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8 Commits (190e395d66d57542ceeced02eabe243b30996504)

Author SHA1 Message Date
I Luk Kim b3ba914a8d Optimize v7 strategy: v7.356 achieves CW 2159% + SQS 90.7 (Pareto improvement over v7.314) 
Key changes from v7.314 baseline (CW 2012%, SQS 90.0):
- max_position_value_pct 15→25, non_a_tier_target_1_fraction 0.2→0
- max_daily_new_risk_pct 30→50 (via v7.330, CW champion 2148%)
- bullish_raised_recovery per_trade_risk_pct 0.71→0.55 (DD improvement)
- bullish_raised_recovery max_holding_days 12→10 (sweet spot, +98pp CW)

Result: v7.356 CW 2159% (+147pp), SQS 90.7 (+0.7), risk 66.2 (+2.9), robustness 94.3 (+0.5)
All metrics improved simultaneously — return increase AND DD reduction achieved.

Also includes: web UI updates, pipeline scripts, v16/v17/v18/v19 experiment pruning,
Form4 preset additions, snapshot registry updates, domain.py enhancements.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
 4 months ago
I Luk Kim 5496059b6c Add v7.120 composed GLD experiment updates 4 months ago
I Luk Kim f21caf23cb Prune Form4 experimental presets 4 months ago
I Luk Kim e38c314a09 Add ownership/risk-off sleeves, v17-v19 experiments, and web app restructure 
New features:
- Ownership 13D/13G residual-cash sleeve with PIT calendar and quality filters
- Risk-off alpha sleeve (GLD/DBC rotation on crisis regime signals)
- Crisis relay target in parking: evaluates before defensive relay
- Bearish symbol allocation split (bearish_alloc_pct + sgov remainder)
- Alternative defensive ETF candidate (cash_parking_defensive_alt_symbol)
- Composite eval and engine ablation tools
- experiment and overfit CLI apps

New experiments:
- v17.x series (v17.1 champion SQS 78.4; v17.5–v17.129 exhausted)
- v18.x and v19.x families from v12.8 OOT defense branch
- v7.119 composed variants (idle alpha + ownership + risk-off sleeves)
- parking_only configs: bufb, jepq, merix, regime_tiered
- empty_strategy baseline config

Web app:
- Restructured into routers/services modules (experiments, leaderboard, runs, sqs, docs)
- Ownership sleeve and risk-off sleeve controls in backtest UI
- Frontend: ComposeStrategy page, tradeSleeves lib, idle decomposition display

Research tools:
- Ownership 13D/13G probe and PIT cache builder
- Dividend capture probe and cache builder
- Insider Form4 idle alpha probe
- Alternative ETF parking probe, put-spread overlay probe
- Wikimedia low-attention and peer-relative idle alpha probes

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
 4 months ago
I Luk Kim f2113b7e06 Fix cash parking phantom-money bug + live engine parking liquidation for events 
Backtester (run.py):
- cash_available = (self._cash + parking_value) * multiplier caused trades to be
  approved even when self._cash ≈ 0 (all money in SGOV/QQQ).  Trades executed
  by deducting from self._cash → negative cash (phantom money).
- Fix: after simulate_entry, if self._cash < actual trade cost and parking exists,
  call _liquidate_parking_for_cash(shortfall) before deducting from cash.
- Verified: 2022-2026 backtest with qqqm_low_dd shows 0 cash_negative events.

Live engine (engine.py):
- Add _parking_liquidate_for_event(): frees parking cash to fund event entries.
  SGOV (virtual) reduces entry_value in DB; QQQM/QQQ sells real shares via broker.
- Both entry loops (engines mode + flat/reaction_close mode) now attempt parking
  liquidation when plan.skip_reason == "insufficient_cash" before giving up.

Also includes prior session work (accumulated since last commit):
- 6 novel parking gate signals: VRP, Market Temperature, Hurst exponent, Rolling
  Kurtosis, Return Autocorrelation, SPY-QQQ Correlation (composite risk score v2)
- QQQM parking symbol support (lower expense ratio vs QQQ)
- Snapshot auto-refresh + bar extension cache (pickle) to avoid 10-min re-fetches
- Bar extension clamps to last market-closed date (ET 4PM check)
- fithia2 refresh command; --no-refresh flag for paper backtest
- Paper backtest macro extension beyond last event date (parking-only periods)
- parking_state DB schema: 7 new columns (peak_price, gate_in_sgov,
  committed_target, pending_target, pending_days, sgov_entry_value, sold_today)
- Live engine: target confirmation (2-day), top-up drawdown gate, trailing stop,
  SGOV interest accrual, full 6-signal gate evaluation
- New PARKING_PRESETS: qqqm_low_dd, composite_v2, vv_24_vrp8, vt_24_t13, etc.
- Web GUI / CLI result parity fix (Oracle URL via get_settings().stock_oracle_url)
- Force-close uses last_exec_date (has bar data); parking liquidates at last_date

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
 5 months ago
I Luk Kim 9ec0b26e10 Implement multi-engine PEAD strategy research workflow 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 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