Three plumbing fixes + one ops doc that close the gaps from the audit.
scripts/rescore_v5.py
Was overwriting only signal/conf/reasoning/sentiment/relevant/
prefilter_reason/analysis_version. Now also persists target_asset,
category, expected_move_pct — without these the bot can't route
rescored posts correctly (would silently fall back to BTC).
app/schemas.py + app/api/posts.py
TrumpPost response model didn't expose target_asset/category/
expected_move_pct, so the frontend had no way to display "this
signal will trade SOL". Added the three fields + mapping in
_post_to_schema(). Pre-v5 posts return null. No frontend changes
yet — display work is a follow-up.
app/services/hyperliquid.py
HL caps max leverage per asset (BTC/ETH 50×, SOL 20×, memes 3-5×).
set_leverage() always tried to push self._leverage — if user set
30× and bot routed to TRUMP, HL rejected the order and the trade
silently dropped. Added _get_max_leverage() (queries meta()'s
maxLeverage field) and _clip_leverage() that caps to HL's max.
set_leverage now returns the effective leverage so callers can
use it for notional sizing if needed.
deploy/ENCRYPTION_KEY_BACKUP.md
Documented mandatory backup procedure for the symmetric key that
encrypts every user's HL API key. Lost key = all users' bots dead
with no recovery. Includes rotation procedure + quarterly test
step + things-not-to-do list.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
scripts/sweep_500.py:
Parameter grid for capital-preservation oriented configs.
Risk filters: max DD <= 30%, max single loss <= 16% of capital.
scripts/sweep_moonshot.py:
Aggressive grid for one-trade-hits-big strategy.
Looser DD ceiling (50%), prioritizes biggest single-trade upside.
Both run on the local v4 dataset to inform initial subscription
parameter choices for live trading. Re-run after v5 accumulates
enough signals (~6 weeks) to recalibrate.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Closes the loop on the asset-routing prompt change. Previously the v5
prompt emitted target_asset (e.g. SOL, TRUMP) but bot_engine still
read price_impact_asset and only ever traded BTC/ETH. Now the trade
actually fires on whatever perp the AI picked.
Schema (alembic 006):
posts.target_asset (str) — HL perp ticker, any of the universe
posts.category (str) — 6-class enum (direct_named, etc.)
posts.expected_move_pct (float) — AI's 1h move estimate
Wiring:
truth_social.py persists the three fields when creating Post rows.
bot_engine.py routing: asset = target_asset || price_impact_asset || BTC
Old rows (target_asset=NULL) fall back to legacy BTC/ETH path — no
retroactive scoring needed; new rows route correctly from now on.
Hyperliquid trader doesn't need changes — `coin` is already a parameter,
and analysis.py validated against HL_PERPS before storing target_asset
so by the time bot_engine reads the field, it's guaranteed tradeable.
Deployment:
alembic upgrade head # adds the 3 columns
Restart api container
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Why
Trading BTC on every actionable signal leaves alpha on the table.
The same Trump post moves different assets very differently:
• "Strategic Reserve including SOL" → BTC +8%, SOL +33%
• "Tokenize Treasuries on ETH" → ETH +5%, BTC +2%
• "$TRUMP coin is GREATEST" → TRUMP +50%+, BTC ~0%
Picking the wrong asset is silent alpha leak.
What changed
• SYSTEM_PROMPT: new ASSET ROUTING section with 6 named categories
(direct_named / crypto_policy / macro_risk_on / macro_risk_off /
defi_thematic / meme_named) + asset-pick rules per category.
• Few-shot examples now show category + target_asset + expected_move.
• Output JSON adds: category, target_asset, target_chain,
expected_move_pct.
• Python normalization:
- HL_PERPS whitelist (current Hyperliquid perp universe).
- CHAIN_FALLBACK map: meme not on HL → trade chain native
(e.g. $FLOKI on Solana → SOL; ETH-chain meme not on HL → ETH).
- Safe default → BTC if everything else fails.
- expected_move_pct < 0.8 → coerce to hold (not worth fees).
- Legacy `asset` field kept aligned to BTC/ETH for the existing
price_impact tracker; alts leave it null until tracker is upgraded.
Note
Bot routing (bot_engine.py) NOT yet updated — it still trades whatever
`price_impact_asset` is, which is BTC/ETH only. The new fields are
emitted and stored in DB but consumed downstream in a follow-up.
This commit is "AI says the right thing" — making the bot ACT on it
is the next step.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
After deploying v5 analysis.py, run this once to overwrite v4 scores in
the DB with v5's interpretation. Idempotent — skips rows already at v5.
Has --dry-run mode to preview the change without AI calls or DB writes.
Live mode prompts for confirmation (skipped if stdin is non-tty so it
also works under `docker exec`).
Touches only AI-derived columns (signal, ai_confidence, ai_reasoning,
sentiment, relevant, prefilter_reason, analysis_version). Leaves all
market-derived columns intact (price_at_post, price_impact_*) — those
stay accurate regardless of which prompt version interpreted the post.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
next_run_time=None means "paused", not "default". The fallback poller was
registered but never triggered, leaving the system on CNN archive only.
Set explicit start time = now + half the poll interval so the two pollers
offset and don't hit upstream simultaneously.
Verified via /api/health/deep: trumpstruth.last_poll was null on the live
server before this fix despite the poller having been deployed.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Why
v4 was firing buy/short on 13% of posts, but only 9% of those had a
≥1% move within the hour. Median move on 'actionable' was 0.298% vs
0.258% on 'hold' — a 1.15× signal-to-noise ratio (random would be 1.0).
The model was confabulating transmission chains to please the user
rather than holding when uncertain.
Separately: 'sell' meant 'close longs / de-risk' in the prompt but
was traded as 'open short' by bot_engine.py, producing systematically
negative results on sell signals (27% win rate vs 57% on real shorts).
What changed
• analysis.py rewritten as v5-extreme-alpha:
- Asymmetric error costs framing (false positive = -$30, FN = $0)
- 7-item checklist that MUST all pass before buy/short
- Only 4 named transmission paths (a/b/c/d); anything else = HOLD
- 5 positive + 5 negative few-shot examples
- UTC hour injected with liquidity context (Asia thin → stricter)
- Adversarial steelman self-check before final output
- confidence < 80 + checklist failure both force-collapse to HOLD
in code, regardless of what the model returns (defense-in-depth)
- 'sell' removed from output schema entirely
• bot_engine.py: stop trading 'sell' signals (treat as hold)
• Case-insensitive normalization on checklist values so model
returning 'None'/'True' (capitalized) doesn't slip through
Expected impact (to validate over next 2-3 weeks of new posts)
• actionable rate: 13% → 2-4%
• signal/hold MFE ratio: 1.15× → 3-5×
• ≥1% hit rate among actionable: 9% → 40-60%
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- New migrations for daily_budget, active_window, and bottrade snapshot
- Add trumpstruth scraper and price_impact_monitor service
- Expand bot_engine, hyperliquid, recovery, and tp_sl_monitor logic
- Update API/schemas/models for new features
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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