Pre-launch hardening: KOL module, Telegram, scanners, WS resilience

Big-picture changes since b941223:

KOL pipeline (new) — Substack/podcast/blog RSS → AI ticker extraction →
on-chain wallet diff → talks-vs-trades divergence detection. Daily polls,
19 feeds, divergence emits Post + Telegram fan-out.

Telegram push (new) — walletless free tier + wallet-linked Pro upgrade,
in-bot preference commands (/trump /btc /funding /kol /conf /quiet),
signed-envelope API for dashboard. Disconnect-wallet keeps free
subscription.

BTC funding-rate reversal scanner (new) — hourly cron, 30d cumulative
funding threshold + mean-revert + 7d price confirm, emits via
/api/signals/ingest. BTC bottom-reversal scanner promoted to System 2.

WS broadcast rewrite — per-client send timeout + parallel fan-out
(asyncio.gather). Fixes "Binance WS no close frame" reconnect storms +
APScheduler 11-min job misses, both caused by one slow client stalling
the kline loop.

Error visibility — three silent-error sites (trumpstruth/truth_social
fetchers, funding_reversal scanner) now include exception type name so
httpx ConnectError-style empty-message errors stop logging blank lines.
Telegram bot loop now classifies ReadTimeout vs network vs unknown +
logger.exception for the unknown bucket.

Security hygiene — trumpsignal.db untracked from git (held subscriber
wallets + encrypted HL keys + 22 bot trades); .gitignore now blocks
*.db/.next/backups. CORS only allows FRONTEND_URL in production.

New ops scripts —
  - scripts/preflight.py: env/DB/Telegram/AI auth verification gate
  - scripts/backup_db.sh: cron-friendly daily DB backup (SQLite + Postgres)
  - scripts/seed_kol_wallets.py: idempotent KOL on-chain wallet seeder

15 new Alembic migrations (007-021) covering convex strategy fields,
phase-1 safety, two-system frozen exits, invalidation prices, dynamic
SYS2 leverage, staged de-risk + pyramiding, peak gain tracking, risk
mode, auto-trade + grow flags, KOL module, KOL on-chain, KOL divergence,
Telegram bindings + walletless.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

.env.example

@@ -1,12 +1,14 @@
 # ── Database ─────────────────────────────────────────────────────────────────
-# For Docker Compose — only POSTGRES_PASSWORD needs to be set;
+# Docker Compose reads SERVICE_* values and injects DATABASE_URL into the API
-# DATABASE_URL is built automatically by docker-compose.yml.
+# container. Bare-metal/systemd reads DATABASE_URL directly.
-# For local dev without Docker, set DATABASE_URL directly:
+SERVICE_USER_POSTGRES=trumpsignal
-# DATABASE_URL=sqlite+aiosqlite:///./trumpsignal.db
+SERVICE_PASSWORD_POSTGRES=change_me_in_production
-POSTGRES_PASSWORD=change_me_in_production
+DATABASE_URL=postgresql+asyncpg://trumpsignal:change_me_in_production@localhost:5432/trumpsignal
 
 # ── CORS / Frontend ──────────────────────────────────────────────────────────
-# Your frontend origin — no trailing slash
+# Your frontend origin — no trailing slash. Used as the only allowed CORS
+# origin in production. Multiple origins not currently supported here; if
+# you need staging + prod simultaneously, edit app/main.py allowed_origins.
 FRONTEND_URL=https://yourdomain.com
 
 # ── Encryption ───────────────────────────────────────────────────────────────
@@ -15,14 +17,55 @@ FRONTEND_URL=https://yourdomain.com
 # WARNING: rotating this invalidates all stored keys.
 ENCRYPTION_KEY=
 
-# ── AI provider ──────────────────────────────────────────────────────────────
+# ── AI provider (System 1 / Trump analysis only) ────────────────────────────
+# OpenAI-compatible endpoint. DeepSeek used in dev.
 AI_API_KEY=
-AI_BASE_URL=https://api.gptsapi.net/v1
+AI_BASE_URL=https://api.deepseek.com/v1
-AI_MODEL=claude-sonnet-4-6
+AI_MODEL=deepseek-v4-pro          # batch / reanalysis (quality)
+AI_LIVE_MODEL=deepseek-v4-flash   # live post analysis (latency)
+# Optional: native Anthropic key takes priority over AI_API_KEY if set.
+# Used for KOL analysis (long-form essays) where reasoning > latency.
+ANTHROPIC_API_KEY=
 
-# ── Hyperliquid ──────────────────────────────────────────────────────────────
+# ── Signal ingest (System 2 scanners + external modules) ─────────────────────
+# Shared secret for POST /api/signals/ingest. Empty = endpoint disabled
+# (fail-closed). Generate: openssl rand -hex 32
+INGEST_API_KEY=
+
+# ── On-chain data providers ──────────────────────────────────────────────────
+# Glassnode — used by the BTC bottom-reversal scanner (MVRV-Z + STH-SOPR).
+# Free tier covers our query volume. Empty = scanner skips with a warning.
+# Sign up: https://glassnode.com → Account → API
+GLASSNODE_API_KEY=
+
+# Etherscan — used by KOL A-tier on-chain poller to read ERC-20 balances for
+# the wallets in `kol_wallets`. Free tier (5 req/s) is plenty for daily polls.
+# Empty = KOL on-chain snapshot pipeline skips Ethereum wallets (HL perps
+# still polled via Hyperliquid's free public API).
+# Sign up: https://etherscan.io/register → My API Keys
+ETHERSCAN_API_KEY=
+
+# ── Telegram push alerts ─────────────────────────────────────────────────────
+# Bot token from @BotFather (https://t.me/BotFather → /newbot). Free.
+# Empty = Telegram alerts disabled (bot loop skipped, notify_signal is a
+# no-op, /api/telegram endpoints return 503).
+TELEGRAM_BOT_TOKEN=
+# Bot username (no @) — used by the Settings UI to render the deep link
+# t.me/<username>?start=<code>. Example: trumpalpha_bot
+TELEGRAM_BOT_USERNAME=
+
+# ── Hyperliquid (server-side fallback only — most ops use user-provided keys) ─
+# These are ONLY for server-controlled signals (e.g. a dev/admin trade) — the
+# user-facing bot reads each subscriber's encrypted key from the DB instead.
+# Leave empty in production unless you're running a single-account demo.
+HL_API_PRIVATE_KEY=
+HL_ACCOUNT_ADDRESS=
+HL_LEVERAGE=3
 HL_MAINNET=true
 
 # ── Runtime ──────────────────────────────────────────────────────────────────
 # development | production
+# development → enables /api/dev/* endpoints (paper-mode toggle, etc.) AND
+#               auto-runs create_all() on startup (Alembic bypass — DEV ONLY).
+# production  → both are off; schema strictly Alembic-managed.
 ENVIRONMENT=production

.gitignore

@@ -7,9 +7,17 @@ env/
 .env
 *.pem
 
-# 数据库 (trumpsignal.db 是例外，用于一次性迁移到 Postgres)
+# 数据库 — 含真实订阅者钱包地址、HL 加密 key、bot trades 等敏感数据。
+# 一旦推到 git 历史回收代价 = 重 init 仓库。全部 untrack。
+# 一次性迁到 Postgres 用 scripts/migrate_sqlite_to_postgres.py 本地跑。
 *.sqlite
 *.sqlite3
+*.db
+*.db.bak-*
+*.db-journal
+backups/
+# Next.js build artifacts (本来在 trumpsignal repo 应忽略；偶尔从 backend 误触)
+.next/
 
 # Python 缓存
 __pycache__/
@@ -21,3 +29,4 @@ __pycache__/
 
 # Alembic 产生的临时迁移记录
 # 注意：alembic/versions 中的迁移脚本应该提交，但 alembic 目录下的其他临时文件可能需要忽略
+.cache/

alembic/versions/007_convex_strategy_fields.py

@@ -0,0 +1,40 @@
+"""Convex-strategy fields: trailing stop, longer hold, manual window.
+
+These columns let the bot run trend-capture (asymmetric payoff) instead of
+mean-reversion scalping:
+
+  - trailing_stop_pct      : Trail distance once profit > activate threshold.
+                             None = use fixed take_profit_pct (legacy behaviour).
+  - trailing_activate_at_pct: Profit % at which trailing kicks in.
+  - max_hold_hours         : Replaces the hardcoded 1-hour cap. Default 168 (7 days)
+                             so runners can run.
+  - manual_window_until    : One-shot "enable for N hours" override. When set and
+                             in the future, bot trades regardless of active_from /
+                             active_until schedule. NULL = use schedule.
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "007"
+down_revision: Union[str, None] = "006"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("subscriptions") as batch_op:
+        batch_op.add_column(sa.Column("trailing_stop_pct",       sa.Float(),    nullable=True))
+        batch_op.add_column(sa.Column("trailing_activate_at_pct", sa.Float(),   nullable=True))
+        batch_op.add_column(sa.Column("max_hold_hours",          sa.Integer(),  nullable=False, server_default="168"))
+        batch_op.add_column(sa.Column("manual_window_until",     sa.DateTime(), nullable=True))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("subscriptions") as batch_op:
+        batch_op.drop_column("manual_window_until")
+        batch_op.drop_column("max_hold_hours")
+        batch_op.drop_column("trailing_activate_at_pct")
+        batch_op.drop_column("trailing_stop_pct")

alembic/versions/008_phase1_safety.py

@@ -0,0 +1,25 @@
+"""Phase 1 safety fields: paper mode + circuit breaker."""
+from typing import Sequence, Union
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "008"
+down_revision: Union[str, None] = "007"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("subscriptions") as batch_op:
+        # Paper mode: trades are simulated, no Hyperliquid call.
+        batch_op.add_column(sa.Column("paper_mode", sa.Boolean(), nullable=False, server_default="0"))
+        # Circuit breaker state.
+        batch_op.add_column(sa.Column("circuit_breaker_tripped_at", sa.DateTime(), nullable=True))
+        batch_op.add_column(sa.Column("circuit_breaker_reason",     sa.String(32), nullable=True))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("subscriptions") as batch_op:
+        batch_op.drop_column("circuit_breaker_reason")
+        batch_op.drop_column("circuit_breaker_tripped_at")
+        batch_op.drop_column("paper_mode")

alembic/versions/009_two_system_and_frozen_exits.py

@@ -0,0 +1,46 @@
+"""Two-system separation + frozen per-trade exit profile.
+
+subscriptions:
+  - sys2_budget_pct          : daily-budget slice for System 2 (default 0.7)
+  - sys2_cb_tripped_at/reason : independent System-2 circuit breaker
+
+bot_trades:
+  - eff_*  : the exit profile FROZEN at open. Recovery rehydrates from these
+             instead of the (mutable) Subscription, so a 90-day reversal
+             isn't silently rewritten to the user's Trump stop on restart.
+"""
+from typing import Sequence, Union
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "009"
+down_revision: Union[str, None] = "008"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("subscriptions") as b:
+        b.add_column(sa.Column("sys2_budget_pct",    sa.Float(),     nullable=False, server_default="0.7"))
+        b.add_column(sa.Column("sys2_cb_tripped_at", sa.DateTime(),  nullable=True))
+        b.add_column(sa.Column("sys2_cb_reason",     sa.String(32),  nullable=True))
+    with op.batch_alter_table("bot_trades") as b:
+        b.add_column(sa.Column("eff_take_profit_pct",       sa.Float(),    nullable=True))
+        b.add_column(sa.Column("eff_stop_loss_pct",         sa.Float(),    nullable=True))
+        b.add_column(sa.Column("eff_trailing_stop_pct",     sa.Float(),    nullable=True))
+        b.add_column(sa.Column("eff_trailing_activate_pct", sa.Float(),    nullable=True))
+        b.add_column(sa.Column("eff_max_hold_hours",        sa.Integer(),  nullable=True))
+        b.add_column(sa.Column("eff_invalidation",          sa.String(24), nullable=True))
+        b.add_column(sa.Column("eff_invalidation_price",    sa.Float(),    nullable=True))
+        b.add_column(sa.Column("eff_min_hold_until_ts",     sa.Float(),    nullable=True))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("bot_trades") as b:
+        for c in ("eff_min_hold_until_ts", "eff_invalidation_price", "eff_invalidation", "eff_max_hold_hours",
+                  "eff_trailing_activate_pct", "eff_trailing_stop_pct",
+                  "eff_stop_loss_pct", "eff_take_profit_pct"):
+            b.drop_column(c)
+    with op.batch_alter_table("subscriptions") as b:
+        for c in ("sys2_cb_reason", "sys2_cb_tripped_at", "sys2_budget_pct"):
+            b.drop_column(c)

alembic/versions/010_add_invalidation_price_fields.py

@@ -0,0 +1,52 @@
+"""Add invalidation_price fields for BTC bottom-reversal trades.
+
+Revision ID: 010
+Revises: 009
+Create Date: 2026-05-16 00:00:00.000000
+
+NOTE: `bot_trades.eff_invalidation_price` is ALSO created by migration 009
+(two_system_and_frozen_exits). The original 010 added it unconditionally,
+which made `alembic upgrade head` on a FRESH database fail at 010 with
+"duplicate column name: eff_invalidation_price" (009 already added it).
+
+This revision is now IDEMPOTENT: each column is added only if it is not
+already present, so it applies cleanly on both fresh DBs (009 already made
+eff_invalidation_price → skip; posts.invalidation_price is new → add) and
+DBs that pre-date 009's column. The genuinely-new column introduced by 010
+is `posts.invalidation_price`; `eff_invalidation_price` is owned by 009 and
+left to 009's downgrade.
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy import inspect
+
+revision: str = "010"
+down_revision: Union[str, None] = "009"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def _columns(table: str) -> set:
+    return {c["name"] for c in inspect(op.get_bind()).get_columns(table)}
+
+
+def upgrade() -> None:
+    if "invalidation_price" not in _columns("posts"):
+        with op.batch_alter_table("posts") as b:
+            b.add_column(sa.Column("invalidation_price", sa.Float(), nullable=True))
+    # eff_invalidation_price may already exist (created by 009). Only add it
+    # if some older DB reached 010 without it.
+    if "eff_invalidation_price" not in _columns("bot_trades"):
+        with op.batch_alter_table("bot_trades") as b:
+            b.add_column(sa.Column("eff_invalidation_price", sa.Float(), nullable=True))
+
+
+def downgrade() -> None:
+    # Only undo 010's genuine addition. eff_invalidation_price is owned by
+    # 009 — dropping it here would double-drop in `downgrade base`.
+    if "invalidation_price" in _columns("posts"):
+        with op.batch_alter_table("posts") as b:
+            b.drop_column("invalidation_price")

alembic/versions/011_sys2_dynamic_leverage.py

@@ -0,0 +1,29 @@
+"""Add System-2 dynamic leverage.
+
+subscriptions.sys2_leverage: per-user leverage for System-2 (bottom reversal),
+independent of the Trump `leverage` field. NULL → platform default.
+
+Revision ID: 011
+Revises: 010
+Create Date: 2026-05-18 00:00:00.000000
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "011"
+down_revision: Union[str, None] = "010"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("subscriptions") as b:
+        b.add_column(sa.Column("sys2_leverage", sa.Integer(), nullable=True))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("subscriptions") as b:
+        b.drop_column("sys2_leverage")

alembic/versions/012_sys2_staged_derisk.py

@@ -0,0 +1,39 @@
+"""Add System-2 staged de-risk (分段式减仓) bookkeeping to bot_trades.
+
+  realized_partial_pnl_usd : cumulative PnL from partial reduces (default 0)
+  remaining_fraction       : fraction of original notional still open (default 1)
+  derisk_steps_done        : number of partial de-risk steps executed (default 0)
+
+Defaults make every existing row behave exactly as before (no partials).
+
+Revision ID: 012
+Revises: 011
+Create Date: 2026-05-18 00:00:00.000000
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "012"
+down_revision: Union[str, None] = "011"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("bot_trades") as b:
+        b.add_column(sa.Column("realized_partial_pnl_usd", sa.Float(),
+                               nullable=False, server_default="0"))
+        b.add_column(sa.Column("remaining_fraction", sa.Float(),
+                               nullable=False, server_default="1"))
+        b.add_column(sa.Column("derisk_steps_done", sa.Integer(),
+                               nullable=False, server_default="0"))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("bot_trades") as b:
+        b.drop_column("derisk_steps_done")
+        b.drop_column("remaining_fraction")
+        b.drop_column("realized_partial_pnl_usd")

alembic/versions/013_sys2_pyramiding.py

@@ -0,0 +1,35 @@
+"""Add System-2 pyramiding (做对了往上加仓) bookkeeping to bot_trades.
+
+  base_size_usd     : original notional at open, immutable (NULL → legacy,
+                      falls back to size_usd)
+  addon_steps_done  : number of pyramid add-ons executed (default 0)
+
+Defaults make every existing row behave exactly as before (no add-ons).
+
+Revision ID: 013
+Revises: 012
+Create Date: 2026-05-18 00:00:00.000000
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "013"
+down_revision: Union[str, None] = "012"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("bot_trades") as b:
+        b.add_column(sa.Column("base_size_usd", sa.Float(), nullable=True))
+        b.add_column(sa.Column("addon_steps_done", sa.Integer(),
+                               nullable=False, server_default="0"))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("bot_trades") as b:
+        b.drop_column("addon_steps_done")
+        b.drop_column("base_size_usd")

alembic/versions/014_bot_trade_peak_gain.py

@@ -0,0 +1,32 @@
+"""Persist tp_sl_monitor peak gain so a restart doesn't reset the regime.
+
+  bot_trades.peak_gain_pct : monotonic peak unrealised gain % (default 0)
+
+Without this, a pyramided / in-profit System-2 trade rehydrates with peak=0
+on restart → wrongly drops into the underwater de-risk regime.
+
+Revision ID: 014
+Revises: 013
+Create Date: 2026-05-18 00:00:00.000000
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "014"
+down_revision: Union[str, None] = "013"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("bot_trades") as b:
+        b.add_column(sa.Column("peak_gain_pct", sa.Float(),
+                               nullable=False, server_default="0"))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("bot_trades") as b:
+        b.drop_column("peak_gain_pct")

alembic/versions/015_sys2_risk_mode.py

@@ -0,0 +1,38 @@
+"""Add System-2 risk mode (standard / aggressive).
+
+  subscriptions.sys2_mode : user-selected mode ("standard" default)
+  bot_trades.sys2_mode    : frozen mode for the trade (NULL → standard)
+
+Aggressive is a separately-funded high-risk/high-explosiveness sleeve. Both
+modes keep the safety invariants (never exchange-liquidated; post-pyramid
+breakeven floor). Defaults make every existing row behave as before.
+
+Revision ID: 015
+Revises: 014
+Create Date: 2026-05-18 00:00:00.000000
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "015"
+down_revision: Union[str, None] = "014"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("subscriptions") as b:
+        b.add_column(sa.Column("sys2_mode", sa.String(16),
+                               nullable=False, server_default="standard"))
+    with op.batch_alter_table("bot_trades") as b:
+        b.add_column(sa.Column("sys2_mode", sa.String(16), nullable=True))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("bot_trades") as b:
+        b.drop_column("sys2_mode")
+    with op.batch_alter_table("subscriptions") as b:
+        b.drop_column("sys2_mode")

alembic/versions/016_auto_trade_and_grow.py

@@ -0,0 +1,48 @@
+"""Simplified operator model: master Auto-Trade + per-trade Grow.
+
+  subscriptions.auto_trade : ONE persistent gate. OFF (default) = scan +
+      ingest signals (feed only), no trades. ON = auto-open on signal.
+  bot_trades.grow_mode     : per-trade pyramiding switch (default off).
+
+Defaults are safe (false) so existing rows do NOT auto-trade / auto-pyramid
+until the user explicitly opts in — no behavior change on upgrade.
+
+Revision ID: 016
+Revises: 015
+Create Date: 2026-05-18 00:00:00.000000
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy import inspect
+
+revision: str = "016"
+down_revision: Union[str, None] = "015"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def _columns(table: str) -> set:
+    return {c["name"] for c in inspect(op.get_bind()).get_columns(table)}
+
+
+def upgrade() -> None:
+    if "auto_trade" not in _columns("subscriptions"):
+        with op.batch_alter_table("subscriptions") as b:
+            b.add_column(sa.Column("auto_trade", sa.Boolean(),
+                                   nullable=False, server_default=sa.false()))
+    if "grow_mode" not in _columns("bot_trades"):
+        with op.batch_alter_table("bot_trades") as b:
+            b.add_column(sa.Column("grow_mode", sa.Boolean(),
+                                   nullable=False, server_default=sa.false()))
+
+
+def downgrade() -> None:
+    if "grow_mode" in _columns("bot_trades"):
+        with op.batch_alter_table("bot_trades") as b:
+            b.drop_column("grow_mode")
+    if "auto_trade" in _columns("subscriptions"):
+        with op.batch_alter_table("subscriptions") as b:
+            b.drop_column("auto_trade")

alembic/versions/017_kol_module.py

@@ -0,0 +1,55 @@
+"""KOL module — Substack/Twitter post ingestion + AI analysis.
+
+Standalone module, no FK into Trump posts/trades. First slice (B-tier):
+  - kol_posts:    raw ingested posts from Substack/Twitter, keyed by URL
+  - (kol_holdings + kol_wallets reserved for A-tier in later migration)
+
+Each post carries its own AI analysis inline (1:1, no separate table):
+summary (one-sentence), tickers_json (list of {ticker, action,
+conviction, quote}), model, analysis_version.
+
+Revision ID: 017
+Revises: 016
+Create Date: 2026-05-23 00:00:00.000000
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+
+revision: str = "017"
+down_revision: Union[str, None] = "016"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "kol_posts",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column("kol_handle", sa.String(64), nullable=False, index=True),
+        sa.Column("source", sa.String(16), nullable=False),  # substack | twitter
+        sa.Column("external_id", sa.String(512), nullable=False),  # canonical URL / tweet id
+        sa.Column("title", sa.String(512), nullable=True),
+        sa.Column("url", sa.String(512), nullable=False),
+        sa.Column("published_at", sa.DateTime(), nullable=False, index=True),
+        sa.Column("raw_text", sa.Text(), nullable=False),
+        sa.Column("content_hash", sa.String(64), nullable=False),
+
+        # ── Inline analysis (1:1, populated by extractor) ─────────────
+        sa.Column("summary",          sa.Text(),    nullable=True),
+        sa.Column("tickers_json",     sa.Text(),    nullable=True),  # JSON list
+        sa.Column("analyzed_at",      sa.DateTime(), nullable=True),
+        sa.Column("analysis_model",   sa.String(64), nullable=True),
+        sa.Column("analysis_version", sa.String(16), nullable=True),
+
+        sa.Column("created_at", sa.DateTime(), nullable=False,
+                  server_default=sa.func.current_timestamp()),
+        sa.UniqueConstraint("source", "external_id", name="uq_kol_post_src_extid"),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("kol_posts")

alembic/versions/018_kol_onchain.py

@@ -0,0 +1,78 @@
+"""KOL A-tier: on-chain wallet tracking + daily holdings snapshots.
+
+Three new tables (standalone, no FK into Trump tables):
+
+  kol_wallets          — curated KOL wallet addresses (one row per chain-addr pair)
+  kol_holdings_snapshots — daily portfolio snapshot per wallet (holdings_json)
+  kol_holding_changes  — detected significant moves (new / closed / ±25%)
+
+Source column distinguishes where the snapshot came from:
+  "arkham"  — Arkham Intelligence API portfolio endpoint
+  "hl"      — Hyperliquid public clearinghouseState (perps only)
+  "manual"  — hand-entered for testing
+
+Revision ID: 018
+Revises: 017
+Create Date: 2026-05-23
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+
+revision: str = "018"
+down_revision: Union[str, None] = "017"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "kol_wallets",
+        sa.Column("id",          sa.Integer(),     primary_key=True),
+        sa.Column("handle",      sa.String(64),    nullable=False, index=True),
+        sa.Column("chain",       sa.String(16),    nullable=False),   # ethereum|solana|hl
+        sa.Column("address",     sa.String(128),   nullable=False),
+        sa.Column("label",       sa.String(128),   nullable=True),    # e.g. "Arthur Hayes hot"
+        sa.Column("source_url",  sa.String(256),   nullable=True),    # Arkham entity URL for reference
+        sa.Column("active",      sa.Boolean(),     nullable=False, server_default="1"),
+        sa.Column("added_at",    sa.DateTime(),    nullable=False,
+                  server_default=sa.func.current_timestamp()),
+        sa.UniqueConstraint("chain", "address", name="uq_kol_wallet_chain_addr"),
+    )
+
+    op.create_table(
+        "kol_holdings_snapshots",
+        sa.Column("id",           sa.Integer(),  primary_key=True),
+        sa.Column("wallet_id",    sa.Integer(),  sa.ForeignKey("kol_wallets.id"), nullable=False, index=True),
+        sa.Column("snapshot_date",sa.String(10), nullable=False),      # YYYY-MM-DD UTC
+        sa.Column("holdings_json",sa.Text(),     nullable=False),       # [{ticker,amount,usd_value,chain}]
+        sa.Column("total_usd",    sa.Float(),    nullable=True),
+        sa.Column("source",       sa.String(16), nullable=False),       # arkham|hl|manual
+        sa.Column("created_at",   sa.DateTime(), nullable=False,
+                  server_default=sa.func.current_timestamp()),
+        sa.UniqueConstraint("wallet_id", "snapshot_date", name="uq_kol_snapshot_wallet_date"),
+    )
+
+    op.create_table(
+        "kol_holding_changes",
+        sa.Column("id",           sa.Integer(),   primary_key=True),
+        sa.Column("wallet_id",    sa.Integer(),   sa.ForeignKey("kol_wallets.id"), nullable=False, index=True),
+        sa.Column("detected_at",  sa.DateTime(),  nullable=False, index=True),
+        sa.Column("ticker",       sa.String(32),  nullable=False),
+        # new_position | closed | increased | decreased
+        sa.Column("change_type",  sa.String(16),  nullable=False),
+        sa.Column("usd_before",   sa.Float(),     nullable=True),
+        sa.Column("usd_after",    sa.Float(),     nullable=True),
+        sa.Column("pct_change",   sa.Float(),     nullable=True),   # signed %
+        sa.Column("created_at",   sa.DateTime(),  nullable=False,
+                  server_default=sa.func.current_timestamp()),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("kol_holding_changes")
+    op.drop_table("kol_holdings_snapshots")
+    op.drop_table("kol_wallets")

alembic/versions/019_kol_divergence.py

@@ -0,0 +1,48 @@
+"""kol_divergence table — talks-vs-trades cross-signal
+
+Revision ID: 019
+Revises: 018
+Create Date: 2026-05-23
+"""
+from alembic import op
+import sqlalchemy as sa
+
+revision = "019"
+down_revision = "018"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "kol_divergence",
+        sa.Column("id",           sa.Integer,  primary_key=True),
+        # Which KOL + asset
+        sa.Column("handle",       sa.String(64),  nullable=False, index=True),
+        sa.Column("ticker",       sa.String(32),  nullable=False),
+        # Content side (B-tier)
+        sa.Column("post_id",      sa.Integer,  sa.ForeignKey("kol_posts.id"), nullable=False),
+        sa.Column("post_action",  sa.String(16), nullable=False),  # buy|sell|bullish|bearish
+        sa.Column("post_conviction", sa.Float, nullable=True),
+        sa.Column("post_at",      sa.DateTime, nullable=False),    # post published_at
+        # On-chain side (A-tier)
+        sa.Column("change_id",    sa.Integer,  sa.ForeignKey("kol_holding_changes.id"), nullable=False),
+        sa.Column("onchain_action", sa.String(16), nullable=False),  # new_position|increased|decreased|closed
+        sa.Column("usd_before",   sa.Float,    nullable=True),
+        sa.Column("usd_after",    sa.Float,    nullable=True),
+        sa.Column("onchain_at",   sa.DateTime, nullable=False),
+        # Classification
+        sa.Column("signal_type",  sa.String(16), nullable=False),  # divergence | alignment
+        sa.Column("direction",    sa.String(8),  nullable=True),   # long | short (net conclusion)
+        sa.Column("days_apart",   sa.Float,    nullable=True),     # abs days between post and onchain event
+        sa.Column("created_at",   sa.DateTime, nullable=False, server_default=sa.func.now()),
+        # Unique: one row per (post, change) pair so re-runs are idempotent
+        sa.UniqueConstraint("post_id", "change_id", name="uq_kol_divergence_pair"),
+    )
+    op.create_index("ix_kol_divergence_handle_ticker", "kol_divergence", ["handle", "ticker"])
+    op.create_index("ix_kol_divergence_signal_type",   "kol_divergence", ["signal_type"])
+    op.create_index("ix_kol_divergence_created_at",    "kol_divergence", ["created_at"])
+
+
+def downgrade() -> None:
+    op.drop_table("kol_divergence")

alembic/versions/020_telegram_bindings.py

@@ -0,0 +1,46 @@
+"""telegram_bindings — wallet ↔ Telegram chat_id mapping with per-source prefs.
+
+Revision ID: 020
+Revises: 019
+Create Date: 2026-05-24
+"""
+from alembic import op
+import sqlalchemy as sa
+
+revision = "020"
+down_revision = "019"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "telegram_bindings",
+        sa.Column("id",             sa.Integer,    primary_key=True),
+        sa.Column("wallet_address", sa.String(64), nullable=False, unique=True, index=True),
+        # Telegram chat_id is an int64 in practice; store as BigInteger to be safe.
+        sa.Column("chat_id",        sa.BigInteger, nullable=False, unique=True, index=True),
+        sa.Column("tg_username",    sa.String(64), nullable=True),  # display only
+        sa.Column("bound_at",       sa.DateTime,   nullable=False, server_default=sa.func.now()),
+        # Master switch
+        sa.Column("alerts_enabled", sa.Boolean,    nullable=False, server_default=sa.true()),
+        # Per-source toggles. KOL divergence is noisier so default-off.
+        sa.Column("alert_trump",          sa.Boolean, nullable=False, server_default=sa.true()),
+        sa.Column("alert_btc_bottom",     sa.Boolean, nullable=False, server_default=sa.true()),
+        sa.Column("alert_funding",        sa.Boolean, nullable=False, server_default=sa.true()),
+        sa.Column("alert_kol_divergence", sa.Boolean, nullable=False, server_default=sa.false()),
+        # Quality floor — don't ping for low-confidence Trump posts.
+        sa.Column("min_confidence", sa.Integer, nullable=False, server_default="70"),
+        # Quiet hours (UTC). 0..23. Both null = always on. If start<end, mute
+        # [start, end); if start>end, mute window wraps midnight.
+        sa.Column("mute_from_hour", sa.Integer, nullable=True),
+        sa.Column("mute_until_hour", sa.Integer, nullable=True),
+        # Audit
+        sa.Column("last_alert_at",      sa.DateTime, nullable=True),
+        sa.Column("total_alerts_sent",  sa.Integer,  nullable=False, server_default="0"),
+        sa.Column("total_alerts_failed", sa.Integer, nullable=False, server_default="0"),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("telegram_bindings")

alembic/versions/021_telegram_walletless.py

@@ -0,0 +1,59 @@
+"""Allow walletless Telegram bindings.
+
+Before: every binding had to be tied to a subscribed wallet.
+After:  anyone who DMs the bot can subscribe to public alerts; the
+        wallet_address column becomes nullable. Uniqueness is on chat_id
+        alone — the same Telegram chat can never have two bindings.
+
+A wallet can still have at most one binding (enforced by a partial unique
+index on wallet_address WHERE wallet_address IS NOT NULL).
+
+Revision ID: 021
+Revises: 020
+Create Date: 2026-05-24
+"""
+from alembic import op
+import sqlalchemy as sa
+
+revision = "021"
+down_revision = "020"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # SQLite + batch mode → recreates the table preserving rows.
+    with op.batch_alter_table("telegram_bindings") as batch:
+        batch.alter_column("wallet_address",
+                           existing_type=sa.String(64),
+                           nullable=True)
+
+    # Drop the old unique constraint on wallet_address (was created via
+    # `unique=True`), then add a partial unique index that allows multiple
+    # NULL wallets but still prevents two bindings for the same wallet.
+    # SQLite quirk: the auto-generated unique index name is
+    # `ix_telegram_bindings_wallet_address` (because we also indexed it).
+    # We replace it with the partial-unique form.
+    with op.batch_alter_table("telegram_bindings") as batch:
+        batch.drop_index("ix_telegram_bindings_wallet_address")
+    op.create_index(
+        "ix_telegram_bindings_wallet_address",
+        "telegram_bindings",
+        ["wallet_address"],
+        unique=True,
+        sqlite_where=sa.text("wallet_address IS NOT NULL"),
+        postgresql_where=sa.text("wallet_address IS NOT NULL"),
+    )
+
+
+def downgrade() -> None:
+    # Note: downgrade FAILS if any walletless rows exist — caller must
+    # delete them first. This is intentional (data preservation > graceful
+    # downgrade for an opt-in safety net).
+    op.drop_index("ix_telegram_bindings_wallet_address", table_name="telegram_bindings")
+    with op.batch_alter_table("telegram_bindings") as batch:
+        batch.alter_column("wallet_address",
+                           existing_type=sa.String(64),
+                           nullable=False)
+        batch.create_index("ix_telegram_bindings_wallet_address",
+                           ["wallet_address"], unique=True)

app/api/dev.py

@@ -57,9 +57,167 @@ async def insert_fake_post(
     return {"id": post.id, "text": post.text[:80]}
 
 
+@router.post("/dev/fake-signal")
+async def inject_fake_signal(
+    symbol: str = "ETHUSDT",
+    close: float = 1850.42,
+    tbr: float = 0.72,
+    vol_mult: float = 3.1,
+    bb_pct: float = 8.5,
+    btc_trend: str = "↑ uptrend",
+):
+    """Inject a synthetic funding_signal for end-to-end testing."""
+    from datetime import datetime, timezone
+    from app.services import funding_signal as fs
+
+    now = datetime.now(timezone.utc)
+    alert = {
+        "type":      "funding_signal",
+        "symbol":    symbol,
+        "time":      now.isoformat(),
+        "close":     close,
+        "tbr":       tbr,
+        "vol_mult":  vol_mult,
+        "bb_pct":    bb_pct,
+        "bb_upper":  round(close * 1.002, 4),
+        "btc_trend": btc_trend,
+        "enabled":   fs.is_enabled(),
+    }
+    fs._recent_signals.append(alert)
+
+    if fs.is_enabled():
+        await manager.broadcast(alert)
+        return {"status": "broadcast", "alert": alert}
+    else:
+        return {"status": "recorded_only (monitor OFF)", "alert": alert}
+
+
+@router.post("/dev/reanalyze")
+async def trigger_reanalyze(
+    background_tasks: BackgroundTasks,
+    limit: int = 500,
+    dry_run: bool = False,
+    delay_secs: float = 0.5,
+    legacy_signals: bool = False,
+    model: str = "",
+):
+    """
+    Batch re-run AI analysis on unscored posts.
+    model: override the AI model (default: ai_live_model=flash for speed).
+    Runs in the background — poll GET /dev/reanalyze/status for progress.
+    """
+    from app.services.reanalyze import reanalyze_unscored, get_state
+    from app.config import settings as _s
+    state = get_state()
+    if state["running"]:
+        return {"status": "already_running", "state": state}
+    effective_model = model or _s.ai_live_model  # default to flash
+    background_tasks.add_task(
+        reanalyze_unscored, AsyncSessionLocal,
+        limit=limit, dry_run=dry_run, delay_secs=delay_secs,
+        legacy_signals=legacy_signals, model=effective_model,
+    )
+    return {
+        "status": "started",
+        "limit": limit,
+        "dry_run": dry_run,
+        "delay_secs": delay_secs,
+        "legacy_signals": legacy_signals,
+        "model": effective_model,
+        "message": f"Re-analyzing up to {limit} posts in background. Poll /api/dev/reanalyze/status.",
+    }
+
+
+@router.get("/dev/reanalyze/status")
+async def reanalyze_status():
+    """Current progress of the background re-analyzer."""
+    from app.services.reanalyze import get_state
+    return get_state()
+
+
 @router.post("/dev/backfill-prices")
 async def trigger_price_backfill(background_tasks: BackgroundTasks, asset: str = "BTC"):
     """触发历史帖子价格回溯（后台运行）"""
     from app.services.price_backfill import backfill_price_impact
     background_tasks.add_task(backfill_price_impact, AsyncSessionLocal, asset)
     return {"status": "started", "asset": asset, "message": "后台回溯中，约需 1-2 分钟"}
+
+
+# ─── Convex-strategy backtest ──────────────────────────────────────────────
+
+@router.post("/dev/backtest/post")
+async def backtest_one_post(
+    post_id: int,
+    stop_loss_pct: float = 1.5,
+    trailing_stop_pct: float = 2.5,
+    trailing_activate_at_pct: float = 5.0,
+    take_profit_pct: float = 0.0,   # 0 = treat as None (no fixed TP)
+    max_hold_hours: int = 168,
+):
+    """Replay one post through the new convex-strategy exit rules.
+
+    Pulls 1m candles from Binance for the post's hold window and simulates
+    trailing-stop + SL + max-hold. Useful for sanity-checking parameters.
+    Pass `take_profit_pct=0` to disable the fixed TP and run pure trailing.
+    """
+    from app.services.backtest import backtest_post, BacktestParams
+    params = BacktestParams(
+        stop_loss_pct=stop_loss_pct,
+        trailing_stop_pct=trailing_stop_pct or None,
+        trailing_activate_at_pct=trailing_activate_at_pct or None,
+        take_profit_pct=take_profit_pct or None,
+        max_hold_hours=max_hold_hours,
+    )
+    r = await backtest_post(post_id, params)
+    if r is None:
+        return {"status": "skipped", "post_id": post_id}
+    return {"status": "ok", "result": r.to_dict()}
+
+
+@router.post("/dev/paper-mode")
+async def toggle_paper_mode(
+    wallet: str,
+    enabled: bool,
+    db: AsyncSession = Depends(get_db),
+):
+    """Flip paper_mode for a subscription. Dev-only — no signature required.
+
+    Paper mode: trades are simulated end-to-end (entry/exit from Binance,
+    DB row with hl_order_id='paper') but no Hyperliquid call is made.
+    """
+    from sqlalchemy import select
+    from app.models import Subscription
+    wallet = wallet.lower().strip()
+    result = await db.execute(select(Subscription).where(Subscription.wallet_address == wallet))
+    sub = result.scalar_one_or_none()
+    if sub is None:
+        return {"status": "not_found", "wallet": wallet}
+    sub.paper_mode = bool(enabled)
+    await db.commit()
+    return {"status": "ok", "wallet": wallet, "paper_mode": sub.paper_mode}
+
+
+@router.post("/dev/backtest/batch")
+async def backtest_batch_route(
+    limit: int = 50,
+    min_confidence: int = 80,
+    stop_loss_pct: float = 1.5,
+    trailing_stop_pct: float = 2.5,
+    trailing_activate_at_pct: float = 5.0,
+    take_profit_pct: float = 0.0,
+    max_hold_hours: int = 168,
+):
+    """Batch backtest: every directional post with conf ≥ min_confidence.
+
+    WARNING: synchronous — for 50 posts at ~10s each on Binance fetches this
+    can take several minutes. Run with limit=5 first to sanity-check.
+    """
+    from app.services.backtest import backtest_batch, BacktestParams
+    params = BacktestParams(
+        stop_loss_pct=stop_loss_pct,
+        trailing_stop_pct=trailing_stop_pct or None,
+        trailing_activate_at_pct=trailing_activate_at_pct or None,
+        take_profit_pct=take_profit_pct or None,
+        max_hold_hours=max_hold_hours,
+    )
+    return await backtest_batch(limit=limit, min_confidence=min_confidence, params=params)

app/api/funding_reversal.py

@@ -0,0 +1,22 @@
+"""
+API endpoints for the BTC funding-rate reversal signal.
+
+GET /api/funding/snapshot
+    Live snapshot of current funding state + signal verdict + 7d history.
+    Powers the BTC page "Funding" tab.
+
+Historical fired signals are returned via the standard /api/posts endpoint
+with source=funding_reversal — same plumbing as btc_bottom_reversal.
+"""
+
+from fastapi import APIRouter
+
+from app.services.scanners.funding_reversal import get_current_snapshot
+
+router = APIRouter(prefix="/funding", tags=["funding"])
+
+
+@router.get("/snapshot")
+async def snapshot() -> dict:
+    """Cheap (≤2 outbound requests). Frontend polls every ~5min."""
+    return await get_current_snapshot()

app/api/funding_signal.py

@@ -0,0 +1,38 @@
+"""
+API endpoints for the breakout signal monitor.
+
+GET  /api/signal/status          — current state (enabled, recent signals)
+POST /api/signal/toggle          — flip the on/off switch
+GET  /api/signal/history         — last N signals (fired regardless of enabled state)
+"""
+
+from fastapi import APIRouter
+
+from app.services.funding_signal import (
+    set_enabled,
+    is_enabled,
+    get_recent_signals,
+    get_status,
+)
+
+router = APIRouter(prefix="/signal", tags=["signal"])
+
+
+@router.get("/status")
+async def status():
+    return get_status()
+
+
+@router.post("/toggle")
+async def toggle(enabled: bool):
+    """
+    Body: ?enabled=true or ?enabled=false
+    Example: POST /api/signal/toggle?enabled=true
+    """
+    set_enabled(enabled)
+    return {"enabled": is_enabled()}
+
+
+@router.get("/history")
+async def history(limit: int = 20):
+    return get_recent_signals(limit)

app/api/kol.py

@@ -0,0 +1,395 @@
+"""KOL module — public read API.
+
+Endpoints:
+  GET /api/kol/posts            list KOL posts (newest first), with summary +
+                                tickers but WITHOUT raw_text to keep payload small
+  GET /api/kol/posts/{id}       full detail incl. raw_text for the modal/page view
+"""
+
+import json
+import logging
+from collections import defaultdict
+from datetime import datetime, timedelta, timezone
+from typing import Any, List, Optional
+
+from fastapi import APIRouter, Depends, Header, HTTPException, Query
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.config import settings
+from app.database import get_db
+from app.models import KolDivergence, KolHoldingChange, KolHoldingSnapshot, KolPost, KolWallet, iso_utc
+
+
+def _verify_admin_key(x_ingest_key: Optional[str]) -> None:
+    """Shared-secret auth for write endpoints (wallet add, scan trigger).
+    Same key as the signal ingest endpoint. Fail-closed if INGEST_API_KEY unset."""
+    expected = settings.ingest_api_key
+    if not expected:
+        raise HTTPException(503, "write endpoint disabled (INGEST_API_KEY not configured)")
+    if not x_ingest_key:
+        raise HTTPException(401, "missing X-Ingest-Key header")
+    if x_ingest_key != expected:
+        raise HTTPException(401, "invalid X-Ingest-Key")
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+
+def _parse_tickers(raw: Optional[str]) -> List[dict]:
+    if not raw:
+        return []
+    try:
+        data = json.loads(raw)
+        return data if isinstance(data, list) else []
+    except json.JSONDecodeError:
+        return []
+
+
+def _summary_dto(post: KolPost) -> dict:
+    """List-view shape: no raw_text, no content_hash."""
+    return {
+        "id": post.id,
+        "kol_handle": post.kol_handle,
+        "source": post.source,
+        "url": post.url,
+        "title": post.title,
+        "published_at": iso_utc(post.published_at),
+        "summary": post.summary,
+        "tickers": _parse_tickers(post.tickers_json),
+        "analyzed_at": iso_utc(post.analyzed_at),
+        "analysis_model": post.analysis_model,
+    }
+
+
+def _detail_dto(post: KolPost) -> dict:
+    base = _summary_dto(post)
+    base["raw_text"] = post.raw_text
+    return base
+
+
+@router.get("/kol/posts")
+async def list_kol_posts(
+    handle: Optional[str] = Query(default=None, description="filter by kol_handle"),
+    source: Optional[str] = Query(default=None, description="substack | twitter"),
+    limit: int = Query(default=50, ge=1, le=200),
+    page: int = Query(default=1, ge=1),
+    db: AsyncSession = Depends(get_db),
+) -> dict[str, Any]:
+    stmt = select(KolPost)
+    if handle:
+        stmt = stmt.where(KolPost.kol_handle == handle)
+    if source:
+        stmt = stmt.where(KolPost.source == source)
+    stmt = stmt.order_by(KolPost.published_at.desc()).offset((page - 1) * limit).limit(limit)
+    rows = (await db.execute(stmt)).scalars().all()
+    return {"items": [_summary_dto(p) for p in rows], "page": page, "limit": limit}
+
+
+@router.get("/kol/posts/{post_id}")
+async def get_kol_post(post_id: int, db: AsyncSession = Depends(get_db)) -> dict:
+    row = (await db.execute(select(KolPost).where(KolPost.id == post_id))).scalar_one_or_none()
+    if row is None:
+        raise HTTPException(status_code=404, detail="KOL post not found")
+    return _detail_dto(row)
+
+
+# ── Action ordering for picking the "dominant" call per ticker ─────────
+# When the same ticker appears across multiple posts with different actions,
+# the strongest signal wins. buy/sell (explicit position) outrank
+# bullish/bearish (directional view) outrank mention (passing reference).
+_ACTION_STRENGTH = {"buy": 3, "sell": 3, "bullish": 2, "bearish": 2, "mention": 1}
+_ACTION_SIDE = {"buy": "long", "bullish": "long",
+                "sell": "short", "bearish": "short", "mention": "neutral"}
+
+
+@router.get("/kol/digest")
+async def kol_digest(
+    days: int = Query(default=7, ge=1, le=90,
+                      description="rolling window in days"),
+    db: AsyncSession = Depends(get_db),
+) -> dict[str, Any]:
+    """Aggregate ticker calls across recent KOL posts.
+
+    For each ticker mentioned in posts within the window, returns the
+    dominant action, the highest conviction, and the list of (KOL, post,
+    action, conviction) calls. Sorted by post_count desc, then conviction.
+    """
+    since = datetime.now(timezone.utc).replace(tzinfo=None) - timedelta(days=days)
+    rows = (await db.execute(
+        select(KolPost)
+        .where(KolPost.published_at >= since)
+        .where(KolPost.tickers_json.is_not(None))
+        .order_by(KolPost.published_at.desc())
+    )).scalars().all()
+
+    # ticker -> list of call dicts
+    bucket: dict[str, list[dict]] = defaultdict(list)
+    for post in rows:
+        try:
+            tickers = json.loads(post.tickers_json or "[]")
+        except json.JSONDecodeError:
+            continue
+        for t in tickers:
+            sym = (t.get("ticker") or "").upper()
+            action = (t.get("action") or "mention").lower()
+            if not sym or action == "mention":
+                # Don't surface 'mention' on the digest — too noisy. Users
+                # can still see it on the post detail.
+                continue
+            bucket[sym].append({
+                "post_id": post.id,
+                "kol_handle": post.kol_handle,
+                "post_title": post.title,
+                "published_at": iso_utc(post.published_at),
+                "action": action,
+                "conviction": float(t.get("conviction") or 0.0),
+                "quote": (t.get("quote") or "")[:240],
+            })
+
+    tickers_out: list[dict] = []
+    for sym, calls in bucket.items():
+        # Dominant action: pick the action with the highest sum of conviction,
+        # broken by strength tier then count. Ensures one strong "buy" beats
+        # three weak "bullish" mentions.
+        action_weights: dict[str, float] = defaultdict(float)
+        for c in calls:
+            action_weights[c["action"]] += c["conviction"]
+        dominant = max(
+            action_weights.items(),
+            key=lambda kv: (_ACTION_STRENGTH.get(kv[0], 0), kv[1]),
+        )[0]
+        max_conv = max(c["conviction"] for c in calls)
+        unique_kols = sorted({c["kol_handle"] for c in calls})
+        tickers_out.append({
+            "ticker": sym,
+            "dominant_action": dominant,
+            "side": _ACTION_SIDE.get(dominant, "neutral"),
+            "max_conviction": round(max_conv, 2),
+            "post_count": len(calls),
+            "kol_count": len(unique_kols),
+            "kols": unique_kols,
+            "calls": sorted(calls, key=lambda c: c["conviction"], reverse=True),
+        })
+
+    tickers_out.sort(
+        key=lambda t: (t["kol_count"], t["post_count"], t["max_conviction"]),
+        reverse=True,
+    )
+
+    return {
+        "window_days": days,
+        "since": iso_utc(since),
+        "post_count": len(rows),
+        "ticker_count": len(tickers_out),
+        "tickers": tickers_out,
+    }
+
+
+# ── A-tier: on-chain wallets + holdings ──────────────────────────────────────
+
+def _wallet_dto(w: KolWallet) -> dict:
+    return {
+        "id": w.id,
+        "handle": w.handle,
+        "chain": w.chain,
+        "address": w.address,
+        "label": w.label,
+        "source_url": w.source_url,
+        "active": w.active,
+        "added_at": iso_utc(w.added_at),
+    }
+
+
+def _snapshot_dto(s: KolHoldingSnapshot) -> dict:
+    return {
+        "id": s.id,
+        "wallet_id": s.wallet_id,
+        "snapshot_date": s.snapshot_date,
+        "holdings": json.loads(s.holdings_json or "[]"),
+        "total_usd": s.total_usd,
+        "source": s.source,
+        "created_at": iso_utc(s.created_at),
+    }
+
+
+def _change_dto(c: KolHoldingChange, handle: str) -> dict:
+    return {
+        "id": c.id,
+        "wallet_id": c.wallet_id,
+        "handle": handle,
+        "detected_at": iso_utc(c.detected_at),
+        "ticker": c.ticker,
+        "change_type": c.change_type,
+        "usd_before": c.usd_before,
+        "usd_after": c.usd_after,
+        "pct_change": c.pct_change,
+    }
+
+
+@router.get("/kol/wallets")
+async def list_kol_wallets(
+    handle: Optional[str] = Query(default=None),
+    db: AsyncSession = Depends(get_db),
+) -> dict[str, Any]:
+    stmt = select(KolWallet).where(KolWallet.active == True)
+    if handle:
+        stmt = stmt.where(KolWallet.handle == handle)
+    stmt = stmt.order_by(KolWallet.handle)
+    rows = (await db.execute(stmt)).scalars().all()
+    return {"wallets": [_wallet_dto(w) for w in rows]}
+
+
+@router.post("/kol/wallets")
+async def add_kol_wallet(
+    body: dict,
+    x_ingest_key: Optional[str] = Header(None, alias="X-Ingest-Key"),
+    db: AsyncSession = Depends(get_db),
+) -> dict:
+    """Add a new KOL wallet address. Body: {handle, chain, address, label?, source_url?}.
+    Auth: requires X-Ingest-Key header matching INGEST_API_KEY."""
+    _verify_admin_key(x_ingest_key)
+    required = {"handle", "chain", "address"}
+    if not required.issubset(body):
+        raise HTTPException(status_code=422, detail=f"Required fields: {required}")
+
+    # Check duplicate
+    existing = (await db.execute(
+        select(KolWallet).where(
+            KolWallet.chain == body["chain"],
+            KolWallet.address == body["address"].lower(),
+        )
+    )).scalar_one_or_none()
+    if existing:
+        raise HTTPException(status_code=409, detail="Wallet already tracked")
+
+    wallet = KolWallet(
+        handle=body["handle"],
+        chain=body["chain"],
+        address=body["address"].lower(),
+        label=body.get("label"),
+        source_url=body.get("source_url"),
+    )
+    db.add(wallet)
+    await db.commit()
+    await db.refresh(wallet)
+    return _wallet_dto(wallet)
+
+
+@router.get("/kol/wallets/{wallet_id}/snapshots")
+async def get_wallet_snapshots(
+    wallet_id: int,
+    limit: int = Query(default=30, ge=1, le=90),
+    db: AsyncSession = Depends(get_db),
+) -> dict[str, Any]:
+    wallet = (await db.execute(
+        select(KolWallet).where(KolWallet.id == wallet_id)
+    )).scalar_one_or_none()
+    if not wallet:
+        raise HTTPException(status_code=404, detail="Wallet not found")
+
+    snaps = (await db.execute(
+        select(KolHoldingSnapshot)
+        .where(KolHoldingSnapshot.wallet_id == wallet_id)
+        .order_by(KolHoldingSnapshot.snapshot_date.desc())
+        .limit(limit)
+    )).scalars().all()
+
+    return {
+        "wallet": _wallet_dto(wallet),
+        "snapshots": [_snapshot_dto(s) for s in snaps],
+    }
+
+
+@router.get("/kol/changes")
+async def list_holding_changes(
+    handle: Optional[str] = Query(default=None),
+    days: int = Query(default=7, ge=1, le=90),
+    db: AsyncSession = Depends(get_db),
+) -> dict[str, Any]:
+    """Recent on-chain position changes across all tracked KOL wallets."""
+    since = datetime.now(timezone.utc).replace(tzinfo=None) - timedelta(days=days)
+
+    stmt = (
+        select(KolHoldingChange, KolWallet.handle)
+        .join(KolWallet, KolHoldingChange.wallet_id == KolWallet.id)
+        .where(KolHoldingChange.detected_at >= since)
+    )
+    if handle:
+        stmt = stmt.where(KolWallet.handle == handle)
+    stmt = stmt.order_by(KolHoldingChange.detected_at.desc()).limit(200)
+
+    rows = (await db.execute(stmt)).all()
+    changes = [_change_dto(c, h) for c, h in rows]
+    return {
+        "window_days": days,
+        "since": iso_utc(since),
+        "count": len(changes),
+        "changes": changes,
+    }
+
+
+# ── Talks-vs-trades divergence ────────────────────────────────────────────────
+
+def _divergence_dto(d: KolDivergence) -> dict:
+    return {
+        "id":             d.id,
+        "handle":         d.handle,
+        "ticker":         d.ticker,
+        "signal_type":    d.signal_type,    # divergence | alignment
+        "direction":      d.direction,      # long | short
+        "post_id":        d.post_id,
+        "post_action":    d.post_action,
+        "post_conviction":d.post_conviction,
+        "post_at":        iso_utc(d.post_at),
+        "onchain_action": d.onchain_action,
+        "usd_before":     d.usd_before,
+        "usd_after":      d.usd_after,
+        "onchain_at":     iso_utc(d.onchain_at),
+        "days_apart":     d.days_apart,
+        "created_at":     iso_utc(d.created_at),
+    }
+
+
+@router.get("/kol/divergence")
+async def list_divergence(
+    handle:      Optional[str] = Query(default=None),
+    ticker:      Optional[str] = Query(default=None),
+    signal_type: Optional[str] = Query(default=None, description="divergence | alignment"),
+    days:        int           = Query(default=30, ge=1, le=180),
+    db:          AsyncSession  = Depends(get_db),
+) -> dict[str, Any]:
+    """List talks-vs-trades cross-signal pairs.
+
+    signal_type=divergence → KOL said X but chain did the opposite (high alpha).
+    signal_type=alignment  → KOL's words matched their on-chain action (reinforced signal).
+    """
+    since = datetime.now(timezone.utc).replace(tzinfo=None) - timedelta(days=days)
+    stmt  = select(KolDivergence).where(KolDivergence.created_at >= since)
+    if handle:
+        stmt = stmt.where(KolDivergence.handle == handle)
+    if ticker:
+        stmt = stmt.where(KolDivergence.ticker == ticker.upper())
+    if signal_type:
+        stmt = stmt.where(KolDivergence.signal_type == signal_type)
+    stmt = stmt.order_by(KolDivergence.created_at.desc()).limit(200)
+    rows = (await db.execute(stmt)).scalars().all()
+    return {
+        "window_days": days,
+        "since":       iso_utc(since),
+        "count":       len(rows),
+        "items":       [_divergence_dto(r) for r in rows],
+    }
+
+
+@router.post("/kol/divergence/scan")
+async def trigger_divergence_scan(
+    lookback_days: int = Query(default=30, ge=1, le=90),
+    x_ingest_key: Optional[str] = Header(None, alias="X-Ingest-Key"),
+) -> dict[str, Any]:
+    """Manually trigger the talks-vs-trades scan. Returns newly written pairs.
+    Auth: requires X-Ingest-Key header matching INGEST_API_KEY."""
+    _verify_admin_key(x_ingest_key)
+    from app.services.kol_divergence import run_divergence_scan
+    results = await run_divergence_scan(lookback_days=lookback_days)
+    return {"new_pairs": len(results), "items": results}

app/api/performance.py

@@ -1,26 +1,43 @@
 import logging
 from datetime import datetime, timedelta, timezone
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, Query
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.database import get_db
 from app.models import BotTrade
 from app.schemas import BotPerformance
+from app.services.signed_request import verify_signed_request
 
 router = APIRouter()
 logger = logging.getLogger(__name__)
 
 PERIOD_DAYS = 30
+ACTION_VIEW_PERFORMANCE = "view_performance"
 
 
 @router.get("/performance", response_model=BotPerformance)
-async def get_performance(db: AsyncSession = Depends(get_db)):
+async def get_performance(
+    wallet: str = Query(..., description="Wallet address (lower-cased internally)"),
+    ts: int = Query(..., description="Signed timestamp (ms)"),
+    sig: str = Query(..., description="EIP-191 signature"),
+    db: AsyncSession = Depends(get_db),
+):
+    wallet = wallet.lower().strip()
+    verify_signed_request(
+        action=ACTION_VIEW_PERFORMANCE,
+        wallet=wallet,
+        timestamp_ms=ts,
+        signature=sig,
+        body=None,
+        allow_replay=True,
+    )
     since = datetime.now(timezone.utc).replace(tzinfo=None) - timedelta(days=PERIOD_DAYS)
 
     result = await db.execute(
         select(BotTrade)
+        .where(BotTrade.wallet_address == wallet)
         .where(BotTrade.closed_at.is_not(None))
         .where(BotTrade.opened_at >= since)
         .order_by(BotTrade.opened_at.asc())

app/api/positions.py

@@ -0,0 +1,392 @@
+"""
+Open positions + today's P&L — surface what every trader checks first.
+
+The existing endpoints answer "what closed?" (trades) and "what's a signal?"
+(posts). Neither answers "what do I currently hold?" — which is the very
+first question on every trading dashboard.
+
+This module exposes two reads for a connected wallet:
+
+  GET /api/positions/open?wallet=...
+      List every BotTrade with closed_at IS NULL for the wallet, enriched
+      with current price (from price_store) and unrealized PnL.
+      Works for both paper and real trades.
+
+  GET /api/positions/today?wallet=...
+      Realized P&L for trades closed since UTC midnight. Plus open count.
+      Cheap aggregate — drives the "today" tile on dashboards.
+
+Both are no-auth (same trust model as /user/{wallet}/public). The wallet
+address is the access key; anyone who knows it can read its state.
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+from typing import Optional
+
+from fastapi import APIRouter, Depends, HTTPException, Query, Request
+from pydantic import BaseModel
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.database import get_db
+from app.models import BotTrade, Subscription, iso_utc
+from app.services.crypto import decrypt_api_key
+from app.services.price_store import price_store
+from app.services.signed_request import verify_signed_request
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+
+# ─── Schemas ────────────────────────────────────────────────────────────────
+
+
+class OpenPosition(BaseModel):
+    trade_id:        int
+    asset:           str
+    side:            str           # "long" | "short"
+    entry_price:     float
+    current_price:   Optional[float] = None   # None if price_store lacks the asset
+    size_usd:        Optional[float] = None   # OPEN notional (after de-risk)
+    leverage:        Optional[int] = None
+    opened_at:       str
+    hold_minutes:    int
+    unrealized_pct:  Optional[float] = None   # signed in trade direction
+    unrealized_usd:  Optional[float] = None   # on the OPEN portion only
+    is_paper:        bool = False
+    trigger_post_id: Optional[int] = None
+    # System-2 staged lifecycle (so the UI doesn't misreport a de-risked /
+    # pyramided trade). realized_usd = PnL already banked by partial de-risk.
+    realized_usd:    Optional[float] = None
+    derisk_steps:    int = 0
+    addon_steps:     int = 0
+    grow_mode:       bool = False
+
+
+class OpenPositionsResponse(BaseModel):
+    wallet: str
+    count:  int
+    positions: list[OpenPosition]
+
+
+class TodayStatsResponse(BaseModel):
+    wallet:           str
+    realized_pnl_usd: float
+    trades_closed:    int
+    wins:             int
+    losses:           int
+    open_count:       int
+    # PnL already banked by staged de-risk on positions that are STILL open
+    # (cumulative for those trades — not date-scoped, since per-step times
+    # aren't tracked). Surfaced separately so it's visible without corrupting
+    # the strict closed-trade realised figure.
+    open_realized_usd: float = 0.0
+
+
+# ─── Helpers ────────────────────────────────────────────────────────────────
+
+
+def _enrich(trade: BotTrade) -> OpenPosition:
+    """Add current price + unrealized PnL to a BotTrade row.
+
+    Uses price_store (in-memory Binance ticks). For assets we don't stream
+    (TRUMP, HYPE, niche perps) current_price will be None and the UI shows
+    "live price unavailable".
+    """
+    now_aware = datetime.now(timezone.utc)
+    opened_aware = trade.opened_at.replace(tzinfo=timezone.utc)
+    hold_min = int((now_aware - opened_aware).total_seconds() // 60)
+
+    # Only the STILL-OPEN fraction is on the book. After staged de-risk,
+    # remaining_fraction < 1.0 and the rest was already realised — marking
+    # the full size_usd to market would overstate both size and PnL.
+    rem_frac = trade.remaining_fraction if trade.remaining_fraction is not None else 1.0
+    open_notional = round((trade.size_usd or 0.0) * rem_frac, 2)
+    realized = round(trade.realized_partial_pnl_usd or 0.0, 2)
+
+    current = price_store.latest_price(trade.asset)
+    unrealized_pct: Optional[float] = None
+    unrealized_usd: Optional[float] = None
+    if current is not None and trade.entry_price:
+        raw_pct = (current - trade.entry_price) / trade.entry_price
+        signed_pct = (raw_pct if trade.side == "long" else -raw_pct) * 100
+        unrealized_pct = round(signed_pct, 3)
+        if open_notional:
+            unrealized_usd = round(open_notional * signed_pct / 100, 2)
+
+    return OpenPosition(
+        trade_id=trade.id,
+        asset=trade.asset,
+        side=trade.side,
+        entry_price=trade.entry_price,
+        current_price=current,
+        size_usd=open_notional,
+        leverage=trade.leverage,
+        opened_at=iso_utc(trade.opened_at) or "",
+        hold_minutes=hold_min,
+        unrealized_pct=unrealized_pct,
+        unrealized_usd=unrealized_usd,
+        is_paper=(trade.hl_order_id == "paper"),
+        trigger_post_id=trade.trigger_post_id,
+        realized_usd=(realized if realized else None),
+        derisk_steps=(trade.derisk_steps_done or 0),
+        addon_steps=(trade.addon_steps_done or 0),
+        grow_mode=bool(getattr(trade, "grow_mode", False)),
+    )
+
+
+# ─── Endpoints ──────────────────────────────────────────────────────────────
+
+
+@router.get("/positions/open", response_model=OpenPositionsResponse)
+async def get_open_positions(
+    wallet: str = Query(..., description="Wallet address (lower-cased internally)"),
+    ts: int = Query(..., description="Signed timestamp (ms)"),
+    sig: str = Query(..., description="EIP-191 signature"),
+    db: AsyncSession = Depends(get_db),
+):
+    """Live open positions for the wallet, with mark-to-market PnL."""
+    wallet = wallet.lower().strip()
+    verify_signed_request(
+        action=ACTION_VIEW_POSITIONS,
+        wallet=wallet,
+        timestamp_ms=ts,
+        signature=sig,
+        body=None,
+        allow_replay=True,
+    )
+    rows = await db.execute(
+        select(BotTrade).where(
+            BotTrade.wallet_address == wallet,
+            BotTrade.closed_at.is_(None),
+        ).order_by(BotTrade.opened_at.desc())
+    )
+    trades = rows.scalars().all()
+    positions = [_enrich(t) for t in trades]
+    return OpenPositionsResponse(wallet=wallet, count=len(positions), positions=positions)
+
+
+@router.get("/positions/today", response_model=TodayStatsResponse)
+async def get_today_stats(
+    wallet: str = Query(...),
+    ts: int = Query(..., description="Signed timestamp (ms)"),
+    sig: str = Query(..., description="EIP-191 signature"),
+    db: AsyncSession = Depends(get_db),
+):
+    """Today's realized P&L (since UTC midnight) + open count.
+
+    Used for the "today" KPI tile. Cheap — one indexed range scan + a
+    one-shot count for opens.
+    """
+    wallet = wallet.lower().strip()
+    verify_signed_request(
+        action=ACTION_VIEW_POSITIONS,
+        wallet=wallet,
+        timestamp_ms=ts,
+        signature=sig,
+        body=None,
+        allow_replay=True,
+    )
+    midnight = datetime.now(timezone.utc).replace(
+        hour=0, minute=0, second=0, microsecond=0, tzinfo=None
+    )
+
+    closed_rows = await db.execute(
+        select(BotTrade).where(
+            BotTrade.wallet_address == wallet,
+            BotTrade.closed_at >= midnight,
+            BotTrade.pnl_usd.is_not(None),
+        )
+    )
+    closed = closed_rows.scalars().all()
+
+    open_rows = await db.execute(
+        select(BotTrade).where(
+            BotTrade.wallet_address == wallet,
+            BotTrade.closed_at.is_(None),
+        )
+    )
+    open_trades = open_rows.scalars().all()
+    open_count = len(open_trades)
+    open_realized = sum(t.realized_partial_pnl_usd or 0.0 for t in open_trades)
+
+    realized = sum(t.pnl_usd or 0 for t in closed)
+    wins = sum(1 for t in closed if (t.pnl_usd or 0) > 0)
+    losses = sum(1 for t in closed if (t.pnl_usd or 0) < 0)
+
+    return TodayStatsResponse(
+        wallet=wallet,
+        realized_pnl_usd=round(realized, 2),
+        trades_closed=len(closed),
+        wins=wins,
+        losses=losses,
+        open_count=open_count,
+        open_realized_usd=round(open_realized, 2),
+    )
+
+
+# ─── Manual close (P0.1 safety valve) ───────────────────────────────────────
+
+
+ACTION_CLOSE_TRADE = "close_trade"
+ACTION_SET_GROW    = "set_trade_grow"
+ACTION_VIEW_POSITIONS = "view_positions"
+
+
+class CloseTradeResponse(BaseModel):
+    status:        str
+    trade_id:      int
+    exit_price:    Optional[float] = None
+    pnl_usd:       Optional[float] = None
+    reason:        str
+    note:          Optional[str]   = None
+
+
+@router.post("/positions/{trade_id}/close", response_model=CloseTradeResponse)
+async def manual_close(
+    trade_id: int,
+    request:  Request,
+    db: AsyncSession = Depends(get_db),
+):
+    """User-initiated emergency close. Always available — bypasses CB, schedule,
+    even circuit breaker lockout. Wallet ownership is verified by signature.
+
+    Path: trade_id → look up wallet → verify signature against THAT wallet →
+    call close_and_finalize. Reason stamped as "manual" so it's distinguishable
+    in the trade log from TP / SL / trailing / max_hold exits.
+
+    The signed body is {"trade_id": <id>} so a leaked sig for trade X can't be
+    replayed to close trade Y.
+    """
+    raw = await request.json()
+    wallet     = (raw.get("wallet") or "").lower().strip()
+    timestamp  = raw.get("timestamp")
+    signature  = raw.get("signature")
+
+    if not wallet or not isinstance(timestamp, int) or not isinstance(signature, str):
+        raise HTTPException(422, "wallet, timestamp, signature required")
+
+    # Load the trade. Must be open AND owned by the signing wallet.
+    trade = (await db.execute(select(BotTrade).where(BotTrade.id == trade_id))).scalar_one_or_none()
+    if trade is None:
+        raise HTTPException(404, f"trade {trade_id} not found")
+    if trade.wallet_address.lower() != wallet:
+        raise HTTPException(403, "trade belongs to a different wallet")
+    if trade.closed_at is not None:
+        raise HTTPException(409, f"trade {trade_id} is already closed")
+
+    verify_signed_request(
+        action=ACTION_CLOSE_TRADE,
+        wallet=wallet,
+        timestamp_ms=timestamp,
+        signature=signature,
+        body={"trade_id": trade_id},
+    )
+
+    # Find the API key from the subscription.
+    sub = (await db.execute(
+        select(Subscription).where(Subscription.wallet_address == wallet)
+    )).scalar_one_or_none()
+    if sub is None:
+        raise HTTPException(404, "subscription not found")
+
+    # Paper trades close synthetically; live trades need the decrypted HL key.
+    api_key = ""
+    if trade.hl_order_id != "paper":
+        if not sub.hl_api_key:
+            raise HTTPException(400, "wallet has no HL API key — cannot close live position")
+        try:
+            api_key = decrypt_api_key(sub.hl_api_key)
+        except Exception as exc:
+            raise HTTPException(500, f"key decryption failed: {exc}")
+
+    # close_and_finalize handles BOTH paper and live branches internally.
+    from app.services.bot_engine import close_and_finalize
+    leverage = trade.leverage if trade.leverage is not None else sub.leverage
+    await close_and_finalize(
+        trade_id=trade.id,
+        api_key=api_key,
+        leverage=leverage,
+        asset=trade.asset,
+        wallet=wallet,
+        reason="manual",
+    )
+
+    closed = (await db.execute(select(BotTrade).where(BotTrade.id == trade_id))).scalar_one()
+    return CloseTradeResponse(
+        status="ok",
+        trade_id=trade_id,
+        exit_price=closed.exit_price,
+        pnl_usd=closed.pnl_usd,
+        reason="manual",
+    )
+
+
+class GrowResponse(BaseModel):
+    trade_id:  int
+    grow_mode: bool
+
+
+@router.post("/positions/{trade_id}/grow", response_model=GrowResponse)
+async def set_trade_grow(
+    trade_id: int,
+    request:  Request,
+    db: AsyncSession = Depends(get_db),
+):
+    """Flip the per-trade Grow switch (pyramiding). Signed + ownership-checked.
+
+    Body: { wallet, timestamp, signature, trade_id, enabled }
+    grow=on  → this winner is scaled INTO on confirmed trend.
+    grow=off → hold + protective de-risk / ratchet only (de-risk + stop-loss
+               always run regardless — never user-toggleable).
+    Takes effect immediately on the live monitor (no restart needed).
+    """
+    raw = await request.json()
+    wallet    = (raw.get("wallet") or "").lower().strip()
+    timestamp = raw.get("timestamp")
+    signature = raw.get("signature")
+    enabled   = raw.get("enabled")
+    body_tid  = raw.get("trade_id")
+
+    if not wallet or not isinstance(timestamp, int) or not isinstance(signature, str):
+        raise HTTPException(422, "wallet, timestamp, signature required")
+    if not isinstance(enabled, bool):
+        raise HTTPException(422, "enabled (bool) required")
+    if body_tid != trade_id:
+        raise HTTPException(400, "trade_id mismatch (path vs signed body)")
+
+    trade = (await db.execute(select(BotTrade).where(BotTrade.id == trade_id))).scalar_one_or_none()
+    if trade is None:
+        raise HTTPException(404, f"trade {trade_id} not found")
+    if trade.wallet_address.lower() != wallet:
+        raise HTTPException(403, "trade belongs to a different wallet")
+    if trade.closed_at is not None:
+        raise HTTPException(409, f"trade {trade_id} is already closed")
+
+    verify_signed_request(
+        action=ACTION_SET_GROW,
+        wallet=wallet,
+        timestamp_ms=timestamp,
+        signature=signature,
+        body={"trade_id": trade_id, "enabled": enabled},
+    )
+
+    trade.grow_mode = enabled
+    await db.commit()
+
+    # Apply to the live monitor immediately so it takes effect this tick.
+    try:
+        from app.services.tp_sl_monitor import _watched
+        wt = _watched.get(trade_id)
+        if wt is not None:
+            wt.grow_mode = enabled
+    except Exception as exc:
+        logger.warning("grow toggle: live monitor update skipped trade %d: %s",
+                        trade_id, exc)
+
+    logger.info("Grow %s for trade %d by %s",
+                "ON" if enabled else "OFF", trade_id, wallet)
+    return GrowResponse(trade_id=trade_id, grow_mode=enabled)

app/api/posts.py

@@ -61,6 +61,7 @@ def _post_to_schema(post: Post) -> TrumpPost:
         target_asset=post.target_asset,
         category=post.category,
         expected_move_pct=post.expected_move_pct,
+        invalidation_price=post.invalidation_price,
     )
 
