Additional tracker/leaderboard updates, overlay leaderboard, and
documentation improvements.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New modules for live/mock broker interface, SQLite session state,
auto-trading engine, and backtest result reporting.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replaces per-experiment rglob with single-pass manifest/metrics indexing
and adds lru_cache. Removes rarely-used commands from help display.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds overlay strategy backtesting, flexible date parsing, --no-trades flag,
--rank range selection, session management improvements, circuit breaker
for screener failures, and bars_cache passthrough for 10x speed gain.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Extends selector with new scoring model support, adds execution
enhancements, and improves snapshot store loading and split handling.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds earnings surprise extraction to parser/features/labeler pipeline,
improves filing fetcher robustness, and extends snapshot export with
new field support.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Bounce engine (buy negative reaction, bet on mean reversion) could not
execute: system architecture ties scoring to single model per backtest,
and selector/store indexes are optimized for positive-reaction PEAD.
Negative-reaction candidates get score=0 from PEAD scoring, blocking
engine selection regardless of engine-level threshold overrides.
Implementing bounce trades requires: dual scoring model support,
selector changes for negative-reaction candidate routing, and
store indexing changes. Deferred to future refactor.
Current best CW return: 293.2% (v6new.255)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Data analysis revealed OBV Q1 (distribution) has 56.4% WR vs Q5 51.2% —
contrarian signal confirmed. Previous OBV bonus was applied in wrong
direction. Corrected with v15 scoring models.
Best result: v6new.185 (entropy + risk 0.058) CW 274.4% but SQS 72.2,
still below v6new.122 (72.4). WFV/robustness offsets CW gains.
v6new.122 confirmed as optimal under current SQS v4 formula.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New data source integration:
- EarningsSurpriseService: GET /api/v1/earnings/surprise/{symbol}
Returns actual vs estimated EPS with surprise_percentage
- Feature builder: creates earnings_surprise_v1 snapshots for earnings events
- Backfill script runs for existing 1,273 tickers (Alpha Vantage rate limited)
New scoring (v11):
- Small beat (0-3% surprise): +10% bonus (82.4% WR in sample)
- Medium beat (3-8%): +5% bonus
- Big beat (>8%): no bonus (already priced in)
- Miss (<=0%): -5% penalty
Signal validation (n=66 sample):
Small beat: 82.4% WR, +1.79% mean 5d return
Big beat: 54.8% WR, +0.47%
Miss: 55.6% WR, -0.10%
Backfill running (~4 hours). Experiment pending data completion.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Phase 5 — Engine selection (both entry paths):
- Added residual_reserve_selected tracking between engines
- Added prelimit amplification (5x) for attention-requiring engines
- Added truncate_to parameter to select_candidates calls
Matches BacktestRunner._select_candidates_for_date() behavior.
Phase 6 — Macro data:
- Added FRED series fetch (VIXCLS, BAMLH0A0HYM2) to _fetch_macro()
- Matches SnapshotStore._fetch_macro() which loads from MacroObservation DB
- Enables VIX/HY regime sizing in live paper trading
All 6 phases of BacktestRunner ↔ PaperTradingEngine unification complete.
450 unit tests pass. Multi-strategy paper backtest verified.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Phase 1-4 of engine unification to eliminate research/live divergence.
Phase 1 — Scoring (event_detector.py):
EventDetector now uses config's scoring_model (v5/v9 etc.) when
event_v1 features are present (parse_confidence_overall not null).
Falls back to compute_entry_score only for incomplete events.
Phase 2 — Execution config (execution.py):
Extracted build_effective_execution_config() as shared function.
BacktestRunner delegates to it. PaperTradingEngine can now use
identical per-engine overrides, adaptive exit, tiered targets.
Phase 3 — Attention filtering (attention.py):
New AttentionFilterService class extracted from BacktestRunner.
Provides: engine_requires_attention, apply_filters, rescoring.
BacktestRunner now delegates to this service.
PaperTradingEngine can import and use the same service.
Phase 4 — Gap cap (execution.py):
check_next_open_gap_cap() shared function for next-open gap rejection.
All 450 unit tests pass. Paper backtest verified working.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Snapshots can be in data/parquet/ or data/datasets/snapshots/.
Now tries default parquet_dir first, falls back to data/datasets/snapshots/
if the snapshot exists there instead.
Fixes FileNotFoundError when running multi-strategy paper backtest with
configs that reference snapshots in the alternate directory.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
run_backtest changed from async to sync function. Pipeline refresh
(async) runs via asyncio.run() before the sync BacktestRunner,
avoiding nested event loop when SnapshotStore.load() calls asyncio.run().
CLI updated to call run_backtest() directly (no asyncio.run wrapper).
