5 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 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