 

app/api/scanners.py

@@ -0,0 +1,182 @@
+"""
+Scanner control endpoints — kill switch + per-scanner toggle + status.
+
+GET /scanners is public (read-only status, polled by the UI).
+
+ALL mutating endpoints (toggle / all-disable / all-enable) require a SIGNED
+request from a SUBSCRIBED wallet. Rationale: the scanner switch is a global
+control — flipping it OFF stops the signal engine for every user. Before, it
+was unauthenticated and any anonymous visitor could kill it. Now the caller
+must prove wallet ownership (EIP-191 signature) AND already be a subscriber.
+
+  # See what's running (still public)
+  curl http://localhost:8000/api/scanners
+
+Mutations are no longer curl-able without a wallet signature — that's the
+point. Use the dashboard (which signs via the connected wallet).
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Optional
+
+from fastapi import APIRouter, Depends, HTTPException, Request
+from pydantic import BaseModel
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.database import get_db
+from app.models import Subscription
+from app.services import scanner_state
+from app.services.signed_request import verify_signed_request
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+ACTION_TOGGLE_SCANNER = "toggle_scanner"
+ACTION_SCANNER_KILL   = "scanner_kill_all"
+ACTION_SCANNER_REVIVE = "scanner_revive_all"
+
+
+class ScannerSummary(BaseModel):
+    name:               str
+    enabled:            bool
+    last_run_at:        Optional[str] = None
+    last_status:        str
+    last_message:       Optional[str] = None
+    last_fired_at:      Optional[str] = None
+    total_runs:         int
+    total_fires:        int
+    consecutive_errors: int
+
+
+class ScannersResponse(BaseModel):
+    count:    int
+    enabled:  int
+    scanners: list[ScannerSummary]
+
+
+class ToggleResponse(BaseModel):
+    status:  str
+    name:    Optional[str] = None
+    enabled: Optional[bool] = None
+    count:   Optional[int]  = None
+
+
+async def _require_subscribed_signer(
+    request: Request,
+    db: AsyncSession,
+    *,
+    action: str,
+    body: Optional[dict],
+) -> str:
+    """Verify the signed envelope and that the signer is a subscriber.
+
+    Expects JSON body: { wallet, timestamp, signature, ...body fields }.
+    `body` is the canonical signed payload (must match what the client
+    hashed) — pass None for no-body actions (kill/revive).
+    Returns the verified lower-cased wallet.
+    """
+    raw = await request.json()
+    wallet    = (raw.get("wallet") or "").lower().strip()
+    timestamp = raw.get("timestamp")
+    signature = raw.get("signature")
+
+    if not wallet:
+        raise HTTPException(422, "wallet required")
+    if not isinstance(timestamp, int):
+        raise HTTPException(422, "timestamp (ms) required")
+    if not isinstance(signature, str) or not signature:
+        raise HTTPException(422, "signature required")
+
+    verify_signed_request(
+        action=action,
+        wallet=wallet,
+        timestamp_ms=timestamp,
+        signature=signature,
+        body=body,
+    )
+
+    sub = (await db.execute(
+        select(Subscription).where(Subscription.wallet_address == wallet)
+    )).scalar_one_or_none()
+    if sub is None:
+        raise HTTPException(403, "Wallet is not a subscriber — scanner control denied")
+    return wallet
+
+
+@router.get("/scanners", response_model=ScannersResponse)
+async def list_scanners() -> ScannersResponse:
+    """Operational snapshot of every registered scanner. Polled by the UI
+    every 15-30s. Idempotent + cheap (in-memory dict read). Public."""
+    all_states = scanner_state.get_all()
+    return ScannersResponse(
+        count=len(all_states),
+        enabled=sum(1 for s in all_states if s.enabled),
+        scanners=[ScannerSummary(**s.to_dict()) for s in all_states],
+    )
+
+
+@router.post("/scanners/{name}/toggle", response_model=ToggleResponse)
+async def toggle_scanner(
+    name: str,
+    request: Request,
+    db: AsyncSession = Depends(get_db),
+) -> ToggleResponse:
+    """Flip ONE scanner. Signed + subscriber-gated.
+
+    Body: { wallet, timestamp, signature, name, enabled }
+    The signed body is { "enabled": <bool>, "name": <str> }.
+    """
+    raw = await request.json()
+    enabled = raw.get("enabled")
+    body_name = raw.get("name")
+    if not isinstance(enabled, bool):
+        raise HTTPException(422, "enabled (bool) required")
+    if body_name != name:
+        raise HTTPException(400, "scanner name mismatch (path vs signed body)")
+
+    # NOTE: request.json() is cached by Starlette, so re-reading inside the
+    # helper returns the same payload.
+    wallet = await _require_subscribed_signer(
+        request, db, action=ACTION_TOGGLE_SCANNER,
+        body={"enabled": enabled, "name": name},
+    )
+
+    s = scanner_state.set_enabled(name, enabled)
+    if s is None:
+        raise HTTPException(404, f"unknown scanner {name!r}")
+    logger.info("Scanner %s set enabled=%s by %s", name, s.enabled, wallet)
+    return ToggleResponse(status="ok", name=name, enabled=s.enabled)
+
+
+@router.post("/scanners/all/disable", response_model=ToggleResponse)
+async def kill_switch(
+    request: Request,
+    db: AsyncSession = Depends(get_db),
+) -> ToggleResponse:
+    """Emergency stop — disables ALL scanners. Signed + subscriber-gated.
+    Body: { wallet, timestamp, signature }. Does NOT close open positions —
+    manage those via /positions/{id}/close or the Trades page."""
+    wallet = await _require_subscribed_signer(
+        request, db, action=ACTION_SCANNER_KILL, body=None,
+    )
+    n = scanner_state.disable_all()
+    logger.warning("ALL scanners disabled (%d) by %s", n, wallet)
+    return ToggleResponse(status="killed", count=n)
+
+
+@router.post("/scanners/all/enable", response_model=ToggleResponse)
+async def revive_all(
+    request: Request,
+    db: AsyncSession = Depends(get_db),
+) -> ToggleResponse:
+    """Re-enable every scanner that was disabled. Signed + subscriber-gated.
+    Body: { wallet, timestamp, signature }."""
+    wallet = await _require_subscribed_signer(
+        request, db, action=ACTION_SCANNER_REVIVE, body=None,
+    )
+    n = scanner_state.enable_all()
+    logger.info("ALL scanners re-enabled (%d) by %s", n, wallet)
+    return ToggleResponse(status="revived", count=n)

app/api/signals.py

@@ -0,0 +1,243 @@
+"""
+Generic signal ingestion endpoint.
+
+The BTC bottom scanner POSTs a signal here and it flows through the shared
+execution pipeline: sizing → risk caps → Hyperliquid execution → trailing
+stop monitor. Unknown sources are accepted into the posts table for audit,
+but the execution layer fails closed and will not trade them.
+
+Why one endpoint instead of many?
+  The trusted scanners and the Trump scraper share the same trade execution
+  path. The downstream code sees a `Post` row with `signal=buy|short`, then
+  checks whether that post source is allowed to trade.
+
+Auth: shared secret in X-Ingest-Key header. Fail-closed if INGEST_API_KEY
+is empty in env (so a fresh deploy can't accidentally accept random POSTs).
+
+Source field convention:
+  - "truth"     → Trump posts (existing scraper). Reserved — rejected here.
+  - "btc_bottom_reversal" → Bitcoin Bottom scanner
+  - "<custom>"            → Audit/storage only unless whitelisted in
+                            signal_categories.py.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import logging
+from datetime import datetime, timezone
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Header, HTTPException
+from pydantic import BaseModel, Field, field_validator
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.config import settings
+from app.database import get_db
+from app.models import Post
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+
+# ─── Schema ─────────────────────────────────────────────────────────────────
+
+
+class SignalIngestRequest(BaseModel):
+    """Payload for POST /api/signals/ingest.
+
+    Mirrors the Post schema closely so a signal here becomes a Post row
+    1:1, with no field translation. Downstream code is unaware of the source.
+    """
+
+    source: str = Field(..., min_length=2, max_length=32,
+                        description="Signal source tag, e.g. 'btc_bottom_reversal'. 'truth' is reserved.")
+    external_id: str = Field(..., min_length=4, max_length=64,
+                             description="Caller-supplied unique key. Used for idempotent retries.")
+    text: str = Field(..., min_length=1, max_length=4000,
+                      description="Human-readable description of why the signal fired. Shown in UI.")
+    signal: str = Field(..., description="'buy' or 'short' — non-actionable signals should not be ingested.")
+    target_asset: str = Field(..., min_length=2, max_length=16,
+                              description="Hyperliquid perp ticker, e.g. 'SOL', 'BTC'.")
+    confidence: int = Field(..., ge=0, le=100,
+                            description="0–100. Drives position sizing (≥90 boosts multiplier).")
+    category: str = Field(..., min_length=2, max_length=24,
+                          description="Subtype within source, e.g. 'btc_bottom_reversal_long'.")
+    expected_move_pct: Optional[float] = Field(None, ge=0, le=100,
+                                               description="Optional: caller's expected % move. UI display only.")
+    invalidation_price: Optional[float] = Field(None, ge=0,
+                                                description="Optional thesis invalidation level for System 2.")
+    published_at: Optional[datetime] = Field(None,
+                                             description="Defaults to server now (UTC). Set explicitly only for backfill.")
+
+    @field_validator("signal")
+    @classmethod
+    def _signal_must_be_directional(cls, v: str) -> str:
+        if v not in ("buy", "short"):
+            raise ValueError(f"signal must be 'buy' or 'short' (got {v!r})")
+        return v
+
+    @field_validator("source")
+    @classmethod
+    def _no_reserved_sources(cls, v: str) -> str:
+        if v.lower() == "truth":
+            raise ValueError("'truth' is reserved for the Trump scraper. Use a different source tag.")
+        return v.lower()
+
+
+class SignalIngestResponse(BaseModel):
+    status: str           # "accepted" | "duplicate" | "skipped"
+    post_id: Optional[int] = None
+    dedup_against: Optional[int] = None
+    note: Optional[str] = None
+
+
+# ─── Auth helper ────────────────────────────────────────────────────────────
+
+
+def _verify_ingest_key(x_ingest_key: Optional[str]) -> None:
+    """Fail-closed auth check.
+
+    - INGEST_API_KEY unset → return 503 (endpoint disabled by config)
+    - Header missing or wrong → 401
+    """
+    expected = settings.ingest_api_key
+    if not expected:
+        raise HTTPException(503, "signal ingest endpoint is disabled (INGEST_API_KEY not configured)")
+    if not x_ingest_key:
+        raise HTTPException(401, "missing X-Ingest-Key header")
+    if x_ingest_key != expected:
+        raise HTTPException(401, "invalid X-Ingest-Key")
+
+
+# ─── Endpoint ───────────────────────────────────────────────────────────────
+
+
+@router.post("/signals/ingest", response_model=SignalIngestResponse)
+async def ingest_signal(
+    body: SignalIngestRequest,
+    x_ingest_key: Optional[str] = Header(None, alias="X-Ingest-Key"),
+    db: AsyncSession = Depends(get_db),
+) -> SignalIngestResponse:
+    """Accept a signal from an external trading module.
+
+    Pipeline behaviour:
+      1. Auth (shared secret in header)
+      2. Dedup by external_id (idempotent — same id returns the existing post_id)
+      3. Insert as a Post row (source = caller-supplied)
+      4. If the source is whitelisted for trading, hand off to
+         bot_engine.process_post. Otherwise store for audit and return
+         status="skipped".
+
+    Note: the entry filter ([A] in the pipeline) is SKIPPED for non-truth
+    sources. That filter is tuned for Trump's writing patterns; technical
+    signals are their own catalyst and don't need text-based gating.
+    """
+    _verify_ingest_key(x_ingest_key)
+
+    # Hash external_id the same way truth_social.py does — keeps the dedup
+    # key short and uniform across sources.
+    ext_id_hashed = hashlib.md5(f"{body.source}:{body.external_id}".encode()).hexdigest()
+
+    # Idempotency check
+    existing = await db.execute(select(Post).where(Post.external_id == ext_id_hashed))
+    prior = existing.scalar_one_or_none()
+    if prior:
+        return SignalIngestResponse(
+            status="duplicate",
+            post_id=prior.id,
+            dedup_against=prior.id,
+            note=f"external_id {body.external_id!r} already ingested",
+        )
+
+    # Publication time defaults to server now (naive UTC, matching other rows)
+    pub_naive = (body.published_at or datetime.now(timezone.utc))
+    if pub_naive.tzinfo is not None:
+        pub_naive = pub_naive.astimezone(timezone.utc).replace(tzinfo=None)
+
+    post = Post(
+        external_id=ext_id_hashed,
+        text=body.text,
+        source=body.source,
+        published_at=pub_naive,
+        sentiment="bullish" if body.signal == "buy" else "bearish",
+        ai_confidence=body.confidence,
+        relevant=True,
+        signal=body.signal,
+        target_asset=body.target_asset.upper(),
+        category=body.category,
+        expected_move_pct=body.expected_move_pct,
+        invalidation_price=body.invalidation_price,
+        # `analysis_version` doubles as a provenance tag — easy to query in DB
+        analysis_version=f"ingest:{body.source}",
+        prefilter_reason="external_signal",  # bypasses entry-filter audit
+    )
+    db.add(post)
+    await db.commit()
+    await db.refresh(post)
+
+    logger.info(
+        "Ingested signal: source=%s id=%s → post_id=%d, %s/%s conf=%d category=%s",
+        body.source, body.external_id, post.id,
+        body.signal, body.target_asset, body.confidence, body.category,
+    )
+
+    from app.services.signal_categories import is_supported_trading_source
+    if not is_supported_trading_source(post.source):
+        logger.info("Signal %d stored but skipped: unsupported trading source=%s",
+                    post.id, post.source)
+        return SignalIngestResponse(
+            status="skipped",
+            post_id=post.id,
+            note=f"source {post.source!r} is not enabled for live trading",
+        )
+
+    # Hand off to the same trade pipeline used by Trump posts.
+    # Runs sizing → risk caps → HL trade → trailing-stop monitor.
+    try:
+        from app.services.bot_engine import process_post
+        await process_post(post, db)
+    except Exception as exc:
+        # Don't fail the HTTP response — the post is already in DB, and
+        # process_post failures shouldn't make the external module retry
+        # (which would duplicate signals). Log and return success.
+        logger.error("process_post failed for ingested signal %d: %s", post.id, exc)
+
+    # Broadcast to UI so the new signal shows up live, same as Trump posts.
+    try:
+        from app.scrapers.truth_social import _post_to_ws_payload
+        from app.ws.manager import manager
+        await manager.broadcast(_post_to_ws_payload(post))
+    except Exception as exc:
+        logger.warning("WS broadcast failed for signal %d: %s", post.id, exc)
+
+    # Fan out to Telegram subscribers (fire-and-forget; never blocks ingest).
+    try:
+        from app.services.telegram import notify_signal
+        notify_signal(post)
+    except Exception as exc:
+        logger.warning("Telegram notify failed for signal %d: %s", post.id, exc)
+
+    return SignalIngestResponse(status="accepted", post_id=post.id)
+
+
+@router.get("/signals/sources")
+async def list_sources(db: AsyncSession = Depends(get_db)) -> dict:
+    """List distinct signal sources we've seen, with counts.
+
+    Helpful for spot-checking that ingestion is working and for the UI to
+    show a source filter without hard-coding the list.
+    """
+    from sqlalchemy import func
+    rows = await db.execute(
+        select(Post.source, func.count(Post.id), func.max(Post.published_at))
+        .group_by(Post.source)
+        .order_by(func.count(Post.id).desc())
+    )
+    return {
+        "sources": [
+            {"source": r[0], "count": r[1], "latest": r[2].isoformat() if r[2] else None}
+            for r in rows.all()
+        ]
+    }

app/api/subscribe.py

@@ -1,7 +1,7 @@
 import logging
 from datetime import datetime, timezone
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, Request
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -17,15 +17,29 @@ ACTION_SUBSCRIBE = "subscribe"
 
 
 @router.post("/subscribe", response_model=SubscribeResponse)
-async def subscribe(body: SubscribeRequest, db: AsyncSession = Depends(get_db)):
+async def subscribe(request: Request, db: AsyncSession = Depends(get_db)):
+    """Activate a subscription. Optional `paper_mode` flag in the body lets
+    new users try the system safely (no Hyperliquid call, simulated fills).
+
+    Signed message body is the RAW dict so the canonical hash matches what
+    the frontend signed — same pattern as set_user_settings.
+    """
+    raw = await request.json()
+    body = SubscribeRequest(**raw)
     wallet = body.wallet.lower().strip()
 
+    # The signed body excludes the envelope fields. For backwards compatibility
+    # with old clients that signed `body=None` (no paper_mode key), accept
+    # either signature.
+    paper_mode = bool(raw.get("paper_mode", False))
+    signed_body = {"paper_mode": paper_mode} if paper_mode else None
+
     verify_signed_request(
         action=ACTION_SUBSCRIBE,
         wallet=wallet,
         timestamp_ms=body.timestamp,
         signature=body.signature,
-        body=None,
+        body=signed_body,
     )
 
     result = await db.execute(
@@ -35,13 +49,22 @@ async def subscribe(body: SubscribeRequest, db: AsyncSession = Depends(get_db)):
     now = datetime.now(timezone.utc).replace(tzinfo=None)
 
     if sub is None:
-        sub = Subscription(wallet_address=wallet, active=True, subscribed_at=now)
+        sub = Subscription(
+            wallet_address=wallet,
+            active=True,
+            subscribed_at=now,
+            paper_mode=paper_mode,
+        )
         db.add(sub)
     else:
         sub.active = True
         if sub.subscribed_at is None:
             sub.subscribed_at = now
+        # Re-subscribing is allowed to change paper mode (e.g. user wants to
+        # promote from paper to live). Otherwise leave existing flag alone.
+        if paper_mode != sub.paper_mode:
+            sub.paper_mode = paper_mode
 
     await db.commit()
-    logger.info("Subscription activated for wallet %s", wallet)
+    logger.info("Subscription activated for %s (paper_mode=%s)", wallet, paper_mode)
-    return SubscribeResponse(status="ok", wallet=wallet)
+    return SubscribeResponse(status="ok", wallet=wallet, paper_mode=paper_mode)

app/api/telegram.py

@@ -0,0 +1,232 @@
+"""
+Telegram binding + preferences API.
+
+All mutating endpoints require a signed envelope from the wallet (same EIP-191
+flow as set_hl_api_key). Read endpoints are unsigned but require the wallet
+in the path so other users' bindings stay private.
+
+  GET  /api/telegram/{wallet}/status            ← unsigned read
+  POST /api/telegram/{wallet}/init              ← signed; returns binding code + deep link
+  POST /api/telegram/{wallet}/preferences       ← signed; update toggles
+  POST /api/telegram/{wallet}/unbind            ← signed; removes binding
+  POST /api/telegram/{wallet}/test              ← signed; sends a self-test
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Optional
+
+from fastapi import APIRouter, Depends, HTTPException
+from pydantic import BaseModel, Field
+from sqlalchemy import select, update
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.config import settings
+from app.database import get_db
+from app.models import TelegramBinding, Subscription
+from app.services.signed_request import verify_signed_request
+from app.services.telegram import send_test_message
+from app.services.telegram_bot import issue_binding_code, unbind_wallet
+
+router = APIRouter(prefix="/telegram", tags=["telegram"])
+logger = logging.getLogger(__name__)
+
+ACTION_TG_INIT        = "telegram_init"
+ACTION_TG_PREFS       = "telegram_prefs"
+ACTION_TG_UNBIND      = "telegram_unbind"
+ACTION_TG_TEST        = "telegram_test"
+
+
+def _require_tg_configured() -> None:
+    if not settings.telegram_bot_token or not settings.telegram_bot_username:
+        raise HTTPException(503, "Telegram alerts are not configured on this server")
+
+
+# ── Schemas ──────────────────────────────────────────────────────────────
+
+
+class SignedEnvelope(BaseModel):
+    wallet:    str
+    timestamp: int
+    signature: str
+
+
+class PrefsBody(SignedEnvelope):
+    alerts_enabled:        Optional[bool] = None
+    alert_trump:           Optional[bool] = None
+    alert_btc_bottom:      Optional[bool] = None
+    alert_funding:         Optional[bool] = None
+    alert_kol_divergence:  Optional[bool] = None
+    min_confidence:        Optional[int]  = Field(None, ge=0, le=100)
+    mute_from_hour:        Optional[int]  = Field(None, ge=0, le=23)
+    mute_until_hour:       Optional[int]  = Field(None, ge=0, le=23)
+
+
+class StatusResponse(BaseModel):
+    configured: bool                # whether server has bot token
+    bot_username: Optional[str] = None
+    bound: bool
+    wallet_address: Optional[str] = None
+    tg_username: Optional[str] = None
+    chat_id: Optional[int] = None
+    alerts_enabled: Optional[bool] = None
+    alert_trump: Optional[bool] = None
+    alert_btc_bottom: Optional[bool] = None
+    alert_funding: Optional[bool] = None
+    alert_kol_divergence: Optional[bool] = None
+    min_confidence: Optional[int] = None
+    mute_from_hour: Optional[int] = None
+    mute_until_hour: Optional[int] = None
+    total_alerts_sent: Optional[int] = None
+
+
+class InitResponse(BaseModel):
+    code: str
+    deep_link: str          # t.me/<bot>?start=<code>
+    expires_in_seconds: int
+
+
+# ── Endpoints ────────────────────────────────────────────────────────────
+
+
+@router.get("/{wallet}/status", response_model=StatusResponse)
+async def status(wallet: str, db: AsyncSession = Depends(get_db)) -> StatusResponse:
+    """Public-by-wallet read. Returns whether server is configured AND
+    whether this wallet has bound a Telegram chat."""
+    wallet = wallet.lower().strip()
+    configured = bool(settings.telegram_bot_token and settings.telegram_bot_username)
+    b = (await db.execute(
+        select(TelegramBinding).where(TelegramBinding.wallet_address == wallet)
+    )).scalar_one_or_none()
+
+    if not b:
+        return StatusResponse(configured=configured,
+                              bot_username=settings.telegram_bot_username or None,
+                              bound=False)
+    return StatusResponse(
+        configured=configured,
+        bot_username=settings.telegram_bot_username or None,
+        bound=True,
+        wallet_address=b.wallet_address,
+        tg_username=b.tg_username,
+        chat_id=b.chat_id,
+        alerts_enabled=b.alerts_enabled,
+        alert_trump=b.alert_trump,
+        alert_btc_bottom=b.alert_btc_bottom,
+        alert_funding=b.alert_funding,
+        alert_kol_divergence=b.alert_kol_divergence,
+        min_confidence=b.min_confidence,
+        mute_from_hour=b.mute_from_hour,
+        mute_until_hour=b.mute_until_hour,
+        total_alerts_sent=b.total_alerts_sent,
+    )
+
+
+@router.post("/{wallet}/init", response_model=InitResponse)
+async def init_binding(
+    wallet: str, body: SignedEnvelope,
+    db: AsyncSession = Depends(get_db),
+) -> InitResponse:
+    """Generate a one-time code and the deep link the frontend renders.
+    Subscriber-gated — only paying wallets can receive alerts."""
+    _require_tg_configured()
+    wallet = wallet.lower().strip()
+    if wallet != body.wallet.lower().strip():
+        raise HTTPException(400, "Wallet mismatch")
+    verify_signed_request(
+        action=ACTION_TG_INIT, wallet=wallet,
+        timestamp_ms=body.timestamp, signature=body.signature, body=None,
+    )
+    sub = (await db.execute(
+        select(Subscription).where(Subscription.wallet_address == wallet)
+    )).scalar_one_or_none()
+    if not sub or not sub.active:
+        raise HTTPException(403, "Wallet must be subscribed to enable Telegram alerts")
+
+    code = issue_binding_code(wallet)
+    deep_link = f"https://t.me/{settings.telegram_bot_username}?start={code}"
+    return InitResponse(code=code, deep_link=deep_link, expires_in_seconds=600)
+
+
+@router.post("/{wallet}/preferences", response_model=StatusResponse)
+async def update_preferences(
+    wallet: str, body: PrefsBody,
+    db: AsyncSession = Depends(get_db),
+) -> StatusResponse:
+    """Toggle alert sources, confidence floor, mute hours. Signed.
+    Idempotent — only fields present in the body are updated."""
+    _require_tg_configured()
+    wallet = wallet.lower().strip()
+    if wallet != body.wallet.lower().strip():
+        raise HTTPException(400, "Wallet mismatch")
+
+    # Build a canonical body for signing: include only the fields the user
+    # is actually trying to change (so the signed payload matches what the
+    # frontend hashed).
+    signed_body = {k: v for k, v in body.model_dump(exclude={"wallet", "timestamp", "signature"}).items()
+                   if v is not None}
+    verify_signed_request(
+        action=ACTION_TG_PREFS, wallet=wallet,
+        timestamp_ms=body.timestamp, signature=body.signature,
+        body=signed_body,
+    )
+
+    b = (await db.execute(
+        select(TelegramBinding).where(TelegramBinding.wallet_address == wallet)
+    )).scalar_one_or_none()
+    if not b:
+        raise HTTPException(404, "No Telegram binding for this wallet — bind via /start first")
+
+    values = {}
+    for f in ["alerts_enabled", "alert_trump", "alert_btc_bottom",
+              "alert_funding", "alert_kol_divergence", "min_confidence",
+              "mute_from_hour", "mute_until_hour"]:
+        v = getattr(body, f)
+        if v is not None:
+            values[f] = v
+    if values:
+        await db.execute(
+            update(TelegramBinding).where(TelegramBinding.id == b.id).values(**values)
+        )
+        await db.commit()
+        await db.refresh(b)
+
+    return await status(wallet, db)
+
+
+@router.post("/{wallet}/unbind")
+async def unbind(
+    wallet: str, body: SignedEnvelope,
+    db: AsyncSession = Depends(get_db),
+) -> dict:
+    _require_tg_configured()
+    wallet = wallet.lower().strip()
+    if wallet != body.wallet.lower().strip():
+        raise HTTPException(400, "Wallet mismatch")
+    verify_signed_request(
+        action=ACTION_TG_UNBIND, wallet=wallet,
+        timestamp_ms=body.timestamp, signature=body.signature, body=None,
+    )
+    n = await unbind_wallet(wallet)
+    return {"removed": n}
+
+
+@router.post("/{wallet}/test")
+async def test(
+    wallet: str, body: SignedEnvelope,
+    db: AsyncSession = Depends(get_db),
+) -> dict:
+    """Send a sample alert to verify the binding works end-to-end."""
+    _require_tg_configured()
+    wallet = wallet.lower().strip()
+    if wallet != body.wallet.lower().strip():
+        raise HTTPException(400, "Wallet mismatch")
+    verify_signed_request(
+        action=ACTION_TG_TEST, wallet=wallet,
+        timestamp_ms=body.timestamp, signature=body.signature, body=None,
+    )
+    ok = await send_test_message(wallet)
+    if not ok:
+        raise HTTPException(400, "Test failed — bind via /start first, or check bot token")
+    return {"sent": True}

app/api/trades.py

@@ -3,17 +3,31 @@ from typing import List
 
 from fastapi import APIRouter, Depends, Query
 from sqlalchemy import select
+from sqlalchemy.orm import joinedload
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.database import get_db
 from app.models import BotTrade, iso_utc
 from app.schemas import BotTrade as BotTradeSchema
+from app.services.signed_request import verify_signed_request
 
 router = APIRouter()
 logger = logging.getLogger(__name__)
 
+ACTION_VIEW_TRADES = "view_trades"
+
 
 def _trade_to_schema(trade: BotTrade) -> BotTradeSchema:
+    # Join against trigger_post to surface the source tag. When a trade was
+    # opened by an ingested signal (VCP scanner, user's module, etc.) the
+    # source reveals WHICH module produced it — critical for "is module X
+    # actually making money?" attribution analysis.
+    trigger_source = None
+    if trade.trigger_post is not None:
+        trigger_source = trade.trigger_post.source
+    # Paper trades are tagged via hl_order_id at open time; that's the only
+    # stable signal we have to distinguish them in aggregate views.
+    is_paper = (trade.hl_order_id == "paper")
     return BotTradeSchema(
         id=trade.id,
         asset=trade.asset,
@@ -25,18 +39,36 @@ def _trade_to_schema(trade: BotTrade) -> BotTradeSchema:
         trigger_post_id=trade.trigger_post_id or 0,
         opened_at=iso_utc(trade.opened_at) or "",
         closed_at=iso_utc(trade.closed_at) or "",
+        trigger_source=trigger_source,
+        is_paper=is_paper,
     )
 
 
 @router.get("/trades", response_model=List[BotTradeSchema])
 async def get_trades(
+    wallet: str = Query(..., description="Wallet address (lower-cased internally)"),
+    ts: int = Query(..., description="Signed timestamp (ms)"),
+    sig: str = Query(..., description="EIP-191 signature"),
     limit: int = Query(default=20, ge=1, le=100),
-    page: int = Query(default=1, ge=1),
+    page:  int = Query(default=1,  ge=1),
     db: AsyncSession = Depends(get_db),
 ):
+    wallet = wallet.lower().strip()
+    verify_signed_request(
+        action=ACTION_VIEW_TRADES,
+        wallet=wallet,
+        timestamp_ms=ts,
+        signature=sig,
+        body=None,
+        allow_replay=True,
+    )
     offset = (page - 1) * limit
+    # joinedload pulls trigger_post in the same query — avoids N+1 lookups
+    # when serialising 100 trades.
     result = await db.execute(
         select(BotTrade)
+        .options(joinedload(BotTrade.trigger_post))
+        .where(BotTrade.wallet_address == wallet)
         .where(BotTrade.closed_at.is_not(None))
         .order_by(BotTrade.opened_at.desc())
         .offset(offset)

app/api/user.py

@@ -15,15 +15,30 @@ from app.schemas import (
     SetSettingsRequest,
 )
 from app.services.crypto import encrypt_api_key
+from app.services.hyperliquid import HyperliquidTrader
 from app.services.signed_request import verify_signed_request
 
 router = APIRouter()
 logger = logging.getLogger(__name__)
 
 # Action names — must match frontend exactly (used in the signed message)
-ACTION_SET_API_KEY = "set_hl_api_key"
+ACTION_SET_API_KEY      = "set_hl_api_key"
-ACTION_SET_SETTINGS = "set_settings"
+ACTION_SET_SETTINGS     = "set_settings"
-ACTION_VIEW_USER = "view_user"
+ACTION_VIEW_USER        = "view_user"
+ACTION_SET_MANUAL_WINDOW = "set_manual_window"
+ACTION_SET_AUTO_TRADE   = "set_auto_trade"
+
+
+async def verify_hl_api_key_can_trade(api_key: str, account_address: str) -> None:
+    """Fail before storing an unusable Hyperliquid API wallet key."""
+    try:
+        trader = HyperliquidTrader(
+            api_private_key=api_key,
+            account_address=account_address,
+        )
+        await trader.get_balance()
+    except Exception as exc:
+        raise HTTPException(422, f"Hyperliquid rejected this API key: {exc}")
 
 
 def _trade_to_schema(trade: BotTrade) -> BotTradeSchema:
@@ -70,12 +85,14 @@ async def set_hl_api_key(
     if sub is None:
         raise HTTPException(404, "Wallet not subscribed. Subscribe first.")
 
+    await verify_hl_api_key_can_trade(api_key=api_key, account_address=wallet)
+
     sub.hl_api_key = encrypt_api_key(api_key)
     await db.commit()
 
     masked = f"...{api_key[-6:]}"
     logger.info("HL API key updated for wallet %s (masked: %s)", wallet, masked)
-    return SetApiKeyResponse(status="ok", masked_key=masked)
+    return SetApiKeyResponse(status="ok", masked_key=masked, verified=True)
 
 
 @router.get("/user/{wallet}/public")
@@ -86,11 +103,22 @@ async def get_user_public(wallet: str, db: AsyncSession = Depends(get_db)):
     result = await db.execute(select(Subscription).where(Subscription.wallet_address == wallet))
     sub = result.scalar_one_or_none()
     if sub is None:
-        return {"wallet_address": wallet, "active": False, "hl_api_key_set": False}
+        return {
+            "wallet_address": wallet, "active": False, "hl_api_key_set": False,
+            "paper_mode": False, "manual_window_until": None,
+            "circuit_breaker_tripped_at": None, "circuit_breaker_reason": None,
+            "auto_trade": False,
+        }
     return {
         "wallet_address": wallet,
         "active": sub.active,
         "hl_api_key_set": bool(sub.hl_api_key),
+        # Operational state shown on /signals page. Not sensitive.
+        "paper_mode": bool(sub.paper_mode),
+        "manual_window_until":        iso_utc(sub.manual_window_until),
+        "circuit_breaker_tripped_at": iso_utc(sub.circuit_breaker_tripped_at),
+        "circuit_breaker_reason":     sub.circuit_breaker_reason,
+        "auto_trade": bool(sub.auto_trade),
     }
 
 
@@ -151,9 +179,12 @@ async def get_user(
             stop_loss_pct=sub.stop_loss_pct,
             min_confidence=sub.min_confidence,
             daily_budget_usd=sub.daily_budget_usd,
+            sys2_leverage=sub.sys2_leverage,
+            sys2_mode=sub.sys2_mode,
             active_from=iso_utc(sub.active_from),
             active_until=iso_utc(sub.active_until),
         ),
+        manual_window_until=iso_utc(sub.manual_window_until),
     )
 
 
@@ -190,6 +221,10 @@ async def set_user_settings(
         raise HTTPException(422, "stop_loss_pct is required (0.1–50)")
     if not (0 <= s.min_confidence <= 100):
         raise HTTPException(422, "min_confidence must be 0–100")
+    if s.sys2_leverage is not None and not (1 <= s.sys2_leverage <= 10):
+        raise HTTPException(422, "sys2_leverage must be 1–10")
+    if s.sys2_mode is not None and s.sys2_mode not in ("standard", "aggressive"):
+        raise HTTPException(422, "sys2_mode must be 'standard' or 'aggressive'")
     if s.daily_budget_usd is None or not (0 < s.daily_budget_usd <= 100000):
         raise HTTPException(422, "daily_budget_usd is required (>0, ≤100,000)")
 
@@ -235,8 +270,145 @@ async def set_user_settings(
     sub.stop_loss_pct = s.stop_loss_pct
     sub.min_confidence = s.min_confidence
     sub.daily_budget_usd = s.daily_budget_usd
+    sub.sys2_leverage = s.sys2_leverage
+    if s.sys2_mode is not None:
+        sub.sys2_mode = s.sys2_mode
     sub.active_from = af
     sub.active_until = au
     await db.commit()
     logger.info("Settings updated for %s: %s", wallet, s.model_dump())
     return s
+
+
+# ─── Manual window (convex-strategy "enable for N hours" override) ───────────
+
+
+@router.post("/user/{wallet}/manual-window")
+async def set_manual_window(
+    wallet: str,
+    request: Request,
+    db: AsyncSession = Depends(get_db),
+):
+    """Arm the bot for the next N hours, overriding the active_from/until schedule.
+
+    Body: { wallet, timestamp, signature, hours }
+      hours: integer 0–168. Pass 0 to clear (immediate disarm).
+
+    The bot's main gate (Subscription.active) still applies. This endpoint only
+    flips the schedule override; an inactive subscription stays paused.
+    """
+    from datetime import datetime as _dt, timezone as _tz, timedelta as _td
+
+    wallet = wallet.lower().strip()
+    raw    = await request.json()
+
+    # Manual auth payload — keep deliberately minimal so the signed string is
+    # short and easy to reason about.
+    body_wallet = (raw.get("wallet") or "").lower().strip()
+    timestamp   = raw.get("timestamp")
+    signature   = raw.get("signature")
+    hours_raw   = raw.get("hours")
+
+    if body_wallet != wallet:
+        raise HTTPException(400, "Wallet mismatch")
+    if not isinstance(timestamp, int):
+        raise HTTPException(422, "timestamp (ms) required")
+    if not isinstance(signature, str) or not signature:
+        raise HTTPException(422, "signature required")
+    if not isinstance(hours_raw, int) or hours_raw < 0 or hours_raw > 168:
+        raise HTTPException(422, "hours must be 0–168")
+
+    verify_signed_request(
+        action=ACTION_SET_MANUAL_WINDOW,
+        wallet=wallet,
+        timestamp_ms=timestamp,
+        signature=signature,
+        body={"hours": hours_raw},
+    )
+
+    result = await db.execute(select(Subscription).where(Subscription.wallet_address == wallet))
+    sub    = result.scalar_one_or_none()
+    if sub is None:
+        raise HTTPException(404, "Wallet not subscribed")
+
+    if hours_raw == 0:
+        sub.manual_window_until = None
+        new_until_iso = None
+        logger.info("Manual window cleared for %s", wallet)
+    else:
+        until = _dt.now(_tz.utc).replace(tzinfo=None) + _td(hours=hours_raw)
+        sub.manual_window_until = until
+        new_until_iso = iso_utc(until)
+        # Explicit re-arm clears any active circuit-breaker trip — human in
+        # the loop has acknowledged the risk and chosen to resume.
+        cb_cleared = False
+        if sub.circuit_breaker_tripped_at is not None:
+            sub.circuit_breaker_tripped_at = None
+            sub.circuit_breaker_reason     = None
+            cb_cleared = True
+        logger.info("Manual window armed for %s: until %s (%dh)%s",
+                    wallet, until, hours_raw, " — CB cleared" if cb_cleared else "")
+
+    await db.commit()
+    return {"manual_window_until": new_until_iso}
+
+
+# ─── Master Auto-Trade switch (simplified operator model) ───────────────────
+
+
+@router.post("/user/{wallet}/auto-trade")
+async def set_auto_trade(
+    wallet: str,
+    request: Request,
+    db: AsyncSession = Depends(get_db),
+):
+    """Flip the ONE persistent Auto-Trade gate.
+
+    Body: { wallet, timestamp, signature, enabled }
+      enabled=false → signals are still ingested/shown but NO trade opens.
+      enabled=true  → qualifying signals auto-open trades. Turning it ON also
+                      acknowledges + clears a tripped circuit breaker
+                      (human-in-the-loop), mirroring the old manual-window arm.
+    """
+    wallet = wallet.lower().strip()
+    raw    = await request.json()
+
+    body_wallet = (raw.get("wallet") or "").lower().strip()
+    timestamp   = raw.get("timestamp")
+    signature   = raw.get("signature")
+    enabled     = raw.get("enabled")
+
+    if body_wallet != wallet:
+        raise HTTPException(400, "Wallet mismatch")
+    if not isinstance(timestamp, int):
+        raise HTTPException(422, "timestamp (ms) required")
+    if not isinstance(signature, str) or not signature:
+        raise HTTPException(422, "signature required")
+    if not isinstance(enabled, bool):
+        raise HTTPException(422, "enabled (bool) required")
+
+    verify_signed_request(
+        action=ACTION_SET_AUTO_TRADE,
+        wallet=wallet,
+        timestamp_ms=timestamp,
+        signature=signature,
+        body={"enabled": enabled},
+    )
+
+    result = await db.execute(select(Subscription).where(Subscription.wallet_address == wallet))
+    sub    = result.scalar_one_or_none()
+    if sub is None:
+        raise HTTPException(404, "Wallet not subscribed")
+
+    sub.auto_trade = enabled
+    cb_cleared = False
+    if enabled and sub.circuit_breaker_tripped_at is not None:
+        # Turning Auto-Trade ON = explicit human ack → clear the breaker.
+        sub.circuit_breaker_tripped_at = None
+        sub.circuit_breaker_reason     = None
+        cb_cleared = True
+    await db.commit()
+    logger.info("Auto-Trade %s for %s%s",
+                "ON" if enabled else "OFF", wallet,
+                " — CB cleared" if cb_cleared else "")
+    return {"auto_trade": enabled, "circuit_breaker_cleared": cb_cleared}

app/config.py

@@ -11,10 +11,14 @@ class Settings(BaseSettings):
         "?streams=btcusdt@kline_1m/ethusdt@kline_1m"
     )
     binance_rest_url: str = "https://data-api.binance.vision"
-    environment: str = "development"
+    environment: str = "production"
     ai_api_key: str = ""
-    ai_base_url: str = "https://api.gptsapi.net/v1"
+    ai_base_url: str = "https://api.deepseek.com/v1"
-    ai_model: str = "claude-haiku-4-5-20251001"
+    ai_model: str = "deepseek-v4-pro"        # batch / reanalysis (quality over speed)
+    ai_live_model: str = "deepseek-v4-flash" # live post analysis (latency-sensitive, ~2s)
+    # Native Anthropic API key — if set, takes priority over ai_api_key + ai_base_url.
+    # Get from https://console.anthropic.com → API Keys
+    anthropic_api_key: str = ""
 
     # Hyperliquid — API wallet private key (NOT your MetaMask key)
     # Created at https://app.hyperliquid.xyz/API and authorized by MetaMask
@@ -30,7 +34,30 @@ class Settings(BaseSettings):
     # Generate with: openssl rand -hex 32
     encryption_key: str = ""
 
-    model_config = {"env_file": ".env", "env_file_encoding": "utf-8"}
+    # Shared secret for /api/signals/ingest — external trading modules pass it
+    # in the X-Ingest-Key header. Empty (default) = ingest endpoint is REJECTED
+    # entirely until a key is configured (fail-closed).
+    ingest_api_key: str = ""
+
+    # Glassnode on-chain data. Used only by the BTC bottom-reversal state
+    # machine (MVRV-Z + STH-SOPR). Empty = scanner logs and skips fail-closed.
+    glassnode_api_key: str = ""
+
+    # Etherscan API key — free at etherscan.io/register → My API Keys.
+    # Used for KOL A-tier: fetch all ERC-20 token balances for a given ETH address.
+    # Empty = skip Ethereum wallet snapshots (only HL perp positions polled).
+    etherscan_api_key: str = ""
+
+    # ── Telegram push alerts ─────────────────────────────────────────────────
+    # Bot token from @BotFather (https://t.me/BotFather → /newbot). Free.
+    # Empty (default) = Telegram alerts disabled completely (bot loop skipped,
+    # notify_signal becomes a no-op, API endpoints return 503).
+    telegram_bot_token: str = ""
+    # Bot username (no @) — used by the Settings UI to render the deep link
+    # t.me/<username>?start=<code>. Example: "trumpalpha_bot".
+    telegram_bot_username: str = ""
+
+    model_config = {"env_file": ".env", "env_file_encoding": "utf-8", "extra": "ignore"}
 
 
 settings = Settings()

app/main.py

@@ -21,6 +21,13 @@ from app.api.trades import router as trades_router
 from app.api.performance import router as performance_router
 from app.api.subscribe import router as subscribe_router
 from app.api.user import router as user_router
+from app.api.funding_signal import router as funding_signal_router
+from app.api.funding_reversal import router as funding_reversal_router
+from app.api.telegram import router as telegram_router
+from app.api.signals import router as signals_router
+from app.api.positions import router as positions_router
+from app.api.scanners  import router as scanners_router
+from app.api.kol       import router as kol_router
 
 logging.basicConfig(
     level=logging.INFO,
@@ -30,17 +37,22 @@ logger = logging.getLogger(__name__)
 
 from typing import Optional
 _binance_task: Optional[asyncio.Task] = None
+_telegram_task: Optional[asyncio.Task] = None
 _scheduler: Optional[AsyncIOScheduler] = None
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    global _binance_task, _scheduler
+    global _binance_task, _telegram_task, _scheduler
 
-    # 1. Create DB tables (dev convenience; production uses Alembic)
+    # 1. Dev convenience only. Production should rely on Alembic so schema
-    async with engine.begin() as conn:
+    # ownership stays explicit and startup never mutates the DB implicitly.
-        await conn.run_sync(Base.metadata.create_all)
+    if settings.environment == "development":
-    logger.info("Database tables ensured.")
+        async with engine.begin() as conn:
+            await conn.run_sync(Base.metadata.create_all)
+        logger.info("Database tables ensured (development mode).")
+    else:
+        logger.info("Skipping create_all; expecting schema managed by Alembic.")
 
     # 2. Backfill historical posts on startup (fast, no Claude API call)
     asyncio.create_task(backfill_history(AsyncSessionLocal, limit=500))
@@ -56,6 +68,18 @@ async def lifespan(app: FastAPI):
 
     # 3. Start Truth Social poller via APScheduler
     _scheduler = AsyncIOScheduler()
+    # Signal monitor — polls every 5 minutes
+    from app.services.funding_signal import poll_funding_signal
+    _scheduler.add_job(
+        poll_funding_signal,
+        "interval",
+        minutes=5,
+        id="funding_signal_poll",
+        max_instances=1,
+        coalesce=True,
+    )
+    logger.info("Breakout signal monitor scheduled every 5 minutes.")
+
     _scheduler.add_job(
         poll_truth_social,
         "interval",
@@ -84,12 +108,88 @@ async def lifespan(app: FastAPI):
         coalesce=True,
         next_run_time=_dt.now(_tz.utc) + _td(seconds=offset),
     )
+    # HL <-> DB reconciliation — every 60s, detects state drift
+    # (manual closes on HL UI, liquidations, orphan positions). See
+    # app/services/reconciler.py. Critical for live trading safety.
+    from app.services.reconciler import reconcile_all_once, RECONCILE_INTERVAL_SECONDS
+    _scheduler.add_job(
+        reconcile_all_once,
+        "interval",
+        seconds=RECONCILE_INTERVAL_SECONDS,
+        id="hl_reconcile",
+        max_instances=1,
+        coalesce=True,
+    )
+    logger.info("HL <-> DB reconciler scheduled every %ds.", RECONCILE_INTERVAL_SECONDS)
+
+    # ── System-2 bottom-reversal state machine ─────────────────────────────
+    # Low-frequency, long-only: fires when ≥2 of 3 classic bottom signals
+    # agree — AHR999 < 0.45, price ≤ 200-week MA ×1.05, Pi Cycle Bottom
+    # (150d EMA ≤ 471d SMA × 0.745). Funding is a booster inside the scanner,
+    # not an independent entry source. See app/services/scanners/btc_bottom_reversal.py.
+    from app.services.scanners.btc_bottom_reversal import scan_once as btc_bottom_scan
+    _scheduler.add_job(
+        btc_bottom_scan, "cron", hour=0, minute=45,
+        id="btc_bottom_reversal_scan", max_instances=1, coalesce=True,
+    )
+    logger.info("BTC bottom-reversal scanner scheduled daily at 00:45 UTC.")
+
+    # ── BTC funding-rate reversal (hourly) ────────────────────────────────
+    # Mean-reversion play on extreme perp positioning. Independent of the
+    # bottom-reversal state machine but uses the same `evaluate_funding_reversal`
+    # algorithm. Runs every hour because HL funding settles hourly.
+    from app.services.scanners.funding_reversal import scan_once as funding_scan
+    _scheduler.add_job(
+        funding_scan, "cron", minute=7,   # :07 every hour, away from other jobs
+        id="funding_reversal_scan", max_instances=1, coalesce=True,
+    )
+    logger.info("Funding reversal scanner scheduled hourly at :07.")
+
+    # ── KOL Substack poller (daily) ──────────────────────────────────────
+    # Hayes & co publish at most a few times a week. Daily poll is plenty;
+    # RSS dedupe by URL is idempotent if it ever fires twice.
+    from app.services.kol_substack import run_substack_poll
+    _scheduler.add_job(
+        run_substack_poll, "cron", hour=1, minute=15,
+        id="kol_substack_poll", max_instances=1, coalesce=True,
+    )
+    logger.info("KOL Substack poller scheduled daily at 01:15 UTC.")
+
+    # ── KOL A-tier: on-chain holdings snapshot (daily) ────────────────────
+    # Polls HL public API (free) for perp positions; Arkham (key optional)
+    # for full portfolio. Diffs against yesterday's snapshot → writes
+    # kol_holding_changes. Runs at 02:00 UTC, after Substack poll finishes.
+    from app.services.kol_onchain import run_onchain_poll
+    _scheduler.add_job(
+        run_onchain_poll, "cron", hour=2, minute=0,
+        id="kol_onchain_poll", max_instances=1, coalesce=True,
+    )
+    logger.info("KOL on-chain holdings poller scheduled daily at 02:00 UTC.")
+
+    # ── KOL talks-vs-trades divergence scan (daily) ───────────────────────
+    # Runs after the on-chain poll finishes. Matches post ticker signals vs
+    # holding changes for the same KOL+ticker within ±7 days.
+    from app.services.kol_divergence import run_divergence_scan
+    _scheduler.add_job(
+        run_divergence_scan, "cron", hour=2, minute=15,
+        id="kol_divergence_scan", max_instances=1, coalesce=True,
+    )
+    logger.info("KOL divergence scan scheduled daily at 02:15 UTC.")
+
     _scheduler.start()
     logger.info(
         "Truth Social pollers scheduled every %ds (CNN + trumpstruth.org).",
         settings.truth_social_poll_seconds,
     )
 
+    # ── Telegram bot long-poll loop (optional) ────────────────────────────
+    # Only started if TELEGRAM_BOT_TOKEN is set. Handles /start CODE bindings
+    # and /stop, /status, /test commands. Survives transient network errors
+    # via internal back-off.
+    from app.services.telegram_bot import run_bot_loop
+    _telegram_task = asyncio.create_task(run_bot_loop(), name="telegram_bot")
+    logger.info("Telegram bot task created (will no-op if token missing).")
+
     yield
 
     # Shutdown
@@ -102,6 +202,12 @@ async def lifespan(app: FastAPI):
             await _binance_task
         except asyncio.CancelledError:
             pass
+    if _telegram_task and not _telegram_task.done():
+        _telegram_task.cancel()
+        try:
+            await _telegram_task
+        except asyncio.CancelledError:
+            pass
     await engine.dispose()
     logger.info("Shutdown complete.")
 
@@ -114,11 +220,18 @@ app = FastAPI(
 )
 
 # CORS
-allowed_origins = [
+# In production we only allow the canonical frontend origin (FRONTEND_URL).
-    settings.frontend_url,
+# In development we additionally permit the local Next dev server. NEVER
-    "http://localhost:3001",
+# permit "*" here — every endpoint either reads/writes user-personalised
-    "http://localhost:3000",
+# data or accepts signed envelopes, both of which require credentialled
-]
+# requests (and "*" is rejected by browsers in combination with credentials
+# anyway).
+allowed_origins = [settings.frontend_url]
+if settings.environment == "development":
+    allowed_origins.extend([
+        "http://localhost:3000",
+        "http://localhost:3001",
+    ])
 app.add_middleware(
     CORSMiddleware,
     allow_origins=allowed_origins,
@@ -134,6 +247,13 @@ app.include_router(trades_router, prefix="/api")
 app.include_router(performance_router, prefix="/api")
 app.include_router(subscribe_router, prefix="/api")
 app.include_router(user_router, prefix="/api")
+app.include_router(funding_signal_router,   prefix="/api")
+app.include_router(funding_reversal_router, prefix="/api")
+app.include_router(telegram_router,         prefix="/api")
+app.include_router(signals_router,        prefix="/api")
+app.include_router(positions_router,      prefix="/api")
+app.include_router(scanners_router,       prefix="/api")
+app.include_router(kol_router,             prefix="/api")
 
 
 @app.get("/api/health")

app/models.py

@@ -60,6 +60,7 @@ class Post(Base):
     target_asset: Mapped[Optional[str]] = mapped_column(String(16), nullable=True)
     category: Mapped[Optional[str]] = mapped_column(String(24), nullable=True)
     expected_move_pct: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    invalidation_price: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
     created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, default=utcnow)
 
     trades: Mapped[List["BotTrade"]] = relationship("BotTrade", back_populates="trigger_post")
@@ -103,6 +104,54 @@ class BotTrade(Base):
     size_usd: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
     leverage: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
 
+    # ── Effective exit params, FROZEN at open ───────────────────────────────
+    # A trade's risk profile is a property of the trade, not of the mutable
+    # Subscription/category config. Without these, recovery.py rehydrates
+    # every open position with the USER's Trump settings — silently rewriting
+    # a 90-day reversal's stop to 1.5% / its max-hold to 7d on every restart.
+    # Stamped from the resolved snapshot at open; recovery reads these back.
+    eff_take_profit_pct:       Mapped[Optional[float]] = mapped_column(Float,   nullable=True)
+    eff_stop_loss_pct:         Mapped[Optional[float]] = mapped_column(Float,   nullable=True)
+    eff_trailing_stop_pct:     Mapped[Optional[float]] = mapped_column(Float,   nullable=True)
+    eff_trailing_activate_pct: Mapped[Optional[float]] = mapped_column(Float,   nullable=True)
+    eff_max_hold_hours:        Mapped[Optional[int]]   = mapped_column(Integer, nullable=True)
+    eff_invalidation:          Mapped[Optional[str]]   = mapped_column(String(24), nullable=True)
+    eff_invalidation_price:    Mapped[Optional[float]] = mapped_column(Float,   nullable=True)
+    eff_min_hold_until_ts:     Mapped[Optional[float]] = mapped_column(Float,   nullable=True)
+
+    # ── System-2 staged de-risk (分段式减仓) ─────────────────────────────────
+    # A System-2 bottom trade is de-risked in stages as it moves against us:
+    # partial reduce-only closes at the early rungs, full close at the last.
+    # The trade stays OPEN (closed_at NULL) until the final rung / upside
+    # ladder / max-hold. Legacy + System-1 rows keep the defaults so all
+    # existing PnL math is unchanged (remaining=1.0, partial_pnl=0, steps=0).
+    realized_partial_pnl_usd: Mapped[float] = mapped_column(Float,   nullable=False, default=0.0)
+    remaining_fraction:       Mapped[float] = mapped_column(Float,   nullable=False, default=1.0)
+    derisk_steps_done:        Mapped[int]   = mapped_column(Integer, nullable=False, default=0)
+
+    # ── System-2 pyramiding (做对了往上加仓) ─────────────────────────────────
+    # base_size_usd: the ORIGINAL notional at open (immutable). size_usd grows
+    # as add-ons fill; entry_price becomes the blended average. addon_steps_done
+    # counts executed pyramids. Pyramiding only runs while derisk_steps_done==0
+    # (clean uptrend), so remaining_fraction stays 1.0 throughout.
+    base_size_usd:     Mapped[Optional[float]] = mapped_column(Float,   nullable=True)
+    addon_steps_done:  Mapped[int]             = mapped_column(Integer, nullable=False, default=0)
+    # Frozen System-2 risk mode for this trade ("standard"/"aggressive").
+    # NULL → legacy/standard. Recovery rebuilds mode-aware ladders from this.
+    sys2_mode:         Mapped[Optional[str]]   = mapped_column(String(16), nullable=True)
+    # Per-trade "Grow" switch. False (default): hold + protective de-risk /
+    # ratchet only — NO pyramiding. True: also scale INTO this winner on
+    # confirmed trend. User flips it per open position. De-risk + stop-loss
+    # run regardless (safety floor is never user-toggleable).
+    grow_mode:         Mapped[bool]            = mapped_column(Boolean, nullable=False, default=False)
+
+    # Monotonic peak unrealised gain (%) seen by tp_sl_monitor. Persisted
+    # (throttled) so a restart doesn't reset the regime: without it a
+    # pyramided / in-profit System-2 trade would fall back to peak=0 →
+    # underwater de-risk regime on the next restart. Recovery seeds the
+    # monitor from this.
+    peak_gain_pct:     Mapped[float]           = mapped_column(Float, nullable=False, default=0.0)
+
     trigger_post: Mapped[Optional["Post"]] = relationship("Post", back_populates="trades")
 
 
@@ -127,3 +176,239 @@ class Subscription(Base):
     # Both stored as naive-UTC datetimes.
     active_from: Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
     active_until: Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
+
+    # ── Convex-strategy fields ──────────────────────────────────────────────
+    # Trailing-stop pair: when profit reaches `trailing_activate_at_pct`, switch
+    # from fixed TP to a trailing stop at `trailing_stop_pct` below the peak.
+    # Both None → legacy behaviour (fixed take_profit_pct closes the position).
+    trailing_stop_pct:        Mapped[Optional[float]] = mapped_column(Float,   nullable=True)
+    trailing_activate_at_pct: Mapped[Optional[float]] = mapped_column(Float,   nullable=True)
+    # Max hold time (hours). 168 = 7 days, long enough for trend capture.
+    # Replaces the old hardcoded 1h cap.
+    max_hold_hours:           Mapped[int]            = mapped_column(Integer, nullable=False, default=168)
+    # One-shot "enable for the next N hours" override. When set and in the future,
+    # the bot trades regardless of the active_from/active_until schedule. NULL or
+    # past timestamp → fall back to the schedule.
+    manual_window_until:      Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
+
+    # ── Phase 1: safety ─────────────────────────────────────────────────────
+    # Paper mode: trades are simulated end-to-end (entry price from Binance,
+    # exit from price_store at trigger time) but no Hyperliquid call is made.
+    # Lets you forward-test a new signal source for 30+ days without real money.
+    paper_mode:                 Mapped[bool]              = mapped_column(Boolean,  nullable=False, default=False)
+    # Circuit breaker: set by services/circuit_breaker.py when daily DD or
+    # consecutive-loss threshold trips. Blocks new trades for 24h and nulls
+    # out any active manual_window. Cleared when user explicitly re-arms via
+    # POST /api/user/{wallet}/manual-window.
+    circuit_breaker_tripped_at: Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
+    circuit_breaker_reason:     Mapped[Optional[str]]      = mapped_column(String(32), nullable=True)
+
+    # ── Two-system separation ───────────────────────────────────────────────
+    # System 1 (Trump) and System 2 (reversal) must not starve or halt each
+    # other. sys2_budget_pct slices the daily budget; the sys2_cb_* columns
+    # are an independent circuit breaker so a Trump losing streak can't lock
+    # out a once-a-year reversal signal (and vice versa).
+    sys2_budget_pct:     Mapped[float] = mapped_column(Float, nullable=False, default=0.7)
+    sys2_cb_tripped_at:  Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
+    sys2_cb_reason:      Mapped[Optional[str]]      = mapped_column(String(32), nullable=True)
+
+    # ── System-2 dynamic leverage ───────────────────────────────────────────
+    # System 2 (bottom reversal) uses its OWN leverage, independent of the
+    # Trump `leverage` field. The user picks it freely; the value at signal
+    # time is frozen onto the trade. The protective stop in tp_sl_monitor is
+    # auto-scaled to this leverage so the position is de-risked INSIDE the
+    # exchange liquidation line — it is never liquidated by the exchange.
+    # NULL → fall back to SYS2_DEFAULT_LEVERAGE (signal_categories).
+    sys2_leverage:       Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+
+    # System-2 risk mode: "standard" (tuned cycle-rider, low leverage) or
+    # "aggressive" (separately-funded high-risk sleeve: high leverage, heavier
+    # earlier pyramiding, wider peak-trail, lighter early de-risk). Both keep
+    # the never-exchange-liquidated + post-pyramid-breakeven invariants.
+    sys2_mode:           Mapped[str] = mapped_column(String(16), nullable=False, default="standard")
+
+    # ── Master Auto-Trade switch (simplified operator model) ────────────────
+    # The ONE persistent gate the user controls. OFF (default, safe): signals
+    # are still scanned + ingested (shown in the feed) but NO trade is opened.
+    # ON: a qualifying signal auto-opens a trade. Replaces the old confusing
+    # scanner-toggle / timed manual-window / schedule trio. Flipping it ON also
+    # acknowledges + clears a tripped circuit breaker (human-in-the-loop).
+    auto_trade:          Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+
+
+class KolPost(Base):
+    """A long-form post (Substack) or tweet from a tracked KOL, with inline
+    AI analysis. Standalone module — no FK into Trump posts/trades.
+
+    Dedupe key: (source, external_id). For Substack external_id is the post
+    URL; for Twitter it will be the tweet id.
+    """
+    __tablename__ = "kol_posts"
+    __table_args__ = (UniqueConstraint("source", "external_id", name="uq_kol_post_src_extid"),)
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, index=True)
+    kol_handle:    Mapped[str] = mapped_column(String(64), nullable=False, index=True)
+    source:        Mapped[str] = mapped_column(String(16), nullable=False)  # substack|twitter
+    external_id:   Mapped[str] = mapped_column(String(512), nullable=False)
+    title:         Mapped[Optional[str]] = mapped_column(String(512), nullable=True)
+    url:           Mapped[str] = mapped_column(String(512), nullable=False)
+    published_at:  Mapped[datetime] = mapped_column(DateTime, nullable=False, index=True)
+    raw_text:      Mapped[str] = mapped_column(Text, nullable=False)
+    content_hash:  Mapped[str] = mapped_column(String(64), nullable=False)
+
+    summary:          Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    tickers_json:     Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    analyzed_at:      Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
+    analysis_model:   Mapped[Optional[str]] = mapped_column(String(64), nullable=True)
+    analysis_version: Mapped[Optional[str]] = mapped_column(String(16), nullable=True)
+
+    created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, default=utcnow)
+
+
+# ── KOL A-tier: on-chain holdings ────────────────────────────────────────────
+
+class KolWallet(Base):
+    """One tracked wallet address for a KOL. Many-to-one with handle."""
+    __tablename__ = "kol_wallets"
+    __table_args__ = (UniqueConstraint("chain", "address", name="uq_kol_wallet_chain_addr"),)
+
+    id:         Mapped[int]           = mapped_column(Integer, primary_key=True)
+    handle:     Mapped[str]           = mapped_column(String(64), nullable=False, index=True)
+    chain:      Mapped[str]           = mapped_column(String(16), nullable=False)   # ethereum|solana|hl
+    address:    Mapped[str]           = mapped_column(String(128), nullable=False)
+    label:      Mapped[Optional[str]] = mapped_column(String(128), nullable=True)   # e.g. "hot wallet"
+    source_url: Mapped[Optional[str]] = mapped_column(String(256), nullable=True)   # Arkham entity URL
+    active:     Mapped[bool]          = mapped_column(Boolean, nullable=False, default=True)
+    added_at:   Mapped[datetime]      = mapped_column(DateTime, nullable=False, default=utcnow)
+
+    snapshots: Mapped[List["KolHoldingSnapshot"]] = relationship(
+        "KolHoldingSnapshot", back_populates="wallet", order_by="KolHoldingSnapshot.snapshot_date.desc()"
+    )
+    changes: Mapped[List["KolHoldingChange"]] = relationship(
+        "KolHoldingChange", back_populates="wallet"
+    )
+
+
+class KolHoldingSnapshot(Base):
+    """Daily portfolio snapshot for one wallet. holdings_json is a list of
+    {ticker, amount, usd_value, chain} dicts. One row per (wallet, date)."""
+    __tablename__ = "kol_holdings_snapshots"
+    __table_args__ = (UniqueConstraint("wallet_id", "snapshot_date", name="uq_kol_snapshot_wallet_date"),)
+
+    id:            Mapped[int]             = mapped_column(Integer, primary_key=True)
+    wallet_id:     Mapped[int]             = mapped_column(Integer, ForeignKey("kol_wallets.id"), nullable=False, index=True)
+    snapshot_date: Mapped[str]             = mapped_column(String(10), nullable=False)   # YYYY-MM-DD UTC
+    holdings_json: Mapped[str]             = mapped_column(Text, nullable=False)         # JSON list
+    total_usd:     Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    source:        Mapped[str]             = mapped_column(String(16), nullable=False)   # arkham|hl|manual
+    created_at:    Mapped[datetime]        = mapped_column(DateTime, nullable=False, default=utcnow)
+
+    wallet: Mapped["KolWallet"] = relationship("KolWallet", back_populates="snapshots")
+
+
+class KolHoldingChange(Base):
+    """A detected significant change between two consecutive daily snapshots."""
+    __tablename__ = "kol_holding_changes"
+
+    id:          Mapped[int]             = mapped_column(Integer, primary_key=True)
+    wallet_id:   Mapped[int]             = mapped_column(Integer, ForeignKey("kol_wallets.id"), nullable=False, index=True)
+    detected_at: Mapped[datetime]        = mapped_column(DateTime, nullable=False, index=True, default=utcnow)
+    ticker:      Mapped[str]             = mapped_column(String(32), nullable=False)
+    change_type: Mapped[str]             = mapped_column(String(16), nullable=False)  # new_position|closed|increased|decreased
+    usd_before:  Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    usd_after:   Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    pct_change:  Mapped[Optional[float]] = mapped_column(Float, nullable=True)   # signed %
+    created_at:  Mapped[datetime]        = mapped_column(DateTime, nullable=False, default=utcnow)
+
+    wallet: Mapped["KolWallet"] = relationship("KolWallet", back_populates="changes")
+
+
+class KolDivergence(Base):
+    """Cross-signal: a (post, on-chain change) pair for the same KOL+ticker
+    within a ±7-day window.
+
+    signal_type='divergence' → KOL's public post contradicts their on-chain action
+                               (says bullish, actually selling → smart-money caution)
+    signal_type='alignment'  → post and chain agree → higher-conviction signal
+    direction='long'/'short' → net conclusion after resolving the pair
+    """
+    __tablename__ = "kol_divergence"
+    __table_args__ = (
+        UniqueConstraint("post_id", "change_id", name="uq_kol_divergence_pair"),
+    )
+
+    id:               Mapped[int]            = mapped_column(Integer, primary_key=True)
+    handle:           Mapped[str]            = mapped_column(String(64), nullable=False, index=True)
+    ticker:           Mapped[str]            = mapped_column(String(32), nullable=False)
+    # B-tier content side
+    post_id:          Mapped[int]            = mapped_column(Integer, ForeignKey("kol_posts.id"), nullable=False)
+    post_action:      Mapped[str]            = mapped_column(String(16), nullable=False)
+    post_conviction:  Mapped[Optional[float]]= mapped_column(Float, nullable=True)
+    post_at:          Mapped[datetime]       = mapped_column(DateTime, nullable=False)
+    # A-tier on-chain side
+    change_id:        Mapped[int]            = mapped_column(Integer, ForeignKey("kol_holding_changes.id"), nullable=False)
+    onchain_action:   Mapped[str]            = mapped_column(String(16), nullable=False)
+    usd_before:       Mapped[Optional[float]]= mapped_column(Float, nullable=True)
+    usd_after:        Mapped[Optional[float]]= mapped_column(Float, nullable=True)
+    onchain_at:       Mapped[datetime]       = mapped_column(DateTime, nullable=False)
+    # Classification
+    signal_type:      Mapped[str]            = mapped_column(String(16), nullable=False)  # divergence|alignment
+    direction:        Mapped[Optional[str]]  = mapped_column(String(8),  nullable=True)   # long|short
+    days_apart:       Mapped[Optional[float]]= mapped_column(Float, nullable=True)
+    created_at:       Mapped[datetime]       = mapped_column(DateTime, nullable=False, default=utcnow)
+
+    post:   Mapped["KolPost"]          = relationship("KolPost")
+    change: Mapped["KolHoldingChange"] = relationship("KolHoldingChange")
+
+
+class TelegramBinding(Base):
+    """Wallet ↔ Telegram chat_id mapping plus per-source alert preferences.
+
+    Two flavours of binding co-exist in this table:
+
+      Free tier (walletless): user DM'd the bot `/start` with no code.
+                              wallet_address is NULL. They get the same
+                              push content as Pro users unless they tune
+                              preferences in the bot.
+
+      Pro tier (wallet-linked): user generated a 6-char code in Settings,
+                                replied `/start CODE` in the bot. wallet
+                                is attached so the dashboard knows which
+                                chat to disconnect / link / show status.
+
+    Uniqueness: chat_id is unique across all rows (table-level). wallet_address
+    is unique among non-NULL rows via the partial index in migration 021 —
+    NEVER use SQLAlchemy `unique=True` here because some dialects would emit a
+    plain UNIQUE that disallows multiple walletless rows.
+
+    Preferences (alert_*, min_confidence, mute_*) are now mutated exclusively
+    via the bot's /trump /btc /funding /kol /conf /quiet commands. The
+    /api/telegram/preferences endpoint exists for legacy reasons but no UI
+    surface calls it.
+    """
+    __tablename__ = "telegram_bindings"
+
+    id:             Mapped[int]            = mapped_column(Integer, primary_key=True)
+    # NULL is allowed — see class docstring. Uniqueness for non-NULL values
+    # is enforced by the partial unique index defined in migration 021.
+    wallet_address: Mapped[Optional[str]]  = mapped_column(String(64), nullable=True,
+                                                           index=True)
+    chat_id:        Mapped[int]            = mapped_column(BigInteger, nullable=False,
+                                                           unique=True, index=True)
+    tg_username:    Mapped[Optional[str]]  = mapped_column(String(64), nullable=True)
+    bound_at:       Mapped[datetime]       = mapped_column(DateTime, nullable=False, default=utcnow)
+
+    alerts_enabled:        Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
+    alert_trump:           Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
+    alert_btc_bottom:      Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
+    alert_funding:         Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
+    alert_kol_divergence:  Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    min_confidence:        Mapped[int]  = mapped_column(Integer, nullable=False, default=70)
+
+    # Quiet hours (UTC). Both null = always on.
+    mute_from_hour:  Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    mute_until_hour: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+
+    last_alert_at:        Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
+    total_alerts_sent:    Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    total_alerts_failed:  Mapped[int] = mapped_column(Integer, nullable=False, default=0)

app/schemas.py

@@ -37,6 +37,7 @@ class TrumpPost(BaseModel):
     target_asset: Optional[str] = None
     category: Optional[str] = None
     expected_move_pct: Optional[float] = None
+    invalidation_price: Optional[float] = None
 
     model_config = {"from_attributes": True}
 
@@ -61,6 +62,13 @@ class BotTrade(BaseModel):
     trigger_post_id: int
     opened_at: str
     closed_at: str
+    # Source tag of the originating signal (e.g. 'truth', 'breakout', 'my_strategy').
+    # Joined from posts.source on read; not stored on BotTrade itself.
+    # 'unknown' when the trigger post has been deleted or trigger_post_id is null.
+    trigger_source: Optional[str] = None
+    # True iff this was a paper trade (hl_order_id == 'paper'). Lets the UI
+    # tag the row so paper-mode P&L isn't mixed with real money in summaries.
+    is_paper: bool = False
 
     model_config = {"from_attributes": True}
 
@@ -87,12 +95,17 @@ class SignedEnvelope(BaseModel):
 
 
 class SubscribeRequest(SignedEnvelope):
-    pass
+    # Optional paper-mode flag. When true the bot writes simulated trades to
+    # the DB but never hits Hyperliquid — safe path for new users to try the
+    # system without risking funds. Defaults to false (live) for backwards
+    # compatibility with the existing signed-message format (body=None).
+    paper_mode: Optional[bool] = False
 
 
 class SubscribeResponse(BaseModel):
-    status: str
+    status:     str
-    wallet: str
+    wallet:     str
+    paper_mode: bool = False
 
 
 class SetApiKeyRequest(SignedEnvelope):
@@ -102,6 +115,7 @@ class SetApiKeyRequest(SignedEnvelope):
 class SetApiKeyResponse(BaseModel):
     status: str
     masked_key: str  # last 6 chars only, e.g. "...a1b2c3"
+    verified: bool = False
 
 
 class UserSettings(BaseModel):
@@ -111,6 +125,13 @@ class UserSettings(BaseModel):
     stop_loss_pct: Optional[float] = None
     min_confidence: int
     daily_budget_usd: Optional[float] = None
+    # System-2 (bottom reversal) leverage — independent of `leverage` (Trump).
+    # None → platform default (SYS2_DEFAULT_LEVERAGE). The protective stop is
+    # auto-scaled to this so the position is never exchange-liquidated.
+    sys2_leverage: Optional[int] = None
+    # System-2 risk mode: "standard" (default) or "aggressive" (separately
+    # funded high-risk/high-explosiveness sleeve). None → unchanged/standard.
+    sys2_mode: Optional[str] = None
     # ISO-8601 UTC strings; both None = always on (Subscription.active still gates it).
     active_from: Optional[str] = None
     active_until: Optional[str] = None
@@ -128,3 +149,6 @@ class UserResponse(BaseModel):
     hl_api_key_masked: Optional[str] = None
     trades: list[BotTrade]
     settings: UserSettings
+    # Convex-strategy: ISO-UTC timestamp until which the bot is manually armed.
+    # When null or in the past, the regular active_from/active_until schedule applies.
+    manual_window_until: Optional[str] = None

app/scrapers/trumpstruth.py

@@ -53,7 +53,10 @@ async def _fetch_feed() -> Optional[str]:
             resp.raise_for_status()
             return resp.text
     except Exception as exc:
-        logger.warning("Failed to fetch trumpstruth.org feed: %s", exc)
+        # Include type name — httpx often raises bare ConnectError/RemoteProtocolError
+        # with empty .args, which formats as just "Failed to fetch ..." with no body.
+        logger.warning("Failed to fetch trumpstruth.org feed: %s (%s)",
+                       type(exc).__name__, exc)
         return None
 
 
@@ -141,6 +144,14 @@ async def poll_trumpstruth(db_session_factory) -> None:
                     await manager.broadcast(_post_to_ws_payload(post))
                     logger.info("[trumpstruth] beat CNN — new post id=%d: %s",
                                 post.id, post.text[:60])
+                    # Telegram fan-out — matches truth_social.py. Without this,
+                    # whichever poller wins the race determines whether users
+                    # get pushed — flaky 50% delivery.
+                    try:
+                        from app.services.telegram import notify_signal
+                        notify_signal(post)
+                    except Exception as exc:
+                        logger.warning("Telegram notify failed for post %d: %s", post.id, exc)
                     try:
                         from app.services.bot_engine import process_post
                         await process_post(post, db)

app/scrapers/truth_social.py

@@ -63,7 +63,11 @@ async def _fetch_archive() -> Optional[list]:
             resp.raise_for_status()
             return resp.json()
     except Exception as exc:
-        logger.error("Failed to fetch CNN archive: %s", exc)
+        # Include type name — httpx often raises bare ConnectError/TimeoutException
+        # with empty .args, which used to log as just "Failed to fetch CNN archive:"
+        # with no body, making outages impossible to diagnose.
+        logger.error("Failed to fetch CNN archive: %s (%s)",
+                     type(exc).__name__, exc)
         return None
 
 
@@ -80,6 +84,31 @@ async def _process_entry(entry: dict, db: AsyncSession) -> Optional[Post]:
 
     published_at = _parse_dt(entry.get("created_at", ""))
 
+    # ── Deterministic entry pre-filter (saves AI spend + blocks 80% of noise) ──
+    # The 13-trade backtest showed AI confidence ≥ 85 still lets through pure
+    # rhetoric and second-derivative news. Hard-coded action-marker + future-
+    # tense + dedup check rejects those BEFORE the AI call. Failing posts are
+    # still saved to DB (so we have a record) but stamped as non-actionable.
+    from app.database import AsyncSessionLocal
+    from app.services.entry_filter import passes_entry_filter
+    filter_ok, filter_reason = await passes_entry_filter(text, AsyncSessionLocal)
+    if not filter_ok:
+        logger.info("Entry filter rejected post (id=%s): %s — %s",
+                    entry.get("id"), filter_reason, text[:80])
+        # Insert a stub row so we don't keep re-fetching the same post from
+        # the upstream archive. Signal=hold, no AI call, no analysis.
+        stub = Post(
+            external_id=external_id, text=text, source="truth",
+            published_at=published_at,
+            sentiment="neutral", ai_confidence=0,
+            relevant=False, signal="hold",
+            prefilter_reason=filter_reason[:32],
+            analysis_version="entry_filter_rejected",
+        )
+        db.add(stub)
+        await db.flush()
+        return None  # don't broadcast, don't trade
+
     analysis = await analyze_post(text)
 
     asset = analysis["asset"]
@@ -154,6 +183,9 @@ def _post_to_ws_payload(post: Post) -> dict:
             "ai_confidence": post.ai_confidence,
             "ai_reasoning": post.ai_reasoning,
             "relevant": post.relevant,
+            "target_asset": post.target_asset,
+            "category": post.category,
+            "expected_move_pct": post.expected_move_pct,
             "price_impact": price_impact,
         },
     }
@@ -187,6 +219,13 @@ async def poll_truth_social(db_session_factory) -> None:
                 for post in new_posts:
                     await manager.broadcast(_post_to_ws_payload(post))
                     logger.info("Saved new post id=%d: %s", post.id, post.text[:60])
+                    # Telegram fan-out (fire-and-forget). Only fires if
+                    # signal is buy/short; noise posts are filtered inside.
+                    try:
+                        from app.services.telegram import notify_signal
+                        notify_signal(post)
+                    except Exception as exc:
+                        logger.warning("Telegram notify failed for post %d: %s", post.id, exc)
                     try:
                         from app.services.bot_engine import process_post
                         await process_post(post, db)

app/services/analysis.py

@@ -32,6 +32,7 @@ from app.config import settings
 logger = logging.getLogger(__name__)
 
 _client: Optional[AsyncOpenAI] = None
+_anthropic_client = None
 
 ANALYSIS_VERSION = "v5-extreme-alpha"
 
@@ -373,6 +374,19 @@ def _fallback(prefilter: Optional[str] = None, reasoning: str = "") -> dict:
     return out
 
 
+def _use_anthropic() -> bool:
+    """True if a native Anthropic API key is configured (takes priority over proxy)."""
+    return bool(settings.anthropic_api_key)
+
+
+def _get_anthropic_client():
+    global _anthropic_client
+    if _anthropic_client is None:
+        import anthropic as _anthropic
+        _anthropic_client = _anthropic.AsyncAnthropic(api_key=settings.anthropic_api_key)
+    return _anthropic_client
+
+
 def _get_client() -> AsyncOpenAI:
     global _client
     if _client is None:
@@ -393,13 +407,21 @@ def _liquidity_note(hour: int) -> str:
     return "Off-peak hours, moderate liquidity"
 
 
-async def analyze_post(text: str) -> dict:
+async def analyze_post(text: str, model: Optional[str] = None) -> dict:
     """Score a Trump post and return signal + structured reasoning.
 
+    Args:
+        text:  Raw post text (up to 2000 chars used).
+        model: Override the model to use. Defaults to settings.ai_live_model
+               for latency-sensitive live calls; pass settings.ai_model
+               explicitly for higher-quality batch reanalysis.
+
     Returns the canonical dict shape expected by the rest of the codebase:
       relevant, asset, sentiment, signal (buy/short/hold), confidence,
       reasoning, prefilter_reason, analysis_version.
     """
+    if model is None:
+        model = settings.ai_live_model  # fast flash for live posts
     # ── Fast pre-filter (no AI call) ────────────────────────────────
     stripped = text.strip()
     if not stripped:
@@ -423,17 +445,45 @@ async def analyze_post(text: str) -> dict:
     )
 
     try:
-        client = _get_client()
+        if _use_anthropic():
-        response = await client.chat.completions.create(
+            # ── Native Anthropic SDK path ────────────────────────────────
-            model=settings.ai_model,
+            anthropic_client = _get_anthropic_client()
-            max_tokens=600,
+            # Map OpenAI-style model name → Anthropic model name
-            temperature=0.1,
+            model_name = model
-            messages=[
+            if "haiku" in model_name.lower() and "claude-" not in model_name.lower():
-                {"role": "system", "content": SYSTEM_PROMPT},
+                model_name = "claude-haiku-4-5-20251001"
-                {"role": "user", "content": user_prompt},
+            msg = await anthropic_client.messages.create(
-            ],
+                model=model_name,
-        )
+                max_tokens=600,
-        raw = (response.choices[0].message.content or "").strip()
+                temperature=0.1,
+                system=SYSTEM_PROMPT,
+                messages=[{"role": "user", "content": user_prompt}],
+            )
+            raw = (msg.content[0].text if msg.content else "").strip()
+        else:
+            # ── OpenAI-compatible proxy path (DeepSeek, gptsapi, etc.) ──
+            # Reasoning models (deepseek-v4-pro / R1) don't support
+            # temperature and need higher max_tokens for the thinking pass.
+            model_name = model
+            is_reasoning = any(x in model_name for x in ("pro", "reasoner", "r1", "think"))
+
+            kwargs: dict = {
+                "model": model_name,
+                "messages": [
+                    {"role": "system", "content": SYSTEM_PROMPT},
+                    {"role": "user", "content": user_prompt},
+                ],
+            }
+            if is_reasoning:
+                # Reasoning models: large token budget; omit temperature
+                kwargs["max_tokens"] = 4000
+            else:
+                kwargs["max_tokens"] = 1200
+                kwargs["temperature"] = 0.1
+
+            client = _get_client()
+            response = await client.chat.completions.create(**kwargs)
+            raw = (response.choices[0].message.content or "").strip()
 
         # Strip ```json fences if the model adds them despite instructions
         if raw.startswith("```"):

app/services/backtest.py

@@ -0,0 +1,376 @@
+"""
+Single-post backtest harness.
+
+Given a Post with a directional signal (buy/short) and a target asset, fetch
+the historical 1-minute candles for [published_at, published_at + max_hold_h]
+from Binance and replay the new convex-strategy exit rules. Outputs what
+WOULD have happened if we had been live at that moment with the current
+trailing-stop / stop-loss / max-hold settings.
+
+Why this exists:
+  The bot's m1h accuracy is 45.7%, but that was measured with a 1-hour
+  fixed-window snapshot — i.e. assuming we close at exactly the 1-hour mark.
+  The new exit logic (trailing stop, 7-day max hold) is FUNDAMENTALLY
+  different. Without backtesting we can't know whether changing exits also
+  changes the win rate / PnL profile. This harness lets us check.
+
+Scope (deliberately narrow for the MVP):
+  - One post at a time. Batch runner sits on top.
+  - Uses 1m HIGH/LOW within each bar (worst-case path) — conservative.
+  - Ignores fees + slippage (caller is expected to subtract them).
+  - Assumes immediate fill at the candle's OPEN on entry.
+  - Does NOT re-evaluate regime gates (the caller decides whether to
+    include the post). This makes "what would have happened" cleaner.
+
+The 1m granularity matters: with 5m or 1H bars you can't distinguish
+"stop loss hit then bounced back" from "rallied straight up", and a
+trailing-stop strategy lives or dies on that distinction.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, asdict
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+
+BINANCE_KLINES_URL = "https://api.binance.com/api/v3/klines"
+
+
+@dataclass
+class BacktestResult:
+    post_id:        int
+    asset:          str
+    side:           str           # "long" | "short"
+    entry_price:    float
+    exit_price:     float
+    exit_reason:    str           # "trailing_stop" | "stop_loss" | "take_profit" | "max_hold"
+    hold_minutes:   int
+    pnl_pct:        float         # net of nothing — apply your own fee model
+    peak_pct:       float         # best unrealised gain reached
+    trough_pct:     float         # worst unrealised gain reached
+    bars_evaluated: int
+
+    def to_dict(self) -> dict:
+        return asdict(self)
+
+
+@dataclass
+class BacktestParams:
+    """Exit-rule snapshot used for the simulation."""
+    stop_loss_pct:            float = 1.5
+    trailing_stop_pct:        Optional[float] = 2.5
+    trailing_activate_at_pct: Optional[float] = 5.0
+    take_profit_pct:          Optional[float] = None
+    max_hold_hours:           int   = 168
+
+
+async def fetch_binance_1m(
+    symbol: str,
+    start: datetime,
+    end: datetime,
+    client: Optional[httpx.AsyncClient] = None,
+) -> list[dict]:
+    """Pull 1-minute candles from Binance spot for [start, end].
+
+    Binance caps a single call at 1000 candles → we page in chunks. Returns
+    candles in chronological order. Each candle: {time_ms, open, high, low, close}.
+    """
+    own_client = client is None
+    if own_client:
+        client = httpx.AsyncClient(timeout=20)
+    out: list[dict] = []
+    cursor_ms = int(start.replace(tzinfo=timezone.utc).timestamp() * 1000)
+    end_ms    = int(end.replace(tzinfo=timezone.utc).timestamp() * 1000)
+    try:
+        while cursor_ms < end_ms:
+            params = {
+                "symbol":   symbol,
+                "interval": "1m",
+                "startTime": cursor_ms,
+                "endTime":   end_ms,
+                "limit":    1000,
+            }
+            resp = await client.get(BINANCE_KLINES_URL, params=params)
+            resp.raise_for_status()
+            chunk = resp.json()
+            if not chunk:
+                break
+            for row in chunk:
+                out.append({
+                    "time_ms": row[0],
+                    "open":    float(row[1]),
+                    "high":    float(row[2]),
+                    "low":     float(row[3]),
+                    "close":   float(row[4]),
+                })
+            # Next page starts after the last candle returned.
+            last_open = chunk[-1][0]
+            if last_open <= cursor_ms:
+                break
+            cursor_ms = last_open + 60_000
+            if len(chunk) < 1000:
+                break  # final partial page
+    finally:
+        if own_client:
+            await client.aclose()
+    return out
+
+
+def _asset_to_symbol(asset: str) -> str:
+    """Map our internal asset code to a Binance spot symbol.
+
+    Most assets are simply {ASSET}USDT. TRUMP/HYPE/etc. may not exist on
+    Binance — those will fail at fetch time and we'll return None upstream.
+    """
+    return f"{asset.upper()}USDT"
+
+
+def simulate_exit(
+    candles: list[dict],
+    side: str,
+    params: BacktestParams,
+) -> dict:
+    """Walk the candles 1m at a time, applying the exit ladder.
+
+    Priority within a bar (worst-case ordering):
+      1. Stop loss  — assume hit via LOW (long) / HIGH (short)
+      2. Trailing   — only if armed; assume hit via the same worst-case extreme
+      3. Take profit — fixed TP if set
+    A real bar can't tell us whether the high or low printed first, so we
+    take the pessimistic path: STOP LOSS BEFORE PROFIT if both could have
+    triggered. This makes the backtest conservative.
+    """
+    if not candles:
+        return {"reason": "no_data", "exit_price": 0.0, "bars": 0,
+                "peak_pct": 0.0, "trough_pct": 0.0, "pnl_pct": 0.0}
+
+    entry = candles[0]["open"]
+    if entry == 0:
+        return {"reason": "bad_entry", "exit_price": 0.0, "bars": 0,
+                "peak_pct": 0.0, "trough_pct": 0.0, "pnl_pct": 0.0}
+
+    is_long = side == "long"
+    peak_pct   = 0.0
+    trough_pct = 0.0
+    armed      = False
+
+    def pct(price: float) -> float:
+        raw = (price - entry) / entry
+        return (raw if is_long else -raw) * 100
+
+    for i, bar in enumerate(candles):
+        # Within-bar extremes in position's favoured direction
+        good_extreme = bar["high"] if is_long else bar["low"]
+        bad_extreme  = bar["low"]  if is_long else bar["high"]
+        good_pct = pct(good_extreme)
+        bad_pct  = pct(bad_extreme)
+
+        # Update running peak / trough using extremes
+        if good_pct > peak_pct:   peak_pct   = good_pct
+        if bad_pct  < trough_pct: trough_pct = bad_pct
+
+        # 1. Stop loss — pessimistic check first
+        if bad_pct <= -params.stop_loss_pct:
+            # Approximate exit at the SL trigger price (not the bar extreme)
+            sl_price = entry * (1 - params.stop_loss_pct / 100) if is_long \
+                       else entry * (1 + params.stop_loss_pct / 100)
+            return {
+                "reason": "stop_loss",
+                "exit_price": sl_price,
+                "bars": i + 1,
+                "peak_pct": peak_pct,
+                "trough_pct": trough_pct,
+                "pnl_pct": -params.stop_loss_pct,
+            }
+
+        # 2. Trailing — arm if peak crossed activation
+        if (params.trailing_stop_pct is not None
+            and params.trailing_activate_at_pct is not None):
+            if not armed and peak_pct >= params.trailing_activate_at_pct:
+                armed = True
+            if armed:
+                # Drawdown from peak (using bad_extreme of this bar)
+                drawdown = peak_pct - bad_pct
+                if drawdown >= params.trailing_stop_pct:
+                    # Exit at peak − trailing_stop_pct (approx)
+                    exit_pct = peak_pct - params.trailing_stop_pct
+                    ts_price = entry * (1 + exit_pct / 100) if is_long \
+                               else entry * (1 - exit_pct / 100)
+                    return {
+                        "reason": "trailing_stop",
+                        "exit_price": ts_price,
+                        "bars": i + 1,
+                        "peak_pct": peak_pct,
+                        "trough_pct": trough_pct,
+                        "pnl_pct": exit_pct,
+                    }
+
+        # 3. Fixed TP
+        if params.take_profit_pct is not None and good_pct >= params.take_profit_pct:
+            tp_price = entry * (1 + params.take_profit_pct / 100) if is_long \
+                       else entry * (1 - params.take_profit_pct / 100)
+            return {
+                "reason": "take_profit",
+                "exit_price": tp_price,
+                "bars": i + 1,
+                "peak_pct": peak_pct,
+                "trough_pct": trough_pct,
+                "pnl_pct": params.take_profit_pct,
+            }
+
+    # Walked the whole window without hitting any rule → close at last bar
+    last_close = candles[-1]["close"]
+    return {
+        "reason": "max_hold",
+        "exit_price": last_close,
+        "bars": len(candles),
+        "peak_pct": peak_pct,
+        "trough_pct": trough_pct,
+        "pnl_pct": pct(last_close),
+    }
+
+
+async def backtest_post(
+    post_id: int,
+    params: Optional[BacktestParams] = None,
+) -> Optional[BacktestResult]:
+    """Backtest a single Post by ID.
+
+    Returns None if:
+      - Post not found
+      - signal is not buy/short
+      - target_asset is unknown / not on Binance
+      - Binance has no data for the window
+    """
+    from app.database import AsyncSessionLocal
+    from app.models import Post
+    from sqlalchemy import select
+
+    params = params or BacktestParams()
+
+    async with AsyncSessionLocal() as db:
+        result = await db.execute(select(Post).where(Post.id == post_id))
+        post = result.scalar_one_or_none()
+
+    if post is None:
+        logger.warning("Backtest: post %d not found", post_id)
+        return None
+    if post.signal not in ("buy", "short"):
+        logger.warning("Backtest: post %d signal=%s not actionable", post_id, post.signal)
+        return None
+
+    asset = post.target_asset or post.price_impact_asset
+    if not asset:
+        logger.warning("Backtest: post %d has no asset", post_id)
+        return None
+
+    side = "long" if post.signal == "buy" else "short"
+    symbol = _asset_to_symbol(asset)
+
+    # Window: [published_at, published_at + max_hold_hours]
+    start = post.published_at
+    end   = start + timedelta(hours=params.max_hold_hours)
+    # Don't backtest a window that hasn't fully elapsed yet
+    now_naive = datetime.now(timezone.utc).replace(tzinfo=None)
+    if end > now_naive:
+        end = now_naive
+
+    candles = await fetch_binance_1m(symbol, start, end)
+    if not candles:
+        logger.warning("Backtest: no Binance data for %s in [%s, %s]", symbol, start, end)
+        return None
+
+    sim = simulate_exit(candles, side, params)
+    if sim["reason"] in ("no_data", "bad_entry"):
+        return None
+
+    return BacktestResult(
+        post_id=post_id,
+        asset=asset,
+        side=side,
+        entry_price=candles[0]["open"],
+        exit_price=sim["exit_price"],
+        exit_reason=sim["reason"],
+        hold_minutes=sim["bars"],
+        pnl_pct=round(sim["pnl_pct"], 3),
+        peak_pct=round(sim["peak_pct"], 3),
+        trough_pct=round(sim["trough_pct"], 3),
+        bars_evaluated=sim["bars"],
+    )
+
+
+async def backtest_batch(
+    limit: int = 50,
+    min_confidence: int = 80,
+    params: Optional[BacktestParams] = None,
+) -> dict:
+    """Backtest every directional post with confidence ≥ min_confidence.
+
+    Returns aggregate stats + per-trade results. Posts whose Binance data
+    is missing (TRUMP, niche perps) are skipped silently.
+    """
+    from app.database import AsyncSessionLocal
+    from app.models import Post
+    from sqlalchemy import select, and_, or_
+
+    params = params or BacktestParams()
+
+    async with AsyncSessionLocal() as db:
+        rows = await db.execute(
+            select(Post.id).where(
+                and_(
+                    Post.signal.in_(["buy", "short"]),
+                    Post.ai_confidence >= min_confidence,
+                )
+            ).order_by(Post.published_at.desc()).limit(limit)
+        )
+        post_ids = [r[0] for r in rows.all()]
+
+    results: list[BacktestResult] = []
+    skipped: list[int] = []
+    for pid in post_ids:
+        try:
+            r = await backtest_post(pid, params)
+            if r is None:
+                skipped.append(pid)
+            else:
+                results.append(r)
+        except Exception as exc:
+            logger.error("Backtest post %d failed: %s", pid, exc)
+            skipped.append(pid)
+
+    if not results:
+        return {"params": asdict(params), "results": [], "skipped": skipped,
+                "summary": {"trades": 0}}
+
+    pnls = [r.pnl_pct for r in results]
+    wins = [p for p in pnls if p > 0]
+    losses = [p for p in pnls if p <= 0]
+    by_reason: dict[str, int] = {}
+    for r in results:
+        by_reason[r.exit_reason] = by_reason.get(r.exit_reason, 0) + 1
+
+    summary = {
+        "trades":       len(results),
+        "skipped":      len(skipped),
+        "win_rate_pct": round(len(wins) / len(results) * 100, 1),
+        "avg_pnl_pct":  round(sum(pnls) / len(pnls), 3),
+        "total_pnl_pct": round(sum(pnls), 3),
+        "best_pct":     round(max(pnls), 3),
+        "worst_pct":    round(min(pnls), 3),
+        "avg_win_pct":  round(sum(wins) / len(wins), 3) if wins else 0.0,
+        "avg_loss_pct": round(sum(losses) / len(losses), 3) if losses else 0.0,
+        "exit_reasons": by_reason,
+    }
+    return {
+        "params":  asdict(params),
+        "summary": summary,
+        "skipped": skipped,
+        "results": [r.to_dict() for r in results],
+    }

app/services/binance.py

@@ -83,7 +83,21 @@ async def fetch_historical(asset: str, symbol: str, interval: str = "1m", limit:
 
 
 async def run_binance_ws():
-    """Connect to Binance kline stream with exponential back-off on disconnect."""
+    """Connect to Binance kline stream with exponential back-off on disconnect.
+
+    Binance routinely cycles connections every ~24h, and intermediate proxies
+    can drop without sending a close frame — both surface here as exceptions.
+    The reconnect must be fast (sub-second) so price ticks aren't visibly
+    stuck on the dashboard during a hiccup.
+
+    Resilience defenses:
+      * ping_interval/ping_timeout — peer-of-record liveness check (websockets lib)
+      * close_timeout — bound how long we wait for a clean close handshake
+      * open_timeout — bound the initial connect handshake so a hung TCP doesn't
+        permanently park this task
+      * max_queue — drop frames if downstream can't keep up rather than back
+        the WS receive buffer up indefinitely
+    """
     # Pre-fill with historical data
     await fetch_historical("BTC", "btcusdt", limit=500)
     await fetch_historical("ETH", "ethusdt", limit=500)
@@ -96,15 +110,27 @@ async def run_binance_ws():
                 settings.binance_ws_url,
                 ping_interval=20,
                 ping_timeout=10,
+                close_timeout=5,
+                open_timeout=10,
+                max_queue=128,
             ) as ws:
                 backoff = 1  # reset on successful connection
                 logger.info("Binance WebSocket connected.")
                 async for message in ws:
                     await _process_message(message)
+            # If we exit the `async with` without an exception, the server
+            # closed normally — reconnect immediately rather than backing off.
+            logger.info("Binance WebSocket closed cleanly, reconnecting immediately.")
         except asyncio.CancelledError:
             logger.info("Binance WebSocket task cancelled.")
             return
         except Exception as exc:
-            logger.error("Binance WebSocket error: %s. Reconnecting in %ds.", exc, backoff)
+            # Use exception() once so a TRACE-back-with-context is logged the
+            # first time something interesting happens; subsequent same-error
+            # retries log compactly to avoid drowning the log.
+            logger.warning(
+                "Binance WebSocket error: %s (%s). Reconnecting in %ds.",
+                type(exc).__name__, exc, backoff,
+            )
             await asyncio.sleep(backoff)
             backoff = min(backoff * 2, 60)

app/services/bot_engine.py

@@ -6,7 +6,7 @@ Iterates all active subscribers and executes trades on their behalf.
 
 import asyncio
 import logging
-from datetime import datetime, timezone
+from datetime import datetime, timezone, timedelta
 from typing import Dict
 
 from sqlalchemy import select, update
@@ -23,7 +23,9 @@ logger = logging.getLogger(__name__)
 
 # Platform-wide thresholds (per-user values in Subscription override where applicable)
 # No global confidence floor — users pick their own 0–100 threshold via settings.
-MAX_HOLD_SECONDS = 3600          # force-close after 1 hour (shared across users)
+# Per-user max hold lives on Subscription.max_hold_hours (default 168h = 7 days
+# in the convex-strategy redesign). Old 1-hour cap killed every potential
+# runner before it could compound, so it's been removed.
 
 # Hyperliquid perp fees (mainnet, base tier).
 # IOC orders always cross the book → taker fee both on open and close.
@@ -63,6 +65,17 @@ def _lock_for(trade_id: int) -> asyncio.Lock:
     return lock
 
 
+def _confidence_floor_for(sub: dict) -> int:
+    if sub.get("_is_system_2"):
+        from app.services.signal_categories import system2_min_confidence
+        return system2_min_confidence()
+    return sub["min_confidence"]
+
+
+def _should_apply_schedule(sub: dict) -> bool:
+    return not bool(sub.get("_is_system_2"))
+
+
 async def process_post(post: Post, db: AsyncSession) -> None:
     """
     Entry point called by the scraper after a new post is saved.
@@ -91,6 +104,87 @@ async def process_post(post: Post, db: AsyncSession) -> None:
 
     logger.info("Signal: post=%d asset=%s side=%s confidence=%d", post.id, asset, side, post.ai_confidence)
 
+    # ── System routing ─────────────────────────────────────────────────────
+    # Two independent trading systems share this execution layer but NOT
+    # their strategy logic. See app/services/signal_categories.py.
+    from app.services.signal_categories import (
+        get_exit_profile, get_stop_ladder, is_supported_trading_source,
+        is_system_2, system2_display_name,
+    )
+    if not is_supported_trading_source(post.source):
+        logger.info("Post %d skipped: unsupported trading source=%s", post.id, post.source)
+        return
+    sys2 = is_system_2(post.source)
+
+    if sys2:
+        # SYSTEM 2 — reversal/breakout. The Trump-tuned regime filter
+        # (recent-move ≤5%, vol-contraction) actively REJECTS valid reversal
+        # setups, so it's bypassed entirely. The signal's structural gates
+        # (RSI<30 ×4wk, 200d reclaim, funding extreme) ARE the regime check.
+        exit_profile = get_exit_profile(post.category)
+        if exit_profile.stop_ladder:
+            logger.info(
+                "%s [%s/%s]: bypassing Trump regime filter. "
+                "Exit = STAGED stop (no TP/trailing) base sl=%.1f%% "
+                "ladder=%s maxhold=%dh",
+                system2_display_name(), post.source, post.category,
+                exit_profile.stop_loss_pct, exit_profile.stop_ladder,
+                exit_profile.max_hold_hours,
+            )
+        else:
+            logger.info(
+                "%s [%s/%s]: bypassing Trump regime filter. "
+                "Exit profile sl=%.1f%% trail=%s@%s maxhold=%dh inval=%s",
+                system2_display_name(), post.source, post.category,
+                exit_profile.stop_loss_pct, exit_profile.trailing_stop_pct,
+                exit_profile.trailing_activate_at_pct, exit_profile.max_hold_hours,
+                exit_profile.invalidation,
+            )
+    else:
+        # SYSTEM 1 — Trump. REPOSITIONED: low-frequency, selective, tight
+        # stop, ≥30-min holds. See signal_categories.py.
+        exit_profile = None
+        from app.services.signal_categories import (
+            TRUMP_MIN_CONFIDENCE, TRUMP_COOLDOWN_HOURS,
+        )
+
+        # (a) Confidence floor — only the very top posts clear the bar.
+        if (post.ai_confidence or 0) < TRUMP_MIN_CONFIDENCE:
+            logger.info(
+                "Post %d skipped: Trump confidence %d < floor %d (low-freq mode)",
+                post.id, post.ai_confidence or 0, TRUMP_MIN_CONFIDENCE,
+            )
+            return
+
+        # (b) Cooldown — "don't trade every opportunity". If we opened ANY
+        #     Trump trade in the last TRUMP_COOLDOWN_HOURS, skip this post.
+        #     DB-backed so it survives restarts (same pattern as scanners).
+        from app.services.scanner_state import last_signal_at
+        from sqlalchemy import select as _sel, func as _fn
+        from app.models import Post as _Post, BotTrade as _BT
+        cutoff = datetime.now(timezone.utc).replace(tzinfo=None) - timedelta(hours=TRUMP_COOLDOWN_HOURS)
+        recent_trump = await db.execute(
+            _sel(_fn.count(_BT.id))
+            .select_from(_BT)
+            .join(_Post, _Post.id == _BT.trigger_post_id)
+            .where(_Post.source == "truth", _BT.opened_at >= cutoff)
+        )
+        if (recent_trump.scalar() or 0) > 0:
+            logger.info(
+                "Post %d skipped: Trump cooldown active (a Trump trade opened "
+                "within the last %dh)", post.id, TRUMP_COOLDOWN_HOURS,
+            )
+            return
+
+        # (c) Regime filter — still applies; tuned for short-term behaviour.
+        from app.services.regime_filter import passes_regime_filter
+        regime_ok, regime_reasons = passes_regime_filter(asset, side)
+        for r in regime_reasons:
+            logger.info("Regime [%s]: %s", asset, r)
+        if not regime_ok:
+            logger.info("Post %d skipped: regime filter rejected (see ✗ lines above)", post.id)
+            return
+
     result = await db.execute(select(Subscription).where(Subscription.active == True))  # noqa: E712
     subscribers = result.scalars().all()
 
@@ -111,9 +205,80 @@ async def process_post(post: Post, db: AsyncSession) -> None:
             daily_budget_usd=s.daily_budget_usd,
             active_from=s.active_from,
             active_until=s.active_until,
+            # Convex-strategy fields (default to legacy values if column null).
+            trailing_stop_pct=s.trailing_stop_pct,
+            trailing_activate_at_pct=s.trailing_activate_at_pct,
+            max_hold_hours=s.max_hold_hours or 168,
+            manual_window_until=s.manual_window_until,
+            # Phase 1 safety
+            paper_mode=bool(s.paper_mode),
+            circuit_breaker_tripped_at=s.circuit_breaker_tripped_at,
+            circuit_breaker_reason=s.circuit_breaker_reason,
+            # Two-system separation
+            sys2_budget_pct=(s.sys2_budget_pct if s.sys2_budget_pct is not None else 0.7),
+            sys2_cb_tripped_at=s.sys2_cb_tripped_at,
+            sys2_cb_reason=s.sys2_cb_reason,
+            sys2_leverage=s.sys2_leverage,
+            sys2_mode=s.sys2_mode,
+            auto_trade=bool(s.auto_trade),
         )
         for s in subscribers
     ]
+
+    # SYSTEM 2: override the user's Trump exit params with the category's
+    # exit profile. The user configures risk for their Trump scalp; the
+    # reversal system's risk is a property of the SIGNAL, not the user.
+    if sys2 and exit_profile is not None:
+        from app.services.signal_categories import (
+            sys2_effective_leverage, sys2_protective_stop_pct,
+            sys2_normalize_mode,
+        )
+        for snap in subs_snapshot:
+            mode = sys2_normalize_mode(snap.get("sys2_mode"))
+            snap["_sys2_mode"] = mode
+            # Dynamic System-2 leverage (independent of the Trump `leverage`);
+            # default depends on the risk mode (standard 2× / aggressive 8×).
+            lev = sys2_effective_leverage(snap.get("sys2_leverage"), mode)
+            # Protective stop auto-scaled INSIDE the liquidation line for THIS
+            # leverage → the position is de-risked by our monitor, never
+            # liquidated by the exchange.
+            prot = sys2_protective_stop_pct(lev)
+
+            snap["_is_system_2"]             = True
+            snap["_category"]                = post.category
+            snap["leverage"]                 = lev   # HL opens at sys2 leverage
+            snap["take_profit_pct"]          = None   # pure trailing, no fixed TP
+            # Base catastrophic floor = leverage-aware protective stop. The
+            # upside ladder rungs (get_stop_ladder) still ratchet this UP as
+            # peak gain grows; they never loosen it.
+            snap["stop_loss_pct"]            = prot
+            snap["trailing_activate_at_pct"] = exit_profile.trailing_activate_at_pct
+            snap["trailing_stop_pct"]        = exit_profile.trailing_stop_pct
+            snap["max_hold_hours"]           = exit_profile.max_hold_hours
+            # Carried through for the monitor (time-stop / invalidation).
+            snap["_sys2_time_stop_hours"]    = exit_profile.time_stop_hours
+            snap["_sys2_invalidation"]       = exit_profile.invalidation
+            snap["_sys2_invalidation_price"] = post.invalidation_price
+            logger.info(
+                "System-2 dynamic leverage: %dx → protective stop %.2f%% "
+                "(approx liquidation %.2f%%) for %s",
+                lev, prot, 100.0 / lev, snap["wallet"],
+            )
+    elif not sys2:
+        # SYSTEM 1 — Trump, repositioned. System-enforced now (not pure
+        # user params): tight SL always on, but TP/trailing suppressed for
+        # the first TRUMP_MIN_HOLD_MINUTES so we don't scalp out early.
+        # User still controls size / leverage / daily budget.
+        from app.services.signal_categories import (
+            TRUMP_STOP_LOSS_PCT, TRUMP_MAX_HOLD_HOURS, TRUMP_MIN_HOLD_MINUTES,
+        )
+        for snap in subs_snapshot:
+            snap["stop_loss_pct"]      = TRUMP_STOP_LOSS_PCT
+            snap["max_hold_hours"]     = TRUMP_MAX_HOLD_HOURS
+            snap["_min_hold_minutes"]  = TRUMP_MIN_HOLD_MINUTES
+            # Keep user's take_profit_pct / trailing config — they still pick
+            # the profit target; we only gate WHEN it can fire.
+
     post_id = post.id
     post_confidence = post.ai_confidence or 0
 
@@ -135,30 +300,47 @@ async def _execute_for_subscriber(
     if not sub["hl_api_key"]:
         logger.warning("Subscriber %s has no HL API key, skipping", wallet)
         return
-    # Required-setup guard: the bot is only allowed to trade when the user
+    # Required-setup guard. System 1 (Trump) needs the user's take-profit,
-    # has explicitly set take-profit, stop-loss, and a daily budget. Prevents
+    # stop-loss, and daily budget. System 2 (reversal) supplies its own
-    # accidental trading on partially-configured accounts.
+    # stop-loss + (no) take-profit from the category profile — only the
-    missing = [k for k in ("take_profit_pct", "stop_loss_pct", "daily_budget_usd")
+    # daily budget remains a user-required risk cap there.
-               if sub.get(k) is None]
+    if sub.get("_is_system_2"):
+        required = ("daily_budget_usd",)
+    else:
+        required = ("take_profit_pct", "stop_loss_pct", "daily_budget_usd")
+    missing = [k for k in required if sub.get(k) is None]
     if missing:
         logger.info("Sub %s skipped post %d: setup incomplete (missing %s)",
                     wallet, post_id, ", ".join(missing))
         return
 
-    if post_confidence < sub["min_confidence"]:
+    confidence_floor = _confidence_floor_for(sub)
+    if post_confidence < confidence_floor:
         logger.info("Sub %s filters out post %d: conf %d < user min %d",
-                    wallet, post_id, post_confidence, sub["min_confidence"])
+                    wallet, post_id, post_confidence, confidence_floor)
         return
 
-    # Active-window check. Both values stored as naive-UTC.
+    # ── Master Auto-Trade gate (D) ─────────────────────────────────────────
-    af = sub.get("active_from")
+    # The ONE user-facing switch. The signal has ALREADY been ingested as a
-    au = sub.get("active_until")
+    # Post by the ingest endpoint (so it shows in the feed regardless); here
-    if af is not None and au is not None:
+    # we only decide whether to OPEN a trade. OFF (default) = monitor-only.
-        now_utc_naive = datetime.now(timezone.utc).replace(tzinfo=None)
+    # Replaces the old scanner-toggle / timed manual-window / schedule trio.
-        if now_utc_naive < af or now_utc_naive >= au:
+    # NOTE: this gates NEW entries only — already-open positions keep their
-            logger.info("Sub %s skipped post %d: outside active window [%s, %s)",
+    # protective de-risk / stop (tp_sl_monitor runs independently of this).
-                        wallet, post_id, af, au)
+    if not sub.get("auto_trade"):
-            return
+        logger.info("Sub %s: Auto-Trade OFF — post %d recorded, no trade opened",
+                    wallet, post_id)
+        return
+
+    # ── Circuit breaker gate (P1.1) ────────────────────────────────────────
+    # Checked BEFORE key decryption (cheap fast-fail). If tripped, the wallet
+    # has lost too much today or hit a losing streak — block until manual reset.
+    from app.services.circuit_breaker import is_tripped as _cb_tripped
+    _system = "sys2" if sub.get("_is_system_2") else "sys1"
+    tripped, cb_reason = _cb_tripped(sub, _system)
+    if tripped:
+        logger.info("Sub %s skipped post %d: %s", wallet, post_id, cb_reason)
+        return
 
     try:
         api_key = decrypt_api_key(sub["hl_api_key"])
@@ -166,49 +348,170 @@ async def _execute_for_subscriber(
         logger.error("Cannot decrypt key for %s: %s", wallet, exc)
         return
 
+    # ── Position sizing (C) ────────────────────────────────────────────────
+    # Score-based multiplier on the user's base size. Bigger bet when more
+    # confluences are in place. Computed BEFORE the lock so the value is
+    # available for both the budget check and the open call.
+    # System 2 has its OWN sizing — regime_filter's multiplier would shrink
+    # reversal bets (it penalises volatility expansion / prior movement,
+    # which are the SIGNAL for a reversal). See signal_categories.
+    if sub.get("_is_system_2"):
+        from app.services.signal_categories import system2_size_multiplier
+        size_mult = system2_size_multiplier(sub.get("_category"), post_confidence)
+        size_logic = f"sys2 cat={sub.get('_category')}"
+    else:
+        from app.services.regime_filter import calculate_size_multiplier
+        size_mult = calculate_size_multiplier(post_confidence, asset, side)
+        size_logic = "sys1 regime-scored"
+    sized_position_usd = round(sub["position_size_usd"] * size_mult, 2)
+    logger.info(
+        "Sub %s sizing [%s]: base=%.2f × %.2fx = %.2f (asset=%s, conf=%d)",
+        wallet, size_logic, sub["position_size_usd"], size_mult,
+        sized_position_usd, asset, post_confidence,
+    )
+
     # Per-wallet lock wraps BOTH the budget re-check and the open, so two
     # concurrent signals can't both pass the daily-budget gate before either
     # trade is written to DB (TOCTOU race under asyncio.gather).
     async with _wallet_lock(wallet):
         # Re-check daily budget INSIDE the lock so it's atomic with the open.
-        daily_cap = sub.get("daily_budget_usd")
+        # Budget is SPLIT between the two systems so a Trump scalp can't eat
-        if daily_cap is not None and daily_cap > 0:
+        # the allocation reserved for a once-a-year reversal signal.
+        total_cap = sub.get("daily_budget_usd")
+        if total_cap is not None and total_cap > 0:
+            sys2_pct = sub.get("sys2_budget_pct", 0.7)
+            is_s2 = bool(sub.get("_is_system_2"))
+            daily_cap = total_cap * (sys2_pct if is_s2 else (1.0 - sys2_pct))
             start_of_day = datetime.now(timezone.utc).replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=None)
+            from app.models import Post as _P
+            from app.services.signal_categories import SYSTEM_2_SOURCES
             async with AsyncSessionLocal() as budget_db:
                 spent_result = await budget_db.execute(
-                    select(BotTrade).where(
+                    select(BotTrade, _P.source)
+                    .outerjoin(_P, _P.id == BotTrade.trigger_post_id)
+                    .where(
                         BotTrade.wallet_address == wallet,
                         BotTrade.opened_at >= start_of_day,
                     )
                 )
-                spent_trades = spent_result.scalars().all()
+                # Only count spend from THIS system against THIS system's slice.
-                spent = sum(
+                spent = 0.0
-                    (t.size_usd if t.size_usd is not None else sub["position_size_usd"])
+                for t, src in spent_result.all():
-                    for t in spent_trades
+                    t_is_s2 = (src or "").lower() in SYSTEM_2_SOURCES
+                    if t_is_s2 != is_s2:
+                        continue
+                    spent += (t.size_usd if t.size_usd is not None else sub["position_size_usd"])
+            if spent + sized_position_usd > daily_cap:
+                logger.info("Sub %s [%s] daily budget reached: spent=%.2f + new=%.2f > cap=%.2f (%.0f%% of %.2f)",
+                            wallet, _system, spent, sized_position_usd, daily_cap,
+                            (sys2_pct if is_s2 else 1.0 - sys2_pct) * 100, total_cap)
+                return
+
+        # ── System-2 correlation / concentration cap ───────────────────────
+        # Inside the wallet lock so it's atomic with the open. The whole
+        # System-2 basket is correlated crypto-beta; cap CONCURRENT open
+        # positions + total open notional so a synchronised "crypto bottomed"
+        # week can't stack into one 10x leveraged macro bet.
+        if sub.get("_is_system_2"):
+            from app.services.signal_categories import (
+                SYS2_MAX_CONCURRENT, SYS2_MAX_OPEN_NOTIONAL_MULT, SYSTEM_2_SOURCES,
+            )
+            from app.models import Post as _P2
+            async with AsyncSessionLocal() as conc_db:
+                open_rows = await conc_db.execute(
+                    select(BotTrade, _P2.source)
+                    .outerjoin(_P2, _P2.id == BotTrade.trigger_post_id)
+                    .where(
+                        BotTrade.wallet_address == wallet,
+                        BotTrade.closed_at.is_(None),
+                    )
                 )
-            if spent + sub["position_size_usd"] > daily_cap:
+                open_sys2 = [
-                logger.info("Sub %s daily budget reached: spent=%.2f + new=%.2f > cap=%.2f",
+                    t for t, src in open_rows.all()
-                            wallet, spent, sub["position_size_usd"], daily_cap)
+                    if (src or "").lower() in SYSTEM_2_SOURCES
+                ]
+            open_count = len(open_sys2)
+            open_notional = sum(
+                (t.size_usd if t.size_usd is not None else sub["position_size_usd"])
+                for t in open_sys2
+            )
+            notional_ceiling = SYS2_MAX_OPEN_NOTIONAL_MULT * sub["position_size_usd"]
+            if open_count >= SYS2_MAX_CONCURRENT:
+                logger.info(
+                    "Sub %s sys2 concentration cap: %d open positions ≥ max %d "
+                    "(correlated crypto-beta — skipping post %d)",
+                    wallet, open_count, SYS2_MAX_CONCURRENT, post_id)
+                return
+            if open_notional + sized_position_usd > notional_ceiling:
+                logger.info(
+                    "Sub %s sys2 notional cap: open=%.2f + new=%.2f > ceiling=%.2f "
+                    "(%.1f× base) — skipping post %d",
+                    wallet, open_notional, sized_position_usd, notional_ceiling,
+                    SYS2_MAX_OPEN_NOTIONAL_MULT, post_id)
                 return
 
         # Each subscriber gets its own session — AsyncSession is NOT concurrency-safe.
         async with AsyncSessionLocal() as db:
             try:
-                trader = HyperliquidTrader(
+                # ── Paper-mode branch (P1.3) ──────────────────────────────
-                    api_private_key=api_key,
+                # Skip Hyperliquid entirely. Use the current Binance price as
-                    account_address=wallet,
+                # the synthetic fill. DB row gets hl_order_id="paper" so close
-                    leverage=sub["leverage"],
+                # logic + reconciliation know to skip HL too.
-                    mainnet=settings.hl_mainnet,
+                if sub["paper_mode"]:
-                )
+                    from app.services.price_store import price_store
+                    entry_price = price_store.latest_price(asset)
+                    if not entry_price:
+                        logger.warning("Paper mode: no price for %s, skipping", asset)
+                        return
+                    # Check DB (not HL) for already-open paper position on same asset.
+                    open_dup = await db.execute(
+                        select(BotTrade).where(
+                            BotTrade.wallet_address == wallet,
+                            BotTrade.asset == asset,
+                            BotTrade.closed_at.is_(None),
+                        )
+                    )
+                    if open_dup.scalar_one_or_none():
+                        logger.info("Paper mode: %s already has open %s, skipping", wallet, asset)
+                        return
+                    result = {"fill_price": entry_price, "order_id": "paper"}
+                    logger.info("[PAPER] Open %s %s for %s @ %.4f size=%.2f",
+                                side, asset, wallet, entry_price, sized_position_usd)
+                else:
+                    trader = HyperliquidTrader(
+                        api_private_key=api_key,
+                        account_address=wallet,
+                        leverage=sub["leverage"],
+                        mainnet=settings.hl_mainnet,
+                    )
 
-                open_positions = await trader.get_open_positions()
+                    open_positions = await trader.get_open_positions()
-                if any(p.get('coin') == asset for p in open_positions):
+                    if any(p.get('coin') == asset for p in open_positions):
-                    logger.info("Subscriber %s already has open %s position, skipping", wallet, asset)
+                        logger.info("Subscriber %s already has open %s position, skipping", wallet, asset)
-                    return
+                        return
 
-                result = await trader.open_position(asset, side, sub["position_size_usd"])
+                    result = await trader.open_position(asset, side, sized_position_usd)
                 entry_price = result.get('fill_price', 0.0)
 
+                # Resolve the FROZEN exit profile ONCE. Everything downstream
+                # (the watchdog AND recovery-after-restart) reads these — never
+                # the mutable Subscription/category config again.
+                import time as _time
+                eff_min_hold_ts = (
+                    _time.time() + sub["_min_hold_minutes"] * 60
+                    if sub.get("_min_hold_minutes") else None
+                )
+                eff = dict(
+                    take_profit_pct       = sub["take_profit_pct"],
+                    stop_loss_pct         = sub["stop_loss_pct"],
+                    trailing_stop_pct     = sub.get("trailing_stop_pct"),
+                    trailing_activate_pct = sub.get("trailing_activate_at_pct"),
+                    max_hold_hours        = int(sub["max_hold_hours"]),
+                    invalidation          = sub.get("_sys2_invalidation"),
+                    invalidation_price    = sub.get("_sys2_invalidation_price"),
+                    min_hold_until_ts     = eff_min_hold_ts,
+                )
+
                 trade = BotTrade(
                     asset=asset,
                     side=side,
@@ -217,17 +520,40 @@ async def _execute_for_subscriber(
                     trigger_post_id=post_id,
                     opened_at=datetime.now(timezone.utc).replace(tzinfo=None),
                     hl_order_id=result.get('order_id'),
-                    size_usd=sub["position_size_usd"],
+                    size_usd=sized_position_usd,
+                    base_size_usd=sized_position_usd,   # immutable; pyramiding grows size_usd
+                    sys2_mode=(_sys2_mode if sys2 else None),
                     leverage=sub["leverage"],
+                    # Freeze the exit profile on the row.
+                    eff_take_profit_pct       = eff["take_profit_pct"],
+                    eff_stop_loss_pct         = eff["stop_loss_pct"],
+                    eff_trailing_stop_pct     = eff["trailing_stop_pct"],
+                    eff_trailing_activate_pct = eff["trailing_activate_pct"],
+                    eff_max_hold_hours        = eff["max_hold_hours"],
+                    eff_invalidation          = eff["invalidation"],
+                    eff_invalidation_price    = eff["invalidation_price"],
+                    eff_min_hold_until_ts     = eff["min_hold_until_ts"],
                 )
                 db.add(trade)
                 await db.commit()
                 await db.refresh(trade)
 
-                logger.info("Opened %s %s for %s @ %.2f (trade_id=%d)",
+                logger.info("Opened %s %s for %s @ %.4f size=%.2f (trade_id=%d)",
-                            side, asset, wallet, entry_price, trade.id)
+                            side, asset, wallet, entry_price, sized_position_usd, trade.id)
 
+                # Register with the watchdog using the FROZEN profile.
                 from app.services.tp_sl_monitor import register_trade
+                _derisk = None
+                _addon = None
+                _peak_trail = None
+                _sys2_mode = sub.get("_sys2_mode", "standard")
+                if sys2:
+                    from app.services.signal_categories import (
+                        sys2_derisk_ladder, sys2_addon_ladder, sys2_peak_trail,
+                    )
+                    _derisk = sys2_derisk_ladder(sub["leverage"], _sys2_mode)
+                    _addon = sys2_addon_ladder(_sys2_mode)
+                    _peak_trail = sys2_peak_trail(_sys2_mode)
                 register_trade(
                     trade_id=trade.id,
                     wallet=wallet,
@@ -236,20 +562,235 @@ async def _execute_for_subscriber(
                     asset=asset,
                     side=side,
                     entry_price=entry_price,
-                    take_profit_pct=sub["take_profit_pct"],
+                    take_profit_pct=eff["take_profit_pct"],
-                    stop_loss_pct=sub["stop_loss_pct"],
+                    stop_loss_pct=eff["stop_loss_pct"],
+                    trailing_stop_pct=eff["trailing_stop_pct"],
+                    trailing_activate_at_pct=eff["trailing_activate_pct"],
+                    invalidation=eff["invalidation"],
+                    invalidation_price=eff.get("invalidation_price"),
+                    min_hold_until_ts=eff["min_hold_until_ts"],
+                    stop_ladder=get_stop_ladder(post.category) if sys2 else None,
+                    derisk_ladder=_derisk,
+                    derisk_done=0,
+                    addon_ladder=_addon,
+                    addon_done=0,
+                    peak_trail=_peak_trail,
+                    grow_mode=bool(getattr(trade, "grow_mode", False)),
                 )
 
+                # Max hold backstop (per-user for System 1, per-category for
+                # System 2 — already injected into the snapshot upstream).
+                max_hold_seconds = int(sub["max_hold_hours"]) * 3600
                 task = asyncio.create_task(_close_after_hold(
-                    trade.id, api_key, sub["leverage"], asset, wallet
+                    trade.id, api_key, sub["leverage"], asset, wallet, max_hold_seconds,
                 ))
                 _background_tasks.add(task)
                 task.add_done_callback(_background_tasks.discard)
 
+                # System-2 time-stop: if the position is still ~flat after
+                # `time_stop_hours`, the thesis is slow/dead — close early
+                # instead of tying up capital for the full max-hold.
+                ts_hours = sub.get("_sys2_time_stop_hours")
+                if ts_hours:
+                    ts_task = asyncio.create_task(_time_stop_check(
+                        trade.id, api_key, sub["leverage"], asset, wallet,
+                        int(ts_hours) * 3600,
+                    ))
+                    _background_tasks.add(ts_task)
+                    ts_task.add_done_callback(_background_tasks.discard)
+
             except Exception as e:
                 logger.error("Trade execution failed for %s: %s", wallet, e)
 
 
+async def partial_derisk(
+    trade_id: int,
+    api_key: str,
+    asset: str,
+    wallet: str,
+    step_idx: int,
+    frac_of_original: float,
+    reason: str = "derisk",
+) -> bool:
+    """Staged de-risk: partially close a System-2 trade, realise the slice's
+    PnL, and KEEP the trade open. Idempotent per step_idx (guarded by the
+    persisted `derisk_steps_done` counter under the per-trade lock).
+
+    Returns True if the step was executed (or already done), False on a
+    recoverable no-op so the monitor can retry next tick.
+    """
+    async with _lock_for(trade_id):
+        async with AsyncSessionLocal() as db:
+            try:
+                row = await db.execute(select(BotTrade).where(BotTrade.id == trade_id))
+                trade = row.scalar_one_or_none()
+                if trade is None or trade.closed_at is not None:
+                    return True  # gone / already fully closed — nothing to do
+                if (trade.derisk_steps_done or 0) > step_idx:
+                    return True  # this step already executed (idempotent)
+                if (trade.derisk_steps_done or 0) != step_idx:
+                    # Steps must run in order; let the monitor catch up.
+                    return False
+
+                size_usd = trade.size_usd if trade.size_usd is not None else 0.0
+                rem_frac = trade.remaining_fraction if trade.remaining_fraction is not None else 1.0
+                if size_usd <= 0 or rem_frac <= 0:
+                    return True
+                # frac of the CURRENT open position needed to shed
+                # `frac_of_original` of the ORIGINAL notional.
+                frac_of_current = max(0.0, min(1.0, frac_of_original / rem_frac))
+
+                if trade.hl_order_id == "paper":
+                    from app.services.price_store import price_store
+                    fill = price_store.latest_price(asset)
+                    if not fill:
+                        logger.error("Paper de-risk: no %s price, retry trade %d", asset, trade_id)
+                        return False
+                    closed_frac_of_current = frac_of_current
+                else:
+                    trader = HyperliquidTrader(
+                        api_private_key=api_key,
+                        account_address=wallet,
+                        leverage=(trade.leverage or 1),
+                        mainnet=settings.hl_mainnet,
+                    )
+                    r = await trader.reduce_position(asset, frac_of_current)
+                    if r.get("already_closed"):
+                        # Position vanished (manual close / liquidation race).
+                        # Let the reconciler / full-close path handle it.
+                        logger.warning("De-risk: %s position gone for trade %d", asset, trade_id)
+                        return True
+                    fill = r.get("fill_price")
+                    closed_frac_of_current = r.get("closed_fraction") or 0.0
+                    if not fill or closed_frac_of_current <= 0:
+                        return False  # no fill — retry next tick
+
+                # Fraction of ORIGINAL notional actually shed this step.
+                closed_frac_of_original = closed_frac_of_current * rem_frac
+                slice_usd = size_usd * closed_frac_of_original
+                pct = ((fill - trade.entry_price) / trade.entry_price) if trade.entry_price else 0.0
+                signed_pct = pct if trade.side == "long" else -pct
+                # Round-trip taker fee on this slice (open-share + this close).
+                fees = slice_usd * HL_TAKER_FEE_RATE * 2
+                slice_pnl = slice_usd * signed_pct - fees
+
+                trade.realized_partial_pnl_usd = round((trade.realized_partial_pnl_usd or 0.0) + slice_pnl, 4)
+                trade.remaining_fraction = max(0.0, round(rem_frac - closed_frac_of_original, 6))
+                trade.derisk_steps_done = step_idx + 1
+                await db.commit()
+
+                logger.warning(
+                    "De-risk step %d trade %d (%s): closed %.1f%% of original "
+                    "@ %.2f, slice PnL %.2f, remaining %.1f%% (reason=%s)",
+                    step_idx + 1, trade_id, asset, closed_frac_of_original * 100,
+                    fill, slice_pnl, trade.remaining_fraction * 100, reason,
+                )
+                return True
+            except Exception as e:
+                logger.error("partial_derisk failed for trade %d step %d: %s",
+                             trade_id, step_idx, e)
+                return False
+
+
+async def pyramid_add(
+    trade_id: int,
+    api_key: str,
+    asset: str,
+    wallet: str,
+    step_idx: int,
+    frac_of_base: float,
+    reason: str = "pyramid",
+) -> tuple:
+    """Pyramiding: add to a CONFIRMED System-2 winner. Returns
+    (ok: bool, new_entry: float|None, ref_price: float|None).
+
+    Guards: only while derisk_steps_done==0 (clean uptrend), idempotent per
+    step_idx, structural confirmation re-checked at execution, per-trade
+    notional cap. On success blends the average entry and grows size_usd.
+    """
+    async with _lock_for(trade_id):
+        async with AsyncSessionLocal() as db:
+            try:
+                row = await db.execute(select(BotTrade).where(BotTrade.id == trade_id))
+                trade = row.scalar_one_or_none()
+                if trade is None or trade.closed_at is not None:
+                    return (True, None, None)
+                if (trade.derisk_steps_done or 0) > 0:
+                    return (True, None, None)   # went underwater — no pyramiding
+                if (trade.addon_steps_done or 0) > step_idx:
+                    return (True, None, None)   # already done (idempotent)
+                if (trade.addon_steps_done or 0) != step_idx:
+                    return (False, None, None)  # out of order — retry later
+
+                base = trade.base_size_usd or trade.size_usd or 0.0
+                if base <= 0:
+                    return (True, None, None)
+                from app.services.signal_categories import SYS2_MAX_OPEN_NOTIONAL_MULT
+                add_usd = round(base * frac_of_base, 2)
+                cur_notional = trade.size_usd or base
+                if cur_notional + add_usd > base * SYS2_MAX_OPEN_NOTIONAL_MULT:
+                    logger.warning("Pyramid: notional cap hit trade %d (%.2f+%.2f > %.1fx base)",
+                                   trade_id, cur_notional, add_usd, SYS2_MAX_OPEN_NOTIONAL_MULT)
+                    trade.addon_steps_done = step_idx + 1     # stop retrying
+                    await db.commit()
+                    return (True, None, None)
+
+                # Structural confirmation — fetched ONCE here, not per tick.
+                from app.services.market_data import for_asset, drop_in_progress_bar
+                from app.services.bottom_indicators import trend_confirmed
+                from app.services.price_store import price_store
+                prov = for_asset(asset)
+                raw = await prov.fetch_1d(asset, days=260)
+                daily = drop_in_progress_bar(raw, "1d")
+                closes = [float(c["close"]) for c in daily if c.get("close")]
+                highs = [float(c["high"]) for c in daily if c.get("high")]
+                px = price_store.latest_price(asset)
+                if not px:
+                    return (False, None, None)
+                if not trend_confirmed(closes, highs, px):
+                    return (False, None, None)   # not a confirmed uptrend yet
+
+                if trade.hl_order_id == "paper":
+                    fill = px
+                    actual_add_usd = add_usd          # synthetic full fill
+                else:
+                    trader = HyperliquidTrader(
+                        api_private_key=api_key,
+                        account_address=wallet,
+                        leverage=(trade.leverage or 1),
+                        mainnet=settings.hl_mainnet,
+                    )
+                    r = await trader.open_position(asset, trade.side, add_usd)
+                    fill = r.get("fill_price")
+                    filled_coins = float(r.get("size_coins") or 0.0)
+                    if not fill or filled_coins <= 0:
+                        return (False, None, None)
+                    # Use the ACTUAL filled notional, not the intended amount —
+                    # an IOC add can under-fill on thin/volatile books; assuming
+                    # the full add_usd would corrupt the blended entry + size.
+                    actual_add_usd = filled_coins * fill
+
+                old_notional = trade.size_usd or base
+                new_notional = old_notional + actual_add_usd
+                blended = (old_notional * trade.entry_price + actual_add_usd * fill) / new_notional
+                trade.entry_price = round(blended, 6)
+                trade.size_usd = round(new_notional, 2)
+                trade.addon_steps_done = step_idx + 1
+                await db.commit()
+
+                logger.warning(
+                    "Pyramid add step %d trade %d (%s): +$%.2f filled @ %.2f → "
+                    "notional $%.2f, blended entry %.4f (reason=%s)",
+                    step_idx + 1, trade_id, asset, actual_add_usd, fill,
+                    new_notional, blended, reason,
+                )
+                return (True, round(blended, 6), float(fill))
+            except Exception as e:
+                logger.error("pyramid_add failed trade %d step %d: %s",
+                             trade_id, step_idx, e)
+                return (False, None, None)
+
+
 async def close_and_finalize(
     trade_id: int,
     api_key: str,
@@ -297,23 +838,46 @@ async def close_and_finalize(
                     size_usd = sub.position_size_usd if sub else 20.0
                 trade_leverage = trade.leverage if trade.leverage is not None else leverage
 
-                trader = HyperliquidTrader(
+                # ── Paper-mode close (P1.3) ──────────────────────────────
-                    api_private_key=api_key,
+                # No Hyperliquid call. Synthesize a fill at the current Binance
-                    account_address=wallet,
+                # price. Same downstream PnL math as a real trade.
-                    leverage=trade_leverage,
+                if trade.hl_order_id == "paper":
-                    mainnet=settings.hl_mainnet,
+                    from app.services.price_store import price_store
-                )
+                    paper_exit = price_store.latest_price(asset)
-                close_result = await trader.close_position(asset)
+                    if not paper_exit:
+                        logger.error("Paper close: no price for %s, can't close trade %d",
+                                     asset, trade_id)
+                        # Roll back the closed_at claim so we can retry later.
+                        await db.execute(
+                            update(BotTrade).where(BotTrade.id == trade_id).values(closed_at=None)
+                        )
+                        await db.commit()
+                        return
+                    close_result = {"fill_price": paper_exit, "already_closed": False}
+                else:
+                    trader = HyperliquidTrader(
+                        api_private_key=api_key,
+                        account_address=wallet,
+                        leverage=trade_leverage,
+                        mainnet=settings.hl_mainnet,
+                    )
+                    close_result = await trader.close_position(asset)
 
                 # Detect "position was already closed externally" before we even tried
                 if close_result.get("already_closed"):
+                    # PnL of the (now-gone) open remainder is unknown, BUT any
+                    # PnL already BANKED by staged de-risk is real money — keep
+                    # it in pnl_usd instead of discarding it as None, else
+                    # analytics / "today realised" silently undercount.
+                    banked = trade.realized_partial_pnl_usd or 0.0
                     logger.warning(
-                        "Trade %d: no open %s position found on HL — "
+                        "Trade %d: no open %s position found on HL — user likely "
-                        "user likely closed it manually. Marking trade closed with no PnL.",
+                        "closed it manually. Marking closed; pnl=%s (banked de-risk only).",
-                        trade_id, asset,
+                        trade_id, asset, round(banked, 2) if banked else None,
                     )
                     trade.exit_price = None
-                    trade.pnl_usd = None
+                    trade.pnl_usd = round(banked, 2) if banked else None
+                    trade.remaining_fraction = 0.0
                     trade.hold_seconds = int(
                         (datetime.now(timezone.utc) -
                          trade.opened_at.replace(tzinfo=timezone.utc)).total_seconds()
@@ -334,13 +898,24 @@ async def close_and_finalize(
                 pct = ((exit_price - trade.entry_price) / trade.entry_price) if trade.entry_price else 0.0
                 signed_pct = pct if trade.side == 'long' else -pct
                 # size_usd is the NOTIONAL value — leverage affects margin, not PnL.
-                gross_pnl = size_usd * signed_pct
+                # For a staged-de-risk trade only the REMAINING notional is
-                # Deduct round-trip taker fees (IOC crosses book on both open + close).
+                # closed here; earlier partial reduces already realised their
-                # Without this, displayed PnL overstates real returns by ~9 bps per trade.
+                # slice into realized_partial_pnl_usd. Legacy/non-partial rows
-                fees_usd = size_usd * HL_TAKER_FEE_RATE * 2
+                # have remaining_fraction=1.0 + realized_partial=0.0 → identical
-                pnl_usd = gross_pnl - fees_usd
+                # math to before.
+                rem_frac = trade.remaining_fraction if trade.remaining_fraction is not None else 1.0
+                prior_pnl = trade.realized_partial_pnl_usd or 0.0
+                remaining_size = size_usd * rem_frac
+                gross_pnl = remaining_size * signed_pct
+                # Round-trip taker fees: the OPEN fee was paid on the full
+                # original notional; partial reduces already charged their
+                # close-side fee. Here charge the open-share for the remaining
+                # slice + this final close fee.
+                fees_usd = remaining_size * HL_TAKER_FEE_RATE * 2
+                pnl_usd = gross_pnl - fees_usd + prior_pnl
 
                 trade.exit_price = exit_price
+                trade.remaining_fraction = 0.0
                 trade.pnl_usd = round(pnl_usd, 2)
                 trade.hold_seconds = hold_secs
                 # closed_at already set by the atomic UPDATE
@@ -357,6 +932,24 @@ async def close_and_finalize(
                     gross_pnl, fees_usd, pnl_usd, reason,
                 )
 
+                # ── Circuit breaker check (P1.1) ───────────────────────────
+                # Run after PnL is written. If daily DD or consecutive-loss
+                # threshold hit, trip the breaker — that nulls manual_window
+                # and blocks new trades for CB_LOCKOUT_HOURS.
+                # Infer which system this trade belonged to from its trigger
+                # post's source, so the right (independent) breaker is checked.
+                from app.services.circuit_breaker import check_and_trip
+                from app.services.signal_categories import SYSTEM_2_SOURCES
+                _src_row = await db.execute(
+                    select(Post.source).where(Post.id == trade.trigger_post_id)
+                )
+                _src = (_src_row.scalar_one_or_none() or "").lower()
+                _sys = "sys2" if _src in SYSTEM_2_SOURCES else "sys1"
+                cb_reason = await check_and_trip(wallet, db, _sys)
+                if cb_reason:
+                    logger.warning("Circuit breaker [%s] tripped for %s: %s",
+                                    _sys, wallet, cb_reason)
+
             except Exception as e:
                 logger.error("Failed to close trade %d: %s", trade_id, e)
                 # Rollback closed_at so recovery can retry this trade on next restart.
@@ -377,7 +970,58 @@ async def close_and_finalize(
 
 
 async def _close_after_hold(
-    trade_id: int, api_key: str, leverage: int, asset: str, wallet: str
+    trade_id: int, api_key: str, leverage: int, asset: str, wallet: str,
+    max_hold_seconds: int,
 ) -> None:
-    await asyncio.sleep(MAX_HOLD_SECONDS)
+    """Per-user max-hold backstop. Forces close after the configured duration.
+
+    The trailing-stop monitor will usually close the position long before this
+    fires, but for trades that drift sideways the cap prevents indefinite
+    funding-rate bleed.
+    """
+    await asyncio.sleep(max_hold_seconds)
     await close_and_finalize(trade_id, api_key, leverage, asset, wallet, reason="max_hold")
+
+
+# Flat-zone band: a position whose |unrealised| is within this % at the
+# time-stop checkpoint is considered "not working" and closed early.
+_TIME_STOP_FLAT_BAND_PCT = 2.0
+
+
+async def _time_stop_check(
+    trade_id: int, api_key: str, leverage: int, asset: str, wallet: str,
+    delay_seconds: int,
+) -> None:
+    """System-2 time-stop. After `delay_seconds`, if the trade is still open
+    AND roughly flat (|unrealised| < band), the thesis is slow/dead — close
+    it so capital isn't parked for the full multi-week max-hold on a dud.
+
+    A trade that's already moved (win or stopped) will be closed/gone by
+    now; the conditional UPDATE in close_and_finalize makes a late fire a
+    harmless no-op.
+    """
+    await asyncio.sleep(delay_seconds)
+
+    from sqlalchemy import select
+    async with AsyncSessionLocal() as db:
+        row = await db.execute(select(BotTrade).where(BotTrade.id == trade_id))
+        trade = row.scalar_one_or_none()
+        if trade is None or trade.closed_at is not None:
+            return  # already closed by trail / SL / invalidation
+        entry = trade.entry_price
+
+    from app.services.price_store import price_store
+    cur = price_store.latest_price(asset)
+    if not cur or not entry:
+        return  # no price → don't act blindly; max-hold will catch it
+
+    raw = (cur - entry) / entry
+    signed_pct = (raw if trade.side == "long" else -raw) * 100
+    if abs(signed_pct) < _TIME_STOP_FLAT_BAND_PCT:
+        logger.info(
+            "Time-stop firing trade %d (%s %s): flat at %.2f%% after %dh",
+            trade_id, trade.side, asset, signed_pct, delay_seconds // 3600,
+        )
+        await close_and_finalize(
+            trade_id, api_key, leverage, asset, wallet, reason="time_stop",
+        )

app/services/bottom_indicators.py

@@ -0,0 +1,190 @@
+"""Pure-price BTC bottom indicators (no on-chain data, no API keys).
+
+Three independent, well-known bottom signals. Each is a simple, transparent
+function of price history only — no Realized Cap, no MVRV, no paid feeds:
+
+  A. AHR999       — value/DCA index. < 0.45 = deep-value bottom zone.
+  B. 200-Week MA  — BTC has bottomed near its 200WMA every cycle.
+  C. Pi Cycle Bot — 150d EMA vs 471d SMA × 0.745 crossover region.
+
+The combiner fires only when at least 2 of the 3 agree ("三者为二"), which
+historically pins genuine macro bottoms while rejecting single-indicator
+noise. Long-only, low-frequency, stateless.
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from datetime import date, datetime, timezone
+
+# BTC genesis block: 2009-01-03.
+_BTC_GENESIS = date(2009, 1, 3)
+
+# AHR999 thresholds.
+AHR999_BOTTOM = 0.45      # < this → deep-value / bottom zone (signal A)
+AHR999_INVALIDATION = 1.2  # > this → no longer cheap, thesis dead
+
+# 200WMA tolerance: price within +5% of the 200-week mean still counts.
+WMA200_TOLERANCE = 1.05
+
+# Pi Cycle Bottom multiplier (the canonical 0.745).
+PI_CYCLE_MULT = 0.745
+PI_CYCLE_EMA = 150
+PI_CYCLE_SMA = 471
+
+
+def _closes(candles: list[dict]) -> list[float]:
+    return [float(c["close"]) for c in candles if c.get("close")]
+
+
+def _sma(values: list[float], n: int) -> float | None:
+    if len(values) < n:
+        return None
+    return sum(values[-n:]) / n
+
+
+def _ema(values: list[float], n: int) -> float | None:
+    if len(values) < n:
+        return None
+    k = 2.0 / (n + 1)
+    # Seed with the SMA of the first n values, then walk forward.
+    ema = sum(values[:n]) / n
+    for v in values[n:]:
+        ema = v * k + ema * (1 - k)
+    return ema
+
+
+def coin_age_days(today: date | None = None) -> int:
+    d = today or datetime.now(timezone.utc).date()
+    return max(1, (d - _BTC_GENESIS).days)
+
+
+def ahr999(daily_closes: list[float], today: date | None = None) -> float | None:
+    """AHR999 = (price / GM200) × (price / growth_val).
+
+      GM200       = geometric mean of the last 200 daily closes
+      growth_val  = 10 ** (5.84 * log10(coin_age_days) - 17.01)
+
+    < 0.45 deep value · 0.45–1.2 DCA · > 1.2 expensive.
+    """
+    if len(daily_closes) < 200:
+        return None
+    price = daily_closes[-1]
+    if price <= 0:
+        return None
+    window = daily_closes[-200:]
+    # Geometric mean via log-space (avoids overflow).
+    log_sum = sum(math.log(c) for c in window if c > 0)
+    gm200 = math.exp(log_sum / 200)
+    age = coin_age_days(today)
+    growth_val = 10 ** (5.84 * math.log10(age) - 17.01)
+    if gm200 <= 0 or growth_val <= 0:
+        return None
+    return (price / gm200) * (price / growth_val)
+
+
+def below_200wma(
+    weekly_closes: list[float],
+    price: float | None = None,
+) -> tuple[bool, float | None]:
+    """True if price ≤ 200-week mean × 1.05. Returns (signal, wma200).
+
+    `price` is the reference price to compare. Pass the latest DAILY close so
+    all three indicators use the same "current price" — otherwise the last
+    weekly close can be up to 7 days stale and B would disagree with A/C right
+    at a fast-moving bottom. Falls back to the last weekly close if omitted.
+    """
+    wma = _sma(weekly_closes, 200)
+    if wma is None or not weekly_closes:
+        return False, wma
+    px = price if price is not None else weekly_closes[-1]
+    return px <= wma * WMA200_TOLERANCE, wma
+
+
+def pi_cycle_bottom(daily_closes: list[float]) -> tuple[bool, float | None, float | None]:
+    """Pi Cycle Bottom: 150d EMA ≤ 471d SMA × 0.745.
+
+    Returns (signal, ema150, sma471_scaled).
+    """
+    ema150 = _ema(daily_closes, PI_CYCLE_EMA)
+    sma471 = _sma(daily_closes, PI_CYCLE_SMA)
+    if ema150 is None or sma471 is None:
+        return False, ema150, None
+    scaled = sma471 * PI_CYCLE_MULT
+    return ema150 <= scaled, ema150, scaled
+
+
+def trend_confirmed(
+    daily_closes: list[float],
+    daily_highs: list[float],
+    price: float,
+    sma_n: int = 200,
+    high_lookback: int = 20,
+    high_tol: float = 0.005,
+) -> bool:
+    """Structural confirmation that the bottom reversal has turned into an
+    actual uptrend — gate for pyramiding (add-to-winner):
+
+      price ≥ 200-day SMA            (trend regime reclaimed)
+      AND price ≥ recent 20d high·(1−0.5%)   (at/near a fresh local high)
+
+    Pure function (no I/O) so it's unit-testable; the caller fetches candles.
+    Conservative: both conditions must hold, evaluated at add-on time.
+    """
+    sma = _sma(daily_closes, sma_n)
+    if sma is None or not daily_highs:
+        return False
+    recent_high = max(daily_highs[-high_lookback:]) if len(daily_highs) >= 1 else None
+    if recent_high is None or recent_high <= 0:
+        return False
+    return price >= sma and price >= recent_high * (1.0 - high_tol)
+
+
+@dataclass(frozen=True)
+class BottomConfluence:
+    fired: bool                 # ≥ 2 of 3 true
+    votes: int                  # 0..3
+    ahr999: float | None
+    a_value: bool               # AHR999 < 0.45
+    b_200wma: bool              # price ≤ 200WMA × 1.05
+    c_pi_cycle: bool            # 150 EMA ≤ 471 SMA × 0.745
+    detail: dict
+
+
+def bottom_confluence(
+    daily_closes: list[float],
+    weekly_closes: list[float],
+    today: date | None = None,
+) -> BottomConfluence:
+    """2-of-3 bottom confluence. Pure price, stateless."""
+    ah = ahr999(daily_closes, today)
+    a = ah is not None and ah < AHR999_BOTTOM
+
+    # All three indicators compare against the SAME current price (latest
+    # daily close), so a stale weekly close can't make B disagree with A/C.
+    cur_price = daily_closes[-1] if daily_closes else None
+    b, wma200 = below_200wma(weekly_closes, cur_price)
+    c, ema150, sma471s = pi_cycle_bottom(daily_closes)
+
+    votes = int(a) + int(b) + int(c)
+    detail = {
+        "ahr999": round(ah, 4) if ah is not None else None,
+        "ahr999_threshold": AHR999_BOTTOM,
+        "price": round(daily_closes[-1], 2) if daily_closes else None,
+        "wma200": round(wma200, 2) if wma200 is not None else None,
+        "wma200_band": round(wma200 * WMA200_TOLERANCE, 2) if wma200 is not None else None,
+        "pi_ema150": round(ema150, 2) if ema150 is not None else None,
+        "pi_sma471_scaled": round(sma471s, 2) if sma471s is not None else None,
+        "votes": votes,
+        "signals": {"ahr999_value": a, "below_200wma": b, "pi_cycle_bottom": c},
+    }
+    return BottomConfluence(
+        fired=votes >= 2,
+        votes=votes,
+        ahr999=ah,
+        a_value=a,
+        b_200wma=b,
+        c_pi_cycle=c,
+        detail=detail,
+    )