Tested: `fithia2 paper backtest --config v6new.24 --start 2025-03-23 --end 2026-03-23` works.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When `fithia2 paper backtest --end <date>` requests a date beyond the
snapshot's latest event, automatically runs the pipeline:
1. Filing poller (discover new 8-Ks)
2. Filing fetcher (download exhibits)
3. Event parser (parse events)
4. Feature builder (compute features)
5. Label generator (compute labels)
6. Dataset export (re-generate Parquet snapshot)
Staleness check: snapshot is stale if its latest event_date is >14 days
before the requested end_date, or if the manifest is >7 days old.
If refresh fails, falls back to existing snapshot data gracefully.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Major refactor: `fithia2 paper backtest` now uses the exact same
BacktestRunner + SnapshotStore pipeline as `apps/backtester/run.py`.
Before: PaperTradingEngine + EventDetector + MockBroker
- Different scoring (compute_entry_score vs config scoring_model)
- Different data source (DB + Oracle vs Parquet snapshot)
- Different feature computation (real-time vs pipeline)
→ Config gate changes didn't take effect in paper backtest
After: BacktestRunner + SnapshotStore (Parquet)
- Identical scoring, engine matching, position sizing
- Same Parquet data as research backtester
- Config changes work identically in both systems
Trade output format preserved for reporter.py compatibility.
PaperTradingEngine still used for live Alpaca trading (unchanged).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The DB-first approach (prefer feature_json over Oracle recalculation) caused:
- LMND (+$782) and M (+$1,052) trades to disappear
- TEM loss to increase from -$321 to -$535
- Overall PnL drop from +$5,948 to +$3,078
Root cause: DB features were computed at a different time with different
Oracle data. When paper trader used DB values, the feature values didn't
match what the backtester's Parquet snapshot had, causing different
engine gate outcomes.
Paper trader must use Oracle real-time enrichment as primary source
(same as the original design). The volume_ratio_20d field name fix
is retained as that was a genuine bug.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The _compute_score → v5 dispatch caused v5's hard gates to reject almost
all events (v5 requires specific direction/guidance combos). This killed
all 2025 trades in paper backtest.
Root cause: BacktestRunner and PaperTradingEngine use different flows.
BacktestRunner applies scoring AFTER engine selection (engines have
score_threshold_override=0.0 that bypasses score gates). But EventDetector
applied scoring BEFORE engine matching, causing v5's hard gates to reject
events that engines would have accepted.
Fix: revert to compute_entry_score for EventDetector. Score is ranking-only
in paper trading; engine gates (reaction_min, close_min, etc.) handle filtering.
The volume_ratio_20d fix and DB-first feature fix remain in place.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three critical inconsistencies between BacktestRunner and PaperTradingEngine
that caused gate fixes to not work in paper trading:
1. DB feature values now take priority over Oracle recalculation
- Previously: Oracle bars always recomputed reaction_day_return etc.
- Now: if DB feature_json has the value, Oracle fallback is skipped
- Root cause of PII bug: DB had react=-5.3% but Oracle recomputed +13.9%
due to different date alignment, bypassing engine reaction_min gate
2. Scoring now uses config's scoring_model (v5/v8/v9/v10 etc.)
- Previously: always used compute_entry_score() regardless of config
- Now: _compute_score() dispatches to the correct scoring function
- Ensures hard gates and weights match between backtest and paper trading
3. volume_ratio_20d field name consistency (from prior commit)
These fixes ensure paper trading results match backtester behavior,
making engine gate changes (reaction_min, close_min, etc.) effective
in both systems.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
EventDetector computed volume_ratio as fallback but selector checks
volume_ratio_20d. When DB feature_json was missing this field, the
volume gate was silently bypassed in paper trading — allowing trades
like LKQ (vol=0.8) that the backtest correctly blocks.
Now sets both volume_ratio_20d and volume_ratio for consistency.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New features:
- _enrich_macro_features() in snapshot_export: adds macro_vix, macro_hy_spread from FRED
- compute_return_max_long_score_v10: +12% bonus in favorable regime (VIX>18+HY>3.25)
- _macro_regime_score(): regime-aware scoring component
Findings:
- VIX signal is statistically strong: 62.3% WR (VIX>18+HY>3.25) vs 50.8% (other)
- But scoring bonus promotes marginal trades, diluting OOS quality
- Same pattern as eps_growth, drift bonus: raw signal ≠ scoring improvement
- v6new.17 SQS 59.1 < v6new.9 SQS 63.3
v6new.9 remains best at SQS 63.3 after 17 experiments.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Track experiment cycles with SQS scoring (0-100), JSONL journal, and
auto-generated leaderboard to prevent duplicate experiments and enable
data-driven strategy decisions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove 5 non-alpha features (earnings surprise, risk penalty, parse confidence,
direction clarity, LM sentiment) from composite score to eliminate double-counting
with hard gates and noise sources. Redistribute weights to 5 alpha features.
Add default-deny for unknown event types, no-follow-through early exit (D+1),
kill switch log-only mode, macro regime size scaler. Remove SUE gate (Gate 8).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fix kill switch reset: remove unreachable drawdown recovery condition
(equity can't change while trading is halted), reset peak_equity and
drawdown_pct to 0 on cooldown expiry
- Raise veto_oneoff_penalty threshold 0.5 → 0.7 (was blocking 67% of
candidates due to high median oneoff_penalty in dataset)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Flip scoring weights so event/document quality is primary signal (55%)
and market confirmation is secondary (35%). Add research mode with
kill-switch cooldown/reset, veto gates for bad events, reduced portfolio
risk, and 4 diagnostic analysis scripts.
Phase A: Research mode kill-switch reset, risk reduction (0.5%/trade,
max 4 positions), bullish-only direction for all event types.
Phase B: 2 new sub-scorers (parse_confidence, direction_clarity),
4 veto gates (oneoff risk, parse confidence, unknown/bearish direction).
Phase C: signal_quality, event_type_decomposition, kill_switch_impact,
concurrent_position analysis scripts.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
equity was computed as cash + unrealized_pnl where unrealized_pnl =
(close - entry) × shares. Since cash already had entry cost subtracted,
this double-counted the cost basis:
buggy: equity = (initial - entry×shares) + (close - entry)×shares
= initial + close×shares − 2×entry×shares ← WRONG
correct: equity = cash + market_value
= (initial - entry×shares) + close×shares
= initial + (close − entry)×shares ← RIGHT
This caused drawdown to spike to ~73% the instant a position opened
(e.g. TSLA $330 × 222 shares → equity appeared to drop from 100k to
27k), falsely triggering the kill switch at 25% and blocking all
subsequent entries.
Before fix: 3 trades, +0.08% return, 39.2% max drawdown (fake)
After fix: 10 trades, -2.63% return, 4.24% max drawdown (real)
Also: when bar data is missing, positions now use entry_price as
fallback market value instead of treating the position as worthless.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- BacktestRunner.run() now iterates all NYSE trading days (not just candidate
days) via SnapshotStore.all_trading_days() so stop/target/time exits are
checked every day, not only on days with new candidates
- Record initial DailyPortfolioState before simulation loop starts so
total_return_pct is computed relative to the true initial equity (100k),
not the first post-entry equity snapshot
- SnapshotStore._fetch_event_metadata() now synthesises event_timestamp from
event_date + 21:00 UTC when filed_at_utc is NULL (transparent enrichment at
loader boundary, not silent substitution in selector)
- SnapshotStore._async_load() maps event_close → entry_price_est when the
column is absent, and derives score from abs(reaction_day_return) when the
Parquet snapshot has no score column
- Add --snapshot-dir CLI flag to BacktestRunner to override the default
parquet_dir base path (needed for non-standard snapshot locations)
- Fix integration test assertion: total_trading_days >= 2 (was == 2)
- Add configs/experiments/realdata_test_v1.json for real Phase 3 snapshot runs
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add `think: False` and `num_ctx: 8192` to Ollama payload:
Qwen3.5 extended thinking mode generated 1300+ internal reasoning
tokens before each response, adding 30-60s latency per LLM call.
Disabling it reduces parse time from 600s timeout to ~13s.
- Rewrite OllamaClient to use sync httpx.Client inside asyncio.to_thread():
Async httpx inside an active asyncpg SQLAlchemy session context on
Python 3.13 hung indefinitely. Synchronous httpx in a thread pool
completely isolates Ollama I/O from the asyncio event loop.
- Fix filing_poller to set issuer_id/symbol_id on Document records:
Missing FK caused feature_builder to reject all events with
event_no_symbol warning. Now looks up IssuerMaster/SymbolMaster
by ticker before creating Document rows.
- Update test_llm_client to mock _sync_call instead of _client attr.
- Raise ollama_timeout default to 600s for large document processing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- filing_poller: add --start-date/--end-date CLI args for historical backfill
(defaults to 7 days ago when omitted)
- OracleClient.get/post: apply with_retry(max_attempts=3) so transient
connection errors, timeouts, and 5xx responses are automatically retried
with exponential backoff (0.1s→0.2s→fail)
- financial_features: new compute_financial_features() extracting latest_eps,
latest_gross_margin, latest_operating_margin, eps_growth_qoq,
revenue_growth_qoq from FinancialDataResponse
- feature_builder: wire FinancialService into build_features_for_event(),
persisting financial_v1 FeatureSnapshot (non-fatal if unavailable)
- tests: 94 pass (81→89 unit + 5 replay); +8 new tests covering financial
features and retry success path
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
I Luk Kim