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>

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()
-        response = await client.chat.completions.create(
-            model=settings.ai_model,
-            max_tokens=600,
-            temperature=0.1,
-            messages=[
-                {"role": "system", "content": SYSTEM_PROMPT},
-                {"role": "user", "content": user_prompt},
-            ],
-        )
-        raw = (response.choices[0].message.content or "").strip()
+        if _use_anthropic():
+            # ── Native Anthropic SDK path ────────────────────────────────
+            anthropic_client = _get_anthropic_client()
+            # Map OpenAI-style model name → Anthropic model name
+            model_name = model
+            if "haiku" in model_name.lower() and "claude-" not in model_name.lower():
+                model_name = "claude-haiku-4-5-20251001"
+            msg = await anthropic_client.messages.create(
+                model=model_name,
+                max_tokens=600,
+                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("```"):