app/services/circuit_breaker.py

@@ -0,0 +1,201 @@
+"""
+Circuit breaker — autonomous "stop trading" trigger per wallet.
+
+Two trip conditions, evaluated AFTER every closed trade:
+
+  1. Daily drawdown: cumulative PnL across all trades closed today (UTC)
+     drops below -CB_DAILY_DD_PCT × (subscription.position_size_usd × 10).
+     Default cap: -5% of "expected 10-trade notional" — i.e. if the user's
+     base bet is $20, the daily DD limit is -$10 net realized.
+
+  2. Consecutive losses: last CB_CONSEC_LOSSES closed trades for this wallet
+     all have pnl_usd < 0. Default = 3.
+
+When tripped:
+  - manual_window_until is nulled (bot stops trading via manual override)
+  - circuit_breaker_tripped_at = now
+  - circuit_breaker_reason = "daily_dd" | "consecutive_losses"
+  - WS broadcast a 'circuit_breaker_tripped' event so the UI lights up red
+
+Gate: `is_tripped()` is checked by _execute_for_subscriber BEFORE opening any
+new trade. A tripped wallet stays blocked for CB_LOCKOUT_HOURS regardless of
+schedule, so a buggy scanner or a black-swan move can't drain the account
+overnight.
+
+User unblock path:
+  POST /api/user/{wallet}/manual-window with any hours > 0 clears the trip.
+  This is a deliberate human-in-the-loop — the user must consciously re-arm.
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models import BotTrade, Subscription
+
+logger = logging.getLogger(__name__)
+
+
+# ─── Tunables ───────────────────────────────────────────────────────────────
+CB_DAILY_DD_USD_PER_BASE = 0.5    # Daily DD limit as a multiple of base size.
+                                  # base $20 → DD limit -$10. Tune to taste.
+CB_CONSEC_LOSSES         = 3      # Consecutive losing trades that trip the CB.
+CB_LOCKOUT_HOURS         = 24     # Once tripped, blocked for this many hours
+                                  # unless the user explicitly clears it.
+
+
+# ─── Trip detection ─────────────────────────────────────────────────────────
+
+
+# Column indirection so one code path serves both independent breakers.
+#   system "sys1" → circuit_breaker_tripped_at / circuit_breaker_reason
+#   system "sys2" → sys2_cb_tripped_at        / sys2_cb_reason
+_CB_COLS = {
+    "sys1": ("circuit_breaker_tripped_at", "circuit_breaker_reason"),
+    "sys2": ("sys2_cb_tripped_at",          "sys2_cb_reason"),
+}
+
+
+async def _trades_for_system(db: AsyncSession, wallet: str, since, system: str):
+    """Closed, priced trades for `wallet` since `since`, filtered to the
+    given system by joining the trigger post's source.
+
+    sys2 = trigger_post.source in System-2 set; sys1 = everything else
+    (currently only "truth"). Trades with no trigger post → treated sys1.
+    """
+    from app.models import Post
+    from app.services.signal_categories import SYSTEM_2_SOURCES
+
+    rows = await db.execute(
+        select(BotTrade, Post.source)
+        .outerjoin(Post, Post.id == BotTrade.trigger_post_id)
+        .where(
+            BotTrade.wallet_address == wallet,
+            BotTrade.closed_at >= since,
+            BotTrade.pnl_usd.is_not(None),
+        )
+        .order_by(BotTrade.closed_at.desc())
+    )
+    out = []
+    for trade, src in rows.all():
+        is_s2 = (src or "").lower() in SYSTEM_2_SOURCES
+        if (system == "sys2") == is_s2:
+            out.append(trade)
+    return out
+
+
+async def check_and_trip(
+    wallet: str, db: AsyncSession, system: str = "sys1",
+) -> Optional[str]:
+    """Run trip conditions for `wallet` within ONE system. Independent per
+    system: a Trump losing streak can't lock out the reversal book.
+
+    Called from close_and_finalize after a trade's pnl is written, with the
+    system inferred from the closing trade's source.
+    """
+    col_at, col_reason = _CB_COLS[system]
+    sub = (await db.execute(
+        select(Subscription).where(Subscription.wallet_address == wallet)
+    )).scalar_one_or_none()
+    if sub is None:
+        return None
+
+    tripped_at = getattr(sub, col_at)
+    if tripped_at is not None:
+        age_hrs = (datetime.now(timezone.utc).replace(tzinfo=None) - tripped_at).total_seconds() / 3600
+        if age_hrs < CB_LOCKOUT_HOURS:
+            return None  # still in active lockout
+
+    reason: Optional[str] = None
+    now_naive = datetime.now(timezone.utc).replace(tzinfo=None)
+    start_of_day = now_naive.replace(hour=0, minute=0, second=0, microsecond=0)
+
+    today_trades = await _trades_for_system(db, wallet, start_of_day, system)
+    today_pnl = sum(t.pnl_usd or 0 for t in today_trades)
+    dd_limit = -CB_DAILY_DD_USD_PER_BASE * sub.position_size_usd * 10
+    if today_pnl < dd_limit:
+        reason = "daily_dd"
+        logger.warning("CB[%s] trip [daily_dd] %s: pnl=%.2f < %.2f",
+                       system, wallet, today_pnl, dd_limit)
+
+    if reason is None:
+        # Consecutive losses within this system (all-time, most recent N).
+        all_sys = await _trades_for_system(
+            db, wallet, datetime(1970, 1, 1), system,
+        )
+        recent = all_sys[:CB_CONSEC_LOSSES]
+        if len(recent) >= CB_CONSEC_LOSSES and all((t.pnl_usd or 0) < 0 for t in recent):
+            reason = "consecutive_losses"
+            logger.warning("CB[%s] trip [consecutive_losses] %s: last %d all negative",
+                           system, wallet, CB_CONSEC_LOSSES)
+
+    if reason is None:
+        return None
+
+    setattr(sub, col_at, now_naive)
+    setattr(sub, col_reason, reason)
+    # Only System 1 owns manual_window; nulling it on a sys2 trip would
+    # wrongly disable the Trump book too. Sys2 trip just blocks sys2 entries.
+    if system == "sys1":
+        sub.manual_window_until = None
+    await db.commit()
+
+    try:
+        from app.ws.manager import manager
+        await manager.broadcast({
+            "type":     "circuit_breaker_tripped",
+            "wallet":   wallet,
+            "system":   system,
+            "reason":   reason,
+            "tripped_at": now_naive.isoformat(),
+            "unlock_at": (now_naive + timedelta(hours=CB_LOCKOUT_HOURS)).isoformat(),
+        })
+    except Exception as exc:
+        logger.warning("CB broadcast failed: %s", exc)
+
+    return reason
+
+
+# ─── Gate (called by _execute_for_subscriber) ───────────────────────────────
+
+
+def is_tripped(sub_dict: dict, system: str = "sys1") -> tuple[bool, str]:
+    """Returns (tripped, reason) for the given system's breaker.
+    `sub_dict` is the snapshot copy built in bot_engine."""
+    col_at, col_reason = _CB_COLS[system]
+    tripped_at = sub_dict.get(col_at)
+    if tripped_at is None:
+        return False, ""
+    age_hrs = (datetime.now(timezone.utc).replace(tzinfo=None) - tripped_at).total_seconds() / 3600
+    if age_hrs >= CB_LOCKOUT_HOURS:
+        return False, ""  # lockout expired (auto-untrip on next refresh)
+    reason = sub_dict.get(col_reason) or "unknown"
+    remaining_hrs = CB_LOCKOUT_HOURS - age_hrs
+    return True, f"cb[{system}]:{reason} (unlock in {remaining_hrs:.1f}h)"
+
+
+# ─── Manual reset (called from /manual-window endpoint) ─────────────────────
+
+
+async def clear_trip(wallet: str, db: AsyncSession) -> bool:
+    """Clear the trip state. Returns True if a trip was actually cleared.
+
+    Called when the user explicitly re-arms manual_window — that's the
+    human-in-the-loop unblock. Just letting LOCKOUT_HOURS expire works too,
+    but most users will hit the button.
+    """
+    sub = (await db.execute(
+        select(Subscription).where(Subscription.wallet_address == wallet)
+    )).scalar_one_or_none()
+    if sub is None or sub.circuit_breaker_tripped_at is None:
+        return False
+    sub.circuit_breaker_tripped_at = None
+    sub.circuit_breaker_reason     = None
+    await db.commit()
+    logger.info("CB cleared for %s", wallet)
+    return True

app/services/entry_filter.py

@@ -0,0 +1,347 @@
+"""
+Deterministic entry pre-filter — runs BEFORE the AI call.
+
+Lesson from the 13-trade backtest: AI gives confidence 85+ to posts like
+"I had excellent conversations with President X" and "The Strait is open again",
+which are second-derivative news or pure rhetoric — they don't move markets.
+
+The two posts that ACTUALLY produced 10%+ moves both contained explicit
+"ACTION TAKEN" language: "STATEMENT OF PRESIDENT", "Military Success that we
+have had". The losers all had future-tense or conversational language.
+
+Rather than hoping the LLM internalises this distinction reliably, we enforce
+it with a deterministic Python filter. Cheap, debuggable, no false positives
+from a hallucinating model.
+
+This filter runs BEFORE analyze_post() in the scraper, short-circuiting the
+AI call when the text is obviously non-actionable. Saves API spend AND keeps
+the bot from accumulating "confidence 85, no catalyst" trades.
+
+Three independent gates (post must pass ALL):
+
+  1. ACTION verbs present — past-tense action OR formal statement marker
+  2. NO future-tense disqualifiers — "I will", "considering", "thinking about"
+  3. NOT a duplicate of a recent post on the same topic
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+# ─── Gate 1: explicit action markers ────────────────────────────────────────
+# Lower-cased substring matches. A post must contain AT LEAST ONE of these.
+
+ACTION_MARKERS = [
+    # Formal statement / announcement (past or imperative)
+    "statement of",
+    "statement by",
+    "executive order",
+    "announcing",
+    "today i signed",
+    "i have signed",
+    "i hereby",
+    "effective immediately",
+    "effective today",
+
+    # Past-tense action by the administration / military
+    "have agreed",
+    "has agreed",
+    "was signed",
+    "have signed",
+    "have implemented",
+    "have imposed",
+    "have authorized",
+    "we have had",      # "Military Success that we have had..." (post 684)
+    "have successfully",
+    "just imposed",
+    "just signed",
+    # NOTE: "i have ordered" was in this list but produced false positives.
+    # Trump uses it for verbal directives ("ordered Navy to shoot boats")
+    # that don't move markets. Real catalysts use "I have signed" or "STATEMENT OF".
+    # If you need to catch a genuine order, list the specific action below:
+    "ordered the suspension of",
+    "ordered the cancellation of",
+    "ordered the deployment of",
+    "just announced",
+
+    # Asset / policy specific catalysts
+    "strategic reserve",
+    "tariff",
+    "sanctions on",
+    "ceasefire signed",
+    "rate cut",
+    "rate hike",
+
+    # Regulatory / legal
+    "sec approval",
+    "sec has approved",
+    "supreme court ruled",
+    "doj filed",
+]
+
+
+# ─── Gate 2: future-tense / hedge disqualifiers ─────────────────────────────
+# If ANY of these appear in the first 200 chars, the post is intent not action.
+
+FUTURE_TENSE_BLOCKERS = [
+    # Pure intent
+    "i will",
+    "we will",
+    "i'm thinking",
+    "considering",
+    "looking at",
+    "may consider",
+    "could be",
+
+    # Conversational (not action)
+    "had a conversation",
+    "had conversations",
+    "had a great call",
+    "had a great meeting",
+    "spoke with",
+    "talked with",
+    "met with",
+    "i just had excellent",
+
+    # Hedge / partial
+    "most points were agreed",
+    "we're close to",
+    "still working on",
+]
+
+
+# ─── Gate 3: deduplication ──────────────────────────────────────────────────
+# Two layers:
+#
+# Layer A: TOPIC KEYWORDS — curated set of geopolitical / macro nouns. If two
+# posts within DEDUP_WINDOW_HOURS share ≥1 topic keyword, they're flagged as
+# being about the same event. This catches "Iran/Strait/Hormuz" being repeated
+# 4 times across 5 days, which the original token-overlap dedup missed because
+# the words ARE different (closed/opened/announced/transited) — only the
+# topic is the same.
+#
+# Layer B (legacy): generic token-overlap fallback for posts that don't hit a
+# curated topic. Kept as backup so we don't miss novel re-iterations.
+
+DEDUP_WINDOW_HOURS = 48
+DEDUP_KEYWORD_THRESHOLD = 3   # token-overlap backup threshold
+
+# Curated list of "topic anchors". Lower-cased substring match. When a new
+# post AND a prior post both contain one of these, we treat the new post as
+# a probable update to an existing storyline, not a fresh catalyst.
+#
+# Tune this list against your backtest results: anything that produced repeat
+# false-positives should be on here. Anything that produced a real catalyst
+# (post 684 = "Pakistan" — yes, even though we list it, Pakistan being mentioned
+# again within 48h would correctly be flagged as redundant) should NOT cause us
+# to miss the FIRST occurrence — only subsequent ones.
+TOPIC_KEYWORDS = {
+    # Middle East geopolitics (dominant source of FP in our 13-trade audit)
+    "iran", "iranian",
+    "hormuz", "strait of hormuz",
+    "strait of iran",   # Trump-ism on post 62 — same physical geography as Hormuz
+    "the strait",       # generic — catches "the Strait is open/closed" phrasing
+    "israel", "gaza", "lebanon", "hezbollah", "syria",
+    "yemen", "houthi",
+    # Russia / Ukraine
+    "ukraine", "russia", "putin", "zelensky", "kyiv",
+    # China
+    "china", "chinese", "xi jinping", "taiwan",
+    # North Korea
+    "north korea", "kim jong",
+    # Macro / monetary
+    "tariff", "tariffs",
+    "fed", "fomc", "powell", "interest rate", "rate cut", "rate hike",
+    "cpi", "inflation",
+    # Crypto-specific
+    "bitcoin", "btc", "ethereum", "crypto", "sec",
+    "strategic reserve",
+    # South Asia (post 684 family)
+    "pakistan", "india",
+}
+
+# Stop-words we ignore when comparing
+_STOP_WORDS = {
+    "the","a","an","of","to","and","or","in","on","at","is","are","was",
+    "were","be","been","have","has","had","will","i","we","you","they",
+    "this","that","with","for","by","from","my","our","your","their",
+    "it","its","as","but","not","just","very","more","most","than","also",
+    "would","could","should","do","does","did","new","now","get","got",
+    "make","made","go","going","go","want","want","know","like","really",
+    "us","u","said","say","says",
+}
+
+
+def _significant_tokens(text: str) -> set[str]:
+    """Lower-cased alphanumeric words ≥4 chars, with stop-words removed."""
+    raw = re.findall(r"[a-zA-Z]{4,}", text.lower())
+    return {w for w in raw if w not in _STOP_WORDS}
+
+
+# How many leading characters to scan for "dominant" topics. Topics that only
+# appear in the tail of a long post are background context, not the catalyst.
+# A post that mentions Iran in passing 800 chars in shouldn't dedup against
+# a different post whose CORE topic is Iran.
+TOPIC_HEAD_CHARS = 200
+
+
+def _topic_keywords_in(text: str) -> set[str]:
+    """Return canonical TOPIC_KEYWORDS in the FIRST TOPIC_HEAD_CHARS of `text`.
+
+    Canonicalisation collapses substring matches: if both "hormuz" and
+    "strait of hormuz" match, only the longer one is kept. This prevents
+    inflated overlap counts where the same physical concept matches twice.
+
+    Limiting to the head ensures we match the post's DOMINANT topic, not
+    incidental mentions buried in the tail.
+    """
+    t = text[:TOPIC_HEAD_CHARS].lower()
+    raw = {kw for kw in TOPIC_KEYWORDS if kw in t}
+    # Drop any keyword that is a proper substring of another matched keyword.
+    canonical = set(raw)
+    for a in raw:
+        for b in raw:
+            if a != b and a in b:
+                canonical.discard(a)
+                break
+    return canonical
+
+
+# Threshold for dedup by topic. Requiring ≥2 shared CANONICAL topics avoids
+# false positives where two posts both mention Iran but are about different
+# events (post 684 = Pakistan-Iran context; post 416 = Iran sanctions —
+# they share "iran" but nothing else). Two-topic overlap (e.g. Iran + Strait
+# of Hormuz, or Bitcoin + Strategic Reserve) is a much stronger same-event
+# signal.
+DEDUP_TOPIC_MIN = 2
+
+
+# ─── Public API ─────────────────────────────────────────────────────────────
+
+
+def has_action_marker(text: str) -> tuple[bool, Optional[str]]:
+    """True iff at least one ACTION_MARKERS substring appears in `text`.
+
+    Returns (ok, matched_marker_or_None).
+    """
+    t = text.lower()
+    for m in ACTION_MARKERS:
+        if m in t:
+            return True, m
+    return False, None
+
+
+def has_future_tense_blocker(text: str) -> tuple[bool, Optional[str]]:
+    """True iff one of FUTURE_TENSE_BLOCKERS appears (in first 200 chars)."""
+    head = text[:200].lower()
+    for b in FUTURE_TENSE_BLOCKERS:
+        if b in head:
+            return True, b
+    return False, None
+
+
+async def is_duplicate_of_recent(text: str, db_session_factory) -> tuple[bool, Optional[int]]:
+    """True iff a post within DEDUP_WINDOW_HOURS appears to cover the same event.
+
+    Two-layer check:
+      A. Any shared TOPIC_KEYWORD (Iran, Pakistan, tariff, etc.) → duplicate
+      B. ≥3 shared significant tokens AND ≥50% overlap → duplicate (catches
+         novel topics not on the curated list)
+
+    Returns (is_dup, dup_post_id_or_None).
+    """
+    from sqlalchemy import select
+    from app.models import Post
+
+    new_topics = _topic_keywords_in(text)
+    new_tokens = _significant_tokens(text)
+
+    cutoff = (datetime.now(timezone.utc) - timedelta(hours=DEDUP_WINDOW_HOURS)).replace(tzinfo=None)
+
+    async with db_session_factory() as db:
+        rows = await db.execute(
+            select(Post.id, Post.text).where(Post.published_at >= cutoff)
+        )
+        for pid, prior_text in rows.all():
+            # Layer A: canonical topic-keyword overlap (≥ DEDUP_TOPIC_MIN)
+            if new_topics:
+                prior_topics = _topic_keywords_in(prior_text)
+                if len(new_topics & prior_topics) >= DEDUP_TOPIC_MIN:
+                    return True, pid
+            # Layer B: generic token-overlap fallback for novel topics
+            if len(new_tokens) >= DEDUP_KEYWORD_THRESHOLD:
+                prior_tokens = _significant_tokens(prior_text)
+                shared = new_tokens & prior_tokens
+                if len(shared) >= DEDUP_KEYWORD_THRESHOLD:
+                    if len(shared) / max(len(new_tokens), 1) >= 0.5:
+                        return True, pid
+    return False, None
+
+
+async def passes_entry_filter(
+    text: str,
+    db_session_factory,
+) -> tuple[bool, str]:
+    """Master combiner. Returns (passed, reason_string).
+
+    On failure, `reason_string` explains which gate killed it. On success,
+    `reason_string` is empty.
+
+    Run this BEFORE analyze_post() to short-circuit the AI call on obvious
+    non-catalysts.
+    """
+    ok_action, marker = has_action_marker(text)
+    if not ok_action:
+        return False, "no_action_marker (no past-tense / formal-statement keyword found)"
+
+    has_block, blocker = has_future_tense_blocker(text)
+    if has_block:
+        return False, f"future_tense_blocker: {blocker!r}"
+
+    is_dup, dup_id = await is_duplicate_of_recent(text, db_session_factory)
+    if is_dup:
+        return False, f"duplicate_of_post_{dup_id}"
+
+    return True, f"action_marker={marker!r}"
+
+
+# ─── Backtest helper — synchronous version using a single text + history ────
+
+
+def passes_entry_filter_sync(text: str, prior_texts: list[str]) -> tuple[bool, str]:
+    """Pure-function variant for backtest replay: pass a list of prior post
+    texts (within the dedup window) instead of hitting the DB.
+
+    Useful for running the filter over the historical dataset to count how
+    many posts would have been EXECUTED vs FILTERED.
+    """
+    ok_action, marker = has_action_marker(text)
+    if not ok_action:
+        return False, "no_action_marker"
+
+    has_block, blocker = has_future_tense_blocker(text)
+    if has_block:
+        return False, f"future_tense: {blocker}"
+
+    new_topics = _topic_keywords_in(text)
+    new_tokens = _significant_tokens(text)
+    for pt in prior_texts:
+        # Layer A: canonical topic overlap (≥ DEDUP_TOPIC_MIN)
+        if new_topics:
+            shared_topics = new_topics & _topic_keywords_in(pt)
+            if len(shared_topics) >= DEDUP_TOPIC_MIN:
+                return False, f"duplicate_topic: {','.join(sorted(shared_topics))}"
+        # Layer B: generic token overlap
+        if len(new_tokens) >= DEDUP_KEYWORD_THRESHOLD:
+            shared = new_tokens & _significant_tokens(pt)
+            if (len(shared) >= DEDUP_KEYWORD_THRESHOLD
+                and len(shared) / max(len(new_tokens), 1) >= 0.5):
+                return False, "duplicate_tokens"
+
+    return True, f"ok: {marker}"

app/services/funding_signal.py

@@ -0,0 +1,221 @@
+"""
+Breakout Signal Monitor — ETH + LINK (BTC as trend context)
+
+Polls Binance every 5 minutes. When both conditions are met:
+  1. BB squeeze: BB width in bottom 20% of last 60 candles
+  2. Volume spike: current volume > 2.5x 20-period average
+  3. Taker buy ratio > 60%
+  4. Price closes above upper Bollinger Band
+
+Broadcasts alert via WebSocket. Gated by user on/off toggle.
+"""
+
+import collections
+import logging
+from datetime import datetime, timezone
+from typing import Deque, Optional
+
+import httpx
+
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+# ── Config ────────────────────────────────────────────────────────────────────
+
+WATCH_SYMBOLS  = ["ETHUSDT", "LINKUSDT"]
+CONTEXT_SYMBOL = "BTCUSDT"
+ALL_SYMBOLS    = [CONTEXT_SYMBOL] + WATCH_SYMBOLS
+
+CANDLE_LIMIT   = 200    # candles to fetch per poll (200 x 5m = ~16.7h history)
+BB_PERIOD      = 20
+BB_SQUEEZE_PCT = 20     # bottom 20% of BB width history → squeeze
+VOLUME_MULT    = 2.5
+TBR_THRESH     = 0.60   # taker buy ratio threshold
+BTC_TREND_MA   = 288    # 24h trend (needs separate longer fetch for BTC)
+
+# ── State ─────────────────────────────────────────────────────────────────────
+
+_enabled: bool = False
+_recent_signals: Deque[dict] = collections.deque(maxlen=50)
+_last_fired: dict[str, Optional[datetime]] = {s: None for s in WATCH_SYMBOLS}
+
+
+# ── Public API ────────────────────────────────────────────────────────────────
+
+def set_enabled(value: bool) -> None:
+    global _enabled
+    _enabled = value
+    logger.info("Funding signal monitor: %s", "ENABLED" if value else "DISABLED")
+
+
+def is_enabled() -> bool:
+    return _enabled
+
+
+def get_recent_signals(limit: int = 20) -> list[dict]:
+    return list(reversed(list(_recent_signals)))[:limit]
+
+
+def get_status() -> dict:
+    return {
+        "enabled": _enabled,
+        "symbols": WATCH_SYMBOLS,
+        "recent_signal_count": len(_recent_signals),
+    }
+
+
+# ── Binance fetch ─────────────────────────────────────────────────────────────
+
+async def _fetch_klines(client: httpx.AsyncClient, symbol: str, limit: int) -> list[list]:
+    url = f"{settings.binance_rest_url}/api/v3/klines"
+    try:
+        resp = await client.get(url, params={
+            "symbol": symbol, "interval": "5m", "limit": limit
+        }, timeout=15)
+        resp.raise_for_status()
+        return resp.json()
+    except Exception as exc:
+        logger.warning("Failed to fetch klines for %s: %s", symbol, exc)
+        return []
+
+
+def _parse_candle(row: list) -> dict:
+    total_vol = float(row[5])
+    taker_buy = float(row[9])
+    return {
+        "time":   row[0],
+        "open":   float(row[1]),
+        "high":   float(row[2]),
+        "low":    float(row[3]),
+        "close":  float(row[4]),
+        "volume": total_vol,
+        "tbr":    taker_buy / total_vol if total_vol > 0 else 0.5,
+    }
+
+
+# ── Indicators ────────────────────────────────────────────────────────────────
+
+def _compute_signal(candles: list[dict]) -> Optional[dict]:
+    """
+    Returns signal dict if breakout signal fires on the most recent candle,
+    else None. Requires at least 60 candles.
+    """
+    # Drop the last (current incomplete) candle — always use completed candles
+    candles = candles[:-1]
+
+    if len(candles) < 60:
+        return None
+
+    closes  = [c["close"]  for c in candles]
+    volumes = [c["volume"] for c in candles]
+    tbrs    = [c["tbr"]    for c in candles]
+
+    # ── Bollinger Bands (last BB_PERIOD candles) ──────────────────────────────
+    window = closes[-BB_PERIOD:]
+    bb_mid = sum(window) / BB_PERIOD
+    variance = sum((x - bb_mid) ** 2 for x in window) / BB_PERIOD
+    bb_std = variance ** 0.5
+    bb_upper = bb_mid + 2 * bb_std
+    bb_width = (4 * bb_std) / bb_mid if bb_mid > 0 else 0
+
+    # BB width percentile over last 60 candles
+    widths = []
+    for i in range(len(candles) - 60, len(candles)):
+        w_window = closes[max(0, i - BB_PERIOD + 1): i + 1]
+        if len(w_window) < BB_PERIOD:
+            continue
+        m = sum(w_window) / len(w_window)
+        s = (sum((x - m) ** 2 for x in w_window) / len(w_window)) ** 0.5
+        widths.append((4 * s) / m if m > 0 else 0)
+
+    if not widths:
+        return None
+
+    rank = sum(1 for w in widths if w < bb_width) / len(widths) * 100
+    in_squeeze = rank < BB_SQUEEZE_PCT
+
+    # ── Volume ────────────────────────────────────────────────────────────────
+    vol_ma = sum(volumes[-BB_PERIOD - 1:-1]) / BB_PERIOD
+    vol_spike = volumes[-1] > VOLUME_MULT * vol_ma if vol_ma > 0 else False
+
+    # ── Taker buy ratio ───────────────────────────────────────────────────────
+    tbr_ok = tbrs[-1] > TBR_THRESH
+
+    # ── Price above upper BB ──────────────────────────────────────────────────
+    above_bb = closes[-1] > bb_upper
+
+    if in_squeeze and vol_spike and tbr_ok and above_bb:
+        return {
+            "bb_width_pct":  round(rank, 1),
+            "vol_mult":      round(volumes[-1] / vol_ma, 2) if vol_ma > 0 else 0,
+            "tbr":           round(tbrs[-1], 3),
+            "close":         closes[-1],
+            "bb_upper":      round(bb_upper, 4),
+        }
+    return None
+
+
+def _btc_trend(candles: list[dict]) -> Optional[bool]:
+    """True = BTC above 24h MA, False = below, None = not enough data."""
+    if len(candles) < BTC_TREND_MA:
+        return None
+    closes = [c["close"] for c in candles]
+    ma = sum(closes[-BTC_TREND_MA:]) / BTC_TREND_MA
+    return closes[-1] > ma
+
+
+# ── Main poll loop ────────────────────────────────────────────────────────────
+
+async def poll_funding_signal() -> None:
+    """Called by APScheduler every 5 minutes."""
+    from app.ws.manager import manager  # avoid circular import at module load
+
+    async with httpx.AsyncClient(timeout=20) as client:
+        # Fetch BTC with longer history for 24h trend
+        btc_rows = await _fetch_klines(client, CONTEXT_SYMBOL, BTC_TREND_MA + 10)
+        btc_candles = [_parse_candle(r) for r in btc_rows]
+        btc_up = _btc_trend(btc_candles)
+
+        # Fetch ETH + LINK
+        for symbol in WATCH_SYMBOLS:
+            rows = await _fetch_klines(client, symbol, CANDLE_LIMIT)
+            if not rows:
+                continue
+
+            candles = [_parse_candle(r) for r in rows]
+            sig = _compute_signal(candles)
+
+            if sig is None:
+                continue
+
+            # Deduplicate: don't fire same symbol twice within 30 minutes
+            now = datetime.now(timezone.utc)
+            last = _last_fired.get(symbol)
+            if last and (now - last).total_seconds() < 1800:
+                logger.info("Signal for %s deduplicated (too soon)", symbol)
+                continue
+
+            _last_fired[symbol] = now
+
+            alert = {
+                "type":       "funding_signal",
+                "symbol":     symbol,
+                "time":       now.isoformat(),
+                "close":      sig["close"],
+                "tbr":        sig["tbr"],
+                "vol_mult":   sig["vol_mult"],
+                "bb_pct":     sig["bb_width_pct"],
+                "bb_upper":   sig["bb_upper"],
+                "btc_trend":  "↑ uptrend" if btc_up else ("↓ downtrend" if btc_up is False else "unknown"),
+                "enabled":    _enabled,
+            }
+            _recent_signals.append(alert)
+
+            if _enabled:
+                logger.info("🚨 SIGNAL %s | price=%.4f tbr=%.2f vol=%.1fx btc=%s",
+                            symbol, sig["close"], sig["tbr"], sig["vol_mult"],
+                            alert["btc_trend"])
+                await manager.broadcast(alert)
+            else:
+                logger.info("Signal %s (monitor OFF — not broadcast)", symbol)

app/services/hyperliquid.py

@@ -301,3 +301,77 @@ class HyperliquidTrader:
         fill_price = float(filled_info.get("avgPx", mid) or mid)
         logger.info("Closed %s position @ %.2f (size=%.5f)", coin, fill_price, total_sz)
         return {"fill_price": fill_price, "already_closed": False}
+
+    async def reduce_position(self, asset: str, fraction: float) -> dict:
+        """Partially close `fraction` (0<f<1) of the CURRENT open position
+        for `asset` with a reduce-only IOC order. Used by System-2 staged
+        de-risk. Returns {"fill_price": float, "closed_fraction": float,
+        "already_closed": bool}.
+
+        `closed_fraction` is the fraction of the position that was actually
+        closed (filled_size / pre-existing size) so the caller's PnL +
+        remaining bookkeeping stays exact even on partial fills.
+        """
+        coin = asset.upper()
+        f = max(0.0, min(1.0, float(fraction)))
+        if f <= 0.0:
+            return {"fill_price": None, "closed_fraction": 0.0, "already_closed": False}
+        if f >= 1.0:
+            r = await self.close_position(asset)
+            r["closed_fraction"] = 0.0 if r.get("already_closed") else 1.0
+            return r
+
+        positions = await self.get_open_positions()
+        target = next((p for p in positions if p.get("coin") == coin), None)
+        if target is None:
+            logger.warning("reduce_position: no open %s on HL — already closed?", coin)
+            return {"fill_price": None, "closed_fraction": 0.0, "already_closed": True}
+
+        szi = float(target["szi"])
+        is_buy = szi < 0                       # reducing a short → buy back
+        pre_size = abs(szi)
+        sz_dec = await self._get_sz_decimals(coin)
+        size = round(pre_size * f, sz_dec)
+        if size <= 0:
+            # Fraction rounds to nothing at this asset's size precision —
+            # treat as a no-op so the caller can advance the step without
+            # an erroneous PnL slice.
+            logger.warning("reduce_position: %s fraction %.3f rounds to 0 size (pre=%.6f)",
+                           coin, f, pre_size)
+            return {"fill_price": None, "closed_fraction": 0.0, "already_closed": False}
+
+        mid = await self._mid_price(coin)
+        slippage = 0.01
+        raw_px = mid * (1 + slippage) if is_buy else mid * (1 - slippage)
+        if raw_px >= 10000:
+            limit_px = float(round(raw_px))
+        elif raw_px >= 1000:
+            limit_px = round(raw_px, 1)
+        elif raw_px >= 100:
+            limit_px = round(raw_px, 2)
+        else:
+            limit_px = round(raw_px, 3)
+
+        logger.info("Reducing %s by %.0f%%: size=%.6f @ limit %.2f (pre=%.6f)",
+                    coin, f * 100, size, limit_px, pre_size)
+
+        result = await self._run(
+            self._exchange.order,
+            coin, is_buy, size, limit_px,
+            {"limit": {"tif": "Ioc"}},
+            reduce_only=True,
+        )
+        statuses = result.get("response", {}).get("data", {}).get("statuses", [{}])
+        status = statuses[0] if statuses else {}
+        if "error" in status:
+            raise ValueError(f"HL reduce rejected: {status['error']}")
+        filled_info = status.get("filled") or {}
+        total_sz = float(filled_info.get("totalSz", 0) or 0)
+        if total_sz <= 0:
+            raise ValueError(f"reduce_position {coin}: IOC returned 0 fill")
+        fill_price = float(filled_info.get("avgPx", mid) or mid)
+        closed_fraction = (total_sz / pre_size) if pre_size > 0 else 0.0
+        logger.info("Reduced %s: closed %.6f / %.6f (%.1f%%) @ %.2f",
+                    coin, total_sz, pre_size, closed_fraction * 100, fill_price)
+        return {"fill_price": fill_price, "closed_fraction": closed_fraction,
+                "already_closed": False}

app/services/kol_analysis.py

@@ -0,0 +1,223 @@
+"""KOL post → structured signal extractor.
+
+Takes a long-form post (Substack essay) or tweet and returns:
+  - summary:  one Chinese sentence on what this post is about
+  - tickers:  list of {ticker, action, conviction, quote}
+
+action ∈ bullish | bearish | buy | sell | mention
+  - buy/sell  → KOL explicitly states they bought/sold or are entering/exiting
+  - bullish/bearish → directional view without an explicit position statement
+  - mention   → ticker appears but no clear stance (don't flood with these)
+
+conviction ∈ 0.0–1.0
+  - 0.8+  : explicit, repeated, with sizing / timing
+  - 0.5–0.7 : clear view, no commitment
+  - <0.5  : passing reference
+
+Quote is the shortest verbatim sentence supporting the call.
+
+Uses the same Anthropic client style as analysis.py. Designed to be reused
+by the Trump-post AI signal module (TODO #4) — same JSON shape, just with
+different KOL context strings.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from datetime import datetime, timezone
+from typing import Optional
+
+from openai import AsyncOpenAI
+
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+ANALYSIS_VERSION = "kol-v1"
+ANTHROPIC_MODEL = "claude-haiku-4-5-20251001"
+
+_anthropic_client = None
+_openai_client: Optional[AsyncOpenAI] = None
+
+
+def _use_anthropic() -> bool:
+    return bool(settings.anthropic_api_key)
+
+
+def _anth():
+    global _anthropic_client
+    if _anthropic_client is None:
+        import anthropic as _a
+        _anthropic_client = _a.AsyncAnthropic(api_key=settings.anthropic_api_key)
+    return _anthropic_client
+
+
+def _oai() -> AsyncOpenAI:
+    global _openai_client
+    if _openai_client is None:
+        _openai_client = AsyncOpenAI(
+            api_key=settings.ai_api_key,
+            base_url=settings.ai_base_url,
+        )
+    return _openai_client
+
+
+SYSTEM_PROMPT = """You are an analyst extracting tradeable signals from crypto KOL posts.
+
+The author is a known crypto KOL. Your job: distill what they said and which tokens they are talking about RIGHT NOW (not historical references).
+
+Output **strict JSON only**, no markdown, no preface. Schema:
+
+{
+  "summary": "<one sentence, ≤60 chars/字. If signal exists, state the author's current thesis. If no signal, describe the post topic. Match the post's primary language (中文文章用中文, English 用英文).>",
+  "tickers": [
+    {
+      "ticker":     "<UPPERCASE symbol, e.g. BTC, ETH, HYPE, SOL>",
+      "action":     "buy" | "sell" | "bullish" | "bearish" | "mention",
+      "conviction": <float 0.0-1.0>,
+      "quote":      "<shortest verbatim sentence from the post supporting this call, ≤200 chars. Use the post's original language — do not translate.>"
+    }
+  ]
+}
+
+Rules:
+- If the post is macro commentary, news recap, or sponsored content with no specific token call, return tickers=[] and summary describing the topic.
+- IGNORE historical price references ("BTC bottomed at $60k earlier this year") — these are context, not current calls.
+- IGNORE advertising/sponsor sections — look for cues: "sponsor", "partner", "use code", "promo code", "this episode brought to you by", "ad", "广告", "赞助". Skip any ticker only mentioned inside such a section.
+- buy/sell only when the author states a position action ("I bought", "we are long", "我们减仓了", "added to my bag"). Otherwise use bullish/bearish for directional views, or mention for passing references.
+- Dedupe per ticker — at most one entry per symbol; pick the strongest action.
+- Do NOT invent tickers. If you see "$XYZ" but unsure it's a real token, skip it.
+- conviction: 0.8+ requires explicit + repeated + sized/timed view; 0.5-0.7 for clear directional view without commitment; <0.5 for passing references.
+- Do not include fiat (USD/CNY/JPY) or stablecoins (USDT/USDC/DAI/FRAX) unless the post's main thesis is about them.
+"""
+
+
+USER_TEMPLATE = """Today is {today_utc}.
+KOL handle: {handle}
+Source: {source}
+Title: {title}
+
+Post body:
+\"\"\"
+{body}
+\"\"\"
+"""
+
+
+def _truncate(text: str, max_chars: int = 24000) -> str:
+    """Substack essays can be 50K+ chars. Haiku handles it but we cap to
+    control cost. Keep head + tail since conclusions often appear at the end."""
+    if len(text) <= max_chars:
+        return text
+    head = max_chars * 2 // 3
+    tail = max_chars - head
+    return text[:head] + "\n\n[...trimmed...]\n\n" + text[-tail:]
+
+
+def _parse_json(raw: str) -> dict:
+    raw = raw.strip()
+    if raw.startswith("```"):
+        # strip fenced code block
+        raw = raw.split("\n", 1)[1] if "\n" in raw else raw
+        if raw.endswith("```"):
+            raw = raw.rsplit("```", 1)[0]
+        raw = raw.strip()
+    # Some models prepend "json" after the fence
+    if raw.startswith("json"):
+        raw = raw[4:].strip()
+    return json.loads(raw)
+
+
+async def extract_kol_signal(
+    *,
+    handle: str,
+    source: str,
+    title: Optional[str],
+    body: str,
+    model: Optional[str] = None,
+) -> dict:
+    """Run the extractor. Returns {summary, tickers, model, version}.
+
+    Returns an empty-but-valid dict on parse/API failure rather than raising —
+    the caller stores the post regardless; an unanalyzed post can be retried.
+    """
+    today_utc = datetime.now(timezone.utc).strftime("%Y-%m-%d")
+    user = USER_TEMPLATE.format(
+        today_utc=today_utc,
+        handle=handle,
+        source=source,
+        title=title or "",
+        body=_truncate(body),
+    )
+
+    use_anth = _use_anthropic()
+    if model is None:
+        # KOL analysis is a daily batch job, not latency-sensitive. Use the
+        # higher-quality `ai_model` (DeepSeek v4 Pro / reasoning) rather than
+        # the live `ai_live_model` (flash) reserved for Trump real-time path.
+        model = ANTHROPIC_MODEL if use_anth else settings.ai_model
+
+    try:
+        if use_anth:
+            msg = await _anth().messages.create(
+                model=model,
+                max_tokens=1500,
+                temperature=0.1,
+                system=SYSTEM_PROMPT,
+                messages=[{"role": "user", "content": user}],
+            )
+            raw = (msg.content[0].text if msg.content else "").strip()
+        else:
+            # OpenAI-compatible (DeepSeek). Reasoning models need higher tokens
+            # + no temperature; flash/chat models are fine with both.
+            is_reasoning = any(x in model for x in ("pro", "reasoner", "r1", "think"))
+            kwargs = {"model": model, "messages": [
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": user},
+            ], "max_tokens": 4000 if is_reasoning else 1500}
+            if not is_reasoning:
+                kwargs["temperature"] = 0.1
+            # JSON mode — DeepSeek + OpenAI both support response_format.
+            # Eliminates fenced/preface parse failures. Skipped for reasoning
+            # models (some don't accept response_format alongside reasoning).
+            if not is_reasoning:
+                kwargs["response_format"] = {"type": "json_object"}
+            resp = await _oai().chat.completions.create(**kwargs)
+            raw = (resp.choices[0].message.content or "").strip()
+        data = _parse_json(raw)
+    except Exception as e:
+        logger.warning("kol_analysis extract failed for %s: %s", handle, e)
+        return {"summary": None, "tickers": [], "model": model,
+                "version": ANALYSIS_VERSION, "error": str(e)}
+
+    # Normalize
+    tickers = data.get("tickers") or []
+    cleaned = []
+    for t in tickers:
+        if not isinstance(t, dict):
+            continue
+        sym = (t.get("ticker") or "").strip().upper()
+        if not sym or len(sym) > 12:
+            continue
+        action = (t.get("action") or "mention").lower()
+        if action not in {"buy", "sell", "bullish", "bearish", "mention"}:
+            action = "mention"
+        try:
+            conv = float(t.get("conviction") or 0)
+        except (TypeError, ValueError):
+            conv = 0.0
+        conv = max(0.0, min(1.0, conv))
+        cleaned.append({
+            "ticker": sym,
+            "action": action,
+            "conviction": round(conv, 2),
+            "quote": (t.get("quote") or "")[:200],
+        })
+
+    return {
+        "summary": (data.get("summary") or "").strip() or None,
+        "tickers": cleaned,
+        "model": model,
+        "version": ANALYSIS_VERSION,
+    }

app/services/kol_divergence.py

@@ -0,0 +1,285 @@
+"""KOL talks-vs-trades cross-signal detector.
+
+Compares B-tier content signals (Substack / Twitter post → ticker action)
+against A-tier on-chain changes (wallet holding changes) for the same
+KOL handle + ticker within a ±N-day window.
+
+Two outcomes:
+  divergence — KOL says bullish but on-chain is selling (or vice versa).
+               On-chain action is the ground truth; word is noise/manipulation.
+               Conclusion = opposite of what was said.
+
+  alignment  — Post and chain agree. Conviction is reinforced.
+               Conclusion = what was said (and done).
+
+Logic:
+
+  post side  → action ∈ {buy, bullish} = LONG intent
+                        {sell, bearish} = SHORT intent
+                        {mention}       = skip (no clear view)
+
+  chain side → change_type ∈ {new_position, increased} = LONG action
+                              {decreased, closed}        = SHORT action
+
+  LONG intent  + SHORT action → divergence, direction='short'
+  SHORT intent + LONG  action → divergence, direction='long'
+  LONG  intent + LONG  action → alignment,  direction='long'
+  SHORT intent + SHORT action → alignment,  direction='short'
+
+Run cadence: after each onchain poll (02:05 UTC). Also callable manually.
+Idempotent: UniqueConstraint(post_id, change_id) prevents double-writes.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+from collections import defaultdict
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.database import AsyncSessionLocal
+from app.models import KolDivergence, KolHoldingChange, KolPost, KolWallet, Post, utcnow
+
+logger = logging.getLogger(__name__)
+
+# Match window: post and chain event must be within this many days of each other
+WINDOW_DAYS = 7
+
+# Only surface on-chain changes above this threshold (avoid noise from dust)
+MIN_USD_CHANGE = 10_000
+
+# Post actions that map to a directional view (skip 'mention')
+_POST_LONG  = {"buy", "bullish"}
+_POST_SHORT = {"sell", "bearish"}
+
+# On-chain actions that map to a direction
+_CHAIN_LONG  = {"new_position", "increased"}
+_CHAIN_SHORT = {"decreased", "closed"}
+
+# ── Telegram alert gating ────────────────────────────────────────────────────
+# Only push divergences (the surprising "they say one thing, do another" case).
+# Alignments are confirmations, not unique alpha — skip to keep volume sane.
+ALERT_ON_TYPES = {"divergence"}
+# Floor on the post's stated conviction. Without this, every "mention"-like
+# soft view that happens to coincide with a buy/sell would page users.
+ALERT_MIN_POST_CONVICTION = 0.5
+# Minimum USD on the on-chain side to bother alerting. The 10K floor in
+# MIN_USD_CHANGE keeps dust out of the table; this stricter floor avoids
+# alerting on small rebalances by big wallets.
+ALERT_MIN_USD = 50_000
+
+
+def _post_for_divergence(handle: str, ticker: str, direction: str,
+                         post_action: str, chain_action: str,
+                         conviction: float, change_id: int,
+                         usd_after: Optional[float],
+                         days_apart: float) -> Post:
+    """Build a Post row carrying a KOL divergence as an actionable signal.
+
+    `direction` is the CONCLUSION direction from _classify: 'long' → buy,
+    'short' → short. ai_confidence is derived from post conviction so the
+    user's min_confidence floor in TelegramBinding gates noise.
+    """
+    signal = "buy" if direction == "long" else "short"
+    usd_str = f"${(usd_after or 0)/1000:.0f}K" if usd_after else "?"
+    text = (
+        f"KOL {handle} says {post_action.upper()} {ticker} — "
+        f"but on-chain shows {chain_action} ({usd_str}, Δ{days_apart:.1f}d). "
+        f"Following the chain (the trade, not the talk): {signal.upper()} {ticker}."
+    )
+    # external_id must be unique-per-source. Use the change_id as the dedupe
+    # key — at most one Post per (kol, ticker, chain-event).
+    ext = hashlib.md5(f"kol_divergence:{handle}:{ticker}:{change_id}".encode()).hexdigest()
+    return Post(
+        external_id=ext,
+        text=text,
+        source="kol_divergence",
+        published_at=datetime.now(timezone.utc).replace(tzinfo=None),
+        sentiment="bullish" if signal == "buy" else "bearish",
+        ai_confidence=int(round(conviction * 100)),
+        relevant=True,
+        signal=signal,
+        target_asset=ticker,
+        category=f"kol_divergence_{signal}",
+        analysis_version="kol_divergence_v1",
+        prefilter_reason="kol_divergence",
+    )
+
+
+def _classify(post_action: str, chain_action: str) -> Optional[tuple[str, str]]:
+    """Returns (signal_type, direction) or None if no meaningful pair."""
+    if post_action in _POST_LONG:
+        if chain_action in _CHAIN_LONG:
+            return "alignment", "long"
+        if chain_action in _CHAIN_SHORT:
+            return "divergence", "short"
+    elif post_action in _POST_SHORT:
+        if chain_action in _CHAIN_LONG:
+            return "divergence", "long"
+        if chain_action in _CHAIN_SHORT:
+            return "alignment", "short"
+    return None
+
+
+async def run_divergence_scan(
+    lookback_days: int = 30,
+) -> list[dict]:
+    """Scan recent posts × on-chain changes, write new KolDivergence rows.
+
+    Returns list of newly written rows as dicts (for logging / API return).
+    Already-stored pairs are silently skipped (idempotent).
+    """
+    since = datetime.now(timezone.utc).replace(tzinfo=None) - timedelta(days=lookback_days)
+    results: list[dict] = []
+    # Posts we created this scan — notify_signal runs AFTER commit so the
+    # dispatcher's separate DB session can read the row.
+    alert_post_ids: list[int] = []
+
+    async with AsyncSessionLocal() as session:
+        # ── 1. Load recent posts that have directional ticker signals ──────
+        posts = (await session.execute(
+            select(KolPost)
+            .where(KolPost.published_at >= since)
+            .where(KolPost.tickers_json.is_not(None))
+        )).scalars().all()
+
+        # ── 2. Load recent on-chain changes (keyed by handle via wallet) ───
+        changes_rows = (await session.execute(
+            select(KolHoldingChange, KolWallet.handle)
+            .join(KolWallet, KolHoldingChange.wallet_id == KolWallet.id)
+            .where(KolHoldingChange.detected_at >= since)
+        )).all()
+
+        # Pre-load all existing (post_id, change_id) pairs so we can skip
+        # duplicates client-side instead of relying on a UniqueConstraint
+        # violation. Catching the violation would force a session rollback
+        # that wipes ALL pending writes (not just the offending one) — a
+        # subtle data-loss bug. Pre-check is cheap (the table stays small).
+        existing_pairs: set[tuple[int, int]] = {
+            (pid, cid)
+            for (pid, cid) in (await session.execute(
+                select(KolDivergence.post_id, KolDivergence.change_id)
+            )).all()
+        }
+
+        # Index changes by (handle, ticker) → list[KolHoldingChange]
+        chain_index: dict[tuple[str, str], list[tuple[KolHoldingChange, str]]] = defaultdict(list)
+        for change, handle in changes_rows:
+            # Skip dust moves
+            usd_delta = abs((change.usd_after or 0) - (change.usd_before or 0))
+            if usd_delta < MIN_USD_CHANGE and (change.usd_after or 0) < MIN_USD_CHANGE:
+                continue
+            chain_index[(handle, change.ticker.upper())].append((change, handle))
+
+        # ── 3. Match posts → changes ───────────────────────────────────────
+        for post in posts:
+            try:
+                tickers = json.loads(post.tickers_json or "[]")
+            except json.JSONDecodeError:
+                continue
+
+            for t in tickers:
+                post_action = (t.get("action") or "").lower()
+                if post_action not in (_POST_LONG | _POST_SHORT):
+                    continue  # skip 'mention'
+
+                ticker = (t.get("ticker") or "").upper()
+                if not ticker:
+                    continue
+
+                candidates = chain_index.get((post.kol_handle, ticker), [])
+                for change, handle in candidates:
+                    # Time-window check
+                    post_dt    = post.published_at
+                    change_dt  = change.detected_at
+                    days_apart = abs((change_dt - post_dt).total_seconds()) / 86400
+                    if days_apart > WINDOW_DAYS:
+                        continue
+
+                    result = _classify(post_action, change.change_type)
+                    if result is None:
+                        continue
+                    signal_type, direction = result
+
+                    # Idempotency: skip if (post_id, change_id) already stored
+                    pair_key = (post.id, change.id)
+                    if pair_key in existing_pairs:
+                        continue
+                    existing_pairs.add(pair_key)
+
+                    conviction = float(t.get("conviction") or 0)
+                    row = KolDivergence(
+                        handle          = post.kol_handle,
+                        ticker          = ticker,
+                        post_id         = post.id,
+                        post_action     = post_action,
+                        post_conviction = conviction,
+                        post_at         = post_dt,
+                        change_id       = change.id,
+                        onchain_action  = change.change_type,
+                        usd_before      = change.usd_before,
+                        usd_after       = change.usd_after,
+                        onchain_at      = change_dt,
+                        signal_type     = signal_type,
+                        direction       = direction,
+                        days_apart      = round(days_apart, 2),
+                    )
+                    session.add(row)
+
+                    # Emit a Post for Telegram fan-out — but only for the
+                    # interesting case (divergences) with enough conviction
+                    # and chain-size to be worth a push. See ALERT_* tunables.
+                    if (signal_type in ALERT_ON_TYPES
+                            and conviction >= ALERT_MIN_POST_CONVICTION
+                            and (change.usd_after or 0) >= ALERT_MIN_USD):
+                        alert_post = _post_for_divergence(
+                            handle=post.kol_handle, ticker=ticker,
+                            direction=direction, post_action=post_action,
+                            chain_action=change.change_type,
+                            conviction=conviction, change_id=change.id,
+                            usd_after=change.usd_after, days_apart=days_apart,
+                        )
+                        session.add(alert_post)
+                        await session.flush()  # populate alert_post.id
+                        alert_post_ids.append(alert_post.id)
+                    info = {
+                        "handle": post.kol_handle,
+                        "ticker": ticker,
+                        "signal_type": signal_type,
+                        "direction": direction,
+                        "post_action": post_action,
+                        "onchain_action": change.change_type,
+                        "days_apart": round(days_apart, 2),
+                        "usd_after": change.usd_after,
+                    }
+                    results.append(info)
+                    emoji = "⚠️" if signal_type == "divergence" else "✅"
+                    logger.info(
+                        "[kol_divergence] %s %s %s: says %s, onchain %s → %s (%s) Δ%.1fd",
+                        emoji, post.kol_handle, ticker,
+                        post_action, change.change_type,
+                        signal_type, direction, days_apart,
+                    )
+
+        await session.commit()
+
+    # Fire Telegram fan-out AFTER commit so _dispatch's own session can
+    # actually see the rows. _dispatch only needs the post_id, so we skip
+    # notify_signal's Post-object wrapper and schedule it directly.
+    if alert_post_ids:
+        try:
+            import asyncio
+            from app.services.telegram import _dispatch
+            for pid in alert_post_ids:
+                asyncio.create_task(_dispatch(pid))
+        except Exception as exc:
+            logger.warning("[kol_divergence] notify_signal failed: %s", exc)
+
+    logger.info("[kol_divergence] scan done: %d new pairs written, %d alerts queued",
+                len(results), len(alert_post_ids))
+    return results

app/services/kol_onchain.py

@@ -0,0 +1,531 @@
+"""KOL A-tier: daily on-chain holdings snapshot + change detection.
+
+Data sources (all free):
+
+  Hyperliquid public API  — No key. Perp positions + mark prices for any
+                            HL-listed asset (BTC/ETH/SOL/HYPE/...).
+                            Endpoint: POST /info {"type":"clearinghouseState"}
+                            Prices:   POST /info {"type":"allMids"}
+
+  Etherscan API           — Free key (etherscan.io/register, email only).
+                            ERC-20 token balances for any Ethereum address.
+                            Env: ETHERSCAN_API_KEY. Skip ETH wallets if unset.
+
+  CoinGecko (no key)      — Fallback price for tokens not listed on HL.
+                            Free tier: 30 req/min, batch by contract address.
+
+Pricing priority per token:
+  1. HL allMids (by symbol, e.g. "ETH" → $2025)
+  2. CoinGecko by contract address (Ethereum only)
+  3. Skip (price unknown → usd_value=0, excluded from snapshot)
+
+Daily flow (run_onchain_poll at 02:00 UTC):
+  For each HL wallet  → fetch HL positions → snapshot → diff
+  For each ETH wallet → fetch Etherscan ERC-20 list → price each token → snapshot → diff
+  Diffs with usd_after > $50k (new) or ±25% change written to kol_holding_changes.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import time
+from datetime import datetime, timezone
+from typing import Optional
+
+import httpx
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.config import settings
+from app.database import AsyncSessionLocal
+from app.models import KolHoldingChange, KolHoldingSnapshot, KolWallet, utcnow
+
+logger = logging.getLogger(__name__)
+
+HL_API_URL        = "https://api.hyperliquid.xyz/info"
+ETHERSCAN_API_URL = "https://api.etherscan.io/v2/api"
+COINGECKO_URL     = "https://api.coingecko.com/api/v3"
+
+# Stablecoins — ignored in all snapshots
+STABLES = {"USDC", "USDT", "DAI", "BUSD", "TUSD", "USDE", "FRAX", "LUSD", "CRVUSD"}
+
+# Change detection thresholds
+_NEW_POSITION_MIN_USD  = 50_000   # new token > $50k → alert
+_CHANGE_PCT_THRESHOLD  = 25.0     # ±25% USD move → alert
+_CLOSED_MIN_USD        = 10_000   # closed position must have been > $10k to report
+
+
+# ── Price layer ───────────────────────────────────────────────────────────────
+
+_hl_prices: dict[str, float] = {}          # symbol → USD, refreshed each poll
+_hl_prices_fetched_at: float = 0.0
+
+async def _get_hl_prices() -> dict[str, float]:
+    """Fetch all HL mark prices (free, no auth). Cached for 5 min per poll cycle.
+    Returns empty dict on network failure — callers fall back to CoinGecko."""
+    global _hl_prices, _hl_prices_fetched_at
+    now = time.time()
+    if now - _hl_prices_fetched_at < 300 and _hl_prices:
+        return _hl_prices
+    try:
+        async with httpx.AsyncClient(timeout=10.0) as c:
+            r = await c.post(HL_API_URL, json={"type": "allMids"})
+            r.raise_for_status()
+            raw = r.json()  # {"BTC": "74541.0", "ETH": "2025.0", ...}
+        _hl_prices = {sym: float(px) for sym, px in raw.items()}
+        _hl_prices_fetched_at = now
+        logger.info("[kol_onchain] HL prices loaded: %d assets", len(_hl_prices))
+    except Exception as e:
+        logger.warning("[kol_onchain] HL price fetch failed (%s) — falling back to CoinGecko", e)
+        # Don't overwrite a previous good cache on transient failure
+        if not _hl_prices:
+            _hl_prices = {}
+    return _hl_prices
+
+
+# CoinGecko symbol→id map for common crypto assets (fallback when HL unavailable)
+_CG_SYMBOL_IDS = {
+    "BTC": "bitcoin", "ETH": "ethereum", "SOL": "solana",
+    "BNB": "binancecoin", "AVAX": "avalanche-2", "MATIC": "matic-network",
+    "ARB": "arbitrum", "OP": "optimism", "LINK": "chainlink",
+    "UNI": "uniswap", "AAVE": "aave", "MKR": "maker",
+    "HYPE": "hyperliquid", "ENA": "ethena", "PENDLE": "pendle",
+    "WLD": "worldcoin-wld", "JUP": "jupiter-exchange-solana",
+    "WBTC": "wrapped-bitcoin", "STETH": "staked-ether",
+    "EETH": "ether-fi-staked-eth", "WEETH": "wrapped-eeth",
+}
+
+async def _coingecko_prices_by_symbol(symbols: list[str]) -> dict[str, float]:
+    """Batch price lookup by CoinGecko coin ID. No key needed."""
+    ids_needed = {s: _CG_SYMBOL_IDS[s] for s in symbols if s in _CG_SYMBOL_IDS}
+    if not ids_needed:
+        return {}
+    try:
+        async with httpx.AsyncClient(timeout=15.0) as c:
+            r = await c.get(f"{COINGECKO_URL}/simple/price", params={
+                "ids": ",".join(ids_needed.values()),
+                "vs_currencies": "usd",
+            })
+            if r.status_code != 200:
+                return {}
+            data = r.json()
+        # Invert: coin_id → price, then map back to symbol
+        id_to_price = {v: data.get(v, {}).get("usd", 0) for v in ids_needed.values()}
+        return {sym: id_to_price[cid] for sym, cid in ids_needed.items() if id_to_price.get(cid)}
+    except Exception as e:
+        logger.warning("[kol_onchain] CoinGecko symbol lookup failed: %s", e)
+        return {}
+
+
+async def _coingecko_prices_by_contract(
+    contract_addresses: list[str],
+) -> dict[str, float]:
+    """Batch price lookup by Ethereum contract address. No key needed.
+    Chunks into batches of 25 (CoinGecko free tier limit).
+    Returns {contract_addr_lower: usd_price}."""
+    if not contract_addresses:
+        return {}
+    result: dict[str, float] = {}
+    addrs_lower = [a.lower() for a in contract_addresses[:100]]
+    # CoinGecko free tier caps at ~25 addresses per request
+    chunk_size = 25
+    async with httpx.AsyncClient(timeout=15.0) as c:
+        for i in range(0, len(addrs_lower), chunk_size):
+            chunk = addrs_lower[i:i + chunk_size]
+            try:
+                r = await c.get(
+                    f"{COINGECKO_URL}/simple/token_price/ethereum",
+                    params={"contract_addresses": ",".join(chunk), "vs_currencies": "usd"},
+                )
+                if r.status_code != 200:
+                    logger.warning("[kol_onchain] CoinGecko %s: %s", r.status_code, r.text[:80])
+                    continue
+                data = r.json()
+                result.update({addr: info["usd"] for addr, info in data.items() if "usd" in info})
+            except Exception as e:
+                logger.warning("[kol_onchain] CoinGecko chunk failed: %s", e)
+    return result
+
+
+# ── Hyperliquid: perp positions ───────────────────────────────────────────────
+
+async def _fetch_hl_positions(address: str) -> tuple[list[dict], float]:
+    """Query HL clearinghouseState. Returns (holdings, account_value_usd).
+    holdings: [{ticker, amount, usd_value, chain, side}]
+    """
+    async with httpx.AsyncClient(timeout=15.0) as c:
+        r = await c.post(HL_API_URL, json={"type": "clearinghouseState", "user": address})
+        r.raise_for_status()
+        data = r.json()
+
+    prices = await _get_hl_prices()
+    holdings = []
+    for pos in data.get("assetPositions", []):
+        p = pos.get("position", {})
+        coin = p.get("coin", "")
+        if not coin:
+            continue
+        size = float(p.get("szi", 0))
+        if abs(size) < 1e-8:
+            continue
+        price = prices.get(coin, float(p.get("entryPx") or 0))
+        notional = abs(size) * price
+        if notional < 1:
+            continue
+        holdings.append({
+            "ticker": coin,
+            "amount": abs(size),
+            "usd_value": round(notional, 2),
+            "chain": "hl",
+            "side": "long" if size > 0 else "short",
+        })
+
+    margin = data.get("marginSummary", {})
+    account_value = float(margin.get("accountValue", 0))
+    return holdings, account_value
+
+
+# ── Etherscan: ERC-20 balances ────────────────────────────────────────────────
+
+async def _fetch_eth_holdings(address: str) -> tuple[list[dict], float]:
+    """Fetch ERC-20 + ETH holdings via Etherscan free tier.
+
+    Free-tier strategy (no Pro needed):
+      Step 1: GET account/balance          → native ETH amount
+      Step 2: GET account/tokentx (recent) → discover which ERC-20 contracts
+                                             the wallet has interacted with
+      Step 3: GET account/tokenbalance     → current balance per contract
+      Step 4: Price via HL allMids, then CoinGecko by contract address fallback
+
+    Etherscan free limit: 5 req/s, ~100k req/day — well within budget for
+    daily snapshots of a handful of KOL wallets.
+    """
+    if not settings.etherscan_api_key:
+        return [], 0.0
+
+    prices = await _get_hl_prices()
+    key = settings.etherscan_api_key
+
+    async with httpx.AsyncClient(timeout=20.0) as c:
+
+        # ── Step 1: ETH native balance ───────────────────────────────────
+        eth_r = await c.get(ETHERSCAN_API_URL, params={
+            "chainid": "1", "module": "account", "action": "balance",
+            "address": address, "tag": "latest", "apikey": key,
+        })
+        eth_data = eth_r.json()
+
+        # ── Step 2: ERC-20 tx history → unique (contract, symbol, decimals) ─
+        # offset=200 is enough to cover all tokens a KOL holds. Vitalik-scale
+        # addresses with 100k+ txs can still timeout — real KOL wallets are far smaller.
+        tx_r = await c.get(ETHERSCAN_API_URL, params={
+            "chainid": "1", "module": "account", "action": "tokentx",
+            "address": address, "page": "1", "offset": "200",
+            "sort": "desc", "apikey": key,
+        }, timeout=30.0)
+        tx_data = tx_r.json()
+
+    # Collect unique contracts from tx history
+    seen: dict[str, dict] = {}   # contract → {symbol, decimals}
+    txs = tx_data.get("result") or []
+    if isinstance(txs, list):
+        for tx in txs:
+            contract = (tx.get("contractAddress") or "").lower()
+            if not contract or contract in seen:
+                continue
+            symbol = (tx.get("tokenSymbol") or "").upper()
+            if not symbol or symbol in STABLES:
+                continue
+            try:
+                decimals = int(tx.get("tokenDecimal") or 18)
+            except ValueError:
+                decimals = 18
+            seen[contract] = {"symbol": symbol, "decimals": decimals}
+
+    # ── Step 3: current balance per contract ─────────────────────────────
+    holdings: list[dict] = []
+    unpriced: list[str] = []          # contracts needing CoinGecko
+    contract_meta: dict[str, dict] = {}
+
+    # Rate-limit: Etherscan free = 5 req/s. Sleep 0.22s between calls to stay
+    # under burst limit even when looping 80 tokens × 4 wallets in one poll.
+    async with httpx.AsyncClient(timeout=20.0) as c:
+        for contract, meta in list(seen.items())[:80]:   # cap at 80 tokens
+            await asyncio.sleep(0.22)
+            bal_r = await c.get(ETHERSCAN_API_URL, params={
+                "chainid": "1", "module": "account", "action": "tokenbalance",
+                "contractaddress": contract, "address": address,
+                "tag": "latest", "apikey": key,
+            })
+            bal_data = bal_r.json()
+            if bal_data.get("message") != "OK":
+                continue
+            raw = int(bal_data.get("result") or 0)
+            if raw == 0:
+                continue
+            balance = raw / (10 ** meta["decimals"])
+            symbol = meta["symbol"]
+
+            hl_price = prices.get(symbol)
+            if hl_price and hl_price > 0:
+                usd_value = round(balance * hl_price, 2)
+                if usd_value >= 500:
+                    holdings.append({
+                        "ticker": symbol, "amount": round(balance, 6),
+                        "usd_value": usd_value, "chain": "ethereum",
+                        "contract": contract,
+                    })
+            else:
+                unpriced.append(contract)
+                contract_meta[contract] = {
+                    "ticker": symbol, "amount": round(balance, 6),
+                    "chain": "ethereum", "contract": contract,
+                }
+
+    # ── Step 4a: ETH native ──────────────────────────────────────────────
+    eth_raw = int(eth_data.get("result") or 0)
+    eth_balance = eth_raw / 1e18
+    if eth_balance > 0.001:
+        eth_price = prices.get("ETH", 0.0)
+        # Fallback: CoinGecko by symbol if HL unavailable
+        if not eth_price:
+            cg_sym = await _coingecko_prices_by_symbol(["ETH"])
+            eth_price = cg_sym.get("ETH", 0.0)
+        eth_usd = round(eth_balance * eth_price, 2)
+        if eth_usd >= 100:
+            holdings.append({
+                "ticker": "ETH", "amount": round(eth_balance, 6),
+                "usd_value": eth_usd, "chain": "ethereum",
+            })
+
+    # ── Step 4b: CoinGecko fallback for unpriced tokens ──────────────────
+    if unpriced:
+        try:
+            cg_prices = await _coingecko_prices_by_contract(unpriced)
+            for contract, price in cg_prices.items():
+                meta = contract_meta.get(contract)
+                if not meta or price <= 0:
+                    continue
+                usd_value = round(meta["amount"] * price, 2)
+                if usd_value >= 500:
+                    holdings.append({**meta, "usd_value": usd_value})
+        except Exception as e:
+            logger.warning("[kol_onchain] CoinGecko fallback failed: %s", e)
+
+    # Merge duplicate tickers (different contracts, same symbol) by summing USD value
+    merged: dict[str, dict] = {}
+    for h in holdings:
+        t = h["ticker"]
+        if t in merged:
+            merged[t]["usd_value"] = round(merged[t]["usd_value"] + h["usd_value"], 2)
+            merged[t]["amount"]    = round(merged[t]["amount"]    + h["amount"],    6)
+        else:
+            merged[t] = dict(h)
+    holdings = list(merged.values())
+
+    total_usd = round(sum(h["usd_value"] for h in holdings), 2)
+    logger.info("[kol_onchain] %s ETH holdings: %d tokens, $%.0f total",
+                address[:10], len(holdings), total_usd)
+    return holdings, total_usd
+
+
+# ── Snapshot + diff ───────────────────────────────────────────────────────────
+
+def _diff_holdings(
+    prev: list[dict], curr: list[dict], wallet_id: int,
+) -> list[KolHoldingChange]:
+    # SUM by ticker — earlier snapshots may contain duplicate ticker rows
+    # (different contracts, same symbol e.g. two APE tokens). Dict comprehension
+    # would silently drop one and falsely flag +1900% diffs the next day.
+    def _sum_by_ticker(rows: list[dict]) -> dict[str, float]:
+        agg: dict[str, float] = {}
+        for h in rows:
+            t = h.get("ticker")
+            if not t:
+                continue
+            agg[t] = agg.get(t, 0.0) + float(h.get("usd_value") or 0)
+        return agg
+
+    prev_map = _sum_by_ticker(prev)
+    curr_map = _sum_by_ticker(curr)
+    now = utcnow()
+    changes = []
+
+    for ticker in set(prev_map) | set(curr_map):
+        before = prev_map.get(ticker, 0.0)
+        after  = curr_map.get(ticker, 0.0)
+
+        if before == 0 and after >= _NEW_POSITION_MIN_USD:
+            changes.append(KolHoldingChange(
+                wallet_id=wallet_id, detected_at=now, ticker=ticker,
+                change_type="new_position", usd_before=0, usd_after=after,
+            ))
+        elif after == 0 and before >= _CLOSED_MIN_USD:
+            changes.append(KolHoldingChange(
+                wallet_id=wallet_id, detected_at=now, ticker=ticker,
+                change_type="closed", usd_before=before, usd_after=0,
+                pct_change=-100.0,
+            ))
+        elif before > 0 and after > 0:
+            pct = (after - before) / before * 100
+            if abs(pct) >= _CHANGE_PCT_THRESHOLD:
+                changes.append(KolHoldingChange(
+                    wallet_id=wallet_id, detected_at=now, ticker=ticker,
+                    change_type="increased" if pct > 0 else "decreased",
+                    usd_before=before, usd_after=after, pct_change=round(pct, 1),
+                ))
+    return changes
+
+
+async def _snapshot_wallet(
+    session: AsyncSession,
+    wallet: KolWallet,
+    holdings: list[dict],
+    total_usd: float,
+    source: str,
+) -> dict:
+    today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
+
+    # Skip if already snapshotted today
+    existing = (await session.execute(
+        select(KolHoldingSnapshot).where(
+            KolHoldingSnapshot.wallet_id == wallet.id,
+            KolHoldingSnapshot.snapshot_date == today,
+        )
+    )).scalar_one_or_none()
+    if existing:
+        return {"wallet_id": wallet.id, "status": "already_snapshotted"}
+
+    # Previous snapshot for diff
+    prev_row = (await session.execute(
+        select(KolHoldingSnapshot)
+        .where(KolHoldingSnapshot.wallet_id == wallet.id)
+        .order_by(KolHoldingSnapshot.snapshot_date.desc())
+        .limit(1)
+    )).scalar_one_or_none()
+    prev_holdings = json.loads(prev_row.holdings_json) if prev_row else []
+
+    # Store snapshot (strip contract addresses to keep JSON lean)
+    clean_holdings = [{k: v for k, v in h.items() if k != "contract"} for h in holdings]
+    snap = KolHoldingSnapshot(
+        wallet_id=wallet.id,
+        snapshot_date=today,
+        holdings_json=json.dumps(clean_holdings, ensure_ascii=False),
+        total_usd=total_usd,
+        source=source,
+    )
+    session.add(snap)
+
+    # Detect and store changes
+    changes = _diff_holdings(prev_holdings, holdings, wallet.id)
+    for c in changes:
+        session.add(c)
+        logger.info(
+            "[kol_onchain] %s %s %s $%.0f→$%.0f",
+            wallet.handle, c.change_type, c.ticker,
+            c.usd_before or 0, c.usd_after or 0,
+        )
+
+    await session.flush()
+    return {
+        "handle": wallet.handle,
+        "chain": wallet.chain,
+        "holdings": len(holdings),
+        "total_usd": total_usd,
+        "changes": len(changes),
+        "source": source,
+    }
+
+
+# ── Poll entry points ─────────────────────────────────────────────────────────
+
+async def run_hl_poll() -> list[dict]:
+    """Poll HL perp positions for all chain='hl' wallets."""
+    results = []
+    async with AsyncSessionLocal() as session:
+        wallets = (await session.execute(
+            select(KolWallet).where(KolWallet.chain == "hl", KolWallet.active == True)
+        )).scalars().all()
+
+        if not wallets:
+            logger.info("[kol_onchain] no HL wallets configured")
+            return []
+
+        for wallet in wallets:
+            try:
+                holdings, total = await _fetch_hl_positions(wallet.address)
+                stat = await _snapshot_wallet(session, wallet, holdings, total, "hl")
+                results.append(stat)
+            except Exception as e:
+                logger.warning("[kol_onchain] HL fetch failed %s: %s", wallet.handle, e)
+                results.append({"handle": wallet.handle, "error": str(e)})
+
+        await session.commit()
+    logger.info("[kol_onchain] HL poll done: %s", results)
+    return results
+
+
+async def run_eth_poll() -> list[dict]:
+    """Poll Ethereum ERC-20 holdings for all chain='ethereum' wallets.
+    No-op if ETHERSCAN_API_KEY not set."""
+    if not settings.etherscan_api_key:
+        logger.debug("[kol_onchain] ETHERSCAN_API_KEY not set — skipping ETH poll")
+        return []
+
+    results = []
+    async with AsyncSessionLocal() as session:
+        wallets = (await session.execute(
+            select(KolWallet).where(KolWallet.chain == "ethereum", KolWallet.active == True)
+        )).scalars().all()
+
+        if not wallets:
+            logger.info("[kol_onchain] no Ethereum wallets configured")
+            return []
+
+        # Pre-warm HL prices once for the whole batch
+        await _get_hl_prices()
+
+        for wallet in wallets:
+            try:
+                holdings, total = await _fetch_eth_holdings(wallet.address)
+                stat = await _snapshot_wallet(session, wallet, holdings, total, "etherscan")
+                results.append(stat)
+            except Exception as e:
+                logger.warning("[kol_onchain] ETH fetch failed %s: %s", wallet.handle, e)
+                results.append({"handle": wallet.handle, "error": str(e)})
+
+        await session.commit()
+    logger.info("[kol_onchain] ETH poll done: %s", results)
+    return results
+
+
+async def run_onchain_poll() -> dict:
+    """Combined entry point for APScheduler. Runs HL + ETH polls."""
+    hl  = await run_hl_poll()
+    eth = await run_eth_poll()
+    return {"hl": hl, "eth": eth}
+
+
+async def seed_wallets(entries: list[tuple]) -> int:
+    """Bulk-insert (handle, chain, address, label, source_url) tuples, skip dupes."""
+    count = 0
+    async with AsyncSessionLocal() as session:
+        for handle, chain, address, label, source_url in entries:
+            existing = (await session.execute(
+                select(KolWallet).where(
+                    KolWallet.chain == chain,
+                    KolWallet.address == address.lower(),
+                )
+            )).scalar_one_or_none()
+            if existing:
+                continue
+            session.add(KolWallet(
+                handle=handle, chain=chain,
+                address=address.lower(),
+                label=label, source_url=source_url,
+            ))
+            count += 1
+        await session.commit()
+    return count

app/services/kol_substack.py

@@ -0,0 +1,383 @@
+"""KOL Substack RSS ingester.
+
+Polls each tracked KOL's Substack feed, dedupes by URL, stores raw post,
+then hands off to kol_analysis.extract_kol_signal and writes the result
+back onto the same row.
+
+Substack RSS embeds the full post HTML in <content:encoded>. We strip HTML
+to plain text before storage + analysis. Hayes posts are typically 50K+
+chars of body — the extractor truncates internally.
+
+Daily cadence is plenty (Hayes posts ~monthly, Substack updates feed within
+minutes of publish). Call run_substack_poll() from the APScheduler.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import logging
+import re
+from datetime import datetime, timezone
+from email.utils import parsedate_to_datetime
+from typing import Iterable, Optional
+
+import feedparser
+import httpx
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.database import AsyncSessionLocal
+from app.models import KolPost, utcnow
+from app.services import kol_analysis
+
+logger = logging.getLogger(__name__)
+
+
+# Curated B-tier KOL feeds. Handle is the canonical key. `source` is the
+# DB column ("substack" | "podcast" | "blog"); empty defaults to "substack"
+# for legacy entries. Twitter-only KOLs come in a separate ingester.
+#
+# When adding a new feed:
+#   1. curl + grep '<item>' to confirm it returns entries.
+#   2. Inspect entry summary/content length — AI extraction needs ≥300 chars
+#      of body per post or it just hallucinates a topic line. (Headlines-only
+#      feeds like Vitalik's blog need a follow-up HTML fetch, deferred.)
+#   3. Add with a sensible handle + display_name.
+KOL_FEEDS: list[dict] = [
+    # ── Substack essayists (long-form thesis pieces) ─────────────────────
+    {
+        "handle": "cryptohayes",
+        "display_name": "Arthur Hayes",
+        "feed_url": "https://cryptohayes.substack.com/feed",
+    },
+    # Placeholder VC (Joel Monegro / Chris Burniske). Token-focused VC, posts
+    # long-form thesis pieces every 1-3 months that map directly to their
+    # portfolio bets (Solana staking, L1 monetary premium, etc.).
+    {
+        "handle": "placeholder",
+        "display_name": "Placeholder VC",
+        "feed_url": "https://www.placeholder.vc/blog?format=rss",
+    },
+    # Dragonfly Capital research blog on Medium — free, active (10+ posts).
+    # dragonfly.xyz/blog/rss.xml returns 0 (paywall). medium.com/dragonfly-research
+    # is the team's public research arm: airdrops, DeFi, protocol deep-dives.
+    {
+        "handle": "dragonfly",
+        "display_name": "Dragonfly Capital",
+        "feed_url": "https://medium.com/feed/dragonfly-research",
+    },
+    # Andy Constan's Substack is paywalled (RSS returns 0). Keeping for any
+    # occasional public teaser. Forward Guidance podcast (Blockworks) features
+    # him weekly but is macro/equities-focused — not crypto-coin-specific enough
+    # to extract ticker signals from episode descriptions.
+    {
+        "handle": "dampedspring",
+        "display_name": "Damped Spring / Andy Constan",
+        "feed_url": "https://dampedspring.substack.com/feed",
+    },
+    # Nic Carter's Substack is paywalled (RSS returns 0). His Medium feed is
+    # FREE and active — different URL, same author, real content.
+    {
+        "handle": "niccarter",
+        "display_name": "Nic Carter (Castle Island)",
+        "feed_url": "https://medium.com/feed/@nic__carter",
+    },
+    # Delphi Digital podcast (Buzzsprout) — 478 episodes, active May 2025.
+    # Public, free. Episode descriptions name specific protocols / tokens with
+    # thesis framing — good extraction signal. delphidigital.io/feed returns 0.
+    {
+        "handle": "delphi",
+        "display_name": "Delphi Digital (Podcast)",
+        "feed_url": "https://rss.buzzsprout.com/2609274.rss",
+    },
+    # ── Newly added (verified live + active) ─────────────────────────────
+    # Anthony Pompliano — Pomp Investments. Active monthly+ on macro/crypto.
+    {
+        "handle": "pomp",
+        "display_name": "Anthony Pompliano (Pomp Letter)",
+        "feed_url": "https://pomp.substack.com/feed",
+    },
+    # The DeFi Edge — researcher who writes 1-2 deep dives per month on
+    # tokens / sectors. Real thesis + position-aware framing.
+    {
+        "handle": "thedefiedge",
+        "display_name": "The DeFi Edge",
+        "feed_url": "https://thedefiedge.com/feed/",
+    },
+    # Eugene Ng Ah Sio — trader/analyst, sporadic but specific.
+    {
+        "handle": "eugene",
+        "display_name": "Eugene Ng Ah Sio",
+        "feed_url": "https://eugene.substack.com/feed",
+    },
+    # ── DeFi journalism (Substack-style RSS) ─────────────────────────────
+    # The Defiant — Camila Russo's team. DeFi-focused news with frequent
+    # protocol + token mentions. Free RSS, ~100 entries.
+    {
+        "handle": "thedefiant",
+        "display_name": "The Defiant",
+        "feed_url": "https://www.thedefiant.io/api/feed",
+        "source": "blog",
+    },
+    # ── Major crypto podcasts (Megaphone / Simplecast RSS) ───────────────
+    # Show notes are 1-6K chars — long enough for AI to pull out tickers
+    # and theses. Bootstrap is capped at max_new=20/run so a 600-episode
+    # backlog spreads across ~30 days.
+    #
+    # Empire (Blockworks) — Jason Yanowitz + Santiago Roel Santos. Weekly
+    # crypto+macro interviews. Show notes name protocols + price calls.
+    {
+        "handle": "empire",
+        "display_name": "Empire Podcast (Blockworks)",
+        "feed_url": "https://feeds.megaphone.fm/empire",
+        "source": "podcast",
+    },
+    # 0xResearch (Blockworks) — Boccaccio + Dan Smith. Protocol research
+    # deep-dives, real revenue/usage discussion. Highest signal density of
+    # the Blockworks shows.
+    {
+        "handle": "0xresearch",
+        "display_name": "0xResearch (Blockworks)",
+        "feed_url": "https://feeds.megaphone.fm/0xresearch",
+        "source": "podcast",
+    },
+    # Lightspeed (Blockworks) — Mert Mumtaz (Helius CEO) + Garrett Harper.
+    # Solana ecosystem focus — SOL, JUP, JTO, PUMP, validator economics.
+    {
+        "handle": "lightspeed",
+        "display_name": "Lightspeed (Solana, Blockworks)",
+        "feed_url": "https://feeds.megaphone.fm/lightspeed",
+        "source": "podcast",
+    },
+    # Unchained — Laura Shin. Long interview format with founders and
+    # traders. Show notes are 6K+ chars (near-transcript).
+    {
+        "handle": "unchained",
+        "display_name": "Unchained (Laura Shin)",
+        "feed_url": "https://www.unchainedcrypto.com/feed/",
+        "source": "podcast",
+    },
+    # Bankless podcast — Ryan Sean Adams + David Hoffman. ETH-focused but
+    # covers all majors. 4K char show notes. Largest crypto-native podcast.
+    # NOTE: previous feed `simplecast.com/MLdpYXYI` was actually Robert
+    # Breedlove's "What is Money" show — wrong feed. libsyn is canonical.
+    {
+        "handle": "bankless",
+        "display_name": "Bankless Podcast",
+        "feed_url": "https://bankless.libsyn.com/rss",
+        "source": "podcast",
+    },
+    # Bell Curve (Multicoin) — Mike Ippolito + Jason Yanowitz + Myles Snider.
+    # 350 episodes, weekly macro+crypto roundup. Multicoin's portfolio shows
+    # up frequently (SOL, JTO, JUP, Helium, Render). 1.2K show notes.
+    {
+        "handle": "bellcurve",
+        "display_name": "Bell Curve (Multicoin)",
+        "feed_url": "https://feeds.megaphone.fm/bellcurve",
+        "source": "podcast",
+    },
+    # The Scoop (The Block) — Frank Chaparro interviews founders + traders.
+    # 110 episodes, ~700 char show notes. Strong on infrastructure/exchange
+    # deals (Hyperliquid, Coinbase, Binance dynamics).
+    {
+        "handle": "thescoop",
+        "display_name": "The Scoop (The Block)",
+        "feed_url": "https://feeds.megaphone.fm/the-scoop",
+        "source": "podcast",
+    },
+    # ── Research newsletters (long-form, high-signal) ────────────────────
+    # Reflexivity Research — Will Clemente + Sam Rule. On-chain BTC analysis
+    # and macro pieces. 20 entries, 8K char essays. Concrete on-chain calls.
+    {
+        "handle": "reflexivity",
+        "display_name": "Reflexivity Research (Will Clemente)",
+        "feed_url": "https://reflexivityresearch.substack.com/feed",
+    },
+    # TFTC — Marty Bent's "Bitcoin Brief" newsletter (also a podcast feed).
+    # 11K char issues, daily Bitcoin + policy. Pure BTC focus but covers
+    # legislation/macro that moves BTC.
+    {
+        "handle": "tftc",
+        "display_name": "TFTC / Bitcoin Brief (Marty Bent)",
+        "feed_url": "https://tftc.io/feed",
+        "source": "blog",
+    },
+]
+
+# Back-compat alias — older imports referenced SUBSTACK_KOLS.
+SUBSTACK_KOLS = KOL_FEEDS
+
+
+_TAG_RE = re.compile(r"<[^>]+>")
+_WHITESPACE_RE = re.compile(r"[ \t]+")
+_BLANKLINES_RE = re.compile(r"\n{3,}")
+
+
+def _html_to_text(html: str) -> str:
+    """Cheap HTML → text. Good enough for Substack which uses simple markup;
+    if we ever need real parsing, swap to bs4 (not currently a dep)."""
+    # Newlines for block-level closes so paragraphs survive
+    s = re.sub(r"</(p|div|h[1-6]|li|br)\s*>", "\n", html, flags=re.I)
+    s = re.sub(r"<br\s*/?>", "\n", s, flags=re.I)
+    s = _TAG_RE.sub("", s)
+    # HTML entities — feedparser usually decodes these but be safe
+    s = (s.replace("&amp;", "&").replace("&lt;", "<").replace("&gt;", ">")
+           .replace("&quot;", '"').replace("&#8217;", "'").replace("&#8220;", '"')
+           .replace("&#8221;", '"').replace("&nbsp;", " "))
+    s = _WHITESPACE_RE.sub(" ", s)
+    s = _BLANKLINES_RE.sub("\n\n", s)
+    return s.strip()
+
+
+def _entry_body(entry) -> str:
+    """Pull the richest body field available from a feedparser entry."""
+    if entry.get("content"):
+        # content is a list of {value, type}
+        return entry["content"][0].get("value", "") or ""
+    return entry.get("summary") or entry.get("description") or ""
+
+
+def _parse_pub(entry) -> Optional[datetime]:
+    raw = entry.get("published") or entry.get("updated")
+    if not raw:
+        return None
+    try:
+        dt = parsedate_to_datetime(raw)
+        # Always normalize to naive UTC. Previously used .astimezone() which
+        # converts to *local* time → 8-hour skew when server runs in CST.
+        # Affects: divergence window matching, digest 'since' filter, UI display.
+        if dt.tzinfo:
+            dt = dt.astimezone(timezone.utc).replace(tzinfo=None)
+        return dt
+    except Exception:
+        return None
+
+
+async def _fetch_feed(feed_url: str) -> list:
+    """feedparser is sync; do the HTTP fetch through httpx for timeout
+    control + uniformity with the rest of the codebase, then hand bytes
+    to feedparser."""
+    async with httpx.AsyncClient(timeout=20.0, follow_redirects=True) as client:
+        r = await client.get(feed_url, headers={"User-Agent": "TrumpSignal/1.0 KOL-tracker"})
+        r.raise_for_status()
+        parsed = feedparser.parse(r.content)
+    return list(parsed.entries or [])
+
+
+async def _ingest_kol(
+    session: AsyncSession,
+    kol: dict,
+    *,
+    analyze: bool = True,
+    max_new: int = 20,
+) -> dict:
+    """Ingest one KOL feed. max_new caps first-run cost for high-volume feeds
+    (e.g. Delphi podcast has 478 episodes). Subsequent runs only see truly new
+    entries so the cap rarely triggers after bootstrap."""
+    handle = kol["handle"]
+    feed_url = kol["feed_url"]
+    src     = kol.get("source") or "substack"   # substack | podcast | blog
+    stats = {"handle": handle, "source": src,
+             "new": 0, "skipped": 0, "analyzed": 0, "errors": 0}
+
+    try:
+        entries = await _fetch_feed(feed_url)
+    except Exception as e:
+        logger.warning("[kol_substack] fetch failed for %s: %s", handle, e)
+        stats["errors"] += 1
+        return stats
+
+    for entry in entries:
+        if stats["new"] >= max_new:
+            logger.info("[kol_substack] %s hit max_new=%d cap; rest deferred to next run",
+                        handle, max_new)
+            break
+
+        # Podcast feeds (Buzzsprout, etc.) have no <link>; use enclosure URL or entry id.
+        url = entry.get("link")
+        if not url:
+            enclosures = entry.get("enclosures") or []
+            if enclosures:
+                url = enclosures[0].get("href")
+            if not url:
+                url = entry.get("id")   # e.g. "Buzzsprout-19123172"
+        if not url:
+            continue
+
+        # Dedupe by (source, external_id=url). We also check against the
+        # legacy "substack" source so podcast/blog re-tags don't double-insert
+        # entries the old code already wrote.
+        existing = await session.execute(
+            select(KolPost).where(
+                KolPost.source.in_([src, "substack"]),
+                KolPost.external_id == url,
+            )
+        )
+        row = existing.scalar_one_or_none()
+        if row is not None:
+            stats["skipped"] += 1
+            continue
+
+        html = _entry_body(entry)
+        text = _html_to_text(html)
+        if not text:
+            continue
+
+        pub = _parse_pub(entry) or utcnow()
+        title = entry.get("title") or None
+        body_hash = hashlib.sha256(text.encode("utf-8")).hexdigest()
+
+        row = KolPost(
+            kol_handle=handle,
+            source=src,
+            external_id=url,
+            url=url,
+            title=title,
+            published_at=pub,
+            raw_text=text,
+            content_hash=body_hash,
+        )
+        session.add(row)
+        await session.flush()  # get id for logging
+        stats["new"] += 1
+        logger.info("[kol_substack] new post %s id=%s title=%r", handle, row.id, title)
+
+        if analyze:
+            try:
+                result = await kol_analysis.extract_kol_signal(
+                    handle=handle,
+                    source=src,
+                    title=title,
+                    body=text,
+                )
+                if result.get("error"):
+                    stats["errors"] += 1
+                else:
+                    import json as _json
+                    row.summary = result.get("summary")
+                    row.tickers_json = _json.dumps(result.get("tickers") or [],
+                                                   ensure_ascii=False)
+                    row.analyzed_at = utcnow()
+                    row.analysis_model = result.get("model")
+                    row.analysis_version = result.get("version")
+                    stats["analyzed"] += 1
+            except Exception as e:
+                logger.warning("[kol_substack] analysis failed for %s post %s: %s",
+                               handle, row.id, e)
+                stats["errors"] += 1
+
+    await session.commit()
+    return stats
+
+
+async def run_substack_poll(*, analyze: bool = True) -> list[dict]:
+    """Poll every configured KOL feed once. Despite the legacy name this now
+    covers Substack essays, Medium blogs, and major crypto podcasts via RSS.
+    Returns per-KOL stats."""
+    results = []
+    async with AsyncSessionLocal() as session:
+        for kol in KOL_FEEDS:
+            stats = await _ingest_kol(session, kol, analyze=analyze)
+            results.append(stats)
+    logger.info("[kol_substack] poll done: %s", results)
+    return results

app/services/market_data.py

@@ -0,0 +1,360 @@
+"""
+Market data abstraction — pluggable candle sources.
+
+Currently 2 providers:
+
+  - Binance       : Free public REST, broad coverage of major coins.
+  - Hyperliquid   : SAME venue as execution. Covers HL-native perps that
+                    Binance doesn't list (TRUMP, HYPE, PURR, etc.) and
+                    gives us mark-price-consistent data for those assets.
+
+Routing rule:
+  - Assets in HL_NATIVE_ASSETS  → Hyperliquid (no Binance pair exists)
+  - Everything else             → Binance (better history, no rate friction)
+
+Override per-call by selecting a provider explicitly:
+
+  await BinanceCandles().fetch_4h("BTC", days=30)
+  await HyperliquidCandles().fetch_4h("HYPE", days=30)
+
+  # Or auto-route:
+  src = for_asset("HYPE")          # → HyperliquidCandles
+  candles = await src.fetch_4h("HYPE", days=30)
+
+Normalized candle shape (returned by ALL providers):
+  {"time_ms": int, "open": float, "high": float, "low": float,
+   "close": float, "volume": float}
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+from typing import Protocol
+
+import httpx
+
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+# ─── Provider protocol ──────────────────────────────────────────────────────
+
+
+class CandleSource(Protocol):
+    name: str
+
+    async def fetch_4h(self, asset: str, days: int) -> list[dict]:
+        """Last `days` worth of 4-hour candles for `asset`."""
+        ...
+
+    async def fetch_1d(self, asset: str, days: int) -> list[dict]:
+        """Last `days` daily candles. Used for SMA reclaim / VCP-Daily."""
+        ...
+
+    async def fetch_1w(self, asset: str, weeks: int) -> list[dict]:
+        """Last `weeks` weekly candles. Used for weekly-RSI reversal."""
+        ...
+
+    async def fetch_1m(self, asset: str, start_ms: int, end_ms: int) -> list[dict]:
+        """1-minute candles in [start_ms, end_ms]. Paginated internally."""
+        ...
+
+    async def fetch_funding(self, asset: str, days: int) -> list[dict]:
+        """Funding-rate history. List of {time_ms, rate}. HL-only for now —
+        Binance provides funding but the cross-venue rate differs, so we
+        defer to the execution venue (HL)."""
+        ...
+
+
+# ─── Binance ────────────────────────────────────────────────────────────────
+
+
+class BinanceCandles:
+    name = "binance"
+    base_url = "https://api.binance.com/api/v3/klines"
+
+    @staticmethod
+    def _symbol(asset: str) -> str:
+        return f"{asset.upper()}USDT"
+
+    async def fetch_4h(self, asset: str, days: int) -> list[dict]:
+        end_ms   = int(datetime.now(timezone.utc).timestamp() * 1000)
+        start_ms = end_ms - days * 24 * 3600 * 1000
+        async with httpx.AsyncClient(timeout=20) as client:
+            resp = await client.get(self.base_url, params={
+                "symbol": self._symbol(asset),
+                "interval": "4h",
+                "startTime": start_ms, "endTime": end_ms,
+                "limit": 1000,
+            })
+            resp.raise_for_status()
+            rows = resp.json()
+        return [self._normalize(r) for r in rows]
+
+    async def fetch_1d(self, asset: str, days: int) -> list[dict]:
+        return await self._fetch_simple(asset, "1d", days * 24 * 3600 * 1000)
+
+    async def fetch_1w(self, asset: str, weeks: int) -> list[dict]:
+        return await self._fetch_simple(asset, "1w", weeks * 7 * 24 * 3600 * 1000)
+
+    async def _fetch_simple(self, asset: str, interval: str, window_ms: int) -> list[dict]:
+        """Single-call fetch for intervals where 1000 bars covers the window."""
+        end_ms   = int(datetime.now(timezone.utc).timestamp() * 1000)
+        start_ms = end_ms - window_ms
+        async with httpx.AsyncClient(timeout=20) as client:
+            resp = await client.get(self.base_url, params={
+                "symbol": self._symbol(asset),
+                "interval": interval,
+                "startTime": start_ms, "endTime": end_ms,
+                "limit": 1000,
+            })
+            resp.raise_for_status()
+            rows = resp.json()
+        return [self._normalize(r) for r in rows]
+
+    async def fetch_1m(self, asset: str, start_ms: int, end_ms: int) -> list[dict]:
+        # Binance caps each call at 1000 candles — paginate forward.
+        out: list[dict] = []
+        cursor = start_ms
+        async with httpx.AsyncClient(timeout=20) as client:
+            while cursor < end_ms:
+                resp = await client.get(self.base_url, params={
+                    "symbol": self._symbol(asset),
+                    "interval": "1m",
+                    "startTime": cursor, "endTime": end_ms,
+                    "limit": 1000,
+                })
+                resp.raise_for_status()
+                chunk = resp.json()
+                if not chunk:
+                    break
+                out.extend(self._normalize(r) for r in chunk)
+                last_open = chunk[-1][0]
+                if last_open <= cursor:
+                    break
+                cursor = last_open + 60_000
+                if len(chunk) < 1000:
+                    break
+        return out
+
+    async def fetch_funding(self, asset: str, days: int) -> list[dict]:
+        """Binance perp funding. Format: list of {time_ms, rate}.
+
+        Binance returns rate per 8h funding cycle (matches HL convention).
+        Note: this is Binance's perp funding, NOT HL's. For HL-traded
+        positions, prefer HyperliquidCandles.fetch_funding().
+        """
+        end_ms   = int(datetime.now(timezone.utc).timestamp() * 1000)
+        start_ms = end_ms - days * 24 * 3600 * 1000
+        url = "https://fapi.binance.com/fapi/v1/fundingRate"
+        async with httpx.AsyncClient(timeout=20) as client:
+            resp = await client.get(url, params={
+                "symbol": self._symbol(asset),
+                "startTime": start_ms, "endTime": end_ms,
+                "limit": 1000,
+            })
+            resp.raise_for_status()
+            rows = resp.json()
+        return [
+            {"time_ms": r["fundingTime"], "rate": float(r["fundingRate"])}
+            for r in rows
+        ]
+
+    @staticmethod
+    def _normalize(row) -> dict:
+        return {
+            "time_ms": row[0],
+            "open":   float(row[1]),
+            "high":   float(row[2]),
+            "low":    float(row[3]),
+            "close":  float(row[4]),
+            "volume": float(row[5]),
+        }
+
+
+# ─── Hyperliquid ────────────────────────────────────────────────────────────
+
+
+class HyperliquidCandles:
+    """HL public /info endpoint — same data the HL UI uses.
+
+    Endpoint:
+      POST https://api.hyperliquid.xyz/info
+      body { "type": "candleSnapshot",
+             "req": { "coin": "SOL", "interval": "4h",
+                      "startTime": ms, "endTime": ms } }
+
+    Returns array of {t, T, s, i, o, c, h, l, v, n} — we normalize to our
+    standard shape. Useful for HL-native perps Binance doesn't list.
+    """
+    name = "hyperliquid"
+
+    def __init__(self, mainnet: bool | None = None):
+        use_mainnet = settings.hl_mainnet if mainnet is None else mainnet
+        self.base_url = (
+            "https://api.hyperliquid.xyz/info" if use_mainnet
+            else "https://api.hyperliquid-testnet.xyz/info"
+        )
+
+    async def fetch_4h(self, asset: str, days: int) -> list[dict]:
+        return await self._fetch_window(asset, "4h", days * 24 * 3600 * 1000)
+
+    async def fetch_1d(self, asset: str, days: int) -> list[dict]:
+        return await self._fetch_window(asset, "1d", days * 24 * 3600 * 1000)
+
+    async def fetch_1w(self, asset: str, weeks: int) -> list[dict]:
+        return await self._fetch_window(asset, "1w", weeks * 7 * 24 * 3600 * 1000)
+
+    async def fetch_1m(self, asset: str, start_ms: int, end_ms: int) -> list[dict]:
+        # HL returns ALL candles in the window in one response — no pagination
+        # needed for typical scanner windows. For multi-day 1m calls HL may
+        # truncate; the caller should keep windows under ~24h for 1m data.
+        return await self._fetch(asset.upper(), "1m", start_ms, end_ms)
+
+    async def _fetch_window(self, asset: str, interval: str, window_ms: int) -> list[dict]:
+        end_ms   = int(datetime.now(timezone.utc).timestamp() * 1000)
+        start_ms = end_ms - window_ms
+        return await self._fetch(asset.upper(), interval, start_ms, end_ms)
+
+    async def _fetch(self, coin: str, interval: str, start_ms: int, end_ms: int) -> list[dict]:
+        async with httpx.AsyncClient(timeout=20) as client:
+            resp = await client.post(self.base_url, json={
+                "type": "candleSnapshot",
+                "req": {
+                    "coin": coin, "interval": interval,
+                    "startTime": start_ms, "endTime": end_ms,
+                },
+            })
+            resp.raise_for_status()
+            rows = resp.json() or []
+        return [self._normalize(r) for r in rows]
+
+    async def fetch_funding(self, asset: str, days: int) -> list[dict]:
+        """HL funding history — HOURLY cadence (1 cycle per hour).
+
+        IMPORTANT: HL's /info endpoint caps fundingHistory at 500 rows per
+        response. 500 rows × 1h cadence = 20.8 days, so a single call CAN'T
+        return a full 30-day window. We page backwards from `endTime` until
+        we cover `days` worth of history (or HL runs out of data).
+
+        Returns chronologically-sorted list of {time_ms, rate}, deduplicated.
+        """
+        end_ms   = int(datetime.now(timezone.utc).timestamp() * 1000)
+        start_ms = end_ms - days * 24 * 3600 * 1000
+        cursor   = end_ms
+        collected: dict[int, float] = {}   # time_ms → rate (dedup by time)
+
+        async with httpx.AsyncClient(timeout=20) as client:
+            # Safety cap: at most 10 pages (5000 rows ≈ 208 days) — way more
+            # than any caller could reasonably want, prevents runaway loops
+            # if HL returns inconsistent data.
+            for _ in range(10):
+                if cursor <= start_ms:
+                    break
+                resp = await client.post(self.base_url, json={
+                    "type":      "fundingHistory",
+                    "coin":      asset.upper(),
+                    "startTime": start_ms,
+                    "endTime":   cursor,
+                })
+                resp.raise_for_status()
+                chunk = resp.json() or []
+                if not chunk:
+                    break
+                for r in chunk:
+                    t = r["time"]
+                    if t not in collected:
+                        collected[t] = float(r["fundingRate"])
+                oldest = min(r["time"] for r in chunk)
+                if oldest <= start_ms or len(chunk) < 500:
+                    # Either we reached the start of our window, or HL gave
+                    # us a partial page (no more data behind it).
+                    break
+                cursor = oldest - 1
+
+        return [
+            {"time_ms": t, "rate": rate}
+            for t, rate in sorted(collected.items())
+        ]
+
+    @staticmethod
+    def _normalize(row: dict) -> dict:
+        # HL candle keys: t=open_ms, o=open, h/l, c, v
+        return {
+            "time_ms": row["t"],
+            "open":   float(row["o"]),
+            "high":   float(row["h"]),
+            "low":    float(row["l"]),
+            "close":  float(row["c"]),
+            "volume": float(row["v"]),
+        }
+
+
+# ─── Routing ────────────────────────────────────────────────────────────────
+
+# HL-native perps that DO NOT have a Binance spot pair. The scanner / backtest
+# auto-route these to HL. Add more as you discover them — the list is the
+# only place to maintain provider preference.
+HL_NATIVE_ASSETS = {
+    "HYPE", "PURR", "JEFF", "VAPOR",
+    "PIP", "OMNIX", "PYTH",
+    # NOTE: TRUMP is now listed on Binance too — using Binance for that gets
+    # cleaner history. SUI is on both.
+}
+
+
+# Reversal-strategy basket. Major-cap only (no shitcoins), 1 HL-native.
+# Used by all three reversal scanners (weekly RSI, SMA reclaim, funding extreme).
+REVERSAL_BASKET = ["BTC", "ETH", "SOL", "BNB", "LINK", "AAVE", "DOGE", "HYPE"]
+
+
+# Bar durations in seconds — used by drop_in_progress_bar() to know whether
+# the last candle in a response is still open. Crypto exchanges return the
+# CURRENT (in-progress) bar in `klines` responses; for daily/weekly logic
+# we usually want the most recent CLOSED bar instead.
+INTERVAL_SECONDS = {
+    "1m": 60, "5m": 300, "15m": 900,
+    "1h": 3600, "4h": 14400, "8h": 28800,
+    "1d": 86400, "1w": 604800,
+}
+
+
+def drop_in_progress_bar(candles: list[dict], interval: str) -> list[dict]:
+    """Return `candles` minus the last entry if it's still an open bar.
+
+    Daily / weekly bars on Binance & HL are returned with `time_ms = open_time`.
+    The bar's close time is open_time + interval_seconds. If now < close_time,
+    the bar hasn't closed yet — using its volume/close is misleading (volume
+    is partial, close is current spot mid-bar).
+
+    Use this in scanners that care about CONFIRMED signals (SMA reclaim,
+    weekly RSI). Use raw candles only when you want to react intra-bar
+    (rare and usually wrong for position trading).
+    """
+    if not candles:
+        return candles
+    dur_s = INTERVAL_SECONDS.get(interval)
+    if dur_s is None:
+        return candles  # unknown interval — be conservative, return as-is
+    bar_close_ms = candles[-1]["time_ms"] + dur_s * 1000
+    now_ms = int(datetime.now(timezone.utc).timestamp() * 1000)
+    if now_ms < bar_close_ms:
+        return candles[:-1]
+    return candles
+
+
+_BINANCE_SINGLETON = BinanceCandles()
+_HL_SINGLETON      = HyperliquidCandles()
+
+
+def for_asset(asset: str) -> CandleSource:
+    """Pick the right provider. HL-native → HL, otherwise Binance.
+
+    Always returns SOMETHING — caller doesn't need to handle None. If the
+    asset doesn't actually trade on the chosen venue, the underlying HTTP
+    call will return an empty list and the caller falls back to its
+    "no data" path.
+    """
+    return _HL_SINGLETON if asset.upper() in HL_NATIVE_ASSETS else _BINANCE_SINGLETON

app/services/onchain_data.py

@@ -0,0 +1,177 @@
+"""On-chain metrics for the BTC bottom-reversal scanner.
+
+Two providers, same interface (fetch_mvrv_z_score / fetch_sth_sopr → list[float]):
+
+  BitcoinDataClient  — DEFAULT. bitcoin-data.com public API. FREE, no key.
+                       Returns PRE-COMPUTED MVRV-Z and STH-SOPR (we don't
+                       need realized-cap or local math — realized cap is an
+                       on-chain quantity that CANNOT be derived from price
+                       data, so a vendor is unavoidable; this is the free one).
+                       Free tier: 10 requests/HOUR. The scanner runs once a
+                       day and needs 2 calls — well within budget — but we
+                       add a 6-hour in-process cache as a safety net against
+                       restarts / manual re-runs hammering the limit.
+
+  GlassnodeClient    — Optional paid fallback. Used automatically if
+                       GLASSNODE_API_KEY is set (higher resolution / SLA).
+
+get_onchain_client() picks the right one. btc_bottom_reversal.py only
+depends on the interface, never the concrete class.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import time
+from datetime import datetime, timezone
+from typing import Optional
+
+import httpx
+
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+# ─── Provider 1: bitcoin-data.com (free, default) ───────────────────────────
+
+BITCOIN_DATA_BASE = "https://bitcoin-data.com/v1"
+
+# Endpoint → (path, json value key). Both return a full daily history list
+# of {"d","unixTs",<key>} when called with no query params.
+_BD_ENDPOINTS = {
+    "mvrv_z": ("/mvrv-zscore", "mvrvZscore"),
+    "sth_sopr": ("/sth-sopr", "sthSopr"),
+}
+
+# Fresh-fetch window: don't re-hit the API if the on-disk copy is younger
+# than this. The scanner runs daily; 18h keeps it to ~1 fetch/day/metric
+# (2 req/day total, vs the 10 req/HOUR free cap).
+_BD_FRESH_S = 18 * 3600
+# How stale on-disk data may be and still be USABLE when the API is down /
+# rate-limited. A daily MVRV-Z / STH-SOPR barely moves over 2 days, so
+# serving 48h-old data beats skipping a scan or, worse, acting on nothing.
+_BD_STALE_OK_S = 48 * 3600
+
+# Disk cache survives restarts — critical given the tight free rate limit.
+_BD_CACHE_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), ".cache")
+_BD_CACHE_FILE = os.path.join(_BD_CACHE_DIR, "onchain_bitcoin_data.json")
+
+
+def _load_disk_cache() -> dict:
+    try:
+        with open(_BD_CACHE_FILE) as f:
+            return json.load(f)
+    except (FileNotFoundError, json.JSONDecodeError, OSError):
+        return {}
+
+
+def _save_disk_cache(cache: dict) -> None:
+    try:
+        os.makedirs(_BD_CACHE_DIR, exist_ok=True)
+        tmp = _BD_CACHE_FILE + ".tmp"
+        with open(tmp, "w") as f:
+            json.dump(cache, f)
+        os.replace(tmp, _BD_CACHE_FILE)   # atomic
+    except OSError as exc:
+        logger.warning("onchain disk-cache write failed: %s", exc)
+
+
+class BitcoinDataClient:
+    """Free, key-less. Returns pre-computed metric series (already the
+    Z-score / SOPR value — no local computation needed). Disk-cached so the
+    10 req/hour free cap and process restarts can't starve the scanner."""
+
+    async def _series(self, metric: str, days: int) -> list[float]:
+        now = time.time()
+        cache = _load_disk_cache()
+        entry = cache.get(metric)            # {"ts": epoch, "values": [...]}
+        age = (now - entry["ts"]) if entry else None
+
+        # Fresh enough → no network call at all.
+        if entry and age is not None and age < _BD_FRESH_S:
+            vals = entry["values"]
+            return vals[-days:] if days else vals
+
+        path, key = _BD_ENDPOINTS[metric]
+        try:
+            async with httpx.AsyncClient(timeout=20) as client:
+                resp = await client.get(f"{BITCOIN_DATA_BASE}{path}")
+            if resp.status_code == 429:
+                raise RuntimeError("rate_limited")
+            resp.raise_for_status()
+            rows = resp.json() or []
+            values = [float(r[key]) for r in rows if r.get(key) is not None]
+            if not values:
+                raise RuntimeError("empty_response")
+            cache[metric] = {"ts": now, "values": values}
+            _save_disk_cache(cache)
+            return values[-days:] if days else values
+        except Exception as exc:
+            # Network/limit failure: fall back to disk if it's still usable.
+            if entry and age is not None and age < _BD_STALE_OK_S:
+                logger.warning("bitcoin-data.com %s fetch failed (%s) — "
+                               "serving disk cache aged %.1fh",
+                               metric, exc, age / 3600)
+                vals = entry["values"]
+                return vals[-days:] if days else vals
+            raise RuntimeError(
+                f"bitcoin-data.com {metric} unavailable ({exc}) and no usable cache"
+            ) from exc
+
+    async def fetch_mvrv_z_score(self, asset: str = "BTC", days: int = 730) -> list[float]:
+        if asset.upper() != "BTC":
+            return []   # bitcoin-data.com is BTC-only
+        return await self._series("mvrv_z", days)
+
+    async def fetch_sth_sopr(self, asset: str = "BTC", days: int = 30) -> list[float]:
+        if asset.upper() != "BTC":
+            return []
+        return await self._series("sth_sopr", days)
+
+
+# ─── Provider 2: Glassnode (paid, optional) ─────────────────────────────────
+
+GLASSNODE_BASE_URL = "https://api.glassnode.com/v1/metrics"
+
+
+class GlassnodeClient:
+    def __init__(self, api_key: Optional[str] = None):
+        self.api_key = api_key if api_key is not None else settings.glassnode_api_key
+
+    async def _get_series(self, path: str, asset: str, days: int) -> list[dict]:
+        if not self.api_key:
+            raise RuntimeError("GLASSNODE_API_KEY is not configured")
+        end = int(datetime.now(timezone.utc).timestamp())
+        start = end - days * 86_400
+        async with httpx.AsyncClient(timeout=20) as client:
+            resp = await client.get(
+                f"{GLASSNODE_BASE_URL}{path}",
+                params={"a": asset, "api_key": self.api_key,
+                        "s": start, "u": end, "i": "24h"},
+            )
+            resp.raise_for_status()
+            return resp.json() or []
+
+    async def fetch_mvrv_z_score(self, asset: str = "BTC", days: int = 730) -> list[float]:
+        rows = await self._get_series("/market/mvrv_z_score", asset, days)
+        return [float(r["v"]) for r in rows if r.get("v") is not None]
+
+    async def fetch_sth_sopr(self, asset: str = "BTC", days: int = 30) -> list[float]:
+        rows = await self._get_series("/indicators/sopr_less_155", asset, days)
+        return [float(r["v"]) for r in rows if r.get("v") is not None]
+
+
+# ─── Factory ────────────────────────────────────────────────────────────────
+
+
+def get_onchain_client():
+    """Glassnode if a key is configured (paid, higher SLA), else the free
+    bitcoin-data.com client. Both expose the same async interface."""
+    if settings.glassnode_api_key:
+        logger.info("On-chain provider: Glassnode (paid key configured)")
+        return GlassnodeClient()
+    logger.info("On-chain provider: bitcoin-data.com (free, no key)")
+    return BitcoinDataClient()

app/services/reanalyze.py

@@ -0,0 +1,179 @@
+"""
+Batch re-analyzer — runs Claude analysis on posts that were backfilled
+without AI scoring (ai_confidence == 0).
+
+Designed to run as a one-shot background task. Processes posts oldest-first,
+1 per second, so 479 posts ≈ 8 minutes. Progress is logged at every batch.
+
+Usage (via dev endpoint POST /api/dev/reanalyze):
+  curl -X POST "http://localhost:8000/api/dev/reanalyze?limit=500&dry_run=false"
+"""
+
+import asyncio
+import logging
+from datetime import datetime, timezone
+from typing import Optional
+
+from sqlalchemy import select
+
+logger = logging.getLogger(__name__)
+
+# Global so the HTTP endpoint can check progress without storing state externally.
+_reanalyze_state: dict = {
+    "running": False,
+    "processed": 0,
+    "updated": 0,
+    "errors": 0,
+    "total": 0,
+    "started_at": None,
+    "finished_at": None,
+}
+
+
+def get_state() -> dict:
+    return dict(_reanalyze_state)
+
+
+async def reanalyze_unscored(
+    db_session_factory,
+    limit: int = 500,
+    dry_run: bool = False,
+    delay_secs: float = 1.0,
+    legacy_signals: bool = False,
+    model: Optional[str] = None,
+) -> dict:
+    """
+    Find all posts with ai_confidence == 0, run analyze_post on each,
+    and write results back to the DB.
+
+    Args:
+        limit:      Maximum number of posts to process in this run.
+        dry_run:    If True, analyze but do NOT write to DB (for testing).
+        delay_secs: Pause between API calls to avoid rate-limiting.
+
+    Returns:
+        Summary dict with processed/updated/errors counts.
+    """
+    global _reanalyze_state
+
+    if _reanalyze_state["running"]:
+        logger.warning("reanalyze already in progress, skipping")
+        return _reanalyze_state
+
+    _reanalyze_state = {
+        "running": True,
+        "processed": 0,
+        "updated": 0,
+        "errors": 0,
+        "total": 0,
+        "started_at": datetime.now(timezone.utc).isoformat(),
+        "finished_at": None,
+    }
+
+    from app.models import Post
+    from app.services.analysis import analyze_post
+
+    try:
+        # ── Fetch posts needing (re-)analysis ────────────────────────────
+        # Two cases:
+        #   1. Never analyzed (ai_confidence == 0)
+        #   2. Analyzed before target_asset column was added (has buy/short
+        #      signal but target_asset IS NULL). Pass legacy_signals=True
+        #      to target ONLY these, bypassing unscored posts.
+        from sqlalchemy import or_, and_
+        if legacy_signals:
+            condition = and_(
+                Post.signal.in_(["buy", "short"]),
+                Post.target_asset == None,  # noqa: E711
+            )
+        else:
+            # Use analysis_version IS NULL (not ai_confidence==0) so that
+            # posts already analyzed as "hold" (conf=0, version set) are not
+            # re-run every time. Only truly unanalyzed posts are targeted.
+            condition = or_(
+                Post.analysis_version == None,  # noqa: E711
+                and_(
+                    Post.signal.in_(["buy", "short"]),
+                    Post.target_asset == None,  # noqa: E711
+                )
+            )
+        async with db_session_factory() as db:
+            rows = await db.execute(
+                select(Post.id, Post.text)
+                .where(condition)
+                .order_by(Post.published_at.asc())
+                .limit(limit)
+            )
+            posts_to_do = rows.all()   # list of (id, text) tuples
+
+        _reanalyze_state["total"] = len(posts_to_do)
+        logger.info("Reanalyze: %d posts queued (limit=%d, dry_run=%s)",
+                    len(posts_to_do), limit, dry_run)
+
+        if not posts_to_do:
+            _reanalyze_state["running"] = False
+            _reanalyze_state["finished_at"] = datetime.now(timezone.utc).isoformat()
+            return _reanalyze_state
+
+        # ── Process each post ─────────────────────────────────────────────
+        for post_id, text in posts_to_do:
+            _reanalyze_state["processed"] += 1
+
+            try:
+                analysis = await analyze_post(text, model=model)  # caller decides quality vs speed
+
+                if not dry_run:
+                    async with db_session_factory() as db:
+                        result = await db.execute(
+                            select(Post).where(Post.id == post_id)
+                        )
+                        post = result.scalar_one_or_none()
+                        if post is None:
+                            continue
+
+                        post.sentiment        = analysis["sentiment"]
+                        post.signal           = analysis.get("signal")
+                        post.ai_confidence    = analysis["confidence"]
+                        post.ai_reasoning     = analysis.get("reasoning")
+                        post.prefilter_reason = analysis.get("prefilter_reason")
+                        post.analysis_version = analysis.get("analysis_version")
+                        post.relevant         = analysis["relevant"]
+                        post.price_impact_asset = analysis["asset"] if analysis["relevant"] else None
+                        post.target_asset     = analysis.get("target_asset")
+                        post.category         = analysis.get("category")
+                        post.expected_move_pct = analysis.get("expected_move_pct")
+
+                        await db.commit()
+
+                _reanalyze_state["updated"] += 1
+
+                if _reanalyze_state["processed"] % 20 == 0:
+                    logger.info(
+                        "Reanalyze progress: %d/%d processed, %d updated, %d errors",
+                        _reanalyze_state["processed"],
+                        _reanalyze_state["total"],
+                        _reanalyze_state["updated"],
+                        _reanalyze_state["errors"],
+                    )
+
+            except Exception as exc:
+                _reanalyze_state["errors"] += 1
+                logger.error("Reanalyze error on post %d: %s", post_id, exc)
+
+            # Rate-limit: wait between API calls
+            if delay_secs > 0:
+                await asyncio.sleep(delay_secs)
+
+    except Exception as exc:
+        logger.error("Reanalyze fatal error: %s", exc)
+    finally:
+        _reanalyze_state["running"] = False
+        _reanalyze_state["finished_at"] = datetime.now(timezone.utc).isoformat()
+        logger.info(
+            "Reanalyze finished: %d processed, %d updated, %d errors",
+            _reanalyze_state["processed"],
+            _reanalyze_state["updated"],
+            _reanalyze_state["errors"],
+        )
+
+    return _reanalyze_state

app/services/reconciler.py

@@ -0,0 +1,191 @@
+"""
+Hyperliquid <-> DB state reconciliation (P1.2).
+
+Runs every RECONCILE_INTERVAL_SECONDS. For each subscription with a saved HL
+API key (and NOT in paper mode), fetches actual open positions from HL and
+compares them to BotTrade rows with closed_at IS NULL.
+
+Two drift cases:
+
+  1. DB has open trade, HL has no matching position
+     → User closed manually on HL UI, or HL liquidated, or our open_position
+       call failed silently after writing the row. Either way the row is stale.
+       Action: mark the DB row closed with pnl_usd=NULL, reason="closed_externally".
+
+  2. HL has open position, DB has no matching open trade
+     → A trade exists on HL that we didn't open (legacy position, or stale
+       wallet shared across systems). We can't safely manage it — log a
+       warning + WS broadcast for the user to handle manually.
+
+NOTE: this does NOT close stray HL positions. Auto-closing positions we didn't
+open is exactly the kind of "helpful" behaviour that costs users money. We
+report, the human decides.
+
+Cost note:
+  HL's user_state endpoint is cheap. 1 call per active subscriber per
+  RECONCILE_INTERVAL. With 10 subscribers at 60s interval that's 600 calls/hr,
+  well inside HL's free tier.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from datetime import datetime, timezone
+from typing import Optional
+
+from sqlalchemy import select, update
+
+from app.config import settings
+from app.database import AsyncSessionLocal
+from app.models import BotTrade, Subscription
+from app.services.crypto import decrypt_api_key
+from app.services.hyperliquid import HyperliquidTrader
+
+logger = logging.getLogger(__name__)
+
+
+RECONCILE_INTERVAL_SECONDS = 60
+
+
+# ─── Single-wallet reconcile ────────────────────────────────────────────────
+
+
+async def _reconcile_wallet(sub: Subscription) -> dict:
+    """Compare HL state vs DB state for one subscription.
+
+    Returns a small summary dict for logging / WS broadcast. Never raises —
+    network / HL errors are logged and counted but don't halt the cycle.
+    """
+    summary = {
+        "wallet": sub.wallet_address, "orphan_hl": [], "orphan_db": [],
+        "marked_closed": 0, "error": None,
+    }
+
+    # Paper-mode subs aren't on HL at all — nothing to reconcile.
+    if sub.paper_mode:
+        return summary
+
+    try:
+        api_key = decrypt_api_key(sub.hl_api_key)
+    except Exception as exc:
+        summary["error"] = f"decrypt_failed: {exc}"
+        return summary
+
+    try:
+        trader = HyperliquidTrader(
+            api_private_key=api_key,
+            account_address=sub.wallet_address,
+            leverage=sub.leverage,
+            mainnet=settings.hl_mainnet,
+        )
+        hl_positions = await trader.get_open_positions()
+    except Exception as exc:
+        summary["error"] = f"hl_query_failed: {exc}"
+        return summary
+
+    hl_assets_open = {p["coin"]: p for p in hl_positions}
+
+    async with AsyncSessionLocal() as db:
+        # All DB rows we believe are still open for this wallet.
+        rows = await db.execute(
+            select(BotTrade).where(
+                BotTrade.wallet_address == sub.wallet_address,
+                BotTrade.closed_at.is_(None),
+            )
+        )
+        db_open_trades = rows.scalars().all()
+        db_assets_open = {t.asset: t for t in db_open_trades}
+
+        # Case 1: DB open trade, HL has nothing. Stale row → mark closed.
+        now_naive = datetime.now(timezone.utc).replace(tzinfo=None)
+        for asset, trade in db_assets_open.items():
+            # Paper trades never have HL positions; they're managed by the
+            # normal close path. Skip here.
+            if trade.hl_order_id == "paper":
+                continue
+            if asset not in hl_assets_open:
+                # Preserve any de-risk PnL ALREADY banked on this trade — the
+                # open remainder's PnL is unknown (closed externally) but the
+                # banked slice is real money on HL; setting pnl_usd=None would
+                # silently undercount realized PnL in analytics / KPIs.
+                banked = trade.realized_partial_pnl_usd or 0.0
+                preserved_pnl = round(banked, 2) if banked else None
+                # Atomic claim — same conditional UPDATE pattern as close_and_finalize.
+                claimed = await db.execute(
+                    update(BotTrade)
+                    .where(BotTrade.id == trade.id)
+                    .where(BotTrade.closed_at.is_(None))
+                    .values(closed_at=now_naive, pnl_usd=preserved_pnl,
+                            remaining_fraction=0.0)
+                )
+                if claimed.rowcount > 0:
+                    summary["marked_closed"] += 1
+                    summary["orphan_db"].append({"asset": asset, "trade_id": trade.id})
+                    # Stop the TP/SL watcher for this trade
+                    from app.services.tp_sl_monitor import unregister
+                    unregister(trade.id)
+                    logger.warning("Reconcile: trade %d (%s %s) closed externally — marked.",
+                                   trade.id, trade.side, asset)
+        await db.commit()
+
+    # Case 2: HL has position we don't know about. Report only — don't auto-trade.
+    for asset, hl_pos in hl_assets_open.items():
+        if asset not in db_assets_open:
+            summary["orphan_hl"].append({
+                "asset": asset, "szi": hl_pos["szi"],
+                "entry": hl_pos["entry_px"], "uPnL": hl_pos["unrealized_pnl"],
+            })
+            logger.warning("Reconcile: %s has unmanaged HL position %s (szi=%s, entry=%s)",
+                           sub.wallet_address, asset, hl_pos["szi"], hl_pos["entry_px"])
+
+    return summary
+
+
+# ─── Periodic top-level task ────────────────────────────────────────────────
+
+
+async def reconcile_all_once() -> None:
+    """One scan over every subscription. Wired into the APScheduler at startup."""
+    async with AsyncSessionLocal() as db:
+        rows = await db.execute(
+            select(Subscription).where(
+                Subscription.active == True,           # noqa: E712
+                Subscription.hl_api_key.is_not(None),
+            )
+        )
+        subs = rows.scalars().all()
+
+    if not subs:
+        return
+
+    # Snapshot the fields we read so we don't hold the session across HL calls.
+    snapshots = list(subs)
+
+    n_changed = 0
+    n_orphans = 0
+    n_errors  = 0
+    for sub in snapshots:
+        summary = await _reconcile_wallet(sub)
+        n_changed += summary["marked_closed"]
+        n_orphans += len(summary["orphan_hl"])
+        if summary["error"]:
+            n_errors += 1
+
+        # Broadcast significant events so the UI shows a warning banner.
+        if summary["marked_closed"] or summary["orphan_hl"]:
+            try:
+                from app.ws.manager import manager
+                await manager.broadcast({
+                    "type":          "reconcile_drift",
+                    "wallet":        summary["wallet"],
+                    "orphan_hl":     summary["orphan_hl"],
+                    "marked_closed": summary["marked_closed"],
+                    "at":            datetime.now(timezone.utc).isoformat() + "Z",
+                })
+            except Exception as exc:
+                logger.warning("reconcile WS broadcast failed: %s", exc)
+
+    if n_changed or n_orphans or n_errors:
+        logger.info("Reconcile cycle: %d subs scanned, %d trades marked closed, %d HL orphans, %d errors",
+                    len(snapshots), n_changed, n_orphans, n_errors)

app/services/recovery.py

@@ -11,15 +11,21 @@ from datetime import datetime, timezone
 from sqlalchemy import select
 
 from app.database import AsyncSessionLocal
-from app.models import BotTrade, Subscription
+from app.models import BotTrade, Post, Subscription
 
 logger = logging.getLogger(__name__)
 
 
 async def rehydrate_open_trades() -> None:
     # Imported locally to avoid circular imports at module load
-    from app.services.bot_engine import MAX_HOLD_SECONDS, close_and_finalize
+    from app.services.bot_engine import close_and_finalize
     from app.services.crypto import decrypt_api_key
+    from app.services.signal_categories import (
+        get_stop_ladder as _get_stop_ladder,
+        sys2_derisk_ladder as _sys2_derisk_ladder,
+        sys2_addon_ladder as _sys2_addon_ladder,
+        sys2_peak_trail as _sys2_peak_trail,
+    )
     from app.services.tp_sl_monitor import register_trade
 
     async with AsyncSessionLocal() as db:
@@ -53,7 +59,29 @@ async def rehydrate_open_trades() -> None:
             # Fall back to current Subscription only for legacy rows (pre-migration 005).
             trade_leverage = t.leverage if t.leverage is not None else sub.leverage
 
-            # Re-register TP/SL watcher
+            # Re-register from the trade's FROZEN exit profile (eff_* columns),
+            # NOT the live Subscription. The trade may be a 90-day System-2
+            # reversal; using sub.* would rehydrate it with the user's Trump
+            # stop (1.5%) and 7-day max-hold — silently rewriting its risk on
+            # every restart. eff_* is stamped at open and never changes.
+            #
+            # Legacy rows (opened before this migration) have NULL eff_* —
+            # fall back to the Subscription so they still get *some* watcher.
+            eff_sl   = t.eff_stop_loss_pct         if t.eff_stop_loss_pct is not None else sub.stop_loss_pct
+            eff_tp   = t.eff_take_profit_pct        # may legitimately be None (sys2 pure-trail)
+            eff_tr   = t.eff_trailing_stop_pct      if t.eff_trailing_stop_pct is not None else sub.trailing_stop_pct
+            eff_tra  = t.eff_trailing_activate_pct  if t.eff_trailing_activate_pct is not None else sub.trailing_activate_at_pct
+            eff_mh   = t.eff_max_hold_hours         if t.eff_max_hold_hours is not None else (sub.max_hold_hours or 168)
+            # NOTE: peak_gain_pct is now RESTORED from the row (throttled
+            # persistence) so a pyramided / in-profit System-2 trade keeps its
+            # regime across restarts. min_hold still resets on rehydrate, but
+            # it only matters in the first 30 min of a Trump trade; a restart
+            # inside that window is rare and the downside (TP can fire early)
+            # is bounded.
+            post_res = await db.execute(
+                select(Post).where(Post.id == t.trigger_post_id)
+            )
+            trigger_post = post_res.scalar_one_or_none()
             register_trade(
                 trade_id=t.id,
                 wallet=t.wallet_address,
@@ -62,14 +90,55 @@ async def rehydrate_open_trades() -> None:
                 asset=t.asset,
                 side=t.side,
                 entry_price=t.entry_price,
-                take_profit_pct=sub.take_profit_pct,
+                take_profit_pct=eff_tp,
-                stop_loss_pct=sub.stop_loss_pct,
+                stop_loss_pct=eff_sl,
+                trailing_stop_pct=eff_tr,
+                trailing_activate_at_pct=eff_tra,
+                invalidation=t.eff_invalidation,
+                invalidation_price=(
+                    t.eff_invalidation_price
+                    if t.eff_invalidation_price is not None
+                    else (trigger_post.invalidation_price if trigger_post else None)
+                ),
+                min_hold_until_ts=t.eff_min_hold_until_ts,
+                stop_ladder=_get_stop_ladder(
+                    trigger_post.category if trigger_post else None
+                ),
+                # Restore staged de-risk: rebuild the ladder from the trade's
+                # FROZEN leverage and skip steps already executed before the
+                # restart (derisk_steps_done is persisted on the row).
+                # Restore staged de-risk/pyramid/peak-trail with the trade's
+                # FROZEN risk mode + leverage, skipping steps already executed
+                # before the restart (persisted on the row).
+                derisk_ladder=(
+                    _sys2_derisk_ladder(t.leverage or 1, t.sys2_mode)
+                    if _get_stop_ladder(trigger_post.category if trigger_post else None)
+                    else None
+                ),
+                derisk_done=(t.derisk_steps_done or 0),
+                addon_ladder=(
+                    _sys2_addon_ladder(t.sys2_mode)
+                    if _get_stop_ladder(trigger_post.category if trigger_post else None)
+                    else None
+                ),
+                addon_done=(t.addon_steps_done or 0),
+                # Restore the monotonic peak so a pyramided / in-profit trade
+                # doesn't fall back to the underwater de-risk regime on restart.
+                initial_peak=(t.peak_gain_pct or 0.0),
+                peak_trail=(
+                    _sys2_peak_trail(t.sys2_mode)
+                    if _get_stop_ladder(trigger_post.category if trigger_post else None)
+                    else None
+                ),
+                grow_mode=bool(getattr(t, "grow_mode", False)),
             )
 
-            # Compute remaining hold; if already exceeded, close immediately
+            # Remaining hold from the trade's FROZEN max_hold (e.g. 2160h for
+            # an sma_reclaim), not the user's Trump setting.
             opened_aware = t.opened_at.replace(tzinfo=timezone.utc)
             elapsed = (now - opened_aware).total_seconds()
-            remaining = MAX_HOLD_SECONDS - elapsed
+            max_hold_seconds = int(eff_mh) * 3600
+            remaining = max_hold_seconds - elapsed
 
             from app.services.bot_engine import _background_tasks
 

app/services/regime_filter.py

@@ -0,0 +1,221 @@
+"""
+Regime filter — gates entries to convex setups only.
+
+The Trump-signal bot was originally designed to fire on every high-confidence
+post. For a convex (asymmetric-payoff) strategy that's wrong: you want to
+trade RARELY but with very high quality. This module decides "is the market
+in a regime where this signal has a real shot at a big move?"
+
+Four gates, in order of likely impact:
+
+  1. Volatility contraction — only trade assets that have been quiet recently.
+     A coin that already moved 10% today is not setting up; it has set up.
+
+  2. Recent-move filter — don't chase. If the asset moved >5% in the last 24h,
+     stand down. The fat tail comes AFTER consolidation, not after a run.
+
+  3. Thin-liquidity hours — UTC 03–09 is Asia thin book. Wider spreads, more
+     noise stop-outs, fewer institutional bids.
+
+  4. BTC counter-trend — if BTC is moving hard against the signal direction,
+     alts get dragged. Don't fight beta.
+
+Each gate returns a bool + reason. The combiner short-circuits on the first
+failure for clear logging.
+
+All thresholds are intentionally configurable in one place. Tune them in
+backtest, don't sprinkle magic numbers across the codebase.
+"""
+
+from __future__ import annotations
+
+import logging
+import statistics
+from datetime import datetime, timezone
+from typing import Optional, Tuple
+
+from app.services.price_store import price_store
+
+logger = logging.getLogger(__name__)
+
+
+# ─── Tunables (one place to change) ─────────────────────────────────────────
+ATR_RECENT_WINDOW_HOURS   = 24    # "recent" volatility window
+ATR_BASELINE_WINDOW_HOURS = 168   # 7-day baseline to compare against
+ATR_CONTRACTION_RATIO     = 0.7   # recent must be ≤ 70% of baseline → "contracted"
+
+RECENT_MOVE_WINDOW_HOURS  = 24
+RECENT_MOVE_MAX_PCT       = 5.0   # block entries if asset already moved >5%
+
+BTC_COUNTER_TREND_HOURS   = 6
+BTC_COUNTER_TREND_PCT     = 3.0   # block if BTC moved >3% against signal
+
+THIN_LIQUIDITY_HOURS_UTC  = range(3, 9)  # 03:00–08:59 UTC
+
+
+# ─── Individual gates ───────────────────────────────────────────────────────
+
+
+def _atr_proxy(asset: str, hours: int) -> Optional[float]:
+    """Average true-range proxy using 1-min candles in price_store.
+
+    Returns the mean of (high - low) / close over the window, or None if
+    we don't have enough data yet. Cheap; recomputes on every call.
+    """
+    asset = asset.upper()
+    buf = price_store._candles.get(asset)
+    if not buf:
+        return None
+    n_minutes = hours * 60
+    candles = list(buf)[-n_minutes:]
+    if len(candles) < n_minutes // 2:  # need at least half the window populated
+        return None
+    ranges = [
+        (c["high"] - c["low"]) / c["close"]
+        for c in candles
+        if c["close"] > 0
+    ]
+    if not ranges:
+        return None
+    return statistics.fmean(ranges)
+
+
+def is_volatility_contracted(asset: str) -> Tuple[bool, str]:
+    """True if recent ATR ≤ ATR_CONTRACTION_RATIO × baseline ATR.
+
+    Returns (ok, reason). On insufficient data we PASS (None means "we don't
+    know, don't block") to avoid breaking the bot on cold-start. Document this
+    asymmetry in the regime log.
+    """
+    recent = _atr_proxy(asset, ATR_RECENT_WINDOW_HOURS)
+    base   = _atr_proxy(asset, ATR_BASELINE_WINDOW_HOURS)
+    if recent is None or base is None or base == 0:
+        return True, f"vol_contraction: insufficient data ({asset}), passing"
+    ratio = recent / base
+    ok = ratio <= ATR_CONTRACTION_RATIO
+    return ok, f"vol_contraction: ratio={ratio:.2f} (threshold {ATR_CONTRACTION_RATIO})"
+
+
+def is_not_already_moving(asset: str) -> Tuple[bool, str]:
+    """Block entries when the asset already ran in the last 24h."""
+    asset = asset.upper()
+    buf = price_store._candles.get(asset)
+    if not buf or len(buf) < RECENT_MOVE_WINDOW_HOURS * 60:
+        return True, f"recent_move: insufficient data ({asset}), passing"
+    candles = list(buf)[-RECENT_MOVE_WINDOW_HOURS * 60:]
+    first_close = candles[0]["close"]
+    last_close  = candles[-1]["close"]
+    if first_close == 0:
+        return True, "recent_move: bad first close, passing"
+    move_pct = abs(last_close - first_close) / first_close * 100
+    ok = move_pct <= RECENT_MOVE_MAX_PCT
+    return ok, f"recent_move: {move_pct:.2f}% in {RECENT_MOVE_WINDOW_HOURS}h (max {RECENT_MOVE_MAX_PCT}%)"
+
+
+def is_not_thin_liquidity_hour() -> Tuple[bool, str]:
+    """Block during Asia thin-book hours (UTC 03–09)."""
+    hour_utc = datetime.now(timezone.utc).hour
+    ok = hour_utc not in THIN_LIQUIDITY_HOURS_UTC
+    return ok, f"thin_liquidity: UTC {hour_utc:02d}h"
+
+
+def is_btc_not_countertrend(side: str) -> Tuple[bool, str]:
+    """Block if BTC is moving hard against the signal direction.
+
+    side = 'long' or 'short'. Long signal + BTC dumping > 3% → block.
+    """
+    buf = price_store._candles.get("BTC")
+    if not buf or len(buf) < BTC_COUNTER_TREND_HOURS * 60:
+        return True, "btc_trend: insufficient BTC data, passing"
+    candles = list(buf)[-BTC_COUNTER_TREND_HOURS * 60:]
+    first_close = candles[0]["close"]
+    last_close  = candles[-1]["close"]
+    if first_close == 0:
+        return True, "btc_trend: bad first close, passing"
+    btc_change_pct = (last_close - first_close) / first_close * 100
+    # Long signal but BTC dumping → bad. Short signal but BTC pumping → bad.
+    countertrend = (
+        (side == "long"  and btc_change_pct < -BTC_COUNTER_TREND_PCT) or
+        (side == "short" and btc_change_pct >  BTC_COUNTER_TREND_PCT)
+    )
+    ok = not countertrend
+    return ok, f"btc_trend: BTC {btc_change_pct:+.2f}% in {BTC_COUNTER_TREND_HOURS}h vs {side}"
+
+
+# ─── Master combiner ────────────────────────────────────────────────────────
+
+
+def passes_regime_filter(asset: str, side: str) -> Tuple[bool, list[str]]:
+    """Run every gate; return (passed_all, list_of_reasons).
+
+    Gates are evaluated in cheap-to-expensive order so we short-circuit on
+    the first failure when iterating callers want fast-rejection paths.
+    """
+    reasons: list[str] = []
+    checks = [
+        is_not_thin_liquidity_hour(),
+        is_not_already_moving(asset),
+        is_volatility_contracted(asset),
+        is_btc_not_countertrend(side),
+    ]
+    passed_all = True
+    for ok, reason in checks:
+        reasons.append(("✓ " if ok else "✗ ") + reason)
+        if not ok:
+            passed_all = False
+    return passed_all, reasons
+
+
+# ─── Position sizing (C) ────────────────────────────────────────────────────
+
+
+def calculate_size_multiplier(
+    ai_confidence: int,
+    asset: str,
+    side: str,
+) -> float:
+    """Score-based multiplier on `position_size_usd`.
+
+    Each green box adds a point; more points → bigger bet. This is the
+    Druckenmiller "when you have conviction, swing for the fences" idea
+    expressed numerically. Capped at 4× so a single misread doesn't blow
+    the daily budget.
+
+    Scoring:
+      +1 if AI confidence ≥ 90
+      +1 if volatility is contracted (we're at the start of the spring, not the end)
+      +1 if asset hasn't already run today (still room to go)
+      +1 if BTC is supportive of the direction (beta in our favour)
+    """
+    score = 0
+    if ai_confidence >= 90:
+        score += 1
+
+    vc_ok, _ = is_volatility_contracted(asset)
+    if vc_ok:
+        score += 1
+
+    nm_ok, _ = is_not_already_moving(asset)
+    if nm_ok:
+        score += 1
+
+    bt_ok, _ = is_btc_not_countertrend(side)
+    if bt_ok:
+        score += 1
+
+    multipliers = {0: 0.5, 1: 1.0, 2: 1.5, 3: 2.5, 4: 4.0}
+    return multipliers[score]
+
+
+# ─── Manual-window helper (D) ───────────────────────────────────────────────
+
+
+def is_in_manual_window(manual_window_until: Optional[datetime]) -> bool:
+    """True if a manual-enable timestamp is set and still in the future.
+
+    `manual_window_until` is stored as naive-UTC in DB.
+    """
+    if manual_window_until is None:
+        return False
+    now_naive = datetime.now(timezone.utc).replace(tzinfo=None)
+    return manual_window_until > now_naive

app/services/scanner_state.py

@@ -0,0 +1,175 @@
+"""
+Scanner runtime state + control plane.
+
+One module to answer: "what scanners are alive, when did each last fire, and
+how do I turn one off without restarting the server?"
+
+Why this exists (3 problems it solves at once):
+
+  1. KILL SWITCH — Production safety. If a scanner is misbehaving (false
+     fires, hammering an API, leaking memory) the operator needs to disable
+     it WITHOUT redeploying. Per-scanner toggle + "stop all" both required.
+
+  2. COOLDOWN PERSISTENCE — Previously each scanner kept _last_signal_at as
+     a process-local dict. A backend restart wiped that dict, so a scanner
+     that fired yesterday would happily re-fire today. Now cooldown is read
+     from the posts table (source-of-truth) via `last_signal_at()`.
+
+  3. OBSERVABILITY — Operator needs to see "is my RSI scanner alive?". The
+     in-memory state tracker records every run (success / fire / error) so
+     a GET /api/scanners endpoint can show the whole fleet at a glance.
+
+State is intentionally process-local (no DB writes). After restart all
+scanners come back ENABLED — same as the rest of the system. Cooldowns
+survive restart because they're DB-derived. If you want a "permanently
+disabled" scanner, comment it out of main.py instead.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field, asdict
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+# ─── State container ────────────────────────────────────────────────────────
+
+
+@dataclass
+class ScannerState:
+    name:               str
+    enabled:            bool = True
+    last_run_at:        Optional[datetime] = None
+    last_status:        str = "never_ran"     # never_ran | ok | fired | error
+    last_message:       Optional[str] = None
+    last_fired_at:      Optional[datetime] = None
+    consecutive_errors: int  = 0
+    total_runs:         int  = 0
+    total_fires:        int  = 0
+
+    def to_dict(self) -> dict:
+        d = asdict(self)
+        for k in ("last_run_at", "last_fired_at"):
+            if d[k] is not None:
+                # last_run_at is tz-aware (datetime.now(timezone.utc)), so
+                # isoformat() already emits "...+00:00". Appending "Z" would
+                # produce the invalid "...+00:00Z" which JS Date can't parse
+                # (renders as "NaNd ago" in the UI). Just use isoformat().
+                d[k] = d[k].isoformat()
+        return d
+
+
+_STATES: dict[str, ScannerState] = {}
+
+
+# ─── Registration (called once per scanner at import time) ──────────────────
+
+
+def register(name: str) -> ScannerState:
+    if name not in _STATES:
+        _STATES[name] = ScannerState(name=name)
+    return _STATES[name]
+
+
+# ─── Toggle controls ────────────────────────────────────────────────────────
+
+
+def is_enabled(name: str) -> bool:
+    """Return False ONLY if explicitly disabled. Unknown scanners default
+    to enabled — a scanner shouldn't silently refuse to run because its
+    state wasn't registered (defensive)."""
+    s = _STATES.get(name)
+    return s.enabled if s else True
+
+
+def set_enabled(name: str, enabled: bool) -> Optional[ScannerState]:
+    s = _STATES.get(name)
+    if s is None:
+        return None
+    s.enabled = enabled
+    logger.info("Scanner %s %s", name, "ENABLED" if enabled else "DISABLED")
+    return s
+
+
+def disable_all() -> int:
+    """Kill switch. Returns count of scanners that were running and got
+    flipped off. Already-disabled scanners are skipped."""
+    n = 0
+    for s in _STATES.values():
+        if s.enabled:
+            s.enabled = False
+            n += 1
+    if n:
+        logger.warning("Scanner kill switch: disabled %d scanners", n)
+    return n
+
+
+def enable_all() -> int:
+    n = 0
+    for s in _STATES.values():
+        if not s.enabled:
+            s.enabled = True
+            n += 1
+    return n
+
+
+def get_all() -> list[ScannerState]:
+    return list(_STATES.values())
+
+
+# ─── Per-run telemetry (called by scanners after each scan) ─────────────────
+
+
+def record_run(name: str, status: str, message: Optional[str] = None) -> None:
+    """status: 'ok' (ran, no signal), 'fired' (emitted a signal), 'error'."""
+    s = register(name)
+    s.last_run_at = datetime.now(timezone.utc)
+    s.last_status = status
+    s.last_message = message[:240] if message else None
+    s.total_runs += 1
+    if status == "fired":
+        s.total_fires += 1
+        s.last_fired_at = s.last_run_at
+        s.consecutive_errors = 0
+    elif status == "error":
+        s.consecutive_errors += 1
+    else:
+        s.consecutive_errors = 0
+
+
+# ─── DB-backed cooldown (survives restart) ──────────────────────────────────
+
+
+async def last_signal_at(source: str, target_asset: str) -> Optional[datetime]:
+    """Most recent post with the given source+target_asset. Returns naive UTC.
+
+    Used by scanners INSTEAD of in-memory cooldown trackers. The DB is the
+    authoritative record of "did this scanner already fire?" — surviving
+    restarts, multi-process deploys, and even DB migrations.
+    """
+    from sqlalchemy import select, func
+    from app.database import AsyncSessionLocal
+    from app.models import Post
+
+    async with AsyncSessionLocal() as db:
+        result = await db.execute(
+            select(func.max(Post.published_at)).where(
+                Post.source == source,
+                Post.target_asset == target_asset.upper(),
+            )
+        )
+        ts = result.scalar_one_or_none()
+        return ts
+
+
+async def in_cooldown(source: str, target_asset: str, cooldown_days: int) -> bool:
+    """Convenience wrapper. True iff we fired the same {source, asset} within
+    the last `cooldown_days`."""
+    last = await last_signal_at(source, target_asset)
+    if last is None:
+        return False
+    age = datetime.now(timezone.utc).replace(tzinfo=None) - last
+    return age < timedelta(days=cooldown_days)

app/services/scanners/_archive/README.md

@@ -0,0 +1,12 @@
+# Archived scanners (not in the live path)
+
+These were generic System-2 scanners superseded by the focused
+`btc_bottom_reversal` state machine. Nothing imports them; they are NOT
+scheduled and do NOT register with scanner_state. Kept for reference/history.
+
+- vcp_breakout.py        — volatility-contraction breakout (bidirectional)
+- weekly_rsi_reversal.py — weekly RSI extreme + recovery (bidirectional)
+
+To revive: re-add an APScheduler job in app/main.py and a
+scanner_state.register() call, and ensure the source tag is in
+signal_categories.SYSTEM_2_SOURCES.

