Extends the local-LLM layer from news to TRADES, reusing the proven enrichment
pattern (structured JSON, Postgres cache, off-hot-path scheduler, graceful
degradation). Advisory only — never gates a trade.
strategy/evaluation/trade_analysis.py: four analyses keyed by analysis_type —
* armed — does a loaded setup still make sense to pursue? (pursue/avoid/
monitor + confidence + concerns), weighing numbers + catalyst
* weak — why a too-soft setup failed and whether a thesis remains
* postmortem — closed-trade lesson (uses r_multiple / exit_reason)
* eod — once-a-day session narrative
Schema-constrained decoding (ANALYSIS_SCHEMA), tolerant _parse, and a
context_hash so an unchanged board isn't re-sent to the GPU. run_* helpers
upsert into ai_trade_analysis_cache; trade_analysis_map / query_trade_analysis
read it back. NEVER raise.
Wiring:
- ai_trade_analysis_cache table (storage/pg_schema.sql)
- OllamaConfig.trade_analysis_{enabled,interval_seconds,batch_limit} + env
- run_live_paper step_analyze on the Scheduler, gated by ollama.enabled AND
TRADE_ANALYSIS_ENABLED (ships OFF); reads the in-memory trigger book + day P&L
- query_trade_analysis projection; api snapshots() attaches the verdict to armed
triggers + approval rows and exposes trade_analysis
- dashboard: per-name AI verdict chip (pursue=green/avoid=red/monitor=amber) +
an "AI session note" panel
- .env.example documents the toggles
Tests: parse/enum/clamp, prompt builders, persistence + context_hash dedupe,
closed-trade + EOD paths, config from_env, and the dashboard projection. 230 pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M5HTMkaadGDnAPtXe2hWwg
Leverage a local Ollama model to interpret news catalysts for the small-cap
momentum strategy — distinguishing bullish catalysts (FDA approval, M&A) from
dilutive offerings the technicals alone can't see. Advisory-first, then automate.
Phase 1 (advisory; no change to trade behavior):
- research/ingestion/news_enrichment.py: stdlib urllib call to Ollama with
structured JSON output, classifying each headline into a catalyst type,
sentiment, conviction and dilution flag. Graceful degradation (None on any
failure) so trading is never blocked.
- news_catalyst_cache table + populate the previously-dead news_events.sentiment.
- Runs on the existing interval Scheduler (off the hot path), gated by
OllamaConfig; deduped by the raw_news_items payload hash.
- Surface the advisory on the dashboard approval queue (projection + api + UI).
Phase 2 (automation; ships gated OFF):
- Optional catalyst_score blended into calculate_setup_quality (None reproduces
the exact legacy weights); pure pass-through via evaluate_setup.
- Dilution veto in TradingExecutionService (slow + fast paths) emitting a
catalyst_dilution_veto risk event, behind NEWS_DILUTION_VETO_ENABLED.
Config (OllamaConfig.from_env), .env.example docs, and tests for parsing,
persistence/dedupe, the projection, the score blend, the veto, and an
end-to-end enrich->advisory flow (Ollama mocked). Default model moves to
qwen2.5:7b-instruct (best small-model JSON, fits a 12GB GPU).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M5HTMkaadGDnAPtXe2hWwg
Day-extension guard (the H1 gap from the prior review): the trigger-extension
ceiling only sees distance above the ORB high, so a name up huge intraday whose
break is itself CLEAN slips through — real example ATPC, entered +1.7% above the
trigger yet +32% above the session open, now a loser. New guard blocks when the
entry is > day_extension_max_pct above the SESSION OPEN. Default 0.30 calibrated
on 22 real fires: normal entries are <=14% above the open, the parabolic tail is
20-83% (ICCM +83/+41, BIRD +36, ATPC +34/+32, NEOV +29). day_open is computed in
step_arm (first RTH bar open), carried on ArmedTrigger, checked in
submit_breakout_now. Env TRADING_DAY_EXTENSION_MAX_PCT; 0 = off.
Percentage breakeven (the "ratchet to a better stop once it works" request): the
broker-side stop ratchet already exists (exit_manager replace_order), but its
earliest move was +1R — a big move for a wide opening-range stop. breakeven_at_pct
moves the stop to entry once price is +this fraction above entry, BEFORE 1R, so a
pop that reverses can't round-trip to a loss (the documented small-cap failure).
Lives in the shared strategy/exits._trail_stop (backtest + live agree), only ever
ratchets up. Default 0.05 live (TRADING_EXIT_BREAKEVEN_PCT); _is_active updated so
the manager runs when it's the only rule set.
tests/unit: 160 passed (+ day-ext block/allow/no-open, + pct-breakeven move/hold/
saves-reverser).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
For going live on a SMALL real account ($300). The sizer assumed a $100k
default -> at $300 it would size $1,000 positions and get rejected. Now:
- _current_equity() reads actual broker equity (cached get_account) and sizes
off it, so a $300 account sizes for $300.
- max_position_value caps each position to ~1/3 of equity (TRADING_MAX_POSITION_PCT)
so N positions fit buying power.
- max_shares liquidity cap (TRADING_LIQUIDITY_MAX_VOLUME_PCT * day volume) wired
through the trigger book -> submit_breakout_now; off at small size, for scaling.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- strategy/exits.py: percentage profit-lock tiers (TRADING_EXIT_PROFIT_TIERS,
e.g. 8:3,15:9,25:18,40:30). Once a trade has been up gain%, the stop is pinned
to entry*(1+lock%) so a winner can only give back to a locked-in minimum gain
and a fade sells you out IN PROFIT. Shared by backtest + live, active-trades
only (ratchets the broker stop up via replace, never cancel).
- momentum_cli.py: open-positions uPnL was reading 'unrealized_pl' but the sync
stores 'unrealized_pnl' -> always blank. Read the right key (+ green/red).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replace the static -1R/+2R bracket with active, parameterised exit management
shared by the backtest and live (strategy/exits.py): move to breakeven, trail
under prior-bar lows or by %, scale out, or first-red exit.
Swept all variants over 187 sessions with a held-out train/test split
(sweep_exits.py), then an adversarial 3-judge workflow picked the robust winner
OUT-OF-SAMPLE: trailLow_a1 (trail under the prior bar's low after +1R) — test
avgR 0.128 vs the old static_2R 0.067 (~1.9x), win 49% vs 38%. The naive
in-sample winner (trailPct10) was correctly rejected as overfit
(train 0.684 -> test 0.085).
- strategy/exits.py: ExitConfig + simulate_exit (backtest) + manage_live (live)
- strategy/backtest/engine.py: simulate the managed exit on entry; Trade.entry_index
- runtime/exit_manager.py: live manager — ratchets the broker stop UP via
REPLACE/PATCH (never cancel = never naked), scales out, first-red exit
- alpaca_paper/client.py: replace_order (PATCH) + partial close_position
- run_live_paper.py: 'exits' step (12s); TRIGGER_MAX_ARMED 6 -> 10
- tests: exits sim/live + manager ratchet-never-cancel
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The "always late / miss the move" problem was structural: entries waited for a
1-min bar to CLOSE above the breakout then walked the ingest->watch->execute
cadence (~2 min), and rested a limit AT the trigger that a runner never fills.
Add a top-6 armed-trigger book (runtime/triggers.py): keep the most-promising
gappers ready with a pre-computed opening-range trigger/stop and FIRE the
instant LIVE price crosses, via TradingExecutionService.submit_breakout_now —
a marketable order (capped above the trigger) that actually fills — reusing the
same risk gates (daily-loss breaker, max-concurrent, 1%-risk sizing, dedup).
- structure.py: opening_range() helper (shared with the ORB detector)
- run_live_paper.py: step_arm (rank + arm, 20s) + step_trigger (price-cross
fire + snapshot, 4s)
- momentum_cli.py: rewrite the watch board — market clock + countdown, loop
heartbeat, the 6 armed triggers with live price / gap / rvol / trigger /
distance / stop, and a plain-English "what it's hunting" line
- tests: opening range, trigger book state machine, submit_breakout_now gates
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Sweep over 172 real gapper sessions (16 days) with a per-setup breakdown found
the opening-range breakout is the ONLY profitable setup (+0.25R); every slow
pattern loses (bull_flag -$78k @ ~42min is the worst). Running ORB-ONLY flips
the book from -$95k to +$7.4k AND enters ~11 min earlier (23 vs 34 min avg).
- classify_setup prefers the ORB when valid and honors STRATEGY_SETUPS env
(comma-sep setup values) to restrict active setups; .env defaults to
STRATEGY_SETUPS=opening_range_break (ORB-only).
- BacktestEngine Trade.setup_type captured for per-setup attribution.
- learn_params load_sessions filters to gapper sessions (mimics live scanner) so
the backtest trades the same universe the system would.
75/75 tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- structure.detect_opening_range_breakout: break of the first ~5 RTH bars' high
on >= OR-average volume; entry=OR high, stop=OR low. Placed first in
classify_setup so it fires minutes after the open instead of waiting for a
pullback to form. Verified: RGNT triggers at bar 6 (~9:36 ET) q=1.0.
- evaluate_setup counts OPENING_RANGE_BREAK toward the structure criterion.
- backfill_history.py: pull ~3 weeks of minute + 60d daily bars for the sub-$20
universe so learn_params optimizes over real sessions.
75/75 tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- learn_params.py: grid-search the strategy timing/threshold knobs over stored
sessions, ranking by total P&L AND 'entry_min' (minutes after the open the
entry fires) — a learning method to find params that enter earlier while
staying green.
- evaluate_setup(min_bars=...) + BacktestEngine(ready_score_pct/criteria/min_bars)
now tunable; watcher passes WATCHER_MIN_BARS through (was a no-op: evaluator
hard-coded 10).
Sweep finding: even the earliest params enter ~28-30 min after the open
(baseline ~33) because the first-pullback setup waits for a pullback to FORM.
Cadence/min_bars are not the bottleneck — the setup design is. Genuine earliness
needs a fast opening-breakout trigger + more historical sessions to learn from.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- SetupEvaluationResult.criteria_detail: per-criterion {name,passed,reason}.
- watcher CriteriaEvaluatedEvent now carries gap_pct, relative_volume, status,
reason, and the per-criterion detail (was only passed/failed names).
- CLI: 'inspect criteria SYMBOL' shows status + gap/rvol + each criterion reason;
new 'inspect signals' = ready board + per-symbol gap/rvol/status.
Verified on real 2026-06-15 bars: RGNT shows 9/9 with reasons (rvol 26.2x),
'late' explained by the 10:30 ET cutoff. 67/67 tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Drop duckdb dependency (pyproject, requirements) -> psycopg2-binary.
- run_live_paper: boot log shows the real Postgres target (masked); add
_db_target_desc() helper.
- .env.example: document DATABASE_URL / PG* vars; mark WATCHER_EVENT_DB_PATH legacy.
- evaluation/storage.py: INSERT OR REPLACE -> INSERT (setup_id is a fresh uuid).
Verified: 67/67 tests green on Postgres with duckdb UNINSTALLED; app boots
against the live endpoint (127.0.0.1:5432 proxy) and wrote 205 daily bars.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Snapshot before the DuckDB->PostgreSQL migration. data/, archive/, the 232MB
rebuild zip, the Windows .venv, and .env are excluded via .gitignore.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude