10 Commits (4d0e773ba0c0e5108b40ac7ddc82aad3bd17a208)

Author SHA1 Message Date
I Luk Kim 4d0e773ba0 feat: overhaul strategy — document quality > price momentum
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>
5 months ago
I Luk Kim cdf6ae3493 feat: Phase 5 fundamental strategy improvements
Fix core strategy flaws identified from academic research and Phase 4
backtest results (23% win rate, 0% target hits, 77% stop exits).

5A — Exit mechanics: ATR-based targets (reachable ~4.5% vs unreachable ~6-8%),
     partial profit-taking at target with breakeven stop on remainder,
     wider catastrophic stop (3.0 ATR), trailing stop enabled by default.
5B — Event-type-specific logic: EventTypeProfile with per-type overrides
     for holding days, ATR multipliers, score thresholds, direction filter.
     Disabled management_change and other_material_event (low evidence).
5C-1 — Expanded universe from 15 to 97 symbols across sectors including
       mid-cap growth where PEAD is stronger.
5C-3 — Bootstrap 95% confidence intervals for key trade metrics.
5D — SUE integration: earnings surprise scoring (eps_growth_qoq) at 10%
     weight, entry gate blocks negative EPS surprise for earnings events.
5F — Extended label horizons to 10D/20D with Alembic migration.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
5 months ago
I Luk Kim d4ae900286 feat: expand data pipeline and integrate event features into scoring
Data expansion:
- Poll SEC 8-K filings from 2025-10-01 to 2026-03-12 (was ~2 months)
- Pipeline: 36 new filings → 24 new events → total 44 events, 15 symbols
- Re-export with merged features (market_v1+event_v1+financial_v1)
- Parquet columns: 7 → 41 (adds signal_strength, guidance_direction,
  document_quality, oneoff_penalty, eps_growth_qoq, etc.)

Score model v2:
- Add event quality component (15% weight): signal_strength_score,
  guidance_direction_score, document_quality_score
- Add risk penalty component (10% weight): inverted oneoff_penalty
- Rebalance market weights: reaction 25%, close 25%, volume 15%, gap 10%
- Graceful degradation when event features are absent (returns 0.5)

Export pipeline:
- Add --feature-versions CLI flag to merge multiple feature types
- export_dataset_snapshot() accepts feature_versions list parameter
- Groups features by event_id and merges feature_json dicts

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
5 months ago
I Luk Kim d387d567c5 fix: correct equity calculation — use market value, not unrealized PnL
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>
5 months ago
I Luk Kim 867d70afae fix: correct simulation loop and equity curve calculation after real-data testing
- 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>
5 months ago
I Luk Kim 2f4d9f61f7 feat: implement Phase 4 -- event-driven backtester
Full backtesting engine that reads Parquet snapshots and simulates a
swing-trading strategy with no look-ahead bias.

## New modules (libs/backtest/)
- domain.py: All Pydantic v2 models (Candidate, PlannedOrder, FilledTrade,
  OpenPosition, DailyPortfolioState, MetricsBundle, BacktestConfig, etc.)
- calendar.py: Thin wrappers over time_utils + reaction_date
- manifests.py: Config load/deep-merge/validate, run-ID generation
- metrics.py: 21 pure-function metrics (no pandas, stdlib statistics only)
- selector.py: build_candidate(), rank_candidates() (score↓ ADV↓ symbol↑)
- allocator.py: 7-gate run_entry_gates(), ATR stop, floor() shares
- execution.py: simulate_entry/exit(), update_trailing_stop() (ratchet-up only)
- splits.py: Walk-forward windows, year/regime split utilities
- snapshot_store.py: Sync load() → asyncio.run(_async_load()), no look-ahead
- artifacts.py: Full run-dir writer (Parquet, CSV, JSON)

## App modules (apps/backtester/)
- run.py: BacktestRunner (exit-first→entry simulation, 25% kill switch) + CLI
- replay.py: Double-run determinism checker

## Config files
- configs/backtest/defaults.json: Base strategy defaults
- configs/experiments/baseline_v1.json: First experiment manifest

## Tests: 142 new tests, all passing
- 132 unit tests (no DB/HTTP required)
- 8 integration tests (synthetic SnapshotStore)
- 3 backtest determinism/replay tests

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
5 months ago
I Luk Kim 9d3427e24e fix: disable Qwen3.5 thinking mode and switch OllamaClient to sync httpx
- 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>
5 months ago
I Luk Kim bbe0e31150 feat: implement Phase 3 -- LLM enrichment, labeler, review queue, dataset export
- libs/llm/: OllamaClient (httpx async, retry), LLMCacheStore (SHA-256 DB cache),
  prompt registry (event_classifier_v1), LLMParser (cache→prompt→validate→repair)
- libs/parser/merger.py: rule+LLM canonical merge with provenance tracking,
  conflict detection (both confident + disagree), should_queue_for_review()
- libs/db/models.py: LLMCallCache, ReviewItem, EventLabel 3개 ORM 모델 추가
- libs/db/migrations/versions/0002_phase3_tables.py: Phase 3 Alembic migration
- libs/labeler/: filing_time_bucket→reaction_date, 1D/3D/5D fwd return, MFE/MAE
- libs/review/queue.py: create(dedup)/resolve/list ReviewItem
- libs/export/snapshot_export.py: temporal split + Parquet + manifest.json
- apps/: label_generator, dataset_export, review CLI, gold set evaluator
- 42개 신규 테스트 추가 (unit 32 + integration 4 + replay 1) — 152/152 통과
- libs/common/config.py: OLLAMA_URL/MODEL/TIMEOUT 설정 추가
- libs/common/logging.py: bugfix — add_logger_name incompatible with PrintLoggerFactory

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
5 months ago
I Luk Kim 38ac53b597 implement Phase 2 gap items: backfill CLI, retry, financial features
- 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>
5 months ago
I Luk Kim 0471018dd8 feat: implement ACE-F v1 Phase 1 -- Stock Oracle 기반 이벤트 파이프라인
Stock Oracle (localhost:18001)을 단일 데이터 소스로 사용하는 미국 주식
이벤트 스윙 트레이딩 시스템의 Phase 1 구현체.

주요 구성:
- libs/oracle_client/: Stock Oracle REST 클라이언트 (filings, price, financial, fred, finra)
- libs/common/: config, logging, time_utils, ids, retries, file_store
- libs/db/: SQLAlchemy 2.0 모델 12개 + Alembic 마이그레이션 0001
- libs/parser/: 규칙 기반 파서 (텍스트 정규화, JSON Schema 검증, LLM 스텁)
- libs/features/: 시장/이벤트 피처 계산기
- apps/pipeline/: filing_poller → filing_fetcher → event_parser → feature_builder
- apps/sync/: macro_sync (FRED), short_volume_sync (FINRA), issuer_sync
- tests/: 단위 81개 + 리플레이 5개 전체 통과, lint clean

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
5 months ago