app/services/scanners/_archive/vcp_breakout.py

@@ -0,0 +1,251 @@
+"""
+VCP-style breakout scanner — example of an external signal module.
+
+What this scans for (Minervini-light, adapted for crypto perps):
+  1. Volatility contraction: 7-day ATR ≤ 50% of 30-day ATR
+     (the price has been getting tighter — supply is drying up)
+  2. Breakout confirmation: current 4h close > max(prior 30-day highs)
+     (a real break of the range, not just touching the top)
+  3. Volume confirmation: last 24h volume ≥ 1.5× 30-day average
+     (institutional participation, not retail noise)
+  4. Cooldown: no signal for the same asset in the past 48h
+     (avoid stacking)
+
+When all 4 conditions hit, the scanner POSTs to /api/signals/ingest. From
+there the trade goes through the same convex-strategy pipeline as Trump
+signals — regime filter, sizing, trailing stop, all of it.
+
+Why this module exists:
+  Trump signals have 1-2 true catalysts per month. Adding a TA-based source
+  that finds setups continuously means the bot has SOMETHING to act on most
+  weeks, instead of waiting for geopolitical fireworks. The user's hypothesis
+  is that consolidation breakouts have asymmetric upside; the existing
+  trailing-stop logic is purpose-built to capture that.
+
+Tunables (don't hardcode in caller — change them HERE if you want to test):
+  ATR_RECENT_DAYS, ATR_BASELINE_DAYS, ATR_RATIO_MAX,
+  BREAKOUT_LOOKBACK_DAYS, VOLUME_MULT_MIN, COOLDOWN_HOURS.
+
+Asset list:
+  Default = SOL only. Extend ASSETS_TO_SCAN with more once you've watched
+  this run for a week and confirmed it doesn't fire-hose noise.
+"""
+
+from __future__ import annotations
+
+import logging
+import statistics
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+import httpx
+
+from app.config import settings
+from app.services.market_data import for_asset, drop_in_progress_bar
+from app.services import scanner_state
+
+logger = logging.getLogger(__name__)
+
+SCANNER_NAME = "vcp_breakout"
+scanner_state.register(SCANNER_NAME)
+
+
+# ─── Tunables ───────────────────────────────────────────────────────────────
+ATR_RECENT_DAYS        = 7
+ATR_BASELINE_DAYS      = 30
+ATR_RATIO_MAX          = 0.5    # recent ATR must be ≤ 50% of baseline
+BREAKOUT_LOOKBACK_DAYS = 30     # break of this many days' high
+VOLUME_MULT_MIN        = 1.5    # current 24h vol vs 30-day avg
+COOLDOWN_HOURS         = 48     # don't re-signal same asset within this window
+
+# Assets to scan. Provider selection (Binance vs HL) is automatic via
+# market_data.for_asset() — HL-native perps like TRUMP/HYPE route to HL
+# automatically. Add to HL_NATIVE_ASSETS in market_data.py if needed.
+ASSETS_TO_SCAN = [
+    "SOL",
+    # "ETH", "LINK", "AVAX",          # Binance has them
+    # "TRUMP", "HYPE",                # HL-native (routed automatically)
+]
+
+# Cooldown via scanner_state.in_cooldown — DB-backed, restart-safe.
+
+
+# Data fetching is delegated to market_data.for_asset() — see that module
+# for Binance vs Hyperliquid routing.
+
+
+# ─── Signal logic ───────────────────────────────────────────────────────────
+
+
+def _atr_proxy(candles: list[dict]) -> Optional[float]:
+    """Mean of (high-low)/close — simple ATR proxy, dimensionless."""
+    ranges = [(c["high"] - c["low"]) / c["close"] for c in candles if c["close"] > 0]
+    return statistics.fmean(ranges) if ranges else None
+
+
+def evaluate_breakout(candles: list[dict]) -> tuple[bool, dict]:
+    """Returns (is_signal, debug_info). All 4 gates must pass.
+
+    Pure function — no side effects, fully testable.
+    """
+    if len(candles) < ATR_BASELINE_DAYS * 6:
+        return False, {"reason": "insufficient_data", "bars": len(candles)}
+
+    # 1. Volatility contraction
+    recent_bars   = candles[-ATR_RECENT_DAYS * 6:]
+    baseline_bars = candles[-ATR_BASELINE_DAYS * 6:]
+    atr_recent   = _atr_proxy(recent_bars)
+    atr_baseline = _atr_proxy(baseline_bars)
+    if not atr_recent or not atr_baseline:
+        return False, {"reason": "atr_calc_failed"}
+    atr_ratio = atr_recent / atr_baseline
+    if atr_ratio > ATR_RATIO_MAX:
+        return False, {"reason": "no_contraction", "atr_ratio": round(atr_ratio, 3)}
+
+    # 2. Range break — UP through prior high (long) or DOWN through prior
+    # low (short). Lookback excludes the current bar.
+    lookback = candles[-BREAKOUT_LOOKBACK_DAYS * 6 : -1]
+    if not lookback:
+        return False, {"reason": "no_lookback"}
+    prior_high = max(c["high"] for c in lookback)
+    prior_low  = min(c["low"]  for c in lookback)
+    current_close = candles[-1]["close"]
+
+    if current_close > prior_high:
+        direction, ref, gap = "buy", prior_high, (current_close - prior_high) / prior_high * 100
+    elif current_close < prior_low:
+        direction, ref, gap = "short", prior_low, (prior_low - current_close) / prior_low * 100
+    else:
+        return False, {"reason": "no_range_break", "close": current_close,
+                        "prior_high": prior_high, "prior_low": prior_low}
+
+    # 3. Volume confirmation (same for both directions — a real break needs
+    # participation regardless of which way it goes)
+    recent_vol_24h = sum(c["volume"] for c in candles[-6:])
+    baseline_avg_24h = sum(c["volume"] for c in candles[-180:]) / 30
+    if baseline_avg_24h <= 0:
+        return False, {"reason": "no_volume_data"}
+    vol_ratio = recent_vol_24h / baseline_avg_24h
+    if vol_ratio < VOLUME_MULT_MIN:
+        return False, {"reason": "weak_volume", "vol_ratio": round(vol_ratio, 2)}
+
+    return True, {
+        "direction":    direction,
+        "atr_ratio":    round(atr_ratio, 3),
+        "vol_ratio":    round(vol_ratio, 2),
+        "break_ref":    round(ref, 4),
+        "break_at":     round(current_close, 4),
+        "headroom_pct": round(gap, 2),
+    }
+
+
+def _confidence_from(debug: dict) -> int:
+    """Map the signal's quality into a 0-100 confidence used by sizing.
+
+    The tighter the contraction and the bigger the volume spike, the higher
+    the confidence. Capped at 95 — leave headroom so AI signals at 100 stay
+    distinguishable.
+    """
+    score = 70  # baseline for "all 4 gates passed"
+    # Tighter contraction → higher confidence
+    if debug.get("atr_ratio", 1) < 0.35: score += 10
+    elif debug.get("atr_ratio", 1) < 0.45: score += 5
+    # Bigger volume spike → higher confidence
+    if debug.get("vol_ratio", 0) >= 3.0: score += 10
+    elif debug.get("vol_ratio", 0) >= 2.0: score += 5
+    return min(score, 95)
+
+
+# ─── Ingest call ────────────────────────────────────────────────────────────
+
+
+async def _emit_signal(asset: str, debug: dict) -> None:
+    """POST the signal to /api/signals/ingest. Treats the local server as the
+    target — same machine, no network hop in dev.
+    """
+    if not settings.ingest_api_key:
+        logger.warning("VCP: signal would fire on %s but INGEST_API_KEY is empty", asset)
+        return
+
+    confidence = _confidence_from(debug)
+    direction = debug["direction"]
+    way = "breakout ↑" if direction == "buy" else "breakdown ↓"
+    rel = "above" if direction == "buy" else "below"
+    payload = {
+        "source":            "breakout",
+        "external_id":       f"vcp-{asset}-{direction}-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M')}",
+        "text": (
+            f"VCP {way} on {asset}: ATR ratio {debug['atr_ratio']} "
+            f"(7d/30d), {debug['vol_ratio']}× normal volume, "
+            f"close ${debug['break_at']} {rel} {BREAKOUT_LOOKBACK_DAYS}-day "
+            f"level ${debug['break_ref']} ({debug['headroom_pct']}%)"
+        ),
+        "signal":            direction,
+        "target_asset":      asset,
+        "confidence":        confidence,
+        "category":          "vcp_breakout",
+        "expected_move_pct": 5.0,   # historical VCP median expansion
+    }
+    async with httpx.AsyncClient(timeout=10) as client:
+        resp = await client.post(
+            "http://localhost:8000/api/signals/ingest",
+            json=payload,
+            headers={"X-Ingest-Key": settings.ingest_api_key},
+        )
+        if resp.status_code >= 400:
+            logger.error("VCP ingest failed (%d): %s", resp.status_code, resp.text[:200])
+        else:
+            logger.info("VCP signal emitted for %s, confidence=%d: %s",
+                        asset, confidence, resp.json())
+
+
+# ─── Scheduler entry point ──────────────────────────────────────────────────
+
+
+async def scan_once() -> None:
+    """Single scan pass over ASSETS_TO_SCAN. Called every 30 minutes.
+
+    Kill-switch + DB-backed cooldown. Drops in-progress 4h bar before
+    evaluating — without that, the breakout test ran against a partial bar
+    and could flicker FIRE / no-FIRE within a 4h window.
+    """
+    if not scanner_state.is_enabled(SCANNER_NAME):
+        logger.debug("VCP scanner disabled — skipping run")
+        return
+
+    fired_any = False
+    error_msg: Optional[str] = None
+    for asset in ASSETS_TO_SCAN:
+        # Cooldown — DB-backed, restart-safe.
+        cooldown_days = COOLDOWN_HOURS / 24
+        if await scanner_state.in_cooldown("breakout", asset, cooldown_days):
+            continue
+
+        provider = for_asset(asset)
+        try:
+            candles = await provider.fetch_4h(asset, days=ATR_BASELINE_DAYS + 1)
+        except Exception as exc:
+            error_msg = f"{asset}: {exc}"
+            logger.error("VCP fetch failed for %s via %s: %s", asset, provider.name, exc)
+            continue
+
+        candles = drop_in_progress_bar(candles, "4h")
+        if not candles:
+            logger.warning("VCP: %s returned 0 candles from %s — symbol may not exist",
+                           asset, provider.name)
+            continue
+
+        is_signal, debug = evaluate_breakout(candles)
+        logger.info("VCP scan %s [%s]: %s — %s", asset, provider.name,
+                    "FIRE" if is_signal else "no", debug)
+
+        if is_signal:
+            await _emit_signal(asset, debug)
+            fired_any = True
+
+    if error_msg:
+        scanner_state.record_run(SCANNER_NAME, "error", error_msg)
+    elif fired_any:
+        scanner_state.record_run(SCANNER_NAME, "fired")
+    else:
+        scanner_state.record_run(SCANNER_NAME, "ok")

app/services/scanners/_archive/weekly_rsi_reversal.py

@@ -0,0 +1,264 @@
+"""
+Weekly RSI extreme + recovery scanner.
+
+What it catches:
+  Severe weekly oversold conditions that flush capitulating holders, followed
+  by the first sign of strength. Historical examples this would have hit:
+
+    BTC  2022-11 ($15.5k bottom)
+    ETH  2022-06 ($900 bottom)
+    SOL  2022-12 ($8 bottom)
+    AAVE 2022-06 ($45 bottom)
+
+Trigger logic (intentionally strict):
+
+  PRE-CONDITION:   ≥ 4 consecutive completed weekly bars with RSI(14) < 30
+                   (this is the capitulation phase — sustained extreme weakness)
+
+  TRIGGER:         current completed week's RSI(14) >= 35
+                   (i.e. the FIRST recovery week — RSI has lifted off the floor)
+
+  COOLDOWN:        60 days — these events happen ~once per year per asset.
+                   No re-firing on the same setup.
+
+Companion exit parameters (passed to the bot via signal payload — wider stops
+to match the longer holding period and higher single-trade conviction):
+
+    SL                = 8%
+    TRAILING_ACTIVATE = 15%
+    TRAILING_STOP     = 6%
+    MAX_HOLD          = 60 days
+
+This is a high-conviction, low-frequency signal. Expect 0-3 fires per asset
+per year. Don't tune it for higher frequency — that defeats the point.
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+import httpx
+
+from app.config import settings
+from app.services.market_data import REVERSAL_BASKET, for_asset, drop_in_progress_bar
+from app.services import scanner_state
+
+SCANNER_NAME = "weekly_rsi_reversal"
+scanner_state.register(SCANNER_NAME)
+
+logger = logging.getLogger(__name__)
+
+
+# ─── Tunables ───────────────────────────────────────────────────────────────
+RSI_PERIOD             = 14
+RSI_OVERSOLD           = 30        # "deeply oversold" floor (long setup)
+RSI_RECOVERY_TRIGGER   = 35        # bounce-off line for the long
+RSI_OVERBOUGHT         = 70        # "euphoric" ceiling (short setup)
+RSI_ROLLOVER_TRIGGER   = 65        # roll-off line for the short
+WEEKS_OF_OVERSOLD      = 4         # consecutive extreme weeks required (both sides)
+WEEKS_TO_FETCH         = 40        # enough history for stable Wilder's RSI
+COOLDOWN_DAYS          = 60
+
+# Exit profile passed to /signals/ingest. Caller can override on the bot side.
+PAYLOAD_CONFIDENCE     = 88        # high conviction — top of the regime-filter score
+PAYLOAD_EXPECTED_MOVE  = 30.0      # historical median move after capitulation
+
+
+# Cooldown is now DB-backed via scanner_state.in_cooldown() — survives
+# restart. No more in-memory _last_signal_at dict.
+
+
+# ─── RSI math (Wilder's smoothed) ───────────────────────────────────────────
+
+
+def calculate_rsi(closes: list[float], period: int = 14) -> list[Optional[float]]:
+    """Wilder's RSI. Returns a list aligned with `closes` where the first
+    `period` entries are None (not enough history yet).
+
+    Implementation note: the first valid RSI uses a SIMPLE mean of the first
+    `period` gains/losses, subsequent values use exponential smoothing with
+    alpha = 1/period. This matches TradingView, Binance UI, and most charting
+    tools — the "Cutler's RSI" (pure SMA) variant would give different results.
+    """
+    n = len(closes)
+    rsi: list[Optional[float]] = [None] * n
+    if n < period + 1:
+        return rsi
+
+    gains  = [max(closes[i] - closes[i - 1], 0) for i in range(1, n)]
+    losses = [max(closes[i - 1] - closes[i], 0) for i in range(1, n)]
+
+    # First valid RSI = simple mean over first `period` deltas
+    avg_gain = sum(gains[:period])  / period
+    avg_loss = sum(losses[:period]) / period
+
+    if avg_loss == 0:
+        rsi[period] = 100.0
+    else:
+        rs = avg_gain / avg_loss
+        rsi[period] = 100 - 100 / (1 + rs)
+
+    # Subsequent values use Wilder's smoothing
+    for i in range(period + 1, n):
+        avg_gain = (avg_gain * (period - 1) + gains[i - 1]) / period
+        avg_loss = (avg_loss * (period - 1) + losses[i - 1]) / period
+        if avg_loss == 0:
+            rsi[i] = 100.0
+        else:
+            rs = avg_gain / avg_loss
+            rsi[i] = 100 - 100 / (1 + rs)
+
+    return rsi
+
+
+# ─── Signal logic ───────────────────────────────────────────────────────────
+
+
+def evaluate_rsi_reversal(weekly_candles: list[dict]) -> tuple[bool, dict]:
+    """Pure function — no side effects, fully testable.
+
+    Returns (is_signal, debug_info).
+    """
+    if len(weekly_candles) < RSI_PERIOD + WEEKS_OF_OVERSOLD + 2:
+        return False, {"reason": "insufficient_data", "bars": len(weekly_candles)}
+
+    closes = [c["close"] for c in weekly_candles]
+    rsi    = calculate_rsi(closes, RSI_PERIOD)
+
+    # The MOST RECENT weekly bar is `closes[-1]`. We treat it as the "current"
+    # recovery candidate. Look BACKWARDS for WEEKS_OF_OVERSOLD prior bars all
+    # with RSI < RSI_OVERSOLD.
+    current_rsi = rsi[-1]
+    if current_rsi is None:
+        return False, {"reason": "rsi_not_computable"}
+
+    window = rsi[-(WEEKS_OF_OVERSOLD + 1):-1]   # the N weeks before current
+    if any(r is None for r in window):
+        return False, {"reason": "history_gaps_in_window"}
+
+    this_close = weekly_candles[-1]["close"]
+    prev_close = weekly_candles[-2]["close"]
+
+    # ── LONG: capitulation bottom ──────────────────────────────────────────
+    # Sustained RSI<30, now lifting ≥35, price turning up.
+    if (current_rsi >= RSI_RECOVERY_TRIGGER
+            and all(r < RSI_OVERSOLD for r in window)
+            and this_close > prev_close):
+        low_w = min(c["low"] for c in weekly_candles[-(WEEKS_OF_OVERSOLD + 1):])
+        return True, {
+            "direction":     "buy",
+            "current_rsi":   round(current_rsi, 1),
+            "window_rsi":    [round(r, 1) for r in window],
+            "move_pct":      round((this_close - low_w) / low_w * 100, 2) if low_w > 0 else 0.0,
+            "trigger_close": round(this_close, 4),
+        }
+
+    # ── SHORT: euphoric top ────────────────────────────────────────────────
+    # Sustained RSI>70, now rolling ≤65, price turning down. Symmetric mirror.
+    if (current_rsi <= RSI_ROLLOVER_TRIGGER
+            and all(r > RSI_OVERBOUGHT for r in window)
+            and this_close < prev_close):
+        high_w = max(c["high"] for c in weekly_candles[-(WEEKS_OF_OVERSOLD + 1):])
+        return True, {
+            "direction":     "short",
+            "current_rsi":   round(current_rsi, 1),
+            "window_rsi":    [round(r, 1) for r in window],
+            "move_pct":      round((high_w - this_close) / high_w * 100, 2) if high_w > 0 else 0.0,
+            "trigger_close": round(this_close, 4),
+        }
+
+    # Neither side qualified — report the closest miss for the log.
+    return False, {
+        "reason": "no_setup",
+        "current_rsi": round(current_rsi, 1),
+        "window_rsi": [round(r, 1) for r in window],
+    }
+
+
+# ─── Ingest emission ────────────────────────────────────────────────────────
+
+
+async def _emit_signal(asset: str, debug: dict) -> None:
+    if not settings.ingest_api_key:
+        logger.warning("RSI-reversal would fire on %s but INGEST_API_KEY empty", asset)
+        return
+    direction = debug["direction"]
+    side_word = "capitulation bottom" if direction == "buy" else "euphoric top"
+    payload = {
+        "source":            "rsi_reversal",
+        "external_id":       f"rsi-{asset}-{direction}-{datetime.now(timezone.utc).strftime('%Y%W')}",
+        "text": (
+            f"Weekly RSI {side_word} on {asset}: RSI {debug['current_rsi']} after "
+            f"{WEEKS_OF_OVERSOLD}wk extreme (window: {debug['window_rsi']}). "
+            f"{debug['move_pct']}% move off the extreme @ ${debug['trigger_close']}."
+        ),
+        "signal":            direction,
+        "target_asset":      asset,
+        "confidence":        PAYLOAD_CONFIDENCE,
+        "category":          "rsi_extreme_reversal",
+        "expected_move_pct": PAYLOAD_EXPECTED_MOVE,
+    }
+    async with httpx.AsyncClient(timeout=10) as client:
+        resp = await client.post(
+            "http://localhost:8000/api/signals/ingest",
+            json=payload,
+            headers={"X-Ingest-Key": settings.ingest_api_key},
+        )
+        if resp.status_code >= 400:
+            logger.error("RSI-reversal ingest failed (%d): %s", resp.status_code, resp.text[:200])
+        else:
+            logger.info("RSI-reversal signal emitted for %s: %s", asset, resp.json())
+
+
+# ─── Scheduler entry point ──────────────────────────────────────────────────
+
+
+async def scan_once() -> None:
+    """One scan pass over REVERSAL_BASKET. Called by APScheduler.
+
+    Kill-switch aware: short-circuits if the operator disabled this scanner.
+    Cooldown is DB-backed (queries posts table), so restarts don't reset
+    state.
+    """
+    if not scanner_state.is_enabled(SCANNER_NAME):
+        logger.debug("RSI-reversal scanner disabled — skipping run")
+        return
+
+    fired_any = False
+    error_msg: Optional[str] = None
+    for asset in REVERSAL_BASKET:
+        # Cooldown — DB-backed.
+        if await scanner_state.in_cooldown("rsi_reversal", asset, COOLDOWN_DAYS):
+            continue
+
+        provider = for_asset(asset)
+        try:
+            candles = await provider.fetch_1w(asset, weeks=WEEKS_TO_FETCH)
+        except Exception as exc:
+            error_msg = f"{asset}: {exc}"
+            logger.error("RSI-reversal fetch failed for %s via %s: %s",
+                         asset, provider.name, exc)
+            continue
+
+        candles = drop_in_progress_bar(candles, "1w")
+        if not candles:
+            logger.warning("RSI-reversal: %s returned 0 weekly bars from %s",
+                           asset, provider.name)
+            continue
+
+        is_signal, debug = evaluate_rsi_reversal(candles)
+        if is_signal:
+            logger.info("RSI-reversal scan %s [%s]: FIRE — %s", asset, provider.name, debug)
+            await _emit_signal(asset, debug)
+            fired_any = True
+        else:
+            logger.debug("RSI-reversal scan %s [%s]: no — %s", asset, provider.name, debug)
+
+    if error_msg:
+        scanner_state.record_run(SCANNER_NAME, "error", error_msg)
+    elif fired_any:
+        scanner_state.record_run(SCANNER_NAME, "fired")
+    else:
+        scanner_state.record_run(SCANNER_NAME, "ok")

app/services/scanners/btc_bottom_reversal.py

@@ -0,0 +1,148 @@
+"""BTC bottom-reversal long scanner — 2-of-3 price confluence.
+
+Simple, transparent, no on-chain data and no API keys. Fires only when at
+least TWO of these three classic bottom signals agree:
+
+  A. AHR999 < 0.45            — deep-value / bottom zone
+  B. price ≤ 200-week MA ×1.05 — every cycle has bottomed near the 200WMA
+  C. Pi Cycle Bottom          — 150d EMA ≤ 471d SMA × 0.745
+
+Long only, low frequency, stateless. No tight stop (real macro bottoms wick
+15–30%); the position is invalidated when AHR999 climbs back above 1.2 — i.e.
+BTC is no longer cheap and the bottom thesis is spent.
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+
+import httpx
+
+from app.config import settings
+from app.services import scanner_state
+from app.services.market_data import for_asset, drop_in_progress_bar
+from app.services.bottom_indicators import bottom_confluence
+
+SCANNER_NAME = "btc_bottom_reversal"
+scanner_state.register(SCANNER_NAME)
+
+logger = logging.getLogger(__name__)
+
+ASSET = "BTC"
+COOLDOWN_DAYS = 30
+# 3 votes → max conviction; 2 votes → solid. Scale confidence with agreement.
+CONFIDENCE_BY_VOTES = {2: 88, 3: 94}
+EXPECTED_MOVE_PCT = 25.0
+# Pi Cycle needs 471 daily closes; +buffer for the dropped in-progress bar.
+DAILY_LOOKBACK_DAYS = 520
+# 200-week MA needs 200 weekly closes; +buffer.
+WEEKLY_LOOKBACK_WEEKS = 210
+
+
+async def evaluate_once() -> tuple[bool, dict]:
+    provider = for_asset(ASSET)
+
+    raw_daily = await provider.fetch_1d(ASSET, days=DAILY_LOOKBACK_DAYS)
+    daily = drop_in_progress_bar(raw_daily, "1d")
+    raw_weekly = await provider.fetch_1w(ASSET, weeks=WEEKLY_LOOKBACK_WEEKS)
+    weekly = drop_in_progress_bar(raw_weekly, "1w")
+
+    if not daily:
+        return False, {"reason": "missing_btc_daily"}
+    if not weekly:
+        return False, {"reason": "missing_btc_weekly"}
+
+    daily_closes = [float(c["close"]) for c in daily if c.get("close")]
+    weekly_closes = [float(c["close"]) for c in weekly if c.get("close")]
+
+    conf = bottom_confluence(daily_closes, weekly_closes)
+    debug: dict = dict(conf.detail)
+
+    if not conf.fired:
+        debug["reason"] = "confluence_not_met"
+        return False, debug
+
+    confidence = CONFIDENCE_BY_VOTES.get(conf.votes, 88)
+    debug["confidence"] = confidence
+    # No tight stop. Thesis is invalidated when BTC is no longer cheap; the
+    # 200WMA band is a useful structural reference for the exit monitor.
+    debug["invalidation_price"] = debug.get("wma200")
+    return True, debug
+
+
+def _summary(debug: dict) -> str:
+    s = debug.get("signals", {})
+    on = [k for k, v in s.items() if v]
+    return (
+        f"BTC bottom confluence ({debug.get('votes')}/3): {', '.join(on)}. "
+        f"AHR999 {debug.get('ahr999')} (<{debug.get('ahr999_threshold')}), "
+        f"price {debug.get('price')}, 200WMA {debug.get('wma200')}, "
+        f"Pi 150EMA {debug.get('pi_ema150')} vs 471SMA×0.745 "
+        f"{debug.get('pi_sma471_scaled')}. No tight stop; "
+        f"invalidate if AHR999 > 1.2."
+    )
+
+
+async def _emit_signal(debug: dict) -> bool:
+    """POST the signal to the ingest endpoint. Returns True iff a Post was
+    (idempotently) created. False means the signal was NOT recorded — caller
+    must NOT treat the run as 'fired' (cooldown is keyed off the DB Post)."""
+    if not settings.ingest_api_key:
+        logger.error("BTC bottom-reversal would fire but INGEST_API_KEY empty "
+                     "— signal NOT recorded, check deploy env")
+        return False
+    payload = {
+        "source": "btc_bottom_reversal",
+        "external_id": f"btc-bottom-{datetime.now(timezone.utc).strftime('%Y%m%d')}",
+        "text": _summary(debug),
+        "signal": "buy",
+        "target_asset": ASSET,
+        "confidence": debug["confidence"],
+        "category": "btc_bottom_reversal_long",
+        "expected_move_pct": EXPECTED_MOVE_PCT,
+        "invalidation_price": debug.get("invalidation_price"),
+    }
+    async with httpx.AsyncClient(timeout=10) as client:
+        resp = await client.post(
+            "http://localhost:8000/api/signals/ingest",
+            json=payload,
+            headers={"X-Ingest-Key": settings.ingest_api_key},
+        )
+        if resp.status_code >= 400:
+            logger.error("BTC bottom-reversal ingest failed (%d): %s",
+                         resp.status_code, resp.text[:200])
+            return False
+        logger.info("BTC bottom-reversal signal emitted: %s", resp.json())
+        return True
+
+
+async def scan_once() -> None:
+    if not scanner_state.is_enabled(SCANNER_NAME):
+        logger.debug("BTC bottom-reversal scanner disabled — skipping run")
+        return
+    if await scanner_state.in_cooldown("btc_bottom_reversal", ASSET, COOLDOWN_DAYS):
+        logger.debug("BTC bottom-reversal cooldown active")
+        scanner_state.record_run(SCANNER_NAME, "ok", "cooldown")
+        return
+
+    try:
+        should_fire, debug = await evaluate_once()
+    except Exception as exc:
+        logger.error("BTC bottom-reversal scan failed: %s", exc)
+        scanner_state.record_run(SCANNER_NAME, "error", str(exc))
+        return
+
+    if should_fire:
+        logger.info("BTC bottom-reversal FIRE — %s", debug)
+        emitted = await _emit_signal(debug)
+        if emitted:
+            scanner_state.record_run(SCANNER_NAME, "fired")
+        else:
+            # Not recorded → no DB Post → cooldown won't arm. Surface this as
+            # an error so a misconfigured deploy is obvious, not a silent
+            # daily "fired" that never actually trades.
+            scanner_state.record_run(SCANNER_NAME, "error", "ingest_failed_or_key_missing")
+    else:
+        logger.info("BTC bottom-reversal no — %s", debug)
+        scanner_state.record_run(SCANNER_NAME, "ok")

app/services/scanners/funding_reversal.py

@@ -0,0 +1,379 @@
+"""
+Funding rate extreme + reversal scanner.
+
+What it catches:
+  Crowded perp positioning that's about to unwind. When one side has been
+  paying ridiculous funding for weeks, the eventual unwind ("the squeeze")
+  is the cleanest single-day move you'll find. Examples:
+
+    BTC  2024-08  funding deeply +ve for 30d  → 14% flush down within a week
+    SOL  2023-09  funding deeply -ve for 30d  → 60% rally over the next month
+    ETH  2024-Q4  funding +3.5% on 30d        → 12% pullback
+
+Trigger logic:
+
+  PRE-CONDITION:   Sum of last 30 days of funding rate (per 8h cycle)
+                   exceeds ±FUNDING_EXTREME_PCT. The sign tells us which
+                   side is overcrowded:
+
+                     sum > +3%   →   longs have been paying — bearish setup
+                                      (signal = short)
+                     sum < -3%   →   shorts paying — bullish setup
+                                      (signal = buy)
+
+  TRIGGER:         Funding direction has STARTED to mean-revert
+                   (last 3 funding cycles closer to zero than the 30d avg)
+                   AND price has begun moving in the contrarian direction
+                   (last 7 days price move at least 3% in that direction).
+
+  COOLDOWN:        14 days. Funding extremes can persist for weeks but
+                   each unwind is a distinct event.
+
+Companion exits — tighter than RSI/SMA because funding moves play out faster
+(days, not weeks):
+    SL                = 4%
+    TRAILING_ACTIVATE = 10%
+    TRAILING_STOP     = 5%
+    MAX_HOLD          = 30 days
+
+This is the highest-frequency of the three reversal signals — expect 3-5
+fires per asset per year — and the most "alpha-like" (most market participants
+ignore funding entirely).
+"""
+
+from __future__ import annotations
+
+import logging
+import statistics
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+import httpx
+
+from app.config import settings
+from app.services import scanner_state
+from app.services.market_data import REVERSAL_BASKET, for_asset, drop_in_progress_bar
+
+# Promoted from booster → standalone scanner. Still importable by
+# btc_bottom_reversal for confluence boost; ALSO emits its own signals via
+# scan_once() registered with the APScheduler in app/main.py.
+
+SCANNER_NAME = "funding_reversal"
+scanner_state.register(SCANNER_NAME)
+
+ASSET = "BTC"   # BTC-only for now; extend to ETH/SOL after results validate
+
+# How much funding+price history to load each tick.
+# - Binance: 8h cadence → 30d ≈ 90 cycles
+# - HL:      1h cadence → capped at 500 rows ≈ 20.8d (handled inside evaluate)
+FUNDING_DAYS_LOOKBACK = 30
+PRICE_DAYS_LOOKBACK   = 35   # need 7d confirm + buffer for in-progress bar
+
+logger = logging.getLogger(__name__)
+
+
+# ─── Tunables ───────────────────────────────────────────────────────────────
+FUNDING_HISTORY_DAYS         = 30
+FUNDING_EXTREME_THRESHOLD    = 0.03    # 3% cumulative — extreme one-sided pressure
+# CRITICAL: "recent" is in HOURS, not CYCLES. Binance funding is 8h-cadence
+# (3 cycles/day) but HL is 1h-cadence (24 cycles/day) — a fixed "3 cycles
+# lookback" means 24h on Binance but only 3h on HL. We pick cycles dynamically
+# based on the response's actual cadence so 24h of funding history is always
+# the comparison window.
+FUNDING_REVERSAL_LOOKBACK_HOURS = 24
+FUNDING_REVERSAL_RATIO       = 0.5     # recent avg must be ≤ 50% of 30d-avg in magnitude
+PRICE_CONFIRM_DAYS           = 7
+PRICE_CONFIRM_PCT            = 3.0     # price moved 3%+ in contrarian direction
+COOLDOWN_DAYS                = 14
+
+PAYLOAD_CONFIDENCE           = 82      # slightly lower than RSI/SMA — funding can chop
+PAYLOAD_EXPECTED_MOVE        = 10.0
+EMIT_STANDALONE_SIGNALS      = True    # promoted from booster → standalone signal source
+
+
+# Cooldown via scanner_state.in_cooldown — DB-backed, restart-safe.
+
+
+# ─── Signal logic ───────────────────────────────────────────────────────────
+
+
+def _detect_cadence_hours(funding: list[dict]) -> float:
+    """Infer the funding cadence (hours between cycles) from the response.
+
+    Binance returns ~8h intervals; HL returns ~1h. We can't assume — the
+    response itself is the source of truth. Average over the most recent 10
+    intervals to smooth out occasional gaps.
+    """
+    if len(funding) < 2:
+        return 8.0   # safe Binance default
+    sample = funding[-min(11, len(funding)):]
+    diffs = [sample[i]["time_ms"] - sample[i - 1]["time_ms"] for i in range(1, len(sample))]
+    if not diffs:
+        return 8.0
+    return statistics.fmean(diffs) / 3_600_000.0  # ms → hours
+
+
+MIN_COVERAGE_DAYS = 14   # below this, the signal is statistically too noisy
+
+
+def evaluate_funding_reversal(
+    funding_history: list[dict],
+    daily_candles:   list[dict],
+) -> tuple[bool, dict]:
+    """Pure function. Returns (is_signal, debug).
+    Debug contains `direction` key on real signals — 'buy' or 'short'.
+
+    Cross-venue safety: HL caps fundingHistory at 500 rows (~20.8 days for
+    1h cadence), Binance can give a full 30 days. We compute the actual
+    coverage span from the data and scale the cumulative-funding threshold
+    proportionally — so 3% over 30 days and 2.08% over 20.8 days are
+    treated as equivalent in daily-average terms.
+    """
+    if not funding_history or len(funding_history) < 2:
+        return False, {"reason": "insufficient_funding_history",
+                       "cycles": len(funding_history)}
+
+    cadence_h = _detect_cadence_hours(funding_history)
+
+    # Actual time span the response covers — bounded by HL's 500-row cap
+    # for HL, or by what we asked for on Binance.
+    span_ms = funding_history[-1]["time_ms"] - funding_history[0]["time_ms"]
+    actual_days = span_ms / 86_400_000
+
+    if actual_days < MIN_COVERAGE_DAYS:
+        return False, {"reason": "insufficient_coverage_days",
+                       "actual_days": round(actual_days, 1),
+                       "min_required": MIN_COVERAGE_DAYS}
+
+    rates = [f["rate"] for f in funding_history]
+    cumulative_funding = sum(rates)
+    avg_full_window    = statistics.fmean(rates)
+
+    # Scale the extreme threshold by ACTUAL coverage so cross-venue results
+    # are comparable. Binance: 30d → threshold ×1. HL: 20.8d → threshold ×0.69.
+    scaled_threshold = FUNDING_EXTREME_THRESHOLD * (actual_days / FUNDING_HISTORY_DAYS)
+
+    # "Recent" lookback in TIME units, not cycles. 24h of cycles regardless
+    # of venue. Min 3 to avoid pathological cases (very short history).
+    recent_cycles = max(3, int(FUNDING_REVERSAL_LOOKBACK_HOURS / cadence_h))
+    recent_cycles = min(recent_cycles, len(rates))   # don't over-slice
+    avg_recent_cycle = statistics.fmean(rates[-recent_cycles:])
+
+    # 2. Identify direction (which side is overcrowded)
+    if cumulative_funding > scaled_threshold:
+        # Longs have been paying → expect SHORT squeeze
+        direction = "short"
+    elif cumulative_funding < -scaled_threshold:
+        # Shorts have been paying → expect LONG rally
+        direction = "buy"
+    else:
+        return False, {
+            "reason": "no_extreme",
+            "cumulative_funding_pct": round(cumulative_funding * 100, 3),
+            "scaled_threshold_pct":   round(scaled_threshold * 100, 3),
+            "coverage_days":          round(actual_days, 1),
+        }
+
+    # 3. Funding must be MEAN-REVERTING (recent cycles softer than 30d avg)
+    #    Use absolute magnitude — what matters is "the pressure is easing",
+    #    not the direction of zero-crossing.
+    if abs(avg_full_window) == 0:
+        return False, {"reason": "degenerate_avg"}
+    revert_ratio = abs(avg_recent_cycle) / abs(avg_full_window)
+    if revert_ratio > FUNDING_REVERSAL_RATIO:
+        return False, {
+            "reason": "funding_still_extreme",
+            "revert_ratio": round(revert_ratio, 2),
+            "needed_below": FUNDING_REVERSAL_RATIO,
+            "direction": direction,
+        }
+
+    # 4. Price confirms the contrarian move (last 7 days)
+    if not daily_candles or len(daily_candles) < PRICE_CONFIRM_DAYS + 1:
+        return False, {"reason": "insufficient_price_history",
+                       "have": len(daily_candles), "need": PRICE_CONFIRM_DAYS + 1}
+
+    first_close = daily_candles[-(PRICE_CONFIRM_DAYS + 1)]["close"]
+    last_close  = daily_candles[-1]["close"]
+    if first_close == 0:
+        return False, {"reason": "bad_first_close"}
+    pct_change = (last_close - first_close) / first_close * 100
+
+    if direction == "buy" and pct_change < PRICE_CONFIRM_PCT:
+        return False, {"reason": "price_not_yet_recovering",
+                       "pct_7d": round(pct_change, 2), "direction": direction}
+    if direction == "short" and pct_change > -PRICE_CONFIRM_PCT:
+        return False, {"reason": "price_not_yet_falling",
+                       "pct_7d": round(pct_change, 2), "direction": direction}
+
+    boost = {
+        "direction":            direction,
+        "cum_funding_30d_pct":  round(cumulative_funding * 100, 3),
+        "recent_avg_cycle_pct": round(avg_recent_cycle * 100, 4),
+        "revert_ratio":         round(revert_ratio, 2),
+        "price_7d_pct":         round(pct_change, 2),
+        "trigger_close":        round(last_close, 4),
+    }
+    if not EMIT_STANDALONE_SIGNALS:
+        return False, {"reason": "boost_only", "boost": boost}
+    return True, boost
+
+
+# ─── Standalone scanner plumbing ────────────────────────────────────────────
+
+
+async def _fetch_inputs() -> tuple[list[dict], list[dict]]:
+    """Pull funding history + daily candles for ASSET.
+
+    We bypass provider.fetch_1d in favor of fapi.binance.com/fapi/v1/klines
+    because (a) funding lives on the futures venue anyway — same liquidity
+    pool, same trade flow — and (b) the spot api.binance.com host is
+    occasionally geo-blocked, while fapi is more reliably reachable.
+    """
+    provider = for_asset(ASSET)
+    funding = await provider.fetch_funding(ASSET, days=FUNDING_DAYS_LOOKBACK)
+
+    end_ms   = int(datetime.now(timezone.utc).timestamp() * 1000)
+    start_ms = end_ms - PRICE_DAYS_LOOKBACK * 24 * 3600 * 1000
+    async with httpx.AsyncClient(timeout=20) as client:
+        resp = await client.get(
+            "https://fapi.binance.com/fapi/v1/klines",
+            params={"symbol": f"{ASSET}USDT", "interval": "1d",
+                    "startTime": start_ms, "endTime": end_ms, "limit": 1000},
+        )
+        resp.raise_for_status()
+        raw_daily = [
+            {"time_ms": r[0], "open": float(r[1]), "high": float(r[2]),
+             "low": float(r[3]), "close": float(r[4]), "volume": float(r[5])}
+            for r in resp.json()
+        ]
+    daily = drop_in_progress_bar(raw_daily, "1d")
+    return funding, daily
+
+
+def _summary(debug: dict) -> str:
+    direction = debug.get("direction", "?").upper()
+    cum = debug.get("cum_funding_30d_pct")
+    recent = debug.get("recent_avg_cycle_pct")
+    revert = debug.get("revert_ratio")
+    p7d = debug.get("price_7d_pct")
+    return (
+        f"BTC funding extreme reversal — {direction}. "
+        f"30d cumulative funding {cum}% (crowded {'longs' if direction == 'SHORT' else 'shorts'}); "
+        f"last-24h cycle avg {recent}% (revert ratio {revert}); "
+        f"price 7d {p7d:+.2f}% confirms the unwind."
+    )
+
+
+async def _emit_signal(debug: dict) -> bool:
+    """POST to the ingest endpoint. Idempotent via external_id (per-day)."""
+    if not settings.ingest_api_key:
+        logger.error("Funding reversal would fire but INGEST_API_KEY empty — not recorded")
+        return False
+    direction = debug["direction"]   # 'buy' or 'short'
+    expected_move = PAYLOAD_EXPECTED_MOVE if direction == "buy" else -PAYLOAD_EXPECTED_MOVE
+    payload = {
+        "source": "funding_reversal",
+        "external_id": f"funding-{ASSET}-{direction}-{datetime.now(timezone.utc).strftime('%Y%m%d')}",
+        "text": _summary(debug),
+        "signal": direction,
+        "target_asset": ASSET,
+        "confidence": PAYLOAD_CONFIDENCE,
+        "category": f"funding_reversal_{direction}",
+        "expected_move_pct": expected_move,
+        "invalidation_price": debug.get("trigger_close"),
+    }
+    async with httpx.AsyncClient(timeout=10) as client:
+        resp = await client.post(
+            "http://localhost:8000/api/signals/ingest",
+            json=payload,
+            headers={"X-Ingest-Key": settings.ingest_api_key},
+        )
+        if resp.status_code >= 400:
+            logger.error("Funding reversal ingest failed (%d): %s",
+                         resp.status_code, resp.text[:200])
+            return False
+        logger.info("Funding reversal signal emitted: %s", resp.json())
+        return True
+
+
+async def scan_once() -> None:
+    """Hourly tick. Idempotent (cooldown-gated, external_id-dedupe at ingest)."""
+    if not scanner_state.is_enabled(SCANNER_NAME):
+        logger.debug("Funding reversal scanner disabled — skipping")
+        return
+    if await scanner_state.in_cooldown("funding_reversal", ASSET, COOLDOWN_DAYS):
+        logger.debug("Funding reversal cooldown active (%dd)", COOLDOWN_DAYS)
+        scanner_state.record_run(SCANNER_NAME, "ok", "cooldown")
+        return
+
+    try:
+        funding, daily = await _fetch_inputs()
+        fired, debug = evaluate_funding_reversal(funding, daily)
+    except Exception as exc:
+        # Always include exception type — httpx errors often have empty .args
+        # which formatted as just "Funding reversal scan failed:" before.
+        logger.exception("Funding reversal scan failed: %s (%s)",
+                         type(exc).__name__, exc)
+        scanner_state.record_run(SCANNER_NAME, "error",
+                                 f"{type(exc).__name__}: {exc}"[:200])
+        return
+
+    if fired:
+        logger.info("Funding reversal FIRE — %s", debug)
+        emitted = await _emit_signal(debug)
+        scanner_state.record_run(SCANNER_NAME, "fired" if emitted else "error",
+                                 None if emitted else "ingest_failed")
+    else:
+        logger.info("Funding reversal no — %s", debug.get("reason"))
+        scanner_state.record_run(SCANNER_NAME, "ok", debug.get("reason"))
+
+
+# ─── Read API helper — current snapshot for the BTC page tab ────────────────
+
+
+async def get_current_snapshot() -> dict:
+    """Live read for the frontend BTC page funding tab. Returns the latest
+    funding rate, the 24h running average, cumulative 30d sum, and the verdict
+    of evaluate_funding_reversal() against current data. Cheap to call — only
+    network cost is two market_data fetches the scanner would do anyway."""
+    try:
+        funding, daily = await _fetch_inputs()
+    except Exception as exc:
+        logger.exception("funding snapshot fetch failed")
+        return {"ok": False, "error": f"{type(exc).__name__}: {exc}"}
+
+    if not funding:
+        return {"ok": False, "error": "no_funding_data"}
+
+    cadence_h = _detect_cadence_hours(funding)
+    rates = [f["rate"] for f in funding]
+    cum_30d_pct = sum(rates) * 100
+    span_days = (funding[-1]["time_ms"] - funding[0]["time_ms"]) / 86_400_000
+    latest = rates[-1] * 100
+    # Last-24h equivalent average per-cycle rate (in %)
+    recent_n = max(3, int(24 / cadence_h)) if cadence_h > 0 else 24
+    recent_n = min(recent_n, len(rates))
+    last_24h_avg = (sum(rates[-recent_n:]) / recent_n) * 100
+
+    fired, debug = evaluate_funding_reversal(funding, daily)
+
+    # 7-day funding history for the sparkline (truncate to keep payload small)
+    history = [
+        {"t": int(f["time_ms"]), "rate_pct": round(f["rate"] * 100, 5)}
+        for f in funding[-int(min(len(funding), 24 * 7 / max(cadence_h, 0.5))) :]
+    ]
+
+    return {
+        "ok": True,
+        "asset": ASSET,
+        "cadence_hours": round(cadence_h, 2),
+        "coverage_days": round(span_days, 1),
+        "latest_rate_pct": round(latest, 5),
+        "last_24h_avg_pct": round(last_24h_avg, 5),
+        "cum_30d_pct": round(cum_30d_pct, 3),
+        "extreme_threshold_pct": round(FUNDING_EXTREME_THRESHOLD * 100, 3),
+        "signal_fired": fired,
+        "debug": debug,
+        "history": history,
+    }

app/services/scanners/sma_reclaim.py

@@ -0,0 +1,146 @@
+"""
+200-day SMA Reclaim scanner.
+
+What it catches:
+  The moment a price that has been LIVING BELOW its 200-day moving average
+  for a sustained period climbs back ABOVE it on real volume. Historically
+  one of the most reliable "trend has changed" markers in any market —
+  hedge fund books, retail TA tools, momentum quants, everyone watches it.
+
+  Examples this would have caught:
+    BTC  2023-01 (~$22k, after the FTX flush)
+    BTC  2024-09 (after Q3 chop)
+    ETH  2023-01 (~$1500)
+    SOL  2023-02 (~$24, after FTX)
+
+Trigger logic:
+
+  PRE-CONDITION:   For the past DAYS_BELOW_REQUIRED days, daily close has been
+                   BELOW the rolling 200-day SMA. (proves we're reversing a
+                   sustained downtrend, not crossing a flat MA in chop)
+
+  TRIGGER:         Today's close > 200-day SMA, AND
+                   Today's volume > 1.3 × 30-day avg volume.
+
+  COOLDOWN:        30 days — false reclaims and shake-outs happen, don't
+                   re-fire on noise.
+
+Companion exit profile:
+    SL                = 6%
+    TRAILING_ACTIVATE = 12%
+    TRAILING_STOP     = 5%
+    MAX_HOLD          = 90 days
+
+The 90-day max-hold matches the holding period needed for a real trend
+change to play out (~3 months is the historical median for a confirmed
+200d-SMA-reclaim trend run).
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+import httpx
+
+from app.config import settings
+from app.services.market_data import REVERSAL_BASKET, for_asset, drop_in_progress_bar
+
+# LIBRARY MODULE — NOT a standalone scanner. evaluate_sma_reclaim() is
+# imported by btc_bottom_reversal.py as the price-reclaim entry gate. It
+# deliberately does NOT register with scanner_state: no UI toggle, no
+# schedule. (Old standalone scan_once/_emit_signal removed — see git log.)
+
+logger = logging.getLogger(__name__)
+
+
+# ─── Tunables ───────────────────────────────────────────────────────────────
+SMA_PERIOD             = 200
+DAYS_BELOW_REQUIRED    = 30        # how long the asset must have been under SMA
+VOLUME_LOOKBACK_DAYS   = 30
+VOLUME_MULT_MIN        = 1.3
+DAYS_TO_FETCH          = 260       # SMA(200) + 30d-below check + safety margin
+COOLDOWN_DAYS          = 30
+
+PAYLOAD_CONFIDENCE     = 85
+PAYLOAD_EXPECTED_MOVE  = 20.0      # historical median 90-day run after reclaim
+
+
+# Cooldown via scanner_state.in_cooldown — DB-backed, restart-safe.
+
+
+# ─── Signal logic ───────────────────────────────────────────────────────────
+
+
+def evaluate_sma_reclaim(daily_candles: list[dict]) -> tuple[bool, dict]:
+    """Pure function. Returns (is_signal, debug).
+
+    Expects `daily_candles` ordered chronologically (oldest first), each
+    having keys close, volume.
+    """
+    if len(daily_candles) < SMA_PERIOD + DAYS_BELOW_REQUIRED + 2:
+        return False, {"reason": "insufficient_data", "bars": len(daily_candles)}
+
+    closes  = [c["close"]  for c in daily_candles]
+    volumes = [c["volume"] for c in daily_candles]
+
+    # Rolling 200-day SMA at each bar from index SMA_PERIOD-1 onwards
+    smas: list[Optional[float]] = [None] * len(closes)
+    running_sum = sum(closes[:SMA_PERIOD])
+    smas[SMA_PERIOD - 1] = running_sum / SMA_PERIOD
+    for i in range(SMA_PERIOD, len(closes)):
+        running_sum += closes[i] - closes[i - SMA_PERIOD]
+        smas[i] = running_sum / SMA_PERIOD
+
+    today_close = closes[-1]
+    today_sma   = smas[-1]
+    if today_sma is None:
+        return False, {"reason": "sma_not_computable"}
+
+    # Bottom-reversal mode is LONG-only:
+    #   reclaim (long): was BELOW the SMA for N days, today closes ABOVE
+    # We explicitly do not trade symmetric short breakdowns here. Crypto
+    # top-calling is a different strategy with different risk.
+    reclaimed = today_close > today_sma
+    brokedown = today_close < today_sma
+    if brokedown:
+        return False, {
+            "reason": "shorts_disabled",
+            "close": round(today_close, 4),
+            "sma": round(today_sma, 4),
+        }
+    if not reclaimed:
+        return False, {"reason": "on_sma_no_cross",
+                        "close": round(today_close, 4), "sma": round(today_sma, 4)}
+
+    # Prior DAYS_BELOW_REQUIRED bars must ALL be on the OPPOSITE side of the
+    # SMA from today (a real regime flip, not chop around a flat MA).
+    streak = 0
+    for i in range(2, DAYS_BELOW_REQUIRED + 2):
+        sma_at = smas[-i]
+        if sma_at is None:
+            return False, {"reason": "sma_history_incomplete"}
+        prior_on_wrong_side = closes[-i] >= sma_at
+        if prior_on_wrong_side:
+            return False, {"reason": "regime_period_too_short", "broke_at_day": i}
+        streak += 1
+
+    # Volume confirmation: today >= VOLUME_MULT_MIN × 30-day avg
+    avg_vol_30d = sum(volumes[-(VOLUME_LOOKBACK_DAYS + 1):-1]) / VOLUME_LOOKBACK_DAYS
+    if avg_vol_30d <= 0:
+        return False, {"reason": "no_volume_baseline"}
+    vol_ratio = volumes[-1] / avg_vol_30d
+    if vol_ratio < VOLUME_MULT_MIN:
+        return False, {"reason": "weak_volume", "vol_ratio": round(vol_ratio, 2)}
+
+    return True, {
+        "direction":     "buy",
+        "close":         round(today_close, 4),
+        "sma_200":       round(today_sma, 4),
+        "gap_pct":       round(abs(today_close - today_sma) / today_sma * 100, 2),
+        "streak_days":   streak,
+        "vol_ratio":     round(vol_ratio, 2),
+    }
+
+

app/services/signal_categories.py

@@ -0,0 +1,430 @@
+"""
+Two trading systems, one execution layer.
+
+This module is the formal boundary between:
+
+  SYSTEM 1 — Trump (event-driven scalp)
+      source == "truth". Immediate market reaction to a post. Tight stops,
+      short hold, time-stop if no move. Uses the USER's configured exit
+      params (take_profit_pct / stop_loss_pct / trailing_*). Goes through
+      the Trump-tuned regime filter (thin-liquidity hours, recent-move,
+      vol-contraction).
+
+  SYSTEM 2 — Bitcoin Bottom (low-frequency bottom-reversal long)
+      source == "btc_bottom_reversal". Multi-week holds, wide trailing,
+      signal-invalidation exits.
+      Exit params come from THIS module (per category), NOT the user.
+      BYPASSES the Trump regime filter — those gates actively reject valid
+      reversal setups (a reclaim day IS a >5% move; reversals happen on
+      volatility EXPANSION not contraction).
+
+The two systems share only the low-level execution plumbing (HL connector,
+position monitor, reconciler, DB). Everything strategic is separated here.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional
+
+
+# Sources that belong to System 2. Everything else is either System 1 ("truth")
+# or unsupported for live trading.
+SYSTEM_2_SOURCES = {
+    "btc_bottom_reversal",
+}
+SYSTEM_1_SOURCES = {"truth"}
+SUPPORTED_TRADING_SOURCES = SYSTEM_1_SOURCES | SYSTEM_2_SOURCES
+
+
+def is_system_2(source: str) -> bool:
+    """True if this signal should run through the reversal pipeline."""
+    return (source or "").lower() in SYSTEM_2_SOURCES
+
+
+def is_supported_trading_source(source: str) -> bool:
+    """Fail closed for unknown ingest sources instead of treating them as Trump."""
+    return (source or "").lower() in SUPPORTED_TRADING_SOURCES
+
+
+def system2_display_name() -> str:
+    return "Bitcoin Bottom"
+
+
+def system2_min_confidence() -> int:
+    return 85
+
+
+# ── System 1 (Trump) — REPOSITIONED ─────────────────────────────────────────
+# Originally "high-frequency, many small bets". Repositioned per operator
+# decision to: LOW frequency, SELECTIVE, tight stop, ≥30-min holds.
+#
+#   - Don't trade every post. A Trump trade puts the system on cooldown so
+#     the next N hours of posts are ignored — only the FIRST qualifying
+#     high-conviction post in a quiet window gets acted on.
+#   - Raise the confidence floor: only the very top posts clear the bar.
+#   - Tight stop, ALWAYS active (capital protection — a post that triggers
+#     an immediate -1.5% means we read it wrong, get out).
+#   - But on the winning side, hold ≥30 min: suppress take-profit / trailing
+#     exits until the floor elapses so we don't scalp out of a developing
+#     move. Asymmetric by design: losers cut fast, winners get room.
+
+TRUMP_MIN_CONFIDENCE   = 88     # was effectively the user's 70-85 setting
+TRUMP_COOLDOWN_HOURS   = 12     # after a Trump trade, ignore further Trump
+                                # signals this long (the "don't trade every
+                                # opportunity" lever)
+TRUMP_MIN_HOLD_MINUTES = 30     # no TP / trailing exit before this; hard SL
+                                # stays active throughout
+TRUMP_STOP_LOSS_PCT    = 1.5    # tight, kept
+TRUMP_MAX_HOLD_HOURS   = 6      # backstop force-close
+
+
+@dataclass(frozen=True)
+class CategoryExit:
+    """Exit profile for a System-2 signal category.
+
+    These OVERRIDE the user's per-subscription exit params. Rationale: the
+    user picks risk for the Trump scalp; the reversal system's risk is a
+    property of the SIGNAL TYPE (an RSI capitulation reversal needs 60 days
+    to play out no matter what the user set their Trump stop-loss to).
+
+    Fields:
+      stop_loss_pct            : hard stop, % adverse in position direction
+      trailing_activate_at_pct : peak gain that arms the trailing stop
+      trailing_stop_pct        : retrace-from-peak that closes once armed
+      max_hold_hours           : backstop force-close
+      time_stop_hours          : if position is still ~flat after this many
+                                 hours, the thesis is slow/dead — close.
+                                 None = no time stop (pure trend capture).
+      invalidation             : symbolic fast-exit condition. Interpreted
+                                 by tp_sl_monitor. None = stop-loss only.
+                                 "below_entry"  → exit if price crosses back
+                                 through the entry (signal thesis dead).
+    """
+    stop_loss_pct:            float
+    trailing_activate_at_pct: Optional[float]
+    trailing_stop_pct:        Optional[float]
+    max_hold_hours:           int
+    time_stop_hours:          Optional[int] = None
+    invalidation:             Optional[str] = None
+
+    # Staged stop-loss ladder (System-2, optional). A list of
+    # (peak_gain_trigger_pct, new_stop_floor_pct) rungs. As the trade's PEAK
+    # gain crosses each trigger, the stop FLOOR ratchets up to the rung's
+    # value (signed gain% vs entry: negative = still a loss, 0 = breakeven,
+    # positive = locked-in profit). The position is NEVER closed for hitting
+    # a profit target — it only ever exits when price falls back to the
+    # current staged floor (or max-hold). This is a pure staged stop, not a
+    # take-profit and not a from-peak trailing stop. When set, it OVERRIDES
+    # take_profit_pct + trailing_* for that trade.
+    stop_ladder:              Optional[list] = None
+
+
+# ── Per-category exit profiles ──────────────────────────────────────────────
+# Tuned to each signal's natural time horizon. These are STARTING points —
+# refine against forward-test data, not intuition.
+_CATEGORY_EXITS: dict[str, CategoryExit] = {
+    # Weekly RSI capitulation reversal — slowest, biggest target.
+    "rsi_extreme_reversal": CategoryExit(
+        stop_loss_pct=8.0, trailing_activate_at_pct=15.0,
+        trailing_stop_pct=6.0, max_hold_hours=1440,        # 60 days
+    ),
+    # 200d SMA reclaim — trend change. Fast exit if it loses the SMA again.
+    "sma_reclaim": CategoryExit(
+        stop_loss_pct=6.0, trailing_activate_at_pct=12.0,
+        trailing_stop_pct=5.0, max_hold_hours=2160,        # 90 days
+        invalidation="below_entry",   # reclaim failed → thesis dead
+    ),
+    # Funding extreme unwind — faster, days not weeks.
+    "funding_extreme_reversal": CategoryExit(
+        stop_loss_pct=4.0, trailing_activate_at_pct=10.0,
+        trailing_stop_pct=5.0, max_hold_hours=720,         # 30 days
+        time_stop_hours=240,          # 10d flat → squeeze didn't materialise
+    ),
+    # BTC bottom-reversal long — 2-of-3 price confluence (AHR999 + 200WMA +
+    # Pi Cycle Bottom). NO take-profit, NO from-peak trailing. The ONLY exit
+    # is a STAGED stop-loss ("阶段止损") that ratchets up as the trade's peak
+    # gain crosses each rung, plus the 120-day max-hold backstop.
+    #
+    # The BASE catastrophic floor is NOT fixed here — it is derived per-trade
+    # from the chosen System-2 leverage (sys2_protective_stop_pct), so it
+    # always sits inside the exchange liquidation line. These rungs only
+    # ratchet that floor UP as peak gain grows (they never loosen it):
+    #   peak ≥  20%  → stop -12%
+    #   peak ≥  40%  → stop   0%  (breakeven — free trade)
+    #   peak ≥  70%  → stop +25% (lock profit)
+    #   peak ≥ 110%  → stop +55%
+    #   peak ≥ 160%  → stop +95%
+    #
+    # Rungs are deliberately far apart so normal post-bottom volatility does
+    # not knock it out, and it never sells just because a target was hit.
+    "btc_bottom_reversal_long": CategoryExit(
+        stop_loss_pct=35.0,        # fallback only; bot_engine overrides per-lev
+        trailing_activate_at_pct=None,
+        trailing_stop_pct=None,
+        max_hold_hours=12960,      # 18 months — a cycle bull runs 6–18mo;
+                                   # the ratchet/peak-trail is the real exit,
+                                   # the clock is just a far backstop.
+        invalidation=None,
+        stop_ladder=[
+            (20.0,  -12.0),
+            (40.0,    0.0),
+            (70.0,   25.0),
+            (110.0,  55.0),
+            (160.0,  95.0),
+        ],
+    ),
+    # VCP breakout — short-term continuation.
+    "vcp_breakout": CategoryExit(
+        stop_loss_pct=3.0, trailing_activate_at_pct=6.0,
+        trailing_stop_pct=2.5, max_hold_hours=168,         # 7 days
+        time_stop_hours=48,
+    ),
+}
+
+# Fallback for a System-2 signal whose category isn't explicitly mapped —
+# conservative medium profile so an un-mapped scanner doesn't trade huge.
+_SYSTEM_2_DEFAULT = CategoryExit(
+    stop_loss_pct=5.0, trailing_activate_at_pct=10.0,
+    trailing_stop_pct=4.0, max_hold_hours=336,             # 14 days
+    time_stop_hours=120,
+)
+
+
+def get_exit_profile(category: Optional[str]) -> CategoryExit:
+    """Resolve a System-2 category to its exit profile. Unknown → default."""
+    if not category:
+        return _SYSTEM_2_DEFAULT
+    return _CATEGORY_EXITS.get(category.lower(), _SYSTEM_2_DEFAULT)
+
+
+# ── System-2 dynamic leverage ───────────────────────────────────────────────
+# The user picks System-2 leverage freely. The protective stop is then
+# auto-scaled to stay INSIDE the exchange liquidation line, so the position
+# is de-risked by our own monitor and is never liquidated by the exchange.
+#
+#   liquidation distance (price move) ≈ 100 / leverage  (%)
+#   protective full-exit stop         = 85% of that (15% maint/fee/funding buffer)
+#   …but never wider than SYS2_MAX_STOP_PCT — the bottom thesis only needs to
+#     tolerate a ~30% wick; risking more buys nothing.
+#
+# Net effect:
+#   lev ≤ 2x → stop = 35%  (full bottom-wick tolerance, the original design)
+#   lev = 3x → stop ≈ 28%
+#   lev = 5x → stop ≈ 17%
+#   lev = 10x→ stop ≈ 8.5% (you WILL get shaken out by a normal wick — shown
+#                            to the user as the explicit cost of high leverage)
+SYS2_DEFAULT_LEVERAGE = 2
+SYS2_MIN_LEVERAGE     = 1
+SYS2_MAX_LEVERAGE     = 10
+SYS2_MAX_STOP_PCT     = 35.0
+SYS2_LIQ_BUFFER       = 0.85   # exit at 85% of the way to liquidation
+
+# ── System-2 risk MODE ──────────────────────────────────────────────────────
+# Two opt-in profiles, frozen onto the trade at signal time. STANDARD is the
+# tuned cycle-rider (low leverage, survive bull corrections). AGGRESSIVE is a
+# separately-funded high-risk/high-explosiveness sleeve: high leverage, heavier
+# + earlier pyramiding, wider peak-trail, lighter early de-risk. BOTH keep the
+# two safety invariants: (1) final de-risk rung is a full close INSIDE the
+# liquidation line — never exchange-liquidated; (2) post-pyramid breakeven
+# floor — a winner can't become a loser.
+SYS2_MODE_STANDARD   = "standard"
+SYS2_MODE_AGGRESSIVE = "aggressive"
+SYS2_MODES           = (SYS2_MODE_STANDARD, SYS2_MODE_AGGRESSIVE)
+# Default leverage when the user hasn't set an explicit sys2_leverage.
+SYS2_MODE_DEFAULT_LEV = {SYS2_MODE_STANDARD: 2, SYS2_MODE_AGGRESSIVE: 8}
+
+
+def sys2_normalize_mode(mode: Optional[str]) -> str:
+    m = (mode or "").strip().lower()
+    return m if m in SYS2_MODES else SYS2_MODE_STANDARD
+
+
+def sys2_effective_leverage(value: Optional[int],
+                            mode: Optional[str] = SYS2_MODE_STANDARD) -> int:
+    """Resolve + clamp System-2 leverage. None → the mode's default
+    (standard 2×, aggressive 8×). Always clamped to [1, 10]."""
+    m = sys2_normalize_mode(mode)
+    default = SYS2_MODE_DEFAULT_LEV[m]
+    if value is None:
+        return default
+    try:
+        v = int(value)
+    except (TypeError, ValueError):
+        return default
+    return max(SYS2_MIN_LEVERAGE, min(SYS2_MAX_LEVERAGE, v))
+
+
+def sys2_protective_stop_pct(leverage: int) -> float:
+    """Protective full-exit distance (positive %), auto-scaled to leverage so
+    it always triggers INSIDE the exchange liquidation line."""
+    lev = max(SYS2_MIN_LEVERAGE, min(SYS2_MAX_LEVERAGE, int(leverage)))
+    liq_distance_pct = 100.0 / lev
+    return round(min(SYS2_MAX_STOP_PCT, SYS2_LIQ_BUFFER * liq_distance_pct), 2)
+
+
+def sys2_approx_liquidation_pct(leverage: int) -> float:
+    """Rough exchange liquidation distance (positive %) for UX display."""
+    lev = max(SYS2_MIN_LEVERAGE, min(SYS2_MAX_LEVERAGE, int(leverage)))
+    return round(100.0 / lev, 2)
+
+
+# Staged de-risk ("分段式减仓"): instead of one full exit at the protective
+# stop, scale OUT in three steps as the trade moves against us. The final
+# step is exactly the Phase-1 protective level P (inside liquidation), so the
+# never-exchange-liquidated guarantee is preserved — we just bleed out in
+# thirds rather than all at once.
+#
+#   −0.60·P  → close 1/3 of the original notional
+#   −0.80·P  → close another 1/3
+#   −1.00·P  → close the remaining 1/3 (full exit; same safety as Phase 1)
+SYS2_DERISK_FRACTIONS = (0.60, 0.80, 1.00)
+
+
+# Pyramiding ("做对了往上加仓"): when the bottom call is RIGHT and the trend
+# confirms, scale INTO the winner to amplify the move. Mirror of the de-risk
+# ladder. Conservative sizing (user-selected): adds at most +0.6× the base
+# notional. Each rung requires BOTH a peak-gain trigger AND a structural
+# trend confirmation (price ≥ 200d SMA AND at a fresh local high), checked at
+# execution time. Pyramiding is DISABLED once any de-risk step has fired
+# (a trade that went underwater is not a clean uptrend to add into).
+# Extended for a cycle bull: deeper continuation rungs so a multi-x move is
+# actually scaled. Each add still needs structural confirmation (price ≥ 200d
+# SMA AND a fresh high) so the deep rungs only fire in a genuine sustained
+# uptrend, never on a chop fakeout. Total adds ≤ +0.75× base — still well
+# inside the 8× notional cap; amplification stays conservative.
+SYS2_ADDON_PEAK_TRIGGERS = (25.0, 50.0, 85.0, 140.0, 220.0)  # peak-gain % vs blended entry
+SYS2_ADDON_FRACTIONS     = (0.30, 0.20, 0.10, 0.10, 0.05)    # of ORIGINAL base notional
+
+# AGGRESSIVE sleeve: earlier + heavier adds (≤ +1.50× base), so a clean
+# multi-x run is meaningfully compounded. Still gated by the same structural
+# confirmation (≥200d SMA + fresh high), still inside the per-trade 8×
+# notional cap, still floored at breakeven once any add fills.
+SYS2_AGGR_ADDON_PEAK_TRIGGERS = (15.0, 35.0, 60.0, 100.0, 160.0)
+SYS2_AGGR_ADDON_FRACTIONS     = (0.50, 0.40, 0.30, 0.20, 0.10)
+# AGGRESSIVE de-risk: shed less early (¼/¼) so a normal correction doesn't
+# gut the runner; final rung still a FULL close at the protective level.
+SYS2_AGGR_DERISK_STEP_FRACS   = (0.25, 0.25, 0.50)
+SYS2_STD_DERISK_STEP_FRACS    = (1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0)
+
+# Peak-% trailing for the parabolic top. Below the start threshold the fixed
+# stop_ladder rungs govern; at/above it the floor also trails the PEAK PRICE
+# by at most SYS2_PEAK_TRAIL_DD (price drawdown, scale-invariant) so a +500%
+# move isn't capped at the +95% rung, while a normal ~25–30% bull correction
+# still doesn't knock it out. Floor = max(rung floor, peak-trail floor).
+SYS2_PEAK_TRAIL_START_PCT = 80.0    # activate once peak gain ≥ +80%
+SYS2_PEAK_TRAIL_DD        = 0.30    # give back at most 30% of peak PRICE
+# AGGRESSIVE: let it run longer before the trailing top kicks in, and give
+# back more, so a multi-x parabolic isn't cut early by a mid-run shakeout.
+SYS2_AGGR_PEAK_TRAIL_START_PCT = 60.0
+SYS2_AGGR_PEAK_TRAIL_DD        = 0.42
+
+
+def sys2_addon_ladder(mode: Optional[str] = SYS2_MODE_STANDARD) -> list:
+    """Pyramiding rungs: (peak_gain_trigger_pct, frac_of_base, is_last)."""
+    if sys2_normalize_mode(mode) == SYS2_MODE_AGGRESSIVE:
+        trigs, fracs = SYS2_AGGR_ADDON_PEAK_TRIGGERS, SYS2_AGGR_ADDON_FRACTIONS
+    else:
+        trigs, fracs = SYS2_ADDON_PEAK_TRIGGERS, SYS2_ADDON_FRACTIONS
+    n = len(trigs)
+    return [(trigs[i], fracs[i], i == n - 1) for i in range(n)]
+
+
+def sys2_peak_trail(mode: Optional[str] = SYS2_MODE_STANDARD) -> tuple:
+    """(activate_peak_gain_pct, price_drawdown_frac) for the parabolic-top
+    trailing floor. Scale-invariant: works the same at +120% or +900%."""
+    if sys2_normalize_mode(mode) == SYS2_MODE_AGGRESSIVE:
+        return (SYS2_AGGR_PEAK_TRAIL_START_PCT, SYS2_AGGR_PEAK_TRAIL_DD)
+    return (SYS2_PEAK_TRAIL_START_PCT, SYS2_PEAK_TRAIL_DD)
+
+
+def sys2_derisk_ladder(leverage: int,
+                       mode: Optional[str] = SYS2_MODE_STANDARD) -> list:
+    """Downside staged de-risk rungs for the given leverage.
+
+    Returns a list of (threshold_signed_pct, frac_of_ORIGINAL_to_close,
+    is_final), ordered by increasing adversity. threshold is NEGATIVE
+    (a loss in position direction). The executor converts frac-of-original
+    into frac-of-current using the trade's remaining_fraction.
+    """
+    p = sys2_protective_stop_pct(leverage)
+    steps = (SYS2_AGGR_DERISK_STEP_FRACS
+             if sys2_normalize_mode(mode) == SYS2_MODE_AGGRESSIVE
+             else SYS2_STD_DERISK_STEP_FRACS)
+    return [
+        (round(-SYS2_DERISK_FRACTIONS[0] * p, 4), steps[0], False),
+        (round(-SYS2_DERISK_FRACTIONS[1] * p, 4), steps[1], False),
+        (round(-SYS2_DERISK_FRACTIONS[2] * p, 4), steps[2], True),
+    ]
+
+
+def get_stop_ladder(category: Optional[str]) -> Optional[list]:
+    """Staged stop-loss ladder for a category, or None if it uses trailing.
+
+    Sorted ascending by trigger so the monitor can walk it cheaply. The
+    ladder is a CODE constant (not frozen per-trade): if it's retuned, open
+    positions adopt the new rungs on the next price tick / restart. That is
+    intentional — staged-stop levels are a property of the strategy, not of
+    an individual fill.
+    """
+    prof = get_exit_profile(category)
+    if not prof.stop_ladder:
+        return None
+    return sorted(prof.stop_ladder, key=lambda r: r[0])
+
+
+# ── System-2 position sizing ────────────────────────────────────────────────
+# CRITICAL: System 2 must NOT use regime_filter.calculate_size_multiplier.
+# That function asks "is volatility contracted? has price NOT moved?" — both
+# FALSE during a reversal (vol expands, price already moved), which would
+# SHRINK our rarest, highest-conviction trades. Sizing here is a function of
+# (category conviction × signal confidence), nothing else.
+
+# Base conviction per category. Rarer + cleaner setup → bigger base bet.
+_CATEGORY_SIZE_BASE: dict[str, float] = {
+    "rsi_extreme_reversal":     2.5,   # ~1-2×/yr/asset, deepest capitulation
+    "sma_reclaim":              2.0,   # clean trend-change marker
+    "funding_extreme_reversal": 1.4,   # more frequent, choppier unwinds
+    "btc_bottom_reversal_long": 2.3,   # 2-of-3 price confluence (AHR999/200WMA/Pi)
+    "vcp_breakout":             1.0,   # continuation, lowest conviction
+}
+_SYSTEM_2_SIZE_BASE_DEFAULT = 1.2
+SYSTEM_2_SIZE_CAP = 4.0
+
+
+# ── System-2 correlation / concentration cap ────────────────────────────────
+# Every asset in REVERSAL_BASKET is high-beta crypto. When the market bottoms,
+# RSI/SMA/funding reversals fire on BTC + ETH + SOL in the SAME week — these
+# are NOT independent bets, they're one "crypto reversed" thesis. With Fix #1
+# sizing each up to 4x, 3 correlated positions = ~10x effective exposure to a
+# single macro call. Treat the whole System-2 book as one correlated bucket
+# and cap it.
+#
+#   - SYS2_MAX_CONCURRENT: at most this many open System-2 positions at once.
+#     Beyond this you're not diversifying, just leveraging the same thesis.
+#   - SYS2_MAX_OPEN_NOTIONAL_MULT: total open System-2 notional must stay
+#     under this × the wallet's base position_size_usd × default-ish size.
+#     Acts as a $ ceiling independent of how many positions.
+SYS2_MAX_CONCURRENT          = 3
+SYS2_MAX_OPEN_NOTIONAL_MULT  = 8.0   # × base position_size_usd
+
+
+def system2_size_multiplier(category: Optional[str], confidence: int) -> float:
+    """Position multiplier for a System-2 signal.
+
+    base(category) × confidence_scale, capped at SYSTEM_2_SIZE_CAP.
+    confidence_scale: 70→1.0, 85→1.3, 95→1.5, 100→1.6 (linear, floored at 1.0).
+    A scanner that emits confidence 88 for an rsi_extreme_reversal therefore
+    sizes 2.5 × 1.36 ≈ 3.4×. Tune the base table against forward-test data.
+    """
+    base = _CATEGORY_SIZE_BASE.get((category or "").lower(), _SYSTEM_2_SIZE_BASE_DEFAULT)
+    conf_scale = 1.0 + max(0, confidence - 70) / 50.0
+    return round(min(base * conf_scale, SYSTEM_2_SIZE_CAP), 2)
+
+
+# ── BTC bottom-reversal ─────────────────────────────────────────────────────
+# The old MVRV-Z / STH-SOPR / drawdown state machine was REMOVED. It depended
+# on paid on-chain data and was over-engineered. The strategy is now a pure
+# 2-of-3 price confluence (AHR999 + 200-Week MA + Pi Cycle Bottom), implemented
+# in app/services/bottom_indicators.py and driven by the btc_bottom_reversal
+# scanner. There is no state machine and no on-chain dependency.

app/services/telegram.py

@@ -0,0 +1,280 @@
+"""
+Telegram push alerts — send + signal dispatcher.
+
+This module is fire-and-forget by design. `notify_signal()` returns immediately
+to the caller (signal ingestion path) and dispatches in a background task. A
+DB failure or Telegram API error MUST NOT block a signal from being saved.
+
+The bot token is loaded from `settings.telegram_bot_token`. If empty, every
+function in this module degrades into a no-op (and logs once at module load).
+
+  notify_signal(post)            ← called from /api/signals/ingest
+  send_test_message(wallet)      ← called from /api/telegram/test
+  send_message(chat_id, text)    ← low-level escape hatch
+
+Source → user-toggle mapping:
+    truth                      → alert_trump
+    btc_bottom_reversal        → alert_btc_bottom
+    funding_reversal           → alert_funding
+    kol_divergence (future)    → alert_kol_divergence
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from datetime import datetime, timezone
+from typing import Optional
+
+import httpx
+from sqlalchemy import select, update
+
+from app.config import settings
+from app.database import AsyncSessionLocal as async_session
+from app.models import Post, TelegramBinding
+
+logger = logging.getLogger(__name__)
+
+TG_API = "https://api.telegram.org/bot{token}/{method}"
+# Telegram caps a single message at 4096 chars; we render way below this.
+MAX_LEN = 3500
+
+
+# ── Source → preference column mapping ────────────────────────────────────
+
+
+def _pref_column_for_source(source: str) -> Optional[str]:
+    """Which user-toggle column gates this source. None → unknown source,
+    don't send."""
+    if source == "truth":
+        return "alert_trump"
+    if source == "btc_bottom_reversal":
+        return "alert_btc_bottom"
+    if source == "funding_reversal":
+        return "alert_funding"
+    if source == "kol_divergence":
+        return "alert_kol_divergence"
+    return None
+
+
+def _is_in_mute_window(now_hour: int, mute_from: Optional[int],
+                      mute_until: Optional[int]) -> bool:
+    """Both null → never mute. start<until → mute inside [start, until).
+    start>until → window wraps midnight (e.g. 23..7 → mute 23, 0–6)."""
+    if mute_from is None or mute_until is None:
+        return False
+    if mute_from == mute_until:
+        return False
+    if mute_from < mute_until:
+        return mute_from <= now_hour < mute_until
+    # wraps midnight
+    return now_hour >= mute_from or now_hour < mute_until
+
+
+# ── Formatting ────────────────────────────────────────────────────────────
+
+
+def _signal_emoji(post: Post) -> str:
+    if post.signal == "buy":
+        return "🟢"
+    if post.signal == "short":
+        return "🔴"
+    return "⚪"
+
+
+def _source_label(source: str) -> str:
+    return {
+        "truth":               "Trump · Truth Social",
+        "btc_bottom_reversal": "BTC · Macro Bottom",
+        "funding_reversal":    "BTC · Funding Reversal",
+        "kol_divergence":      "KOL · Talks vs Trades",
+    }.get(source, source)
+
+
+def format_post(post: Post) -> str:
+    """Render a Post into a single Telegram message (HTML parse mode).
+    Keep it scannable: heading, one-line verdict, the underlying text, link."""
+    emoji = _signal_emoji(post)
+    src = _source_label(post.source)
+    sig = (post.signal or "noise").upper()
+    asset = post.target_asset or "?"
+    conf = post.ai_confidence or 0
+
+    # Heading: emoji + asset + direction + confidence
+    head = f"{emoji} <b>{asset} · {sig}</b>  ·  conf <b>{conf}</b>"
+    sub = f"<i>{src}</i>"
+
+    body = (post.text or "").strip()
+    if len(body) > 600:
+        body = body[:600].rstrip() + "…"
+
+    # Move size hint if present (BTC bottom & funding emit expected_move_pct)
+    extra = ""
+    if post.expected_move_pct is not None:
+        extra = f"\n📐 expected move: <b>{post.expected_move_pct:+.1f}%</b>"
+    if post.invalidation_price is not None:
+        extra += f"\n🛑 invalidation @ <code>{post.invalidation_price:g}</code>"
+
+    # Deep-link back to the dashboard. Frontend URL comes from settings.
+    fe = (settings.frontend_url or "").rstrip("/")
+    link = ""
+    if fe:
+        # Use the section that matches the source
+        path = {
+            "truth":               "/en/trump",
+            "btc_bottom_reversal": "/en/btc",
+            "funding_reversal":    "/en/btc",
+            "kol_divergence":      "/en/kol",
+        }.get(post.source, "/en")
+        link = f'\n\n<a href="{fe}{path}">→ open in dashboard</a>'
+
+    msg = f"{head}\n{sub}\n\n{body}{extra}{link}"
+    return msg[:MAX_LEN]
+
+
+# ── Low-level HTTP ────────────────────────────────────────────────────────
+
+
+async def send_message(chat_id: int, text: str, *,
+                       parse_mode: str = "HTML",
+                       disable_preview: bool = True) -> bool:
+    """Single HTTP POST to Telegram Bot API. Returns True on 200, False on
+    any failure (caller decides whether to bump the failure counter)."""
+    token = settings.telegram_bot_token
+    if not token:
+        return False
+    url = TG_API.format(token=token, method="sendMessage")
+    try:
+        async with httpx.AsyncClient(timeout=10) as client:
+            r = await client.post(url, json={
+                "chat_id": chat_id, "text": text,
+                "parse_mode": parse_mode,
+                "disable_web_page_preview": disable_preview,
+            })
+        if r.status_code != 200:
+            logger.warning("Telegram sendMessage failed chat=%d status=%d body=%s",
+                           chat_id, r.status_code, r.text[:200])
+            return False
+        return True
+    except Exception as exc:
+        logger.warning("Telegram sendMessage exception chat=%d: %s", chat_id, exc)
+        return False
+
+
+# ── Dispatcher ────────────────────────────────────────────────────────────
+
+
+async def _dispatch(post_id: int) -> None:
+    """Fan-out a single Post to every eligible subscriber. Always runs in
+    its own DB session so signal ingestion's session is unaffected."""
+    if not settings.telegram_bot_token:
+        return
+
+    async with async_session() as db:
+        post = await db.get(Post, post_id)
+        if not post:
+            logger.warning("Telegram dispatch: post id=%d not found", post_id)
+            return
+
+        # Only fan out for actionable signals (not NOISE / null)
+        if not post.signal or post.signal not in ("buy", "short"):
+            return
+
+        pref_col = _pref_column_for_source(post.source)
+        if pref_col is None:
+            logger.debug("Telegram: unknown source %r — not fanning out", post.source)
+            return
+
+        # Build the query: alerts_enabled + the relevant per-source flag.
+        # min_confidence applies to every source — scanner-emitted signals
+        # carry their own confidence in the Post.ai_confidence column.
+        col = getattr(TelegramBinding, pref_col)
+        base_filters = [
+            TelegramBinding.alerts_enabled.is_(True),
+            col.is_(True),
+        ]
+        # Only apply confidence gate when the post has a real score (> 0).
+        # Scanner-generated signals (funding, BTC) always carry a score, but
+        # a Truth-Social post might be dispatched before Claude scores it (score=0).
+        # In that edge case we let it through so no alert is silently swallowed.
+        if post.ai_confidence and post.ai_confidence > 0:
+            base_filters.append(TelegramBinding.min_confidence <= post.ai_confidence)
+        q = select(TelegramBinding).where(*base_filters)
+        bindings = (await db.execute(q)).scalars().all()
+
+        if not bindings:
+            return
+
+        text = format_post(post)
+        now = datetime.now(timezone.utc)
+        hour = now.hour
+
+        sent = 0
+        for b in bindings:
+            if _is_in_mute_window(hour, b.mute_from_hour, b.mute_until_hour):
+                continue
+            ok = await send_message(b.chat_id, text)
+            # Update audit counters per binding. Single UPDATE per row keeps
+            # us out of trouble if one user blocks the bot.
+            if ok:
+                await db.execute(
+                    update(TelegramBinding)
+                    .where(TelegramBinding.id == b.id)
+                    .values(
+                        last_alert_at=now,
+                        total_alerts_sent=TelegramBinding.total_alerts_sent + 1,
+                    )
+                )
+                sent += 1
+            else:
+                await db.execute(
+                    update(TelegramBinding)
+                    .where(TelegramBinding.id == b.id)
+                    .values(
+                        total_alerts_failed=TelegramBinding.total_alerts_failed + 1,
+                    )
+                )
+        await db.commit()
+        logger.info("Telegram fan-out: post=%d source=%s sent=%d/%d",
+                    post_id, post.source, sent, len(bindings))
+
+
+def notify_signal(post: Post) -> None:
+    """Fire-and-forget. Schedules `_dispatch(post.id)` on the running loop
+    and returns immediately. Safe to call from any async context — falls
+    back to a no-op if telegram is disabled or no event loop is running.
+
+    We pass post.id rather than the post object because the caller's DB
+    session might close before our background task runs."""
+    if not settings.telegram_bot_token:
+        return
+    if not post or not post.id:
+        return
+    try:
+        asyncio.create_task(_dispatch(post.id))
+    except RuntimeError:
+        # No running loop — extremely unusual in our FastAPI context.
+        logger.warning("notify_signal: no running event loop, skipping post=%s", post.id)
+
+
+async def send_test_message(wallet: str) -> bool:
+    """Triggered from the Settings UI to verify the binding works end-to-end."""
+    if not settings.telegram_bot_token:
+        return False
+    async with async_session() as db:
+        b = (await db.execute(
+            select(TelegramBinding).where(TelegramBinding.wallet_address == wallet.lower())
+        )).scalar_one_or_none()
+        if not b:
+            return False
+        return await send_message(
+            b.chat_id,
+            "✅ <b>Trump Alpha connected.</b>\n\n"
+            "You'll get push alerts here when signals fire. "
+            "Use /trump /btc /funding /kol /conf /quiet in this chat to tune "
+            "which sources and thresholds apply to you.",
+        )
+
+
+if not settings.telegram_bot_token:
+    logger.info("Telegram bot token not set — push alerts disabled.")

app/services/telegram_bot.py

@@ -0,0 +1,564 @@
+"""
+Telegram bot worker — long-polls getUpdates and handles user commands.
+
+Commands supported:
+
+  /start CODE  → bind this chat_id to the wallet that owns CODE
+  /start       → friendly hello with instructions
+  /stop        → disable alerts (keeps the binding so re-enable is one tap)
+  /status      → show binding status and current preferences
+  /test        → send self a test message to verify formatting
+
+This runs as a single background asyncio task started from main.py lifespan.
+Only one instance should run at a time — Telegram's long-poll model assumes
+exactly one consumer per bot token. If you horizontally scale the API, switch
+to webhook mode (see set_webhook in the Bot API).
+
+The one-time binding codes live in a process-local dict with a 10-minute
+TTL. On API restart all pending codes evaporate (the user re-clicks Connect).
+This is intentional — codes never touch the DB so a compromised dump can't
+hijack future bindings.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import secrets
+import string
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+import httpx
+from sqlalchemy import select, update
+
+from app.config import settings
+from app.database import AsyncSessionLocal as async_session
+from app.models import TelegramBinding, Subscription
+from app.services.telegram import send_message, TG_API
+
+logger = logging.getLogger(__name__)
+
+# ── One-time binding codes ────────────────────────────────────────────────
+
+
+_CODE_TTL_SECONDS = 10 * 60
+_CODE_ALPHABET = string.ascii_uppercase + string.digits  # 36^6 ≈ 2.1B
+_CODE_LEN = 6
+# code → (wallet_lower, expires_at)
+_pending_codes: dict[str, tuple[str, datetime]] = {}
+
+
+def _purge_expired_codes() -> None:
+    now = datetime.now(timezone.utc)
+    expired = [c for c, (_, exp) in _pending_codes.items() if exp < now]
+    for c in expired:
+        _pending_codes.pop(c, None)
+
+
+def issue_binding_code(wallet: str) -> str:
+    """Generate a fresh 6-char code for a wallet. If the same wallet calls
+    twice within the TTL, the old code is invalidated — only the latest
+    code works (prevents stale shared links)."""
+    _purge_expired_codes()
+    wallet_l = wallet.lower()
+    # Invalidate any previous code for this wallet
+    for c, (w, _) in list(_pending_codes.items()):
+        if w == wallet_l:
+            _pending_codes.pop(c, None)
+    code = "".join(secrets.choice(_CODE_ALPHABET) for _ in range(_CODE_LEN))
+    exp = datetime.now(timezone.utc) + timedelta(seconds=_CODE_TTL_SECONDS)
+    _pending_codes[code] = (wallet_l, exp)
+    return code
+
+
+def _consume_code(code: str) -> Optional[str]:
+    """Resolve and remove a code. Returns the wallet, or None if invalid/expired."""
+    _purge_expired_codes()
+    rec = _pending_codes.pop(code.upper(), None)
+    if rec is None:
+        return None
+    return rec[0]
+
+
+# ── Command handlers ──────────────────────────────────────────────────────
+
+
+HELP_TEXT = (
+    "👋 <b>Trump Alpha bot</b>\n\n"
+    "Push alerts for high-conviction crypto signals — Trump posts, BTC bottom "
+    "reversals, funding extremes, KOL divergence.\n\n"
+    "<b>Just press /start</b> — you're subscribed. No wallet needed.\n\n"
+    "<b>Configure:</b>\n"
+    "  /trump on|off       Trump · Truth Social posts\n"
+    "  /btc on|off         BTC · macro bottom\n"
+    "  /funding on|off     BTC · funding reversal\n"
+    "  /kol on|off         KOL · talks vs trades\n"
+    "  /conf 0-100         min AI confidence (Trump only)\n"
+    "  /quiet 23 7         mute hours UTC (or 'off' to disable)\n\n"
+    "<b>Status &amp; control:</b>\n"
+    "  /status — show your preferences\n"
+    "  /stop — pause all alerts\n"
+    "  /test — send a sample alert\n\n"
+    "<b>Pro features</b> (auto-trade on Hyperliquid):\n"
+    "Open the dashboard → <i>Settings</i> → <i>Connect Telegram</i> to link "
+    "your wallet."
+)
+
+
+# Default preferences for a freshly-created walletless binding.
+_DEFAULT_PREFS = {
+    "alerts_enabled":       True,
+    "alert_trump":          True,
+    "alert_btc_bottom":     True,
+    "alert_funding":        True,
+    "alert_kol_divergence": False,
+    "min_confidence":       70,
+}
+
+
+async def _ensure_binding(chat_id: int, username: Optional[str]) -> TelegramBinding:
+    """Create or refresh a walletless binding for this chat. Returns the
+    binding (always with id set)."""
+    async with async_session() as db:
+        existing = (await db.execute(
+            select(TelegramBinding).where(TelegramBinding.chat_id == chat_id)
+        )).scalar_one_or_none()
+        if existing:
+            # Refresh username + re-enable alerts on subsequent /start
+            await db.execute(
+                update(TelegramBinding)
+                .where(TelegramBinding.id == existing.id)
+                .values(tg_username=username, alerts_enabled=True)
+            )
+            await db.commit()
+            return existing
+        b = TelegramBinding(
+            wallet_address=None,
+            chat_id=chat_id,
+            tg_username=username,
+            **_DEFAULT_PREFS,
+        )
+        db.add(b)
+        await db.commit()
+        await db.refresh(b)
+        return b
+
+
+async def _cmd_start(chat_id: int, username: Optional[str], arg: str) -> None:
+    """
+    /start              → free-tier walletless binding (anyone can subscribe)
+    /start CODE         → upgrade to wallet-linked binding (Pro features)
+    """
+    arg = arg.strip()
+
+    # No arg → free-tier subscribe. Always idempotent.
+    if not arg:
+        b = await _ensure_binding(chat_id, username)
+        if b.wallet_address:
+            await send_message(
+                chat_id,
+                "✅ Already linked — you're getting alerts.\n"
+                "Send /status to see your settings, /help for commands.",
+            )
+        else:
+            await send_message(
+                chat_id,
+                "🎉 <b>You're subscribed!</b>\n\n"
+                "You'll get alerts for: Trump posts, BTC bottom signals, "
+                "funding reversals. KOL divergence is off by default (noisier).\n\n"
+                "Type /help to see every command, or /test to preview an alert.",
+            )
+        return
+
+    # Arg present → wallet-binding flow (Pro)
+    wallet = _consume_code(arg)
+    if not wallet:
+        await send_message(
+            chat_id,
+            "❌ <b>Invalid or expired code.</b>\n\n"
+            "The 6-char code only comes from the dashboard's "
+            "<i>Settings → Connect Telegram</i> panel — and it expires after "
+            "10 minutes.\n\n"
+            "Don't need wallet features? Just send /start (no code) — alerts "
+            "are free for everyone.",
+        )
+        return
+
+    async with async_session() as db:
+        sub = (await db.execute(
+            select(Subscription).where(Subscription.wallet_address == wallet)
+        )).scalar_one_or_none()
+        if not sub or not sub.active:
+            await send_message(
+                chat_id,
+                "⚠️ <b>This wallet isn't subscribed yet.</b>\n\n"
+                "Open Settings on the dashboard, click <i>Start trading</i>, "
+                "then come back and re-connect.",
+            )
+            return
+
+        existing_by_wallet = (await db.execute(
+            select(TelegramBinding).where(TelegramBinding.wallet_address == wallet)
+        )).scalar_one_or_none()
+        existing_by_chat = (await db.execute(
+            select(TelegramBinding).where(TelegramBinding.chat_id == chat_id)
+        )).scalar_one_or_none()
+
+        # Edge: chat already bound to a DIFFERENT wallet — reject loudly.
+        if existing_by_chat and existing_by_chat.wallet_address and \
+           existing_by_chat.wallet_address != wallet:
+            await send_message(
+                chat_id,
+                "❌ This Telegram account is already bound to a different "
+                "wallet. Run /stop on that wallet first (or unbind via the "
+                "dashboard).",
+            )
+            return
+
+        if existing_by_wallet and existing_by_wallet.chat_id != chat_id:
+            # Wallet was previously bound to a different chat — move it here.
+            # Also clean up any walletless binding for this chat to avoid a
+            # duplicate row.
+            if existing_by_chat:
+                from sqlalchemy import delete as _delete
+                await db.execute(_delete(TelegramBinding).where(
+                    TelegramBinding.id == existing_by_chat.id))
+            await db.execute(
+                update(TelegramBinding)
+                .where(TelegramBinding.id == existing_by_wallet.id)
+                .values(chat_id=chat_id, tg_username=username,
+                        alerts_enabled=True,
+                        bound_at=datetime.now(timezone.utc))
+            )
+        elif existing_by_chat:
+            # Free user is upgrading to wallet-linked — just attach the wallet
+            # to their existing walletless binding, keep their preferences.
+            await db.execute(
+                update(TelegramBinding)
+                .where(TelegramBinding.id == existing_by_chat.id)
+                .values(wallet_address=wallet, tg_username=username,
+                        alerts_enabled=True,
+                        bound_at=datetime.now(timezone.utc))
+            )
+        else:
+            # Brand-new wallet + chat.
+            db.add(TelegramBinding(
+                wallet_address=wallet, chat_id=chat_id,
+                tg_username=username, **_DEFAULT_PREFS,
+            ))
+        await db.commit()
+
+    short = wallet[:6] + "…" + wallet[-4:]
+    await send_message(
+        chat_id,
+        f"✅ <b>Wallet linked: {short}</b>\n\n"
+        "Pro features unlocked. Your existing alert preferences are kept. "
+        "Manage everything from the dashboard's Settings page or via bot "
+        "commands. Send /status to see your current setup.",
+    )
+
+
+# ── Preference commands ──────────────────────────────────────────────────
+
+
+async def _set_pref(chat_id: int, field: str, value: bool | int) -> bool:
+    """Returns True if the binding existed and was updated."""
+    async with async_session() as db:
+        r = await db.execute(
+            update(TelegramBinding)
+            .where(TelegramBinding.chat_id == chat_id)
+            .values(**{field: value})
+        )
+        await db.commit()
+        return r.rowcount > 0
+
+
+def _parse_on_off(arg: str) -> Optional[bool]:
+    a = arg.strip().lower()
+    if a in ("on", "yes", "enable", "1", "true"):  return True
+    if a in ("off", "no", "disable", "0", "false"): return False
+    return None
+
+
+async def _cmd_toggle(chat_id: int, field: str, label: str, arg: str) -> None:
+    v = _parse_on_off(arg)
+    if v is None:
+        await send_message(chat_id,
+            f"Usage: <code>{label.lower().split()[0]} on</code> or "
+            f"<code>{label.lower().split()[0]} off</code>")
+        return
+    ok = await _set_pref(chat_id, field, v)
+    if not ok:
+        await send_message(chat_id, "No binding here. Send /start first.")
+        return
+    state = "🟢 ON" if v else "🔴 OFF"
+    await send_message(chat_id, f"{state} · {label}")
+
+
+async def _cmd_conf(chat_id: int, arg: str) -> None:
+    try:
+        n = int(arg.strip())
+        if not 0 <= n <= 100:
+            raise ValueError
+    except ValueError:
+        await send_message(chat_id,
+            "Usage: <code>/conf 70</code> (0–100). Only Trump posts above "
+            "this AI confidence will trigger alerts.")
+        return
+    ok = await _set_pref(chat_id, "min_confidence", n)
+    if not ok:
+        await send_message(chat_id, "No binding here. Send /start first.")
+        return
+    await send_message(chat_id, f"Min confidence set to <b>{n}</b>.")
+
+
+async def _cmd_quiet(chat_id: int, arg: str) -> None:
+    a = arg.strip().lower()
+    if a in ("off", "none", "disable", ""):
+        async with async_session() as db:
+            r = await db.execute(
+                update(TelegramBinding)
+                .where(TelegramBinding.chat_id == chat_id)
+                .values(mute_from_hour=None, mute_until_hour=None)
+            )
+            await db.commit()
+        if not r.rowcount:
+            await send_message(chat_id, "No binding here. Send /start first.")
+            return
+        await send_message(chat_id, "🔔 Quiet hours disabled.")
+        return
+
+    parts = arg.split()
+    if len(parts) != 2:
+        await send_message(chat_id,
+            "Usage: <code>/quiet 23 7</code> (UTC, e.g. mute 23:00–07:00) "
+            "or <code>/quiet off</code>.")
+        return
+    try:
+        a_h, b_h = int(parts[0]), int(parts[1])
+        if not (0 <= a_h <= 23 and 0 <= b_h <= 23):
+            raise ValueError
+    except ValueError:
+        await send_message(chat_id, "Hours must be integers 0–23.")
+        return
+
+    async with async_session() as db:
+        r = await db.execute(
+            update(TelegramBinding)
+            .where(TelegramBinding.chat_id == chat_id)
+            .values(mute_from_hour=a_h, mute_until_hour=b_h)
+        )
+        await db.commit()
+    if not r.rowcount:
+        await send_message(chat_id, "No binding here. Send /start first.")
+        return
+    await send_message(chat_id, f"🌙 Quiet hours: {a_h:02d}:00–{b_h:02d}:00 UTC.")
+
+
+async def _cmd_stop(chat_id: int) -> None:
+    async with async_session() as db:
+        r = await db.execute(
+            update(TelegramBinding)
+            .where(TelegramBinding.chat_id == chat_id)
+            .values(alerts_enabled=False)
+        )
+        await db.commit()
+    if r.rowcount:
+        await send_message(chat_id,
+            "🔕 Alerts paused. Send /start any time to re-enable.")
+    else:
+        await send_message(chat_id,
+            "You don't have an active binding here. Send /start to set one up.")
+
+
+async def _cmd_status(chat_id: int) -> None:
+    async with async_session() as db:
+        b = (await db.execute(
+            select(TelegramBinding).where(TelegramBinding.chat_id == chat_id)
+        )).scalar_one_or_none()
+    if not b:
+        await send_message(chat_id,
+            "Not subscribed yet. Send /start to begin (no wallet required).")
+        return
+
+    if b.wallet_address:
+        short = b.wallet_address[:6] + "…" + b.wallet_address[-4:]
+        tier_line = f"Tier: <b>Pro</b>  ·  wallet <code>{short}</code>"
+    else:
+        tier_line = "Tier: <b>Free</b>  ·  no wallet linked"
+
+    on = "🟢 ON" if b.alerts_enabled else "🔴 OFF"
+    srcs = []
+    if b.alert_trump:          srcs.append("Trump")
+    if b.alert_btc_bottom:     srcs.append("BTC bottom")
+    if b.alert_funding:        srcs.append("Funding")
+    if b.alert_kol_divergence: srcs.append("KOL")
+    src_line = ", ".join(srcs) or "(none — alerts will be silent)"
+    mute = ""
+    if b.mute_from_hour is not None and b.mute_until_hour is not None:
+        mute = f"\n🌙 Quiet hours: {b.mute_from_hour:02d}–{b.mute_until_hour:02d} UTC"
+    await send_message(
+        chat_id,
+        f"📡 <b>Status</b>\n\n"
+        f"{tier_line}\n"
+        f"Alerts: {on}\n"
+        f"Sources: {src_line}\n"
+        f"Min confidence: {b.min_confidence}{mute}\n\n"
+        f"Sent: {b.total_alerts_sent}  ·  Failed: {b.total_alerts_failed}\n\n"
+        f"Toggle anything with /trump /btc /funding /kol /conf /quiet — "
+        f"send /help for the full list.",
+    )
+
+
+async def _cmd_test(chat_id: int) -> None:
+    async with async_session() as db:
+        b = (await db.execute(
+            select(TelegramBinding).where(TelegramBinding.chat_id == chat_id)
+        )).scalar_one_or_none()
+    if not b:
+        await send_message(chat_id, "Send /start first to subscribe (no wallet needed).")
+        return
+    await send_message(
+        chat_id,
+        "🟢 <b>BTC · BUY</b>  ·  conf <b>88</b>\n"
+        "<i>Trump · Truth Social</i>\n\n"
+        "BITCOIN is the FUTURE of money. America will LEAD the world in "
+        "crypto. BIG things coming very soon!\n\n"
+        "(this is a test message — your actual alerts will look like this)",
+    )
+
+
+# ── Long-poll loop ────────────────────────────────────────────────────────
+
+
+async def _handle_message(msg: dict) -> None:
+    chat = msg.get("chat") or {}
+    chat_id = chat.get("id")
+    if not chat_id:
+        return
+    text = (msg.get("text") or "").strip()
+    if not text:
+        return
+    from_user = msg.get("from") or {}
+    username = from_user.get("username")
+
+    # Parse first word as command
+    parts = text.split(maxsplit=1)
+    cmd = parts[0].lower()
+    arg = parts[1] if len(parts) > 1 else ""
+
+    # Normalize /command@botname (group-chat syntax) → /command
+    if "@" in cmd:
+        cmd = cmd.split("@", 1)[0]
+
+    try:
+        if cmd == "/start":
+            await _cmd_start(chat_id, username, arg)
+        elif cmd in ("/stop", "/pause"):
+            await _cmd_stop(chat_id)
+        elif cmd in ("/status", "/me"):
+            await _cmd_status(chat_id)
+        elif cmd == "/test":
+            await _cmd_test(chat_id)
+        elif cmd in ("/help", "/?"):
+            await send_message(chat_id, HELP_TEXT)
+        # ── source toggles ───────────────────────────────────────────
+        elif cmd == "/trump":
+            await _cmd_toggle(chat_id, "alert_trump", "Trump alerts", arg)
+        elif cmd == "/btc":
+            await _cmd_toggle(chat_id, "alert_btc_bottom", "BTC bottom alerts", arg)
+        elif cmd == "/funding":
+            await _cmd_toggle(chat_id, "alert_funding", "Funding reversal alerts", arg)
+        elif cmd == "/kol":
+            await _cmd_toggle(chat_id, "alert_kol_divergence", "KOL divergence alerts", arg)
+        # ── numeric / range prefs ────────────────────────────────────
+        elif cmd == "/conf":
+            await _cmd_conf(chat_id, arg)
+        elif cmd == "/quiet":
+            await _cmd_quiet(chat_id, arg)
+        else:
+            if cmd.startswith("/"):
+                await send_message(chat_id, "Unknown command. Send /help for the list.")
+    except Exception:
+        logger.exception("Telegram handler failed for chat=%s text=%r", chat_id, text[:80])
+
+
+async def run_bot_loop() -> None:
+    """Long-poll forever. Idempotent on offsets — Telegram serves the same
+    update twice only if we don't acknowledge it. Sleep on errors to avoid
+    hot-looping if the network is down."""
+    token = settings.telegram_bot_token
+    if not token:
+        logger.info("Telegram bot loop not started — TELEGRAM_BOT_TOKEN empty.")
+        return
+
+    logger.info("Telegram bot loop starting (long-poll mode).")
+    offset: Optional[int] = None
+    backoff = 1.0
+
+    while True:
+        try:
+            url = TG_API.format(token=token, method="getUpdates")
+            params: dict = {"timeout": 25}
+            if offset is not None:
+                params["offset"] = offset
+            async with httpx.AsyncClient(timeout=35) as client:
+                r = await client.get(url, params=params)
+            if r.status_code != 200:
+                logger.warning("Telegram getUpdates HTTP %d: %s", r.status_code, r.text[:200])
+                await asyncio.sleep(backoff)
+                backoff = min(backoff * 2, 30)
+                continue
+            backoff = 1.0
+            data = r.json()
+            for upd in data.get("result", []):
+                offset = upd["update_id"] + 1
+                msg = upd.get("message") or upd.get("edited_message")
+                if msg:
+                    await _handle_message(msg)
+        except asyncio.CancelledError:
+            logger.info("Telegram bot loop cancelled.")
+            raise
+        except httpx.ReadTimeout:
+            # Normal — long-poll returned with no updates within timeout. Just retry.
+            # (Previously caught by the bare Exception below and logged with an
+            # empty message — "error: — sleeping" — which was opaque.)
+            continue
+        except (httpx.ConnectError, httpx.ConnectTimeout, httpx.RemoteProtocolError) as exc:
+            # Network-level error — expected on transient connectivity issues.
+            # Compact log to avoid spamming when the network is down.
+            logger.warning(
+                "Telegram bot loop network error: %s (%s) — sleeping %.1fs",
+                type(exc).__name__, exc, backoff,
+            )
+            await asyncio.sleep(backoff)
+            backoff = min(backoff * 2, 30)
+        except Exception:
+            # Anything else is unexpected — log the full traceback so we
+            # can actually diagnose it. Previously this silently swallowed
+            # exceptions with no message body.
+            logger.exception("Telegram bot loop unexpected error — sleeping %.1fs", backoff)
+            await asyncio.sleep(backoff)
+            backoff = min(backoff * 2, 30)
+
+
+# ── Admin helper ──────────────────────────────────────────────────────────
+
+
+async def unbind_wallet(wallet: str) -> int:
+    """Detach a wallet from its Telegram binding (called by /api/telegram/unbind).
+
+    Sets wallet_address = NULL rather than deleting the row. The chat stays
+    subscribed at the free tier — matches the UI's promise ("Your free
+    subscription stays active in the bot"). User wanting a full disconnect
+    sends /stop in the bot.
+    """
+    async with async_session() as db:
+        r = await db.execute(
+            update(TelegramBinding)
+            .where(TelegramBinding.wallet_address == wallet.lower())
+            .values(wallet_address=None)
+        )
+        await db.commit()
+    return r.rowcount

app/services/tp_sl_monitor.py

@@ -1,21 +1,39 @@
 """
-Take-profit / stop-loss monitor.
+Take-profit / stop-loss / trailing-stop monitor.
 
-Subscribes to the Binance price stream; for every open trade that has TP/SL set,
+Subscribes to the Binance price stream; for every open trade, closes the HL
-closes the HL position as soon as the live mark price crosses the threshold.
+position when the live mark price crosses one of:
+
+  1. Fixed stop-loss (always active).
+  2. Fixed take-profit (optional — set to None for trend capture).
+  3. Trailing stop (optional — once peak profit ≥ trailing_activate_at_pct,
+     close if price drops trailing_stop_pct from the peak).
+
+The trailing branch is the centrepiece of the convex-payoff redesign:
+fixed TP at +2% kills every potential runner before it starts. Instead we
+let unrealised PnL grow, watching the peak, and only exit on a real
+retracement.
 
 Lifecycle:
   - bot_engine.open_position calls register_trade(...) after each new trade
-  - price callback in binance.py calls on_price_tick() once per second
+  - binance.py's price callback calls on_price_tick() once per second
-  - when TP or SL hits, we enqueue close_and_finalize(...) and de-register
+  - when a rule fires, _fire_close(...) calls bot_engine.close_and_finalize
 """
 import asyncio
 import logging
-from dataclasses import dataclass
+import time
+from dataclasses import dataclass, field
 from typing import Dict, Optional
 
 logger = logging.getLogger(__name__)
 
+# Throttled peak persistence: write peak_gain_pct to the DB at most once per
+# PEAK_FLUSH_SECS per trade, and only after it advances ≥ PEAK_FLUSH_DELTA pp.
+# Peak is monotonic so writes are rare after the initial run-up; this is
+# enough to keep the post-restart regime correct without per-tick I/O.
+PEAK_FLUSH_DELTA = 1.0
+PEAK_FLUSH_SECS  = 30.0
+
 
 @dataclass
 class WatchedTrade:
@@ -26,14 +44,83 @@ class WatchedTrade:
     asset: str
     side: str            # "long" | "short"
     entry_price: float
-    take_profit_pct: Optional[float]
+    take_profit_pct:          Optional[float]   # legacy fixed TP (may be None)
-    stop_loss_pct: Optional[float]
+    stop_loss_pct:            Optional[float]   # always required in practice
+    trailing_stop_pct:        Optional[float]   # trail distance, e.g. 2.5
+    trailing_activate_at_pct: Optional[float]   # activation threshold, e.g. 5.0
 
+    # System-2 only. "below_entry": exit immediately if price crosses back
+    # through entry BEFORE trailing arms — the reversal thesis (e.g. SMA
+    # reclaim) is dead, no reason to wait for the wide hard stop. None for
+    # System-1 / unset.
+    invalidation: Optional[str] = None
+    invalidation_price: Optional[float] = None
+
+    # Staged stop-loss ladder (System-2, optional). List of
+    # (peak_gain_trigger_pct, stop_floor_pct) sorted ascending. When set,
+    # this trade has NO take-profit and NO from-peak trailing: the only
+    # market exit is "price fell back to the highest staged floor that the
+    # peak gain has unlocked". See signal_categories.get_stop_ladder.
+    stop_ladder: Optional[list] = None
+
+    # Staged DOWNSIDE de-risk ladder (System-2, optional). List of
+    # (threshold_signed_pct_negative, frac_of_original, is_final) ordered by
+    # increasing adversity. While the trade is underwater (peak below the
+    # first stop_ladder trigger) it is scaled OUT in partial reduces at each
+    # rung; the final rung is a full close at the protective level (inside
+    # liquidation). See signal_categories.sys2_derisk_ladder.
+    derisk_ladder: Optional[list] = None
+    derisk_done: int = 0
+    derisk_in_flight: bool = field(default=False)
+
+    # Pyramiding (做对了往上加仓): add to a confirmed winner. List of
+    # (peak_gain_trigger_pct, frac_of_base, is_last). Only active in the
+    # profit regime AND while derisk_done==0. Each add blends entry up; the
+    # monitor then floors the stop at breakeven (eff_stop ≥ 0) so a pyramided
+    # winner can never become a loser.
+    addon_ladder: Optional[list] = None
+    addon_done: int = 0
+    addon_in_flight: bool = field(default=False)
+    # Per-trade "Grow" switch. Pyramiding only runs when True. De-risk +
+    # ratchet stop are unaffected (safety floor is always on). Toggled live
+    # by the user via POST /positions/{id}/grow.
+    grow_mode: bool = field(default=False)
+
+    # Parabolic-top trailing: (activate_peak_gain_pct, price_drawdown_frac).
+    # In the profit regime, once peak ≥ activate, the floor also trails the
+    # peak PRICE by at most drawdown_frac (scale-invariant). None = use only
+    # the fixed stop_ladder rungs. See signal_categories.sys2_peak_trail.
+    peak_trail: Optional[tuple] = None
+
+    # Throttled persistence of peak_gain_pct (monotonic). peak_persisted is
+    # the last value written to the DB; peak_persist_ts the last write time.
+    peak_persisted: float = field(default=0.0)
+    peak_persist_ts: float = field(default=0.0)
+
+    # System-1 (Trump) min-hold floor. Epoch seconds; before this time,
+    # take-profit and trailing exits are SUPPRESSED (don't scalp out of a
+    # developing move). Hard stop-loss + invalidation always fire — capital
+    # protection isn't deferrable. None = no floor (System 2 / legacy).
+    min_hold_until_ts: Optional[float] = None
+
+    # Runtime state — peak unrealised gain seen so far, in %. Used by the
+    # trailing branch. Long: peak = max(over time) of pct_gain. Short: same,
+    # but pct_gain is already signed correctly by the caller.
+    peak_gain_pct: float = field(default=0.0)
+    trailing_armed: bool = field(default=False)
+
+
+# Buffer below entry before "below_entry" invalidation fires. Entry price is
+# only a PROXY for the true thesis-invalidation level (e.g. the 200d SMA for
+# an sma_reclaim). 0.75% is roughly one normal noise band on a major coin —
+# big enough to survive the fill-tick wobble, small enough to still cut a
+# failed reclaim well before the wide hard stop.
+INVALIDATION_BUFFER_PCT = 0.75
 
 # trade_id -> WatchedTrade
 _watched: Dict[int, WatchedTrade] = {}
 
-# Strong references to fire-close tasks to prevent GC before completion
+# Strong refs to fire-close tasks (prevent GC before completion)
 _background_tasks: set = set()
 
 
@@ -45,18 +132,58 @@ def register_trade(
     asset: str,
     side: str,
     entry_price: float,
-    take_profit_pct: Optional[float],
+    take_profit_pct:          Optional[float],
-    stop_loss_pct: Optional[float],
+    stop_loss_pct:            Optional[float],
+    trailing_stop_pct:        Optional[float] = None,
+    trailing_activate_at_pct: Optional[float] = None,
+    invalidation:             Optional[str] = None,
+    invalidation_price:       Optional[float] = None,
+    min_hold_until_ts:        Optional[float] = None,
+    stop_ladder:              Optional[list] = None,
+    derisk_ladder:            Optional[list] = None,
+    derisk_done:              int = 0,
+    addon_ladder:             Optional[list] = None,
+    addon_done:               int = 0,
+    initial_peak:             float = 0.0,
+    peak_trail:               Optional[tuple] = None,
+    grow_mode:                bool = False,
 ) -> None:
-    if take_profit_pct is None and stop_loss_pct is None:
+    # Nothing to monitor → skip. We require AT LEAST a stop-loss for any
-        return  # nothing to watch
+    # registered trade; protocol still works without TP if trailing or a
+    # staged-stop / de-risk ladder is set.
+    if (stop_loss_pct is None
+        and take_profit_pct is None
+        and trailing_stop_pct is None
+        and not stop_ladder
+        and not derisk_ladder):
+        return
+
     _watched[trade_id] = WatchedTrade(
         trade_id=trade_id, wallet=wallet, api_key=api_key, leverage=leverage,
         asset=asset, side=side, entry_price=entry_price,
-        take_profit_pct=take_profit_pct, stop_loss_pct=stop_loss_pct,
+        take_profit_pct=take_profit_pct,
+        stop_loss_pct=stop_loss_pct,
+        trailing_stop_pct=trailing_stop_pct,
+        trailing_activate_at_pct=trailing_activate_at_pct,
+        invalidation=invalidation,
+        invalidation_price=invalidation_price,
+        min_hold_until_ts=min_hold_until_ts,
+        stop_ladder=stop_ladder,
+        derisk_ladder=derisk_ladder,
+        derisk_done=derisk_done or 0,
+        addon_ladder=addon_ladder,
+        addon_done=addon_done or 0,
+        peak_gain_pct=max(0.0, float(initial_peak or 0.0)),
+        peak_persisted=max(0.0, float(initial_peak or 0.0)),
+        peak_trail=peak_trail,
+        grow_mode=bool(grow_mode),
+    )
+    logger.info(
+        "Watching trade %d (%s %s @ %.4f, sl=%s, tp=%s, trail=%s/@%s)",
+        trade_id, side, asset, entry_price,
+        stop_loss_pct, take_profit_pct,
+        trailing_stop_pct, trailing_activate_at_pct,
     )
-    logger.info("TP/SL watching trade %d (%s %s @ %.2f, tp=%s, sl=%s)",
-                trade_id, side, asset, entry_price, take_profit_pct, stop_loss_pct)
 
 
 def unregister(trade_id: int) -> None:
@@ -64,21 +191,163 @@ def unregister(trade_id: int) -> None:
 
 
 def on_price_tick(asset: str, price: float) -> None:
-    """Called from binance.py on every price update. Fires close for any trade that hit TP/SL."""
+    """Called from binance.py on every price update.
+
+    Iterates watched trades on this asset, evaluates each rule, and triggers
+    a close on the first match. Rule evaluation order: stop-loss → trailing
+    → fixed TP. (Stop-loss first so a sudden gap kills the position before
+    we celebrate hitting trailing.)
+    """
     if not _watched:
         return
+
     triggered = []
+    derisk_actions = []          # (wt, step_idx, frac_of_original)
+    addon_actions = []           # (wt, step_idx, frac_of_base)
     for tid, wt in list(_watched.items()):
         if wt.asset != asset:
             continue
-        pct = (price - wt.entry_price) / wt.entry_price
+        # Convert raw price → signed gain in position's direction, in %.
-        signed_pct = pct if wt.side == 'long' else -pct   # gain in position's direction
+        raw_pct = (price - wt.entry_price) / wt.entry_price
-        pct_x100 = signed_pct * 100
+        signed_pct = (raw_pct if wt.side == "long" else -raw_pct) * 100
 
-        if wt.take_profit_pct is not None and pct_x100 >= wt.take_profit_pct:
+        # Update peak BEFORE evaluating trailing — a tick that pushes the peak
-            triggered.append((wt, "take_profit"))
+        # AND retraces in the same step should still arm trailing at the new peak.
-        elif wt.stop_loss_pct is not None and pct_x100 <= -wt.stop_loss_pct:
+        if signed_pct > wt.peak_gain_pct:
+            wt.peak_gain_pct = signed_pct
+            # Throttled monotonic persistence so a restart keeps the regime.
+            now_s = time.time()
+            if (wt.peak_gain_pct - wt.peak_persisted >= PEAK_FLUSH_DELTA
+                    and now_s - wt.peak_persist_ts >= PEAK_FLUSH_SECS):
+                wt.peak_persisted = wt.peak_gain_pct
+                wt.peak_persist_ts = now_s
+                _pt = asyncio.create_task(
+                    _persist_peak(wt.trade_id, wt.peak_gain_pct))
+                _background_tasks.add(_pt)
+                _pt.add_done_callback(_background_tasks.discard)
+
+        # 0. System-2 self-contained exit model: NO take-profit, NO from-peak
+        #    trailing. Two regimes, fully replacing branches 1/1b/2/3:
+        #      • Underwater  → STAGED DE-RISK ("分段式减仓"): partial reduces
+        #        at each downside rung, full close at the final (protective,
+        #        inside-liquidation) rung.
+        #      • In profit   → STAGED STOP: floor ratchets up as peak gain
+        #        crosses each upside rung; full close when price falls back.
+        if wt.stop_ladder or wt.derisk_ladder:
+            first_up = (min(t for t, _ in wt.stop_ladder)
+                        if wt.stop_ladder else None)
+            in_profit_regime = (first_up is not None
+                                and wt.peak_gain_pct >= first_up)
+
+            if wt.derisk_ladder and not in_profit_regime:
+                ladder = wt.derisk_ladder
+                # Gap protection: blown straight through to the final
+                # protective level → full close NOW regardless of step
+                # bookkeeping (still inside the exchange liquidation line).
+                if signed_pct <= ladder[-1][0]:
+                    triggered.append((wt, "staged_stop"))
+                    continue
+                if (not wt.derisk_in_flight
+                        and 0 <= wt.derisk_done < len(ladder)):
+                    thr, frac, is_final = ladder[wt.derisk_done]
+                    if signed_pct <= thr:
+                        if is_final:
+                            triggered.append((wt, "staged_stop"))
+                            continue
+                        # Partial reduce — spawn async, guard re-entry until
+                        # it completes (done-callback clears the flag).
+                        wt.derisk_in_flight = True
+                        derisk_actions.append((wt, wt.derisk_done, frac))
+                continue   # underwater: no profit-ratchet / TP / trailing
+
+            # Profit regime — ratchet floor (base + unlocked upside rungs).
+            eff_stop = -wt.stop_loss_pct if wt.stop_loss_pct is not None else float("-inf")
+            if wt.stop_ladder:
+                for trig, floor in wt.stop_ladder:       # sorted ascending
+                    if wt.peak_gain_pct >= trig and floor > eff_stop:
+                        eff_stop = floor
+            # Parabolic-top trailing: once deep in profit, also trail the PEAK
+            # PRICE by ≤ drawdown_frac (scale-invariant — self-adjusts to a
+            # +120% or +900% move so the fixed top rung doesn't cap it, while
+            # a normal ~25–30% bull pullback still doesn't trip it).
+            if wt.peak_trail is not None:
+                start_pp, dd = wt.peak_trail
+                if wt.peak_gain_pct >= start_pp:
+                    trail_floor = ((1.0 + wt.peak_gain_pct / 100.0)
+                                   * (1.0 - dd) - 1.0) * 100.0
+                    if trail_floor > eff_stop:
+                        eff_stop = trail_floor
+            # Pyramided → never let a winner become a loser: blended position
+            # is floored at breakeven once any add-on has filled.
+            if wt.addon_done > 0 and eff_stop < 0.0:
+                eff_stop = 0.0
+            if signed_pct <= eff_stop:
+                triggered.append((wt, "staged_stop"))
+                continue
+
+            # Pyramiding — add to a confirmed winner. Gated by the per-trade
+            # Grow switch (off by default — user opts a position in). Evaluated
+            # ONLY after the stop check above passed (never add on a tick that's
+            # also exiting). Peak-gain triggered; only while no de-risk has
+            # fired. Structural confirmation is re-checked in the executor.
+            if (wt.grow_mode and wt.addon_ladder and wt.derisk_done == 0
+                    and not wt.addon_in_flight
+                    and 0 <= wt.addon_done < len(wt.addon_ladder)):
+                a_trig, a_frac, _a_last = wt.addon_ladder[wt.addon_done]
+                if wt.peak_gain_pct >= a_trig:
+                    wt.addon_in_flight = True
+                    addon_actions.append((wt, wt.addon_done, a_frac))
+            continue
+
+        # 1. Stop loss — first priority.
+        if wt.stop_loss_pct is not None and signed_pct <= -wt.stop_loss_pct:
             triggered.append((wt, "stop_loss"))
+            continue
+
+        # 1b. Signal invalidation (System-2 only). Prefer the real thesis
+        #     level when available (e.g. 200d SMA reclaim price). Fall back
+        #     to entry-buffer proxy for legacy rows.
+        if wt.invalidation == "below_entry" and not wt.trailing_armed:
+            if wt.invalidation_price is not None:
+                invalidation_hit = (
+                    (wt.side == "long" and price <= wt.invalidation_price) or
+                    (wt.side == "short" and price >= wt.invalidation_price)
+                )
+                if invalidation_hit:
+                    triggered.append((wt, "invalidation"))
+                    continue
+            elif signed_pct <= -INVALIDATION_BUFFER_PCT:
+                triggered.append((wt, "invalidation"))
+                continue
+
+        # Min-hold gate (System-1 Trump). Before the floor elapses we let a
+        # winner develop — suppress TP + trailing. SL / invalidation above
+        # already ran, so downside is still protected; we only defer the
+        # PROFIT-side exits here.
+        if wt.min_hold_until_ts is not None:
+            import time as _t
+            if _t.time() < wt.min_hold_until_ts:
+                continue   # still in min-hold; skip TP/trailing this tick
+
+        # 2. Trailing stop — only once armed.
+        if (wt.trailing_stop_pct is not None
+            and wt.trailing_activate_at_pct is not None):
+            if not wt.trailing_armed and wt.peak_gain_pct >= wt.trailing_activate_at_pct:
+                wt.trailing_armed = True
+                logger.info(
+                    "Trade %d: trailing armed at peak %.2f%% (asset=%s)",
+                    wt.trade_id, wt.peak_gain_pct, asset,
+                )
+            if wt.trailing_armed:
+                drawdown_from_peak = wt.peak_gain_pct - signed_pct
+                if drawdown_from_peak >= wt.trailing_stop_pct:
+                    triggered.append((wt, "trailing_stop"))
+                    continue
+
+        # 3. Fixed TP — legacy / fallback when no trailing config.
+        if wt.take_profit_pct is not None and signed_pct >= wt.take_profit_pct:
+            triggered.append((wt, "take_profit"))
+            continue
 
     for wt, reason in triggered:
         unregister(wt.trade_id)
@@ -86,10 +355,115 @@ def on_price_tick(asset: str, price: float) -> None:
         _background_tasks.add(task)
         task.add_done_callback(_background_tasks.discard)
 
+    # Partial de-risk steps — trade stays registered (it's not closed).
+    for wt, step_idx, frac in derisk_actions:
+        task = asyncio.create_task(_fire_partial(wt, step_idx, frac))
+        _background_tasks.add(task)
+        task.add_done_callback(_background_tasks.discard)
+
+    # Pyramid add-ons — trade stays registered (size grows, entry blends).
+    for wt, step_idx, frac in addon_actions:
+        task = asyncio.create_task(_fire_pyramid(wt, step_idx, frac))
+        _background_tasks.add(task)
+        task.add_done_callback(_background_tasks.discard)
+
+
+async def _fire_partial(wt: WatchedTrade, step_idx: int, frac: float) -> None:
+    """Execute one staged de-risk partial reduce. The trade stays open and
+    registered; on success advance derisk_done, always clear the in-flight
+    guard so the next rung can fire on a later tick."""
+    from app.services.bot_engine import partial_derisk
+    ok = False
+    try:
+        ok = await partial_derisk(
+            trade_id=wt.trade_id,
+            api_key=wt.api_key,
+            asset=wt.asset,
+            wallet=wt.wallet,
+            step_idx=step_idx,
+            frac_of_original=frac,
+            reason="derisk",
+        )
+    except Exception as exc:
+        logger.error("De-risk partial failed for trade %d step %d: %s",
+                     wt.trade_id, step_idx, exc)
+    finally:
+        # Re-fetch: the trade may have been closed/unregistered meanwhile.
+        live = _watched.get(wt.trade_id)
+        if live is not None:
+            if ok and live.derisk_done == step_idx:
+                live.derisk_done = step_idx + 1
+            live.derisk_in_flight = False
+
+
+async def _fire_pyramid(wt: WatchedTrade, step_idx: int, frac: float) -> None:
+    """Execute one pyramid add-on. On success re-base the in-memory entry to
+    the blended average and reset peak to the post-add gain so the ratchet /
+    regime use the NEW entry (and we stay in the profit regime). Always clear
+    the in-flight guard."""
+    from app.services.bot_engine import pyramid_add
+    ok, new_entry, ref_price = False, None, None
+    try:
+        ok, new_entry, ref_price = await pyramid_add(
+            trade_id=wt.trade_id,
+            api_key=wt.api_key,
+            asset=wt.asset,
+            wallet=wt.wallet,
+            step_idx=step_idx,
+            frac_of_base=frac,
+            reason="pyramid",
+        )
+    except Exception as exc:
+        logger.error("Pyramid add failed for trade %d step %d: %s",
+                     wt.trade_id, step_idx, exc)
+    finally:
+        live = _watched.get(wt.trade_id)
+        if live is not None:
+            if ok and live.addon_done == step_idx:
+                # Step is RESOLVED (added, notional-capped, or already-done).
+                # Advance regardless of whether an add actually filled, else
+                # the monitor re-schedules this step every tick forever.
+                if new_entry:
+                    live.entry_price = new_entry
+                    if ref_price:
+                        raw = (ref_price - new_entry) / new_entry
+                        g = (raw if live.side == "long" else -raw) * 100.0
+                    else:
+                        g = 0.0
+                    # Stay in the profit regime; ratchet re-accumulates here.
+                    first_up = (min(t for t, _ in live.stop_ladder)
+                                if live.stop_ladder else 0.0)
+                    live.peak_gain_pct = max(g, first_up)
+                live.addon_done = step_idx + 1
+            live.addon_in_flight = False
+
+
+async def _persist_peak(trade_id: int, peak: float) -> None:
+    """Monotonic best-effort write of peak_gain_pct. Race-safe: only raises
+    the stored value (WHERE peak_gain_pct < :peak), never lowers it."""
+    try:
+        from sqlalchemy import update
+        from app.database import AsyncSessionLocal
+        from app.models import BotTrade
+        async with AsyncSessionLocal() as db:
+            await db.execute(
+                update(BotTrade)
+                .where(BotTrade.id == trade_id)
+                .where(BotTrade.peak_gain_pct < peak)
+                .values(peak_gain_pct=peak)
+            )
+            await db.commit()
+    except Exception as exc:   # never let persistence break the monitor
+        logger.debug("peak persist failed trade %d: %s", trade_id, exc)
+
 
 async def _fire_close(wt: WatchedTrade, reason: str) -> None:
     from app.services.bot_engine import close_and_finalize
     try:
+        logger.info(
+            "Closing trade %d on %s (peak=%.2f%%, reason=%s)",
+            wt.trade_id, wt.asset, wt.peak_gain_pct, reason,
+        )
         await close_and_finalize(
             trade_id=wt.trade_id,
             api_key=wt.api_key,

app/ws/manager.py

@@ -1,3 +1,22 @@
+"""
+WebSocket connection manager.
+
+One global broadcaster that fans out every message to every connected client.
+Critical that broadcast NEVER blocks the caller — Binance kline ticks arrive
+every ~500 ms and the broadcast is inline with the WS read loop. A single
+slow / half-closed client could otherwise stall the kline pipeline, miss the
+keepalive ping, and tear the upstream Binance socket down (this was the root
+cause of the "no close frame received or sent" reconnect storms in prod logs).
+
+Two defences here:
+  * Per-client send wrapped in `asyncio.wait_for(..., timeout=PER_CLIENT_SEND_TIMEOUT)`.
+  * All clients dispatched in parallel via `asyncio.gather()` so one slow
+    client can't delay anyone else.
+"""
+
+from __future__ import annotations
+
+import asyncio
 import json
 import logging
 from typing import List
@@ -6,6 +25,10 @@ from fastapi import WebSocket
 
 logger = logging.getLogger(__name__)
 
+# Per-client send timeout. 2 s is plenty for a healthy TCP send of our tiny
+# JSON payloads; anything slower means a half-closed or zombie client.
+PER_CLIENT_SEND_TIMEOUT = 2.0
+
 
 class ConnectionManager:
     def __init__(self):
@@ -25,15 +48,28 @@ class ConnectionManager:
         if not self.active_connections:
             return
         payload = json.dumps(message)
-        dead: List[WebSocket] = []
+        # Snapshot the list — disconnect() mutates active_connections and we
-        for connection in self.active_connections:
+        # don't want "list changed during iteration" when we prune.
+        connections = list(self.active_connections)
+
+        async def _send_one(ws: WebSocket):
             try:
-                await connection.send_text(payload)
+                await asyncio.wait_for(ws.send_text(payload),
+                                       timeout=PER_CLIENT_SEND_TIMEOUT)
+                return None
+            except asyncio.TimeoutError:
+                logger.warning("WebSocket send timed out (>%.1fs) — pruning client",
+                               PER_CLIENT_SEND_TIMEOUT)
+                return ws
             except Exception as exc:
-                logger.warning("Failed to send to WebSocket: %s", exc)
+                logger.warning("WebSocket send failed: %s — pruning client", exc)
-                dead.append(connection)
+                return ws
-        for ws in dead:
+
-            await self.disconnect(ws)
+        # Fan out concurrently — one slow client can't stall the others.
+        results = await asyncio.gather(*[_send_one(ws) for ws in connections])
+        for dead_ws in results:
+            if dead_ws is not None:
+                await self.disconnect(dead_ws)
 
 
 manager = ConnectionManager()

scripts/backtest.py

@@ -0,0 +1,195 @@
+"""
+Backtest: replay AI signals against actual price moves stored in the DB.
+
+Zero AI calls. Uses pre-computed `price_impact_m5/m15/m1h` peak excursions
+that the price_impact_monitor already filled in for every relevant post.
+
+Methodology (be transparent — this is for honest disclosure):
+  • Universe        : posts with signal in (buy/sell/short) and m1h filled
+  • Side            : buy → long, sell/short → short
+  • Entry           : price_at_post (close of the minute candle when post landed)
+  • Exit window     : 1 hour (matches live bot MAX_HOLD_SECONDS)
+  • The peak_m1h field is the *side-adjusted* max favorable excursion (MFE) in %
+    (i.e. positive means market moved in the bot's predicted direction)
+  • Fees            : 9 bps round-trip (HL taker × 2), matches live bot
+  • TP simulation   : if peak ≥ TP threshold, exit at TP; else conservative 0
+                      (we don't have the actual close price, so 0 = breakeven
+                       on direction — captures only the trades that hit TP)
+
+Caveats reported up front:
+  • MFE is the peak during the window. We don't have the trough (MAE) or the
+    final close price, so we cannot fully simulate stop-loss behavior or
+    "what if you held the full hour with no TP". This is a TP-only sim.
+  • No slippage modeled beyond the 9 bps fee. Real fills on illiquid moments
+    add 1-3 bps more.
+  • Sample is whatever's in the DB right now (~70 actionable signals).
+"""
+import sqlite3
+import statistics
+from pathlib import Path
+
+DB = Path(__file__).resolve().parents[1] / "trumpsignal.db"
+FEE_BPS_ROUND_TRIP = 0.0009     # 9 bps = HL taker × 2
+TP_LEVELS = [0.10, 0.20, 0.30, 0.50, 1.00]   # in percent
+
+
+def fetch_signals():
+    con = sqlite3.connect(DB)
+    con.row_factory = sqlite3.Row
+    rows = con.execute("""
+        SELECT id, signal, price_impact_asset, price_at_post,
+               price_impact_m5, price_impact_m15, price_impact_m1h,
+               ai_confidence, published_at, text
+          FROM posts
+         WHERE signal IN ('buy', 'short', 'sell')
+           AND price_at_post IS NOT NULL
+           AND price_impact_m1h IS NOT NULL
+         ORDER BY published_at
+    """).fetchall()
+    con.close()
+    return [dict(r) for r in rows]
+
+
+def simulate(rows, tp_pct, window_field="price_impact_m1h"):
+    """For each signal: if MFE in window ≥ tp_pct, count as +tp_pct fill,
+    else count as 0 (no fill, but we still pay fees if we'd opened — we model
+    "no entry" so fees aren't charged on misses; this is generous, see below).
+
+    Note on the "fees on misses" question: the bot opens the position the
+    moment the signal fires. Even if TP isn't hit and you exit at the 1h mark,
+    you paid the round-trip fees. So a stricter sim charges fees on EVERY
+    trade. We do that — this is the conservative interpretation.
+    """
+    pnl_pcts = []
+    for r in rows:
+        peak = r[window_field]              # already side-adjusted, in %
+        if peak is None:
+            continue
+        # If MFE crosses TP, exit at TP. Otherwise we hold to window expiry —
+        # but we don't have the close price, so use peak/2 as a midpoint estimate
+        # (it must be between 0 and peak by definition; assume linear-ish reversion).
+        if peak >= tp_pct:
+            gross = tp_pct
+        else:
+            gross = peak / 2.0 if peak > 0 else peak  # peak<0 means market moved against → loss
+        net_pct = gross / 100.0 - FEE_BPS_ROUND_TRIP
+        pnl_pcts.append(net_pct * 100)      # back to percent for display
+    return pnl_pcts
+
+
+def stats(pcts):
+    if not pcts:
+        return None
+    n = len(pcts)
+    wins = sum(1 for p in pcts if p > 0)
+    losses = sum(1 for p in pcts if p < 0)
+    flat = n - wins - losses
+    win_rate = wins / n
+    mean = statistics.mean(pcts)
+    median = statistics.median(pcts)
+    pmax = max(pcts)
+    pmin = min(pcts)
+    # Profit factor: sum of wins / abs(sum of losses)
+    gross_win = sum(p for p in pcts if p > 0)
+    gross_loss = abs(sum(p for p in pcts if p < 0))
+    pf = gross_win / gross_loss if gross_loss > 0 else float("inf")
+    # Cumulative PnL (linear sum, not compounded — conservative)
+    total = sum(pcts)
+    return {
+        "n": n,
+        "wins": wins,
+        "losses": losses,
+        "flat": flat,
+        "win_rate_pct": round(win_rate * 100, 1),
+        "mean_pct": round(mean, 3),
+        "median_pct": round(median, 3),
+        "max_pct": round(pmax, 3),
+        "min_pct": round(pmin, 3),
+        "profit_factor": round(pf, 2) if pf != float("inf") else "∞",
+        "total_pct": round(total, 2),
+    }
+
+
+def main():
+    rows = fetch_signals()
+    print(f"BACKTEST — TrumpSignal AI strategy on Truth Social posts")
+    print(f"=" * 70)
+    print(f"Universe: {len(rows)} actionable signals (buy/sell/short)")
+    by_signal = {}
+    for r in rows:
+        by_signal[r['signal']] = by_signal.get(r['signal'], 0) + 1
+    print(f"  by signal: {by_signal}")
+    by_asset = {}
+    for r in rows:
+        a = r['price_impact_asset'] or 'UNKNOWN'
+        by_asset[a] = by_asset.get(a, 0) + 1
+    print(f"  by asset:  {by_asset}")
+    if rows:
+        print(f"  date span: {rows[0]['published_at'][:10]} → {rows[-1]['published_at'][:10]}")
+    print()
+
+    print(f"\n=== MFE distribution (m1h window, side-adjusted) ===")
+    raw_peaks = [r['price_impact_m1h'] for r in rows]
+    moved_correct = sum(1 for p in raw_peaks if p > 0)
+    print(f"  trades where market moved in predicted direction: "
+          f"{moved_correct}/{len(raw_peaks)} = {round(moved_correct/len(raw_peaks)*100,1)}%")
+    for thresh in [0.1, 0.2, 0.3, 0.5, 1.0, 2.0]:
+        hit = sum(1 for p in raw_peaks if p >= thresh)
+        print(f"  MFE ≥ {thresh:>4.1f}% : {hit:>3}/{len(raw_peaks)}  ({round(hit/len(raw_peaks)*100,1)}%)")
+    print(f"  mean MFE   : {round(statistics.mean(raw_peaks),3)}%")
+    print(f"  median MFE : {round(statistics.median(raw_peaks),3)}%")
+    print(f"  max MFE    : {round(max(raw_peaks),3)}%")
+    print(f"  min MFE    : {round(min(raw_peaks),3)}%")
+
+    print(f"\n=== Strategy comparison: different TP thresholds (1h window, 9 bps fees) ===")
+    print(f"  {'TP%':>5}  {'n':>4}  {'win%':>6}  {'mean':>7}  {'median':>7}  "
+          f"{'max':>7}  {'min':>7}  {'PF':>5}  {'total%':>8}")
+    print(f"  {'-'*5}  {'-'*4}  {'-'*6}  {'-'*7}  {'-'*7}  "
+          f"{'-'*7}  {'-'*7}  {'-'*5}  {'-'*8}")
+    for tp in TP_LEVELS:
+        s = stats(simulate(rows, tp))
+        if s:
+            print(f"  {tp:>5.2f}  {s['n']:>4}  {s['win_rate_pct']:>5.1f}%  "
+                  f"{s['mean_pct']:>+6.3f}%  {s['median_pct']:>+6.3f}%  "
+                  f"{s['max_pct']:>+6.3f}%  {s['min_pct']:>+6.3f}%  "
+                  f"{str(s['profit_factor']):>5}  {s['total_pct']:>+7.2f}%")
+
+    # Also break down by signal direction
+    print(f"\n=== By signal direction (TP=0.30%, 1h window) ===")
+    for sig in ['buy', 'sell', 'short']:
+        sub = [r for r in rows if r['signal'] == sig]
+        if not sub: continue
+        s = stats(simulate(sub, 0.30))
+        print(f"  {sig:>5}: n={s['n']:>3}  win_rate={s['win_rate_pct']:>5.1f}%  "
+              f"mean={s['mean_pct']:>+6.3f}%  total={s['total_pct']:>+7.2f}%  PF={s['profit_factor']}")
+
+    # Confidence-bucket performance
+    print(f"\n=== By AI confidence bucket (TP=0.30%, 1h window) ===")
+    for lo, hi in [(0,49), (50,69), (70,89), (90,100)]:
+        sub = [r for r in rows if lo <= (r['ai_confidence'] or 0) <= hi]
+        if not sub:
+            print(f"  conf {lo:>3}-{hi:<3}: (empty)")
+            continue
+        s = stats(simulate(sub, 0.30))
+        print(f"  conf {lo:>3}-{hi:<3}: n={s['n']:>3}  win_rate={s['win_rate_pct']:>5.1f}%  "
+              f"mean={s['mean_pct']:>+6.3f}%  total={s['total_pct']:>+7.2f}%  PF={s['profit_factor']}")
+
+    # Window comparison: which TP horizon best captures the move?
+    print(f"\n=== Best window comparison (TP=0.30%) ===")
+    print(f"  Same TP, different exit windows. Tells you which horizon to trade.")
+    for win, label in [("price_impact_m5", "5min"), ("price_impact_m15", "15min"), ("price_impact_m1h", "1hour")]:
+        s = stats(simulate(rows, 0.30, window_field=win))
+        if s:
+            print(f"  {label:>6}: n={s['n']:>3}  win_rate={s['win_rate_pct']:>5.1f}%  "
+                  f"mean={s['mean_pct']:>+6.3f}%  total={s['total_pct']:>+7.2f}%  PF={s['profit_factor']}")
+
+    print(f"\n{'=' * 70}")
+    print(f"DISCLAIMER: This is a TP-only simulation using max favorable excursion")
+    print(f"  data. Real performance will differ — no trough/MAE captured, no")
+    print(f"  slippage beyond fees. Sample = {len(rows)}, drawn from live AI scoring")
+    print(f"  on actual Trump posts {rows[0]['published_at'][:10] if rows else '—'} → "
+          f"{rows[-1]['published_at'][:10] if rows else '—'}.")
+
+
+if __name__ == "__main__":
+    main()

scripts/backtest_7d.py

@@ -0,0 +1,214 @@
+"""
+Past-7-day backtest under user-specified params:
+  - Margin: $100, leverage 20x → notional $2000 per trade
+  - SL:     -30% on margin = -1.5% on notional move
+  - TP:     "exit at the future peak" (look-ahead — see caveat at end)
+  - Window: 1 hour after publish
+  - Fees:   9 bps round-trip (HL taker × 2)
+
+We only have the price_impact_m5/m15/m1h fields, which store the MAXIMUM
+FAVORABLE EXCURSION (MFE). We do NOT have the trough / max ADVERSE excursion
+(MAE), so we cannot detect a real intra-hour SL hit. That makes the
+optimistic sim a CEILING, not a real-world outcome.
+
+We compute three scenarios so you can see the honest spread:
+
+  A) "Perfect-foresight":  exit at MFE peak. SL ignored (assume MAE = 0).
+                           This is what the user asked for. Upper bound only.
+
+  B) "Pessimistic":        if MFE < 0, assume SL hit (-1.5% × $2000 = -$30).
+                           If MFE ≥ 0, exit at MFE peak.
+                           Closer to reality but still optimistic on the wins.
+
+  C) "Realistic fixed TP": TP at +1.5%, SL at -1.5% (symmetric).
+                           If MFE ≥ TP → win.  Else → unknown end-of-window
+                           price, conservative model: take MFE × 0.5 as exit.
+                           This is the closest to a real bot's behavior.
+"""
+import sqlite3
+import statistics
+from pathlib import Path
+
+DB = Path(__file__).resolve().parents[1] / "trumpsignal.db"
+
+NOTIONAL = 2000.0
+MARGIN = 100.0
+SL_PCT = 1.5                     # 1.5% notional move = -30% margin
+FEE_BPS_RT = 0.0009              # 9 bps × $2000 = $1.80 per trade
+
+
+def fetch_week():
+    """Last 7 days of actionable signals, anchored to the LATEST post in the
+    DB (not wall-clock now) so the script works regardless of how stale the
+    snapshot is."""
+    con = sqlite3.connect(DB)
+    con.row_factory = sqlite3.Row
+    latest = con.execute("SELECT MAX(published_at) FROM posts").fetchone()[0]
+    rows = con.execute("""
+        SELECT id, signal, ai_confidence, price_at_post,
+               price_impact_m5, price_impact_m15, price_impact_m1h,
+               published_at, text
+          FROM posts
+         WHERE signal IN ('buy', 'short')           -- exclude sell (semantic bug)
+           AND price_at_post IS NOT NULL
+           AND price_impact_m1h IS NOT NULL
+           AND published_at >= datetime(?, '-7 days')
+         ORDER BY published_at
+    """, (latest,)).fetchall()
+    con.close()
+    return [dict(r) for r in rows], latest
+
+
+def trade_pnl_usd(gross_pct: float) -> float:
+    """Convert a notional % move into $ PnL on $2000 notional, after fees."""
+    return NOTIONAL * (gross_pct / 100.0) - NOTIONAL * FEE_BPS_RT
+
+
+def sim_perfect(rows):
+    """A) Exit at peak. No SL. (User's request — look-ahead bias.)"""
+    pnls = []
+    for r in rows:
+        peak = r["price_impact_m1h"]   # already side-adjusted, %
+        # Even peak < 0 still "exits at peak" per user spec
+        pnls.append(trade_pnl_usd(peak))
+    return pnls
+
+
+def sim_pessimistic(rows):
+    """B) If peak < 0, assume SL hit. Else exit at peak."""
+    pnls = []
+    for r in rows:
+        peak = r["price_impact_m1h"]
+        if peak < 0:
+            pnls.append(trade_pnl_usd(-SL_PCT))   # SL = -$30 + fees
+        else:
+            pnls.append(trade_pnl_usd(peak))
+    return pnls
+
+
+def sim_fixed_tp(rows, tp_pct=1.5):
+    """C) TP=tp_pct, SL=-1.5%. Real-bot behavior."""
+    pnls = []
+    for r in rows:
+        peak = r["price_impact_m1h"]
+        if peak >= tp_pct:
+            pnls.append(trade_pnl_usd(tp_pct))           # TP hit
+        elif peak < 0:
+            pnls.append(trade_pnl_usd(-SL_PCT))          # SL likely
+        else:
+            # Held the hour, peak was below TP → exit somewhere between
+            # 0 and peak. Use peak/2 as a midpoint estimate (conservative).
+            pnls.append(trade_pnl_usd(peak / 2.0))
+    return pnls
+
+
+def stats(pnls, label):
+    n = len(pnls)
+    if n == 0:
+        return None
+    wins = sum(1 for p in pnls if p > 0)
+    losses = sum(1 for p in pnls if p < 0)
+    total = sum(pnls)
+    biggest_win = max(pnls)
+    biggest_loss = min(pnls)
+    win_rate = wins / n * 100
+    # Margin return: total $ / total margin used
+    total_margin_used = MARGIN * n
+    roi_on_margin = (total / total_margin_used) * 100
+    return {
+        "label": label,
+        "n": n,
+        "wins": wins,
+        "losses": losses,
+        "win_rate_pct": round(win_rate, 1),
+        "total_usd": round(total, 2),
+        "avg_per_trade_usd": round(total / n, 2),
+        "biggest_win_usd": round(biggest_win, 2),
+        "biggest_loss_usd": round(biggest_loss, 2),
+        "roi_on_margin_pct": round(roi_on_margin, 1),
+    }
+
+
+def print_stats(s):
+    if s is None:
+        print("  (no trades)")
+        return
+    print(f"  n trades:           {s['n']}")
+    print(f"  win rate:           {s['win_rate_pct']}%   ({s['wins']}W / {s['losses']}L)")
+    print(f"  total PnL (USD):    ${s['total_usd']:+,.2f}")
+    print(f"  avg per trade:      ${s['avg_per_trade_usd']:+,.2f}")
+    print(f"  biggest win:        ${s['biggest_win_usd']:+,.2f}")
+    print(f"  biggest loss:       ${s['biggest_loss_usd']:+,.2f}")
+    print(f"  ROI on margin:      {s['roi_on_margin_pct']:+.1f}%   "
+          f"(${s['total_usd']:+.0f} on ${MARGIN * s['n']:.0f} total margin used)")
+
+
+def main():
+    rows, anchor = fetch_week()
+    print("=" * 72)
+    print(f"PAST-7-DAY BACKTEST — TrumpSignal AI strategy")
+    print(f"(7 days back from latest DB post: {anchor[:19]})")
+    print("=" * 72)
+    print(f"Position size:    ${MARGIN} margin × 20x leverage = ${NOTIONAL:.0f} notional")
+    print(f"Stop loss:        -30% margin = -{SL_PCT}% notional")
+    print(f"Take profit:      see scenarios below")
+    print(f"Fees:             9 bps round-trip = ${NOTIONAL * FEE_BPS_RT:.2f} per trade")
+    print(f"Hold window:      1 hour")
+    print(f"Sample:           {len(rows)} actionable signals (buy/short, sell excluded)")
+    if rows:
+        print(f"Date span:        {rows[0]['published_at'][:10]} → {rows[-1]['published_at'][:10]}")
+    print()
+
+    if not rows:
+        print("⚠️  No actionable signals in the last 7 days.")
+        print("    Either Trump didn't post anything market-relevant, or all signals")
+        print("    were 'hold'. Cannot run backtest.")
+        return
+
+    print("=" * 72)
+    print("SCENARIO A — 'Perfect foresight' (exit at peak, ignore SL)  ← user spec")
+    print("           This is the THEORETICAL CEILING. No real bot can hit this.")
+    print("=" * 72)
+    print_stats(stats(sim_perfect(rows), "perfect"))
+    print()
+
+    print("=" * 72)
+    print("SCENARIO B — 'Pessimistic' (SL hits if peak<0, else exit at peak)")
+    print("           Closer to honest, but wins are still cherry-picked at peak.")
+    print("=" * 72)
+    print_stats(stats(sim_pessimistic(rows), "pessimistic"))
+    print()
+
+    print("=" * 72)
+    print("SCENARIO C — 'Realistic fixed TP/SL' (TP=+1.5%, SL=-1.5%)")
+    print("           Closest to what a real bot with these params would yield.")
+    print("           THIS is the only one defensible for marketing.")
+    print("=" * 72)
+    print_stats(stats(sim_fixed_tp(rows, tp_pct=1.5), "realistic"))
+    print()
+
+    # Also try a couple of TP variations to find sweet spot
+    print("=" * 72)
+    print("Realistic sim — TP threshold sweep (SL fixed at -1.5%)")
+    print("=" * 72)
+    print(f"  {'TP%':>6}  {'n':>4}  {'win%':>6}  {'total$':>10}  {'avg$':>8}  {'ROI%margin':>11}")
+    for tp in [0.5, 1.0, 1.5, 2.0, 3.0]:
+        s = stats(sim_fixed_tp(rows, tp), f"tp={tp}")
+        print(f"  {tp:>5.1f}%  {s['n']:>4}  {s['win_rate_pct']:>5.1f}%  "
+              f"${s['total_usd']:>+8.2f}  ${s['avg_per_trade_usd']:>+6.2f}  "
+              f"{s['roi_on_margin_pct']:>+9.1f}%")
+
+    print()
+    print("=" * 72)
+    print("BOTTOM LINE")
+    print("=" * 72)
+    perfect = stats(sim_perfect(rows), "")
+    realistic = stats(sim_fixed_tp(rows, 1.5), "")
+    print(f"  Perfect-foresight ceiling: ${perfect['total_usd']:+,.2f}  "
+          f"(do NOT put on homepage — look-ahead bias)")
+    print(f"  Realistic fixed TP=1.5%:   ${realistic['total_usd']:+,.2f}  "
+          f"(this is the marketable number, IF positive)")
+
+
+if __name__ == "__main__":
+    main()

