e393a8af89
## Summary Third major