scripts/backtest_signals.py

@@ -0,0 +1,357 @@
+"""
+Backtest: Reversal + Breakout signals on Binance Futures 5m klines
+Covers 2023-01-01 to present across SOL, ETH, AVAX, LINK, DOGE
+"""
+
+import io
+import time
+import zipfile
+import subprocess
+import warnings
+import pandas as pd
+import numpy as np
+from datetime import datetime, timezone
+from dateutil.relativedelta import relativedelta
+from tabulate import tabulate
+
+warnings.filterwarnings("ignore")
+
+# ── Config ────────────────────────────────────────────────────────────────────
+
+SYMBOLS = ["ETHUSDT", "LINKUSDT", "AVAXUSDT", "SOLUSDT", "DOGEUSDT"]
+BTC_SYMBOL = "BTCUSDT"
+START_TS = int(datetime(2023, 1, 1, tzinfo=timezone.utc).timestamp() * 1000)
+END_TS   = int(datetime(2025, 12, 31, tzinfo=timezone.utc).timestamp() * 1000)
+
+STOP_LOSS   = -0.03   # -3%
+TAKE_PROFIT =  0.035  # +3.5%
+MAX_HOLD_CANDLES = 288  # 24h at 5m
+
+TREND_MA_PERIOD = 48    # 4h trend filter (48 x 5m = 4h)
+BTC_TREND_MA    = 288   # BTC 24h trend filter (288 x 5m = 24h)
+
+# Signal params
+REVERSAL_TAKER_BUY_THRESH  = 0.65
+REVERSAL_PREV_TAKER_MAX    = 0.45
+REVERSAL_MA_PERIOD         = 20
+REVERSAL_4H_DECLINE        = -0.05
+
+BREAKOUT_BB_PERIOD         = 20
+BREAKOUT_BB_SQUEEZE_PCT    = 20   # bottom 20% of BB width history (60 candles)
+BREAKOUT_VOLUME_MULT       = 2.5
+BREAKOUT_TAKER_BUY_THRESH  = 0.60
+
+DATA_BASE  = "https://data.binance.vision/data/futures/um/monthly/klines"
+CACHE_DIR  = "/tmp/binance_klines_cache"
+KLINE_COLS = [
+    "open_time", "open", "high", "low", "close", "volume",
+    "close_time", "quote_volume", "trades",
+    "taker_buy_base", "taker_buy_quote", "ignore"
+]
+
+# ── Data fetch ────────────────────────────────────────────────────────────────
+
+def _fetch_month(symbol: str, year: int, month: int):
+    """Download one monthly zip from data.binance.vision and return DataFrame."""
+    url = f"{DATA_BASE}/{symbol}/5m/{symbol}-5m-{year}-{month:02d}.zip"
+    for attempt in range(3):
+        result = subprocess.run(
+            ["curl", "-s", "-x", "http://127.0.0.1:7890",
+             "--max-time", "120", "--output", "-", url],
+            capture_output=True,
+        )
+        if result.returncode == 0 and result.stdout:
+            try:
+                with zipfile.ZipFile(io.BytesIO(result.stdout)) as zf:
+                    csv_name = zf.namelist()[0]
+                    with zf.open(csv_name) as f:
+                        df = pd.read_csv(f, names=KLINE_COLS, skiprows=1)
+                return df
+            except Exception:
+                pass
+        time.sleep(2 ** attempt)
+    return None
+
+
+def fetch_klines(symbol: str, start_ms: int, end_ms: int) -> pd.DataFrame:
+    import os
+    os.makedirs(CACHE_DIR, exist_ok=True)
+    cache_file = f"{CACHE_DIR}/{symbol}.pkl"
+
+    start_dt = datetime.fromtimestamp(start_ms / 1000, tz=timezone.utc)
+    end_dt   = datetime.fromtimestamp(end_ms   / 1000, tz=timezone.utc)
+
+    # Load from cache if available
+    if os.path.exists(cache_file):
+        print(f"  Loading {symbol} from cache...", end="", flush=True)
+        df = pd.read_pickle(cache_file)
+        df = df[(df.index >= pd.Timestamp(start_dt)) & (df.index <= pd.Timestamp(end_dt))]
+        print(f" {len(df)} candles (cached)")
+        return df
+
+    frames = []
+    cur = start_dt.replace(day=1)
+    print(f"  Fetching {symbol}", end="", flush=True)
+
+    while cur <= end_dt:
+        df = _fetch_month(symbol, cur.year, cur.month)
+        if df is not None:
+            frames.append(df)
+            print(".", end="", flush=True)
+        else:
+            print("x", end="", flush=True)
+        cur += relativedelta(months=1)
+
+    if not frames:
+        print(f" FAILED")
+        return pd.DataFrame()
+
+    df = pd.concat(frames, ignore_index=True)
+    for col in ["open", "high", "low", "close", "volume", "taker_buy_base"]:
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+    df["open_time"] = pd.to_datetime(df["open_time"], unit="ms", utc=True)
+    df = df.set_index("open_time").sort_index()
+
+    # Save to cache before filtering
+    df.to_pickle(cache_file)
+
+    df = df[(df.index >= pd.Timestamp(start_dt)) & (df.index <= pd.Timestamp(end_dt))]
+    print(f" {len(df)} candles")
+    return df
+
+
+# ── Indicators ────────────────────────────────────────────────────────────────
+
+def add_indicators(df: pd.DataFrame) -> pd.DataFrame:
+    df = df.copy()
+
+    # Taker buy ratio
+    df["tbr"] = df["taker_buy_base"] / df["volume"].replace(0, np.nan)
+
+    # MA20
+    df["ma20"] = df["close"].rolling(REVERSAL_MA_PERIOD).mean()
+
+    # 4h trend MA (48 x 5m candles)
+    df["ma_trend"] = df["close"].rolling(TREND_MA_PERIOD).mean()
+
+    # 4h decline: compare current close to close 48 candles ago (48 * 5m = 4h)
+    df["decline_4h"] = df["close"].pct_change(48)
+
+    # Bollinger Bands
+    rolling = df["close"].rolling(BREAKOUT_BB_PERIOD)
+    df["bb_mid"]   = rolling.mean()
+    df["bb_std"]   = rolling.std()
+    df["bb_upper"] = df["bb_mid"] + 2 * df["bb_std"]
+    df["bb_width"] = (4 * df["bb_std"]) / df["bb_mid"]
+
+    # BB width percentile over past 60 candles
+    df["bb_width_pct"] = df["bb_width"].rolling(60).rank(pct=True) * 100
+
+    # Volume MA20
+    df["vol_ma20"] = df["volume"].rolling(20).mean()
+
+    return df
+
+
+# ── Signal detection ──────────────────────────────────────────────────────────
+
+def detect_signals(df: pd.DataFrame) -> pd.DataFrame:
+    df = df.copy()
+
+    # Previous 3 candle TBR max
+    df["tbr_prev3_max"] = df["tbr"].shift(1).rolling(3).max()
+
+    # Trend up filter: price above 4h MA
+    trend_up = df["close"] > df["ma_trend"]
+
+    # Reversal signal
+    df["sig_reversal"] = (
+        (df["tbr"] > REVERSAL_TAKER_BUY_THRESH) &
+        (df["tbr_prev3_max"] < REVERSAL_PREV_TAKER_MAX) &
+        (df["close"] < df["ma20"]) &
+        (df["decline_4h"] < REVERSAL_4H_DECLINE) &
+        trend_up
+    )
+
+    # Breakout signal
+    df["sig_breakout"] = (
+        (df["bb_width_pct"] < BREAKOUT_BB_SQUEEZE_PCT) &
+        (df["volume"] > BREAKOUT_VOLUME_MULT * df["vol_ma20"]) &
+        (df["tbr"] > BREAKOUT_TAKER_BUY_THRESH) &
+        (df["close"] > df["bb_upper"]) &
+        trend_up
+    )
+
+    df["signal"] = np.where(
+        df["sig_reversal"], "reversal",
+        np.where(df["sig_breakout"], "breakout", None)
+    )
+
+    return df
+
+
+# ── Trade simulation ──────────────────────────────────────────────────────────
+
+def simulate_trades(df: pd.DataFrame, symbol: str) -> list[dict]:
+    trades = []
+    in_trade = False
+    entry_price = 0.0
+    entry_time = None
+    entry_signal = None
+    hold_count = 0
+
+    closes = df["close"].values
+    signals = df["signal"].values
+    times = df.index
+
+    for i in range(1, len(df)):
+        if in_trade:
+            hold_count += 1
+            pct = (closes[i] - entry_price) / entry_price
+
+            hit_sl = pct <= STOP_LOSS
+            hit_tp = pct >= TAKE_PROFIT
+            hit_max = hold_count >= MAX_HOLD_CANDLES
+
+            if hit_sl or hit_tp or hit_max:
+                reason = "SL" if hit_sl else ("TP" if hit_tp else "MAX")
+                trades.append({
+                    "symbol": symbol,
+                    "signal": entry_signal,
+                    "entry_time": entry_time,
+                    "exit_time": times[i],
+                    "entry_price": entry_price,
+                    "exit_price": closes[i],
+                    "pct": pct,
+                    "result": "win" if pct > 0 else "loss",
+                    "reason": reason,
+                    "month": entry_time.strftime("%Y-%m"),
+                })
+                in_trade = False
+
+        # Enter on next candle after signal
+        if not in_trade and i > 0 and signals[i - 1] is not None:
+            in_trade = True
+            entry_price = closes[i]  # next candle open ≈ prev close (5m)
+            entry_time = times[i]
+            entry_signal = signals[i - 1]
+            hold_count = 0
+
+    return trades
+
+
+# ── Analysis ──────────────────────────────────────────────────────────────────
+
+def analyze(trades: list[dict]) -> None:
+    if not trades:
+        print("No trades found.")
+        return
+
+    df = pd.DataFrame(trades)
+
+    print("\n" + "=" * 70)
+    print("OVERALL SUMMARY")
+    print("=" * 70)
+
+    for sig_type in ["reversal", "breakout", "all"]:
+        sub = df if sig_type == "all" else df[df["signal"] == sig_type]
+        if sub.empty:
+            continue
+        wins = (sub["result"] == "win").sum()
+        total = len(sub)
+        avg_pct = sub["pct"].mean() * 100
+        total_pct = sub["pct"].sum() * 100
+        print(f"\n[{sig_type.upper()}]  trades={total}  win_rate={wins/total:.1%}"
+              f"  avg={avg_pct:.2f}%  total_return={total_pct:.1f}%")
+
+    print("\n" + "=" * 70)
+    print("MONTHLY BREAKDOWN (all signals combined)")
+    print("=" * 70)
+
+    monthly = (
+        df.groupby("month")
+        .agg(
+            trades=("pct", "count"),
+            wins=("result", lambda x: (x == "win").sum()),
+            avg_pct=("pct", lambda x: x.mean() * 100),
+            total_pct=("pct", lambda x: x.sum() * 100),
+        )
+        .reset_index()
+    )
+    monthly["win_rate"] = monthly["wins"] / monthly["trades"]
+    monthly["avg_pct"] = monthly["avg_pct"].map("{:.2f}%".format)
+    monthly["total_pct"] = monthly["total_pct"].map("{:.1f}%".format)
+    monthly["win_rate"] = monthly["win_rate"].map("{:.1%}".format)
+    print(tabulate(monthly, headers="keys", tablefmt="simple", showindex=False))
+
+    print("\n" + "=" * 70)
+    print("PER SYMBOL SUMMARY")
+    print("=" * 70)
+
+    by_sym = (
+        df.groupby("symbol")
+        .agg(
+            trades=("pct", "count"),
+            wins=("result", lambda x: (x == "win").sum()),
+            avg_pct=("pct", lambda x: x.mean() * 100),
+            total_pct=("pct", lambda x: x.sum() * 100),
+        )
+        .reset_index()
+    )
+    by_sym["win_rate"] = by_sym["wins"] / by_sym["trades"]
+    by_sym["avg_pct"] = by_sym["avg_pct"].map("{:.2f}%".format)
+    by_sym["total_pct"] = by_sym["total_pct"].map("{:.1f}%".format)
+    by_sym["win_rate"] = by_sym["win_rate"].map("{:.1%}".format)
+    print(tabulate(by_sym, headers="keys", tablefmt="simple", showindex=False))
+
+    print("\n" + "=" * 70)
+    print("EXIT REASON BREAKDOWN")
+    print("=" * 70)
+    print(df.groupby(["signal", "reason"]).size().to_string())
+
+
+# ── Main ──────────────────────────────────────────────────────────────────────
+
+def main():
+    all_trades = []
+
+    # ── Load BTC trend ────────────────────────────────────────────────────────
+    print("Loading BTC trend data...")
+    btc = fetch_klines(BTC_SYMBOL, START_TS, END_TS)
+    if btc.empty:
+        print("  BTC data failed, running without BTC filter")
+        btc_trend = None
+    else:
+        btc["btc_ma"] = btc["close"].rolling(BTC_TREND_MA).mean()
+        btc_trend = (btc["close"] > btc["btc_ma"]).rename("btc_uptrend")
+        print(f"  BTC uptrend {btc_trend.mean():.1%} of the time")
+
+    # ── Per-symbol backtest ───────────────────────────────────────────────────
+    for symbol in SYMBOLS:
+        df = fetch_klines(symbol, START_TS, END_TS)
+        if df.empty:
+            print(f"  → skipped")
+            continue
+        df = add_indicators(df)
+        df = detect_signals(df)
+
+        # Apply BTC trend filter
+        if btc_trend is not None:
+            btc_aligned = btc_trend.reindex(df.index, method="ffill")
+            df.loc[~btc_aligned.fillna(False), "signal"] = None
+            filtered = df["signal"].notna().sum()
+        else:
+            filtered = df["signal"].notna().sum()
+
+        sig_count = df["signal"].notna().sum()
+        print(f"  → {sig_count} signals after BTC filter (was {filtered})")
+
+        trades = simulate_trades(df, symbol)
+        print(f"  → {len(trades)} trades executed")
+        all_trades.extend(trades)
+
+    analyze(all_trades)
+
+
+if __name__ == "__main__":
+    main()

scripts/backup_db.sh

@@ -0,0 +1,62 @@
+#!/usr/bin/env bash
+#
+# Daily DB backup. Cron-friendly. Detects SQLite vs Postgres from DATABASE_URL.
+#
+# Example crontab:
+#   15 3 * * * /path/to/scripts/backup_db.sh >> /var/log/trumpsignal-backup.log 2>&1
+#
+# Retention: keeps the last RETAIN_DAYS daily backups (default 14).
+# Storage:   writes to $BACKUP_DIR (default ./backups/).
+#
+# DATABASE_URL must be available in env. Loads .env if present.
+
+set -euo pipefail
+
+cd "$(dirname "$0")/.."
+
+# Load .env if it exists (so cron jobs without inherited env still work)
+if [[ -f .env ]]; then
+  set -a
+  # shellcheck disable=SC1091
+  source .env
+  set +a
+fi
+
+: "${DATABASE_URL:?DATABASE_URL not set — refusing to back up nothing}"
+BACKUP_DIR="${BACKUP_DIR:-./backups}"
+RETAIN_DAYS="${RETAIN_DAYS:-14}"
+TS="$(date -u +%Y%m%d_%H%M%S)"
+
+mkdir -p "$BACKUP_DIR"
+
+if [[ "$DATABASE_URL" == sqlite* ]]; then
+  # Extract path from sqlite+aiosqlite:///./trumpsignal.db
+  DB_PATH="$(echo "$DATABASE_URL" | sed -E 's#^sqlite(\+[^:]+)?:///+##')"
+  if [[ ! -f "$DB_PATH" ]]; then
+    echo "[backup] SQLite file not found: $DB_PATH" >&2
+    exit 1
+  fi
+  OUT="$BACKUP_DIR/sqlite_${TS}.db"
+  # sqlite3 .backup is online-safe (uses backup API, not a raw copy).
+  # Fall back to cp if sqlite3 CLI isn't installed.
+  if command -v sqlite3 >/dev/null 2>&1; then
+    sqlite3 "$DB_PATH" ".backup '$OUT'"
+  else
+    cp "$DB_PATH" "$OUT"
+  fi
+  gzip -f "$OUT"
+  echo "[backup] wrote $OUT.gz ($(du -h "$OUT.gz" | cut -f1))"
+elif [[ "$DATABASE_URL" == postgres* ]] || [[ "$DATABASE_URL" == postgresql* ]]; then
+  OUT="$BACKUP_DIR/pg_${TS}.sql.gz"
+  # pg_dump reads DATABASE_URL natively if we strip the driver prefix.
+  PG_URL="${DATABASE_URL/+asyncpg/}"
+  PG_URL="${PG_URL/+psycopg2/}"
+  pg_dump "$PG_URL" --no-owner --no-privileges | gzip > "$OUT"
+  echo "[backup] wrote $OUT ($(du -h "$OUT" | cut -f1))"
+else
+  echo "[backup] unsupported DATABASE_URL scheme: $DATABASE_URL" >&2
+  exit 1
+fi
+
+# Prune backups older than RETAIN_DAYS days
+find "$BACKUP_DIR" -name "*.gz" -mtime "+$RETAIN_DAYS" -print -delete

scripts/preflight.py

@@ -0,0 +1,239 @@
+#!/usr/bin/env python3
+"""
+Pre-launch readiness check.
+
+Run this RIGHT BEFORE flipping production traffic. Verifies that:
+  * Every required env var is set
+  * KEK / shared secrets have plausible entropy (not "change_me")
+  * DB is reachable + schema is at the latest Alembic head
+  * The Telegram bot token authenticates with the actual @username
+  * AI provider answers with a real model id
+  * No leftover open positions in bot_trades that the bot doesn't know about
+
+Exits non-zero on any failure so you can wire it into CI / a deploy gate.
+
+    DATABASE_URL=... venv/bin/python scripts/preflight.py
+"""
+from __future__ import annotations
+
+import asyncio
+import os
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+import httpx
+from sqlalchemy import text
+from app.config import settings
+from app.database import AsyncSessionLocal, engine
+
+
+# Vars that MUST be non-empty for production. If you genuinely don't need one
+# (e.g. running without Telegram), comment it out — don't push junk values.
+REQUIRED = [
+    ("database_url",         "Where the API stores everything"),
+    ("frontend_url",         "Used as the single CORS origin"),
+    ("encryption_key",       "KEK for HL API key envelope encryption"),
+    ("ingest_api_key",       "Shared secret for /api/signals/ingest"),
+]
+# Required for the listed feature; non-fatal but logged as a warning.
+OPTIONAL_BUT_RECOMMENDED = [
+    ("ai_api_key",           "Trump signal AI scoring (or anthropic_api_key)"),
+    ("anthropic_api_key",    "Required if ai_api_key empty AND you want KOL analysis"),
+    ("telegram_bot_token",   "Telegram push alerts (whole feature disabled if empty)"),
+    ("telegram_bot_username","Required for the dashboard's Telegram connect deep link"),
+    ("etherscan_api_key",    "KOL on-chain (talks-vs-trades) — Ethereum side"),
+    # NOTE: glassnode_api_key is no longer required — the BTC bottom-reversal
+    # scanner switched to AHR999 + 200-week MA + Pi Cycle Bottom (all derived
+    # from public price candles, no Glassnode call). The setting is kept on
+    # config.Settings for future Glassnode-backed signals but isn't checked.
+]
+# These should NEVER appear unchanged in production.
+SUSPECT_DEFAULTS = {
+    "change_me_in_production",
+    "your_key_here",
+    "CHANGE_ME",
+    "",
+}
+
+
+def red(s):    return f"\033[31m{s}\033[0m"
+def green(s):  return f"\033[32m{s}\033[0m"
+def yellow(s): return f"\033[33m{s}\033[0m"
+
+
+async def check_env() -> list[str]:
+    errors = []
+    print("── env vars ──────────────────────────────────────")
+    for name, why in REQUIRED:
+        v = getattr(settings, name, "")
+        if not v or v in SUSPECT_DEFAULTS:
+            print(red(f"  ✗ {name:25s} EMPTY or default — required: {why}"))
+            errors.append(f"{name} empty")
+        else:
+            shown = v if len(v) < 30 else v[:8] + "…" + v[-4:]
+            print(green(f"  ✓ {name:25s} = {shown}"))
+    print()
+    print("── recommended (warnings only) ────────────────────")
+    for name, why in OPTIONAL_BUT_RECOMMENDED:
+        v = getattr(settings, name, "")
+        if not v:
+            print(yellow(f"  ! {name:25s} empty — {why}"))
+        else:
+            print(green(f"  ✓ {name:25s} set"))
+    return errors
+
+
+async def check_db() -> list[str]:
+    errors = []
+    print()
+    print("── database ──────────────────────────────────────")
+    try:
+        async with AsyncSessionLocal() as db:
+            await db.execute(text("SELECT 1"))
+        print(green("  ✓ DB reachable"))
+    except Exception as exc:
+        print(red(f"  ✗ DB unreachable: {exc}"))
+        errors.append("db unreachable")
+        return errors
+
+    # Schema check — current head
+    try:
+        async with engine.begin() as conn:
+            r = await conn.execute(text("SELECT version_num FROM alembic_version"))
+            row = r.fetchone()
+            current = row[0] if row else None
+        # Read the latest version from alembic/versions/
+        versions_dir = Path(__file__).resolve().parent.parent / "alembic" / "versions"
+        head_files = sorted(p.stem for p in versions_dir.glob("*.py")
+                            if p.stem != "__init__")
+        latest_prefix = max(int(f.split("_")[0]) for f in head_files
+                            if f.split("_")[0].isdigit())
+        if current and current.startswith(f"{latest_prefix:03d}".rstrip("0") or "0"):
+            print(green(f"  ✓ alembic at head {current}"))
+        elif current:
+            print(yellow(f"  ! alembic at {current} but versions/ has up to {latest_prefix:03d}"))
+            print(yellow(f"    → run: alembic upgrade head"))
+        else:
+            print(red("  ✗ alembic_version table empty — schema unmanaged"))
+            errors.append("schema unmanaged")
+    except Exception as exc:
+        print(yellow(f"  ! schema version check failed: {exc}"))
+
+    # Orphan open positions
+    try:
+        async with AsyncSessionLocal() as db:
+            r = await db.execute(text(
+                "SELECT COUNT(*) FROM bot_trades WHERE closed_at IS NULL"
+            ))
+            n = r.scalar() or 0
+        if n:
+            print(yellow(f"  ! {n} open bot_trades rows — verify these match HL state before launch"))
+        else:
+            print(green("  ✓ no orphan open positions"))
+    except Exception as exc:
+        print(yellow(f"  ! open-positions check failed: {exc}"))
+
+    return errors
+
+
+async def check_telegram() -> list[str]:
+    errors = []
+    print()
+    print("── telegram ──────────────────────────────────────")
+    if not settings.telegram_bot_token:
+        print(yellow("  ! token empty — skipping"))
+        return errors
+    try:
+        async with httpx.AsyncClient(timeout=10) as c:
+            r = await c.get(
+                f"https://api.telegram.org/bot{settings.telegram_bot_token}/getMe"
+            )
+        if r.status_code != 200:
+            print(red(f"  ✗ getMe HTTP {r.status_code}: {r.text[:120]}"))
+            errors.append("telegram auth failed")
+            return errors
+        data = r.json()
+        if not data.get("ok"):
+            print(red(f"  ✗ getMe returned ok=false: {data}"))
+            errors.append("telegram auth failed")
+            return errors
+        bot_username = data["result"]["username"]
+        print(green(f"  ✓ token authenticates as @{bot_username}"))
+        if settings.telegram_bot_username and bot_username != settings.telegram_bot_username:
+            print(red(f"  ✗ TELEGRAM_BOT_USERNAME='{settings.telegram_bot_username}'"
+                      f" but token is for @{bot_username}"))
+            errors.append("telegram username mismatch")
+    except Exception as exc:
+        print(red(f"  ✗ telegram check failed: {exc}"))
+        errors.append("telegram unreachable")
+    return errors
+
+
+async def check_ai() -> list[str]:
+    errors = []
+    print()
+    print("── ai provider ───────────────────────────────────")
+    if settings.anthropic_api_key:
+        # Cheap, free models endpoint check.
+        try:
+            async with httpx.AsyncClient(timeout=10) as c:
+                r = await c.get(
+                    "https://api.anthropic.com/v1/models",
+                    headers={
+                        "x-api-key": settings.anthropic_api_key,
+                        "anthropic-version": "2023-06-01",
+                    },
+                )
+            if r.status_code == 200:
+                print(green("  ✓ anthropic_api_key authenticates"))
+            else:
+                print(red(f"  ✗ anthropic /v1/models HTTP {r.status_code}: {r.text[:120]}"))
+                errors.append("anthropic auth failed")
+        except Exception as exc:
+            print(red(f"  ✗ anthropic check failed: {exc}"))
+    elif settings.ai_api_key:
+        try:
+            async with httpx.AsyncClient(timeout=10) as c:
+                r = await c.get(
+                    f"{settings.ai_base_url.rstrip('/')}/models",
+                    headers={"Authorization": f"Bearer {settings.ai_api_key}"},
+                )
+            if r.status_code == 200:
+                print(green(f"  ✓ ai_api_key authenticates at {settings.ai_base_url}"))
+            else:
+                print(red(f"  ✗ /models HTTP {r.status_code}: {r.text[:120]}"))
+                errors.append("ai auth failed")
+        except Exception as exc:
+            print(red(f"  ✗ ai check failed: {exc}"))
+    else:
+        print(yellow("  ! no ai provider key set — Trump signals will not be scored"))
+    return errors
+
+
+async def main() -> int:
+    print(f"Preflight check — env: {settings.environment}\n")
+    if settings.environment != "production":
+        print(yellow("WARNING: settings.environment is not 'production'. Some dev-only"))
+        print(yellow("         endpoints (/api/dev/*) and auto-create_all() are enabled.\n"))
+
+    all_errors: list[str] = []
+    all_errors += await check_env()
+    all_errors += await check_db()
+    all_errors += await check_telegram()
+    all_errors += await check_ai()
+    await engine.dispose()
+
+    print()
+    if all_errors:
+        print(red(f"❌ {len(all_errors)} fatal issue(s):"))
+        for e in all_errors:
+            print(red(f"    - {e}"))
+        return 1
+    print(green("✅ All checks passed. Safe to launch."))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(asyncio.run(main()))

scripts/seed_kol_wallets.py

@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""
+Seed the kol_wallets table with publicly attributed on-chain addresses.
+
+Why this exists:
+  The talks-vs-trades divergence module can only fire on KOLs whose wallets
+  we know. As of 2026-05-24 only 3 KOLs had wallets configured, which is
+  why we had ~3 divergence detections across the whole dataset.
+
+Adding a new wallet here REQUIRES:
+  1. Public attribution — Arkham label, the KOL's own X bio, a public
+     investigation by ZachXBT / Inspex / similar, or the KOL's ENS clearly
+     visible in transactions.
+  2. The `handle` field MUST exactly match a `handle` value in
+     app/services/kol_substack.py KOL_FEEDS — otherwise the divergence
+     scanner cannot match "post by handle X" against "wallet activity by
+     handle X".
+  3. The `source_url` must link to that public attestation. Don't add
+     speculative addresses, even if "everyone knows" — misattribution
+     damages both the KOL and our credibility.
+
+Idempotent: runs UPSERT-style. Existing rows for (handle, address) are
+preserved; only new ones are inserted.
+
+Usage:
+    DATABASE_URL='sqlite+aiosqlite:///./trumpsignal.db' \\
+        venv/bin/python scripts/seed_kol_wallets.py
+"""
+from __future__ import annotations
+
+import asyncio
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from sqlalchemy import select
+from app.database import AsyncSessionLocal
+from app.models import KolWallet
+
+
+# ────────────────────────────────────────────────────────────────────────────
+# Verified seed list.
+#
+# Each entry MUST have a working source_url. If you can't link to a public
+# attestation, DON'T add it.
+#
+# To find more candidates yourself:
+#   • https://platform.arkhamintelligence.com/ — search by handle, click
+#     "labels" tab. Labels prefixed with "Entity:" are Arkham-verified.
+#   • https://etherscan.io/labelcloud — Etherscan public label registry.
+#   • ZachXBT investigations on X — he posts the underlying tx evidence.
+#   • Many KOLs put their address in their X bio or pinned tweet.
+#
+# When the KOL's handle in KOL_FEEDS doesn't match the on-chain handle here,
+# add it under the handle that's IN KOL_FEEDS — divergence join is by handle.
+# ────────────────────────────────────────────────────────────────────────────
+
+SEED_WALLETS: list[dict] = [
+    # ── Already in DB (kept here so the script is self-documenting) ──────
+    {
+        "handle": "cryptohayes",
+        "chain": "ethereum",
+        "address": "0xa86e3d1c80a750a310b484fb9bdc470753a7506f",
+        "label": "Arthur Hayes (main)",
+        "source_url": "https://etherscan.io/address/0xa86e3d1c80a750a310b484fb9bdc470753a7506f",
+    },
+    {
+        "handle": "cryptohayes",
+        "chain": "ethereum",
+        "address": "0x534a0076fb7c2b1f83fa21497429ad7ad3bd7587",
+        "label": "Arthur Hayes (secondary)",
+        "source_url": "https://etherscan.io/address/0x534a0076fb7c2b1f83fa21497429ad7ad3bd7587",
+    },
+    {
+        "handle": "andrewkang",
+        "chain": "ethereum",
+        "address": "0xff3879b8a363aed92a6eaba8f61f1a96a9ec3c1e",
+        "label": "Andrew Kang (beanwhale.eth)",
+        "source_url": "https://etherscan.io/address/0xff3879b8a363aed92a6eaba8f61f1a96a9ec3c1e",
+    },
+    {
+        "handle": "murad",
+        "chain": "ethereum",
+        "address": "0x93f019699ef400df7dc3477dbb6400ed9445a657",
+        "label": "Murad Mahmudov (via ZachXBT investigation)",
+        "source_url": "https://www.blocmates.com/news-posts/24million-in-memecoins-zachxbt-unveils-murad-mahmudov-s-alleged-wallets",
+    },
+
+    # ── Add new VERIFIED entries below this line ─────────────────────────
+    #
+    # Template — copy, replace fields, push only after verifying source_url:
+    #
+    # {
+    #     "handle": "<matches handle in KOL_FEEDS>",
+    #     "chain":  "ethereum",            # or "solana", "base", "arbitrum"
+    #     "address": "0x...",
+    #     "label": "<KOL display name (annotation)>",
+    #     "source_url": "<public link proving attribution>",
+    # },
+    #
+    # Candidates to research (NOT seeded — verify before adding):
+    #
+    #   • niccarter        — Nic Carter has spoken openly about his wallet
+    #                        history on podcasts; check Coin Center filings.
+    #   • pomp             — Pompliano has a public BTC-only treasury; less
+    #                        useful for ETH-side divergence detection.
+    #   • dragonfly        — Dragonfly Capital portfolio wallets often
+    #                        labeled on Arkham as "Dragonfly Fund".
+    #   • placeholder      — Placeholder VC fund wallets per their public
+    #                        investment disclosures.
+    #   • eugene           — Eugene Ng Ah Sio — verify before adding.
+    #
+    # Also worth tracking even if not in KOL_FEEDS (would need to add the
+    # corresponding feed entry first or they'll never have post-side data):
+    #
+    #   • justinsuntron    — Tron founder, very active ETH trader, well-labeled.
+    #   • cobie            — Jordan Fish, address known via Arkham labels.
+    #   • gcr / sam        — anon traders, no reliably verifiable address.
+]
+
+
+async def main() -> int:
+    inserted = 0
+    skipped = 0
+    async with AsyncSessionLocal() as session:
+        for entry in SEED_WALLETS:
+            # Idempotency: skip if (handle, address) already present.
+            existing = await session.execute(
+                select(KolWallet).where(
+                    KolWallet.handle == entry["handle"],
+                    KolWallet.address == entry["address"].lower(),
+                )
+            )
+            if existing.scalar_one_or_none():
+                skipped += 1
+                continue
+            row = KolWallet(
+                handle=entry["handle"],
+                chain=entry["chain"],
+                address=entry["address"].lower(),
+                label=entry["label"],
+                source_url=entry["source_url"],
+                active=True,
+                added_at=datetime.now(timezone.utc).replace(tzinfo=None),
+            )
+            session.add(row)
+            inserted += 1
+            print(f"  + {entry['handle']:18s} {entry['address']}  ({entry['label']})")
+        await session.commit()
+
+    print()
+    print(f"Inserted {inserted} wallets, skipped {skipped} existing.")
+    print()
+    print("Next step: edit SEED_WALLETS above and re-run. Each new wallet")
+    print("MUST cite a public attestation in source_url — see the docstring.")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(asyncio.run(main()))

scripts/verify_sys2_lifecycle.py

@@ -0,0 +1,196 @@
+"""
+System-2 生命周期·小额真单端到端验证
+
+验证我们新写的三个动钱路径在真实 Hyperliquid 上的行为，并和账面记账对账：
+  开仓 → 加仓(pyramid) → 部分减仓(de-risk) → 全平
+每一步都把"预期"和"HL 实际"并排打印，并用和 bot_engine 完全一致的
+公式做 PnL 自洽校验。
+
+用法:
+  source venv/bin/activate
+  HL_API_PRIVATE_KEY="0x..." HL_ACCOUNT_ADDRESS="0x..." \
+  python scripts/verify_sys2_lifecycle.py            # 干跑(不下单, 只打印计划)
+  ... python scripts/verify_sys2_lifecycle.py --live  # 真下单(小额, 需确认)
+
+安全:
+  - 默认 DRY-RUN, 不下任何单
+  - --live 才真下单, 且会要求手动输入 YES 确认
+  - 名义金额默认 $20, 上限 $40 (超过需 --force), 杠杆默认 2x
+  - 任何异常 / 结束都会尝试把仓位平掉 (best-effort flatten)
+  - mainnet/testnet 跟随 settings.hl_mainnet
+"""
+
+import argparse
+import asyncio
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from app.config import settings
+from app.services.hyperliquid import HyperliquidTrader
+from app.services.bot_engine import HL_TAKER_FEE_RATE
+
+ASSET = "BTC"
+SIDE = "long"
+HARD_CAP_USD = 40.0
+
+
+def _slice_pnl(notional: float, entry: float, exit_px: float, side: str) -> float:
+    """Identical to bot_engine: notional × signed move − round-trip taker."""
+    pct = (exit_px - entry) / entry if entry else 0.0
+    signed = pct if side == "long" else -pct
+    return notional * signed - notional * HL_TAKER_FEE_RATE * 2
+
+
+def _pos(positions: list):
+    return next((p for p in positions if p.get("coin") == ASSET), None)
+
+
+def _row(label, expected, actual):
+    print(f"  {label:<28} expected={expected!s:<22} actual={actual!s}")
+
+
+async def _flatten(trader, why: str):
+    try:
+        pos = _pos(await trader.get_open_positions())
+        if pos:
+            print(f"\n🧹 安全平仓 ({why}) — 当前 szi={pos['szi']}")
+            r = await trader.close_position(ASSET)
+            print(f"   平仓结果: {r}")
+        else:
+            print(f"\n🧹 无残留仓位 ({why})")
+    except Exception as exc:
+        print(f"\n⚠️  安全平仓失败 ({why}): {exc} — 请手动检查 HL!")
+
+
+async def main(live: bool, size_usd: float, leverage: int, force: bool):
+    api_key = os.getenv("HL_API_PRIVATE_KEY") or os.getenv("HL_API_KEY", "")
+    account = os.getenv("HL_ACCOUNT_ADDRESS", "")
+    if not api_key or not account:
+        print("❌ 需要 HL_API_PRIVATE_KEY 和 HL_ACCOUNT_ADDRESS 环境变量")
+        sys.exit(1)
+    if size_usd > HARD_CAP_USD and not force:
+        print(f"❌ size_usd ${size_usd} 超过安全上限 ${HARD_CAP_USD}（加 --force 才允许）")
+        sys.exit(1)
+
+    net = "MAINNET 真钱" if settings.hl_mainnet else "TESTNET"
+    add_usd = round(size_usd * 0.30, 2)          # 模拟 pyramid 第1档 (+30% base)
+    derisk_frac_of_cur = 1.0 / 3.0               # 模拟 de-risk 第1档 (减当前 1/3)
+
+    print("=" * 64)
+    print(f"System-2 生命周期验证 · {net} · {ASSET} {SIDE} {leverage}x")
+    print(f"计划: 开 ${size_usd}  → 加 ${add_usd}  → 减当前 1/3  → 全平")
+    print(f"模式: {'🔴 LIVE 真下单' if live else '🟢 DRY-RUN 仅打印'}")
+    print("=" * 64)
+
+    if not live:
+        print("\n干跑结束。确认计划无误后加 --live 真跑（小额）。")
+        return
+
+    confirm = input(f"\n⚠️  将在 {net} 下真单（约 ${size_usd}）。输入大写 YES 继续: ")
+    if confirm.strip() != "YES":
+        print("已取消。")
+        return
+
+    trader = HyperliquidTrader(
+        api_private_key=api_key, account_address=account,
+        leverage=leverage, mainnet=settings.hl_mainnet,
+    )
+
+    bal = await trader.get_balance()
+    print(f"\n账户可用 USDC: ${bal:.2f}")
+    if bal < size_usd:
+        print("❌ 余额不足，放弃。")
+        return
+    if _pos(await trader.get_open_positions()):
+        print(f"❌ 已存在 {ASSET} 持仓 — 为避免干扰，请先手动清空后再跑。")
+        return
+
+    try:
+        # ── 1. 开仓 ──────────────────────────────────────────────────────
+        print("\n[1] 开仓 open_position")
+        o = await trader.open_position(ASSET, SIDE, size_usd)
+        entry = float(o["fill_price"])
+        base_coins = float(o["size_coins"])
+        base_notional = base_coins * entry
+        pos = _pos(await trader.get_open_positions())
+        _row("entry fill", "~mkt", entry)
+        _row("size_coins", f"~{size_usd/entry:.6f}", base_coins)
+        _row("HL szi", f"~{base_coins:.6f}", pos and pos["szi"])
+        assert pos and abs(abs(pos["szi"]) - base_coins) / base_coins < 0.05, "开仓后 HL 仓位不符"
+
+        # ── 2. 加仓 (pyramid 第1档) ──────────────────────────────────────
+        print("\n[2] 加仓 open_position（模拟 pyramid +30%）")
+        a = await trader.open_position(ASSET, SIDE, add_usd)
+        add_fill = float(a["fill_price"])
+        add_coins = float(a["size_coins"])
+        actual_add_notional = add_coins * add_fill          # ← 和修过的 pyramid_add 一致
+        # 混合均价：按名义加权（与 bot_engine.pyramid_add 完全相同的公式）
+        old_notional = base_notional
+        new_notional = old_notional + actual_add_notional
+        blended = (old_notional * entry + actual_add_notional * add_fill) / new_notional
+        pos = _pos(await trader.get_open_positions())
+        exp_coins = base_coins + add_coins
+        _row("add fill", "~mkt", add_fill)
+        _row("add size_coins", f"~{add_usd/add_fill:.6f}", add_coins)
+        _row("blended entry", f"{blended:.2f}", "(账面)")
+        _row("HL szi", f"~{exp_coins:.6f}", pos and pos["szi"])
+        assert pos and abs(abs(pos["szi"]) - exp_coins) / exp_coins < 0.05, "加仓后 HL 仓位不符"
+
+        # ── 3. 部分减仓 (de-risk 第1档：减当前 1/3) ──────────────────────
+        print("\n[3] 部分减仓 reduce_position(1/3)")
+        pre_coins = abs(pos["szi"])
+        r = await trader.reduce_position(ASSET, derisk_frac_of_cur)
+        cut_fill = float(r["fill_price"])
+        closed_frac = float(r["closed_fraction"])
+        pos = _pos(await trader.get_open_positions())
+        post_coins = abs(pos["szi"]) if pos else 0.0
+        actually_cut = pre_coins - post_coins
+        slice_notional = actually_cut * cut_fill
+        slice_pnl = _slice_pnl(slice_notional, blended, cut_fill, SIDE)
+        _row("reduce fill", "~mkt", cut_fill)
+        _row("closed_fraction", f"~{derisk_frac_of_cur:.3f}", round(closed_frac, 4))
+        _row("coins cut", f"~{pre_coins/3:.6f}", round(actually_cut, 6))
+        _row("剩余 szi", f"~{pre_coins*2/3:.6f}", pos and pos["szi"])
+        _row("该片已实现PnL($)", "—", round(slice_pnl, 4))
+        assert 0.25 < closed_frac < 0.42, "减仓比例偏离 1/3 过大"
+
+        # ── 4. 全平 ──────────────────────────────────────────────────────
+        print("\n[4] 全平 close_position")
+        c = await trader.close_position(ASSET)
+        close_fill = float(c["fill_price"])
+        remaining_notional = post_coins * close_fill
+        remaining_pnl = _slice_pnl(remaining_notional, blended, close_fill, SIDE)
+        pos = _pos(await trader.get_open_positions())
+        _row("close fill", "~mkt", close_fill)
+        _row("收尾后持仓", "None", pos)
+        assert pos is None, "全平后仍有残留仓位！"
+
+        total_pnl = slice_pnl + remaining_pnl
+        print("\n" + "=" * 64)
+        print("对账小结（账面 vs 行为）")
+        print(f"  混合均价        : {blended:.2f}")
+        print(f"  分片PnL(减1/3)  : {slice_pnl:+.4f} USD")
+        print(f"  收尾PnL(剩2/3)  : {remaining_pnl:+.4f} USD")
+        print(f"  合计PnL         : {total_pnl:+.4f} USD（应≈HL账户实际变动，含滑点/费）")
+        print("  ✅ 开/加/减/平 四步与 HL 实际持仓全部一致")
+        print("=" * 64)
+        print("\n说明: 这里用市价瞬时往返，PnL≈ -往返taker费(~0.09%)±滑点，属正常。")
+
+    except AssertionError as e:
+        print(f"\n❌ 校验失败: {e}")
+    except Exception as e:
+        print(f"\n❌ 异常: {e}")
+    finally:
+        await _flatten(trader, "收尾")
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--live", action="store_true", help="真下单（默认干跑）")
+    ap.add_argument("--size", type=float, default=20.0, help="开仓名义USD（默认20）")
+    ap.add_argument("--leverage", type=int, default=2, help="杠杆（默认2x）")
+    ap.add_argument("--force", action="store_true", help="允许 size 超过安全上限")
+    a = ap.parse_args()
+    asyncio.run(main(a.live, a.size, a.leverage, a.force))

tests/conftest.py

@@ -0,0 +1,13 @@
+import os
+import sys
+from pathlib import Path
+
+
+BACKEND_ROOT = Path(__file__).resolve().parents[1]
+
+if str(BACKEND_ROOT) not in sys.path:
+    sys.path.insert(0, str(BACKEND_ROOT))
+
+# Tests import app.config at module import time. Provide a harmless default
+# DB URL so collection doesn't depend on the caller's shell env.
+os.environ.setdefault("DATABASE_URL", "sqlite+aiosqlite:///./test.db")

tests/test_bottom_indicators_and_leverage.py

@@ -0,0 +1,425 @@
+"""Tests for the current BTC bottom strategy:
+
+  - pure-price indicators (AHR999 / 200WMA / Pi Cycle) + 2-of-3 confluence
+  - leverage-aware dynamic System-2 leverage + protective stop
+  - staged stop-loss ladder math (no take-profit, ratchet-up only)
+"""
+
+import math
+
+from app.services.bottom_indicators import (
+    ahr999, below_200wma, pi_cycle_bottom, bottom_confluence,
+    AHR999_BOTTOM,
+)
+from app.services.signal_categories import (
+    sys2_effective_leverage, sys2_protective_stop_pct,
+    sys2_approx_liquidation_pct, get_stop_ladder, sys2_derisk_ladder,
+    sys2_addon_ladder, sys2_peak_trail, get_exit_profile,
+    SYS2_DEFAULT_LEVERAGE, SYS2_MAX_STOP_PCT,
+)
+from app.services.bottom_indicators import trend_confirmed
+
+
+# ── Indicators ──────────────────────────────────────────────────────────────
+
+def test_ahr999_none_when_insufficient_history():
+    assert ahr999([100.0] * 199) is None
+
+
+def test_ahr999_low_in_deep_decline():
+    # Long steady decline → price far below the geometric mean & growth model.
+    daily = [60000 * math.exp(-0.002 * i) for i in range(400)]
+    v = ahr999(daily)
+    assert v is not None and v < AHR999_BOTTOM
+
+
+def test_below_200wma_uses_explicit_price_not_stale_weekly():
+    weekly = [100.0] * 199 + [130.0]   # 200-wk mean ≈ 100.15, last close 130
+    # Using the stale weekly close → above band → False.
+    sig_stale, wma = below_200wma(weekly)
+    assert sig_stale is False and wma is not None
+    # Passing the fresh (lower) daily price → below band → True.
+    sig_fresh, _ = below_200wma(weekly, price=95.0)
+    assert sig_fresh is True
+
+
+def test_pi_cycle_bottom_none_when_short():
+    assert pi_cycle_bottom([1.0] * 470)[0] is False
+
+
+def test_confluence_fires_only_with_two_of_three():
+    # Deep decline: AHR999 deep-value + 200WMA both true → ≥2 → fired.
+    daily = [60000 * math.exp(-0.002 * i) for i in range(520)]
+    weekly = [60000 * math.exp(-0.01 * i) for i in range(210)]
+    c = bottom_confluence(daily, weekly)
+    assert c.votes >= 2 and c.fired is True
+    # Strong bull: AHR999 expensive (>0.45) and Pi not in bottom region →
+    # at most 1 vote → confluence requires ≥2 so it must NOT fire.
+    up_d = [20000 * math.exp(0.004 * i) for i in range(520)]
+    up_w = [20000 * math.exp(0.02 * i) for i in range(210)]
+    c2 = bottom_confluence(up_d, up_w)
+    assert c2.detail["signals"]["ahr999_value"] is False
+    assert c2.detail["signals"]["pi_cycle_bottom"] is False
+    assert c2.votes < 2 and c2.fired is False
+
+
+def test_confluence_b_uses_latest_daily_price():
+    # weekly close stale-high but the latest daily close is at the lows.
+    daily = [60000 * math.exp(-0.002 * i) for i in range(520)]
+    weekly = [30000.0] * 209 + [90000.0]   # last weekly close artificially high
+    c = bottom_confluence(daily, weekly)
+    # B must compare the 200wma against the latest DAILY close, not 90000.
+    assert c.detail["price"] == round(daily[-1], 2)
+
+
+# ── Dynamic System-2 leverage ────────────────────────────────────────────────
+
+def test_effective_leverage_clamps_and_defaults():
+    assert sys2_effective_leverage(None) == SYS2_DEFAULT_LEVERAGE
+    assert sys2_effective_leverage(0) == 1
+    assert sys2_effective_leverage(99) == 10
+    assert sys2_effective_leverage(5) == 5
+    assert sys2_effective_leverage("bad") == SYS2_DEFAULT_LEVERAGE
+
+
+def test_protective_stop_always_inside_liquidation():
+    # The protective full-exit must trigger BEFORE the exchange liquidates,
+    # for every allowed leverage.
+    for lev in range(1, 11):
+        stop = sys2_protective_stop_pct(lev)
+        liq = sys2_approx_liquidation_pct(lev)
+        assert stop < liq, f"lev {lev}: stop {stop} not inside liq {liq}"
+    # Low leverage keeps the full bottom-wick tolerance.
+    assert sys2_protective_stop_pct(1) == SYS2_MAX_STOP_PCT
+    assert sys2_protective_stop_pct(2) == SYS2_MAX_STOP_PCT
+    # High leverage tightens automatically.
+    assert sys2_protective_stop_pct(5) < 20
+    assert sys2_protective_stop_pct(10) < 10
+
+
+def test_stop_ladder_has_no_negative_base_rung_and_only_ratchets_up():
+    ladder = get_stop_ladder("btc_bottom_reversal_long")
+    assert ladder is not None
+    triggers = [t for t, _ in ladder]
+    floors = [f for _, f in ladder]
+    # Sorted ascending by trigger.
+    assert triggers == sorted(triggers)
+    # Floors strictly increase (pure ratchet-up: never loosens).
+    assert floors == sorted(floors)
+    # No 0%-trigger catastrophic rung — that floor is leverage-derived now.
+    assert triggers[0] > 0
+    # Reaches locked-in profit (a positive floor exists).
+    assert max(floors) > 0
+
+
+def test_non_bottom_category_has_no_ladder():
+    assert get_stop_ladder("sma_reclaim") is None
+    assert get_stop_ladder(None) is None
+
+
+# ── Staged-stop monitor math (mirror of tp_sl_monitor branch 0) ──────────────
+
+def _eff_stop(peak: float, base_stop_pct: float, ladder) -> float:
+    eff = -base_stop_pct
+    for trig, floor in ladder:
+        if peak >= trig and floor > eff:
+            eff = floor
+    return eff
+
+
+def test_staged_stop_no_take_profit_and_locks_profit():
+    ladder = get_stop_ladder("btc_bottom_reversal_long")
+    base = sys2_protective_stop_pct(2)   # 35%
+
+    # Never exits for a big unrealised gain (no take-profit).
+    assert 300.0 > _eff_stop(peak=300.0, base_stop_pct=base, ladder=ladder)
+
+    # Catastrophic floor before any rung: -35% at 2x.
+    assert _eff_stop(peak=0.0, base_stop_pct=base, ladder=ladder) == -35.0
+
+    # After +70% peak the floor has ratcheted to a locked-in profit.
+    locked = _eff_stop(peak=75.0, base_stop_pct=base, ladder=ladder)
+    assert locked > 0
+
+    # High leverage → tighter base floor, ladder still ratchets identically.
+    base10 = sys2_protective_stop_pct(10)
+    assert _eff_stop(peak=0.0, base_stop_pct=base10, ladder=ladder) == -base10
+    assert _eff_stop(peak=75.0, base_stop_pct=base10, ladder=ladder) == locked
+
+
+# ── Staged de-risk ladder (分段式减仓) ───────────────────────────────────────
+
+def test_derisk_ladder_shape_and_safety():
+    for lev in (1, 2, 3, 5, 10):
+        p = sys2_protective_stop_pct(lev)
+        liq = sys2_approx_liquidation_pct(lev)
+        ladder = sys2_derisk_ladder(lev)
+        assert len(ladder) == 3
+        thrs = [t for t, _, _ in ladder]
+        fracs = [f for _, f, _ in ladder]
+        finals = [fin for _, _, fin in ladder]
+        # All thresholds negative, increasing in adversity.
+        assert all(t < 0 for t in thrs)
+        assert thrs == sorted(thrs, reverse=True)   # -21, -28, -35 …
+        # Exactly the last rung is the full close.
+        assert finals == [False, False, True]
+        # Fractions sum to the whole position (thirds of original).
+        assert abs(sum(fracs) - 1.0) < 1e-9
+        # Final rung == the protective level == inside liquidation.
+        assert abs(thrs[-1] - (-p)) < 1e-6
+        assert -thrs[-1] < liq, f"lev {lev}: final {thrs[-1]} not inside liq {liq}"
+
+
+def test_derisk_pnl_accounting_matches_single_close():
+    """Summing the staged slices + the remaining close must equal a single
+    full close at the same final price (the staged path must not create or
+    destroy PnL vs closing all at once)."""
+    notional = 1000.0
+    entry = 100.0
+    final_price = 70.0   # -30% (long)
+
+    def slice_pnl(frac, px):
+        return notional * frac * ((px - entry) / entry)
+
+    # Staged: 1/3 closed at 85, 1/3 at 78, final 1/3 at 70.
+    staged = (
+        slice_pnl(1/3, 85.0) +
+        slice_pnl(1/3, 78.0) +
+        slice_pnl(1/3, 70.0)
+    )
+    # If instead all three thirds were closed at the final price:
+    single = slice_pnl(1.0, final_price)
+    # Staged exits EARLIER on the way down, so it must lose LESS than a
+    # single close at the worst price (the whole point of de-risking).
+    assert staged > single
+    # And closing all at entry-price slices would be zero — sanity.
+    assert abs(slice_pnl(1/3, entry) * 3) < 1e-9
+
+
+def test_derisk_regime_switch_underwater_vs_profit():
+    """Mirror the monitor's regime decision: underwater → de-risk ladder,
+    in-profit (peak ≥ first upside rung) → ratchet stop."""
+    stop_ladder = get_stop_ladder("btc_bottom_reversal_long")
+    first_up = min(t for t, _ in stop_ladder)   # 20
+
+    def regime(peak):
+        return "profit" if peak >= first_up else "derisk"
+
+    assert regime(0.0) == "derisk"
+    assert regime(19.9) == "derisk"
+    assert regime(20.0) == "profit"
+    assert regime(150.0) == "profit"
+
+
+# ── Pyramiding (做对了往上加仓) ──────────────────────────────────────────────
+
+def test_addon_ladder_shape_conservative():
+    ladder = sys2_addon_ladder()
+    trigs = [t for t, _, _ in ladder]
+    fracs = [f for _, f, _ in ladder]
+    lasts = [x for _, _, x in ladder]
+    assert trigs == sorted(trigs) and all(t > 0 for t in trigs)   # peak-gain rungs
+    assert fracs[:3] == [0.30, 0.20, 0.10]                        # conservative base
+    assert sum(fracs) <= 0.80                                     # still modest total
+    assert lasts[-1] is True and lasts.count(True) == 1           # exactly one final
+
+
+def test_trend_confirmed_requires_sma_and_new_high():
+    # Uptrend: price above 200d SMA and at a fresh 20d high → confirmed.
+    up = [100.0 + i for i in range(260)]
+    highs = [c + 1 for c in up]
+    price = up[-1] + 1
+    assert trend_confirmed(up, highs, price) is True
+    # Below the 200d SMA → not confirmed even at a local high.
+    assert trend_confirmed(up, highs, price=50.0) is False
+    # Above SMA but well below the recent high (still chopping) → not confirmed.
+    mid = sum(up[-200:]) / 200
+    assert trend_confirmed(up, highs, price=mid + 1) is False
+    # Too little history → fail closed.
+    assert trend_confirmed([1.0] * 50, [1.0] * 50, 1.0) is False
+
+
+def test_pyramiding_blended_entry_math():
+    """Blended entry after an add must be the notional-weighted average, and
+    it must sit BELOW the add price for a long that added higher (so the
+    aggregate is still in profit)."""
+    base = 1000.0
+    entry = 100.0
+    add_usd = base * 0.30
+    fill = 130.0                       # added after +30%
+    new_notional = base + add_usd
+    blended = (base * entry + add_usd * fill) / new_notional
+    assert entry < blended < fill      # weighted average
+    # Aggregate still profitable at the add price.
+    assert (fill - blended) / blended > 0
+    # Conservative sizing only modestly lifts the average (< 7% here).
+    assert (blended - entry) / entry < 0.07
+
+
+def test_recovery_restores_peak_keeps_profit_regime():
+    """A pyramided / in-profit trade rehydrated after a restart must seed the
+    monitor's peak from the persisted value so it stays in the profit regime
+    (not fall back to the underwater de-risk regime)."""
+    from app.services.tp_sl_monitor import register_trade, _watched, unregister
+    from app.services.signal_categories import (
+        sys2_derisk_ladder, sys2_addon_ladder, get_stop_ladder,
+    )
+    tid = 990011
+    try:
+        register_trade(
+            trade_id=tid, wallet="0xabc", api_key="k", leverage=2,
+            asset="BTC", side="long", entry_price=100.0,
+            take_profit_pct=None, stop_loss_pct=35.0,
+            stop_ladder=get_stop_ladder("btc_bottom_reversal_long"),
+            derisk_ladder=sys2_derisk_ladder(2),
+            addon_ladder=sys2_addon_ladder(), addon_done=1,
+            initial_peak=90.0,
+        )
+        wt = _watched[tid]
+        assert wt.peak_gain_pct == 90.0
+        assert wt.peak_persisted == 90.0
+        first_up = min(t for t, _ in wt.stop_ladder)
+        # peak 90 ≥ first upside rung → profit regime, NOT underwater de-risk.
+        assert wt.peak_gain_pct >= first_up
+    finally:
+        unregister(tid)
+
+
+def test_addon_ladder_extended_for_cycle_bull():
+    ladder = sys2_addon_ladder()
+    trigs = [t for t, _, _ in ladder]
+    fracs = [f for _, f, _ in ladder]
+    lasts = [x for _, _, x in ladder]
+    assert len(ladder) == 5                       # deeper continuation rungs
+    assert trigs == sorted(trigs) and trigs[-1] >= 200
+    assert lasts == [False, False, False, False, True]
+    assert abs(sum(fracs) - 0.75) < 1e-9          # ≤ +0.75× base, still modest
+
+
+def test_btc_bottom_maxhold_is_18_months():
+    p = get_exit_profile("btc_bottom_reversal_long")
+    assert p.max_hold_hours == 12960              # 540 days ≈ 18 months
+
+
+def _peak_trail_floor(peak_pp: float):
+    start, dd = sys2_peak_trail()
+    if peak_pp < start:
+        return None
+    return ((1.0 + peak_pp / 100.0) * (1.0 - dd) - 1.0) * 100.0
+
+
+def test_peak_trail_scale_invariant_and_never_loosens():
+    start, dd = sys2_peak_trail()
+    # Inactive below the start threshold.
+    assert _peak_trail_floor(start - 1) is None
+    # Self-scales: a +500% move locks far above the old fixed +95% top rung.
+    f500 = _peak_trail_floor(500.0)
+    assert f500 is not None and f500 > 300.0      # ≈ +320%
+    # A +900% move scales further still (not capped).
+    assert _peak_trail_floor(900.0) > f500
+    # Survives a normal bull pullback: at peak +120% the floor is a ≤30%
+    # PRICE drawdown from the peak, i.e. price 2.2→1.54 (gain +54%), so a
+    # typical 20–25% dip (still >+54%) does NOT trip it.
+    f120 = _peak_trail_floor(120.0)
+    assert 50.0 < f120 < 60.0                      # ((2.2*0.7)-1)*100 = 54
+    # Combined with the fixed rungs the floor only ratchets UP (max of both).
+    rung_at_160 = 95.0
+    assert max(rung_at_160, _peak_trail_floor(160.0)) == rung_at_160
+    assert max(95.0, _peak_trail_floor(400.0)) == _peak_trail_floor(400.0)
+
+
+def test_sys2_risk_mode_params():
+    from app.services.signal_categories import (
+        sys2_normalize_mode, sys2_addon_ladder, sys2_derisk_ladder,
+        sys2_peak_trail,
+    )
+    assert sys2_normalize_mode("AGGRESSIVE") == "aggressive"
+    assert sys2_normalize_mode("garbage") == "standard"
+    assert sys2_normalize_mode(None) == "standard"
+
+    # Leverage default depends on mode; explicit value still clamped.
+    assert sys2_effective_leverage(None, "standard") == 2
+    assert sys2_effective_leverage(None, "aggressive") == 8
+    assert sys2_effective_leverage(99, "aggressive") == 10
+
+    # Aggressive pyramiding: earlier + heavier, ≤ +1.5× base, one final.
+    ag = sys2_addon_ladder("aggressive")
+    st = sys2_addon_ladder("standard")
+    assert [t for t, _, _ in ag] == [15.0, 35.0, 60.0, 100.0, 160.0]
+    assert abs(sum(f for _, f, _ in ag) - 1.50) < 1e-9
+    assert [x for _, _, x in ag][-1] is True
+    assert ag != st                                  # genuinely different
+    assert abs(sum(f for _, f, _ in st) - 0.75) < 1e-9   # standard unchanged
+
+    # Aggressive de-risk keeps a bigger runner (¼/¼/½) but final still FULL.
+    agd = sys2_derisk_ladder(8, "aggressive")
+    assert [round(f, 4) for _, f, _ in agd] == [0.25, 0.25, 0.5]
+    assert agd[-1][2] is True                        # final = full close
+    std_d = sys2_derisk_ladder(8, "standard")
+    assert abs(std_d[0][1] - 1.0 / 3.0) < 1e-9       # standard unchanged
+    assert std_d[-1][2] is True
+
+    # Aggressive peak-trail: earlier start, wider give-back.
+    assert sys2_peak_trail("aggressive") == (60.0, 0.42)
+    assert sys2_peak_trail("standard") == (80.0, 0.30)
+    assert sys2_peak_trail() == (80.0, 0.30)         # default = standard
+
+    # Safety invariant holds in BOTH modes: final de-risk rung is the
+    # protective level (inside liquidation) and is a full close.
+    for m in ("standard", "aggressive"):
+        for lev in (2, 5, 8, 10):
+            lad = sys2_derisk_ladder(lev, m)
+            assert lad[-1][2] is True
+            assert -lad[-1][0] < sys2_approx_liquidation_pct(lev)
+
+
+def test_register_trade_default_peak_is_zero():
+    from app.services.tp_sl_monitor import register_trade, _watched, unregister
+    tid = 990012
+    try:
+        register_trade(
+            trade_id=tid, wallet="0xabc", api_key="k", leverage=3,
+            asset="BTC", side="long", entry_price=100.0,
+            take_profit_pct=None, stop_loss_pct=6.0,
+        )
+        assert _watched[tid].peak_gain_pct == 0.0
+    finally:
+        unregister(tid)
+
+
+def test_register_trade_grow_mode_default_off_and_settable():
+    """Per-trade Grow defaults OFF (pyramiding opt-in) and is settable."""
+    from app.services.tp_sl_monitor import register_trade, _watched, unregister
+    a, b = 990021, 990022
+    try:
+        register_trade(
+            trade_id=a, wallet="0xabc", api_key="k", leverage=3,
+            asset="BTC", side="long", entry_price=100.0,
+            take_profit_pct=None, stop_loss_pct=6.0,
+        )
+        assert _watched[a].grow_mode is False          # default OFF
+        register_trade(
+            trade_id=b, wallet="0xabc", api_key="k", leverage=2,
+            asset="BTC", side="long", entry_price=100.0,
+            take_profit_pct=None, stop_loss_pct=35.0,
+            grow_mode=True,
+        )
+        assert _watched[b].grow_mode is True
+    finally:
+        unregister(a); unregister(b)
+
+
+def test_auto_trade_gate_skips_when_off():
+    """The master gate: process_post must NOT open a trade when the sub has
+    auto_trade falsy (the signal Post is still created upstream by ingest)."""
+    import inspect
+    from app.services import bot_engine
+    # The per-subscriber executor holds the gate: keyed off auto_trade,
+    # returns (no trade opened) when off.
+    gate = inspect.getsource(bot_engine._execute_for_subscriber)
+    assert 'sub.get("auto_trade")' in gate
+    assert "Auto-Trade OFF" in gate
+    assert "return" in gate.split('sub.get("auto_trade")')[1][:260]
+    # process_post builds the snapshot carrying auto_trade so the gate reads it.
+    pp = inspect.getsource(bot_engine.process_post)
+    assert "auto_trade=bool(s.auto_trade)" in pp

tests/test_bottom_reversal_strategy.py

@@ -0,0 +1,95 @@
+from app.services.scanners.funding_reversal import evaluate_funding_reversal
+from app.services.scanners.sma_reclaim import evaluate_sma_reclaim
+from app.services.signal_categories import (
+    is_system_2,
+    is_supported_trading_source,
+    system2_display_name,
+    system2_min_confidence,
+    system2_size_multiplier,
+)
+from app.services.bot_engine import _confidence_floor_for, _should_apply_schedule
+from app.services.tp_sl_monitor import register_trade, _watched
+
+
+def _daily(close: float, volume: float = 100.0) -> dict:
+    return {"close": close, "volume": volume}
+
+
+def test_sma_reclaim_no_longer_emits_short_breakdowns():
+    candles = [_daily(110.0) for _ in range(200)]
+    candles.extend(_daily(110.0) for _ in range(31))
+    candles.append(_daily(90.0, volume=200.0))
+
+    is_signal, debug = evaluate_sma_reclaim(candles)
+
+    assert is_signal is False
+    assert debug["reason"] == "shorts_disabled"
+
+
+def test_funding_reversal_reports_boost_without_standalone_signal():
+    funding = [
+        {"time_ms": i * 3_600_000, "rate": -0.00005}
+        for i in range(24 * 29)
+    ]
+    funding.extend(
+        {"time_ms": (24 * 29 + i) * 3_600_000, "rate": -0.00001}
+        for i in range(24)
+    )
+    candles = [_daily(100.0) for _ in range(8)]
+    candles[-1] = _daily(104.0)
+
+    is_signal, debug = evaluate_funding_reversal(funding, candles)
+
+    assert is_signal is True
+    assert debug["direction"] == "buy"
+
+
+def test_bottom_reversal_source_routes_to_system_2():
+    assert is_system_2("btc_bottom_reversal")
+
+
+def test_old_scanner_sources_no_longer_route_to_module_2():
+    for source in ("rsi_reversal", "sma_reclaim", "funding_reversal", "breakout", "vcp_breakout"):
+        assert not is_system_2(source)
+
+
+def test_unknown_external_sources_are_not_supported_for_trading():
+    assert is_supported_trading_source("truth")
+    assert is_supported_trading_source("btc_bottom_reversal")
+    assert not is_supported_trading_source("manual")
+    assert not is_supported_trading_source("sma_reclaim")
+
+
+def test_module_2_uses_own_confidence_floor_and_bypasses_schedule():
+    sys2_sub = {"_is_system_2": True, "min_confidence": 95}
+    sys1_sub = {"_is_system_2": False, "min_confidence": 95}
+
+    assert system2_display_name() == "Bitcoin Bottom"
+    assert _confidence_floor_for(sys2_sub) == system2_min_confidence()
+    assert _confidence_floor_for(sys1_sub) == 95
+    assert _should_apply_schedule(sys2_sub) is False
+    assert _should_apply_schedule(sys1_sub) is True
+
+
+def test_register_trade_keeps_invalidation_price():
+    register_trade(
+        trade_id=99991,
+        wallet="0xabc",
+        api_key="key",
+        leverage=3,
+        asset="BTC",
+        side="long",
+        entry_price=100.0,
+        take_profit_pct=None,
+        stop_loss_pct=6.0,
+        trailing_stop_pct=5.0,
+        trailing_activate_at_pct=12.0,
+        invalidation="below_entry",
+        invalidation_price=92.5,
+        min_hold_until_ts=None,
+    )
+    try:
+        wt = _watched[99991]
+        assert wt.invalidation_price == 92.5
+    finally:
+        _watched.pop(99991, None)

tests/test_production_readiness.py

@@ -0,0 +1,110 @@
+import pytest
+
+from app.config import Settings
+from app.api import positions
+
+
+class _Scalar:
+    def __init__(self, value):
+        self._value = value
+
+    def scalar_one_or_none(self):
+        return self._value
+
+    def scalar_one(self):
+        return self._value
+
+    def scalars(self):
+        return self
+
+    def all(self):
+        return self._value
+
+
+class _Request:
+    async def json(self):
+        return {
+            "wallet": "0xabc",
+            "timestamp": 123,
+            "signature": "0xsig",
+        }
+
+
+class _Trade:
+    id = 7
+    wallet_address = "0xabc"
+    closed_at = None
+    hl_order_id = "paper"
+    leverage = 3
+    asset = "BTC"
+    exit_price = 101.5
+    pnl_usd = 2.25
+
+
+class _Sub:
+    leverage = 3
+    hl_api_key = None
+
+
+class _Db:
+    def __init__(self, responses):
+        self._responses = list(responses)
+
+    async def execute(self, _stmt):
+        return _Scalar(self._responses.pop(0))
+
+
+@pytest.mark.asyncio
+async def test_manual_close_returns_close_result(monkeypatch):
+    async def fake_close_and_finalize(**_kwargs):
+        return None
+
+    monkeypatch.setattr(positions, "verify_signed_request", lambda **_kwargs: None)
+
+    from app.services import bot_engine
+
+    monkeypatch.setattr(bot_engine, "close_and_finalize", fake_close_and_finalize)
+
+    db = _Db([_Trade(), _Sub(), _Trade()])
+
+    result = await positions.manual_close(7, _Request(), db)
+
+    assert result.status == "ok"
+    assert result.trade_id == 7
+    assert result.exit_price == 101.5
+    assert result.pnl_usd == 2.25
+    assert result.reason == "manual"
+
+
+@pytest.mark.asyncio
+async def test_open_positions_requires_signed_wallet_read(monkeypatch):
+    calls = []
+
+    def fake_verify(**kwargs):
+        calls.append(kwargs)
+
+    monkeypatch.setattr(positions, "verify_signed_request", fake_verify)
+    db = _Db([[]])
+
+    result = await positions.get_open_positions(
+        wallet="0xABC",
+        ts=123,
+        sig="0xsig",
+        db=db,
+    )
+
+    assert result.wallet == "0xabc"
+    assert calls == [{
+        "action": positions.ACTION_VIEW_POSITIONS,
+        "wallet": "0xabc",
+        "timestamp_ms": 123,
+        "signature": "0xsig",
+        "body": None,
+        "allow_replay": True,
+    }]
+
+
+def test_settings_default_to_production_when_environment_not_explicit():
+    settings = Settings(database_url="sqlite+aiosqlite:///./test.db")
+
+    assert settings.environment == "production"

tests/test_user_hl_api_key.py

@@ -0,0 +1,83 @@
+import pytest
+from fastapi import HTTPException
+
+from app.api import user
+
+
+def _make_sub():
+    class Sub:
+        wallet_address = "0xabc"
+        hl_api_key = None
+
+    return Sub()
+
+
+class _Scalar:
+    def __init__(self, value):
+        self._value = value
+
+    def scalar_one_or_none(self):
+        return self._value
+
+
+class _Db:
+    def __init__(self, sub):
+        self.sub = sub
+        self.committed = False
+
+    async def execute(self, _stmt):
+        return _Scalar(self.sub)
+
+    async def commit(self):
+        self.committed = True
+
+
+class _Body:
+    wallet = "0xabc"
+    timestamp = 123
+    signature = "0xsig"
+    api_key = "0x" + "1" * 64
+
+
+@pytest.mark.asyncio
+async def test_set_hl_api_key_saves_only_after_hyperliquid_verification(monkeypatch):
+    sub = _make_sub()
+    db = _Db(sub)
+
+    monkeypatch.setattr(user, "verify_signed_request", lambda **_kwargs: None)
+    monkeypatch.setattr(user, "encrypt_api_key", lambda value: f"encrypted:{value[-6:]}")
+
+    calls = []
+
+    async def verify_key(**kwargs):
+        calls.append(kwargs)
+
+    monkeypatch.setattr(user, "verify_hl_api_key_can_trade", verify_key)
+
+    result = await user.set_hl_api_key("0xabc", _Body(), db)
+
+    assert db.committed is True
+    assert sub.hl_api_key == "encrypted:111111"
+    assert result.status == "ok"
+    assert result.verified is True
+    assert calls == [{"api_key": _Body.api_key, "account_address": "0xabc"}]
+
+
+@pytest.mark.asyncio
+async def test_set_hl_api_key_does_not_save_when_hyperliquid_verification_fails(monkeypatch):
+    sub = _make_sub()
+    db = _Db(sub)
+
+    monkeypatch.setattr(user, "verify_signed_request", lambda **_kwargs: None)
+
+    async def fail_verify(**_kwargs):
+        raise HTTPException(422, "Hyperliquid rejected this API key")
+
+    monkeypatch.setattr(user, "verify_hl_api_key_can_trade", fail_verify)
+
+    with pytest.raises(HTTPException) as exc:
+        await user.set_hl_api_key("0xabc", _Body(), db)
+
+    assert exc.value.status_code == 422
+    assert db.committed is False
+    assert sub.hl_api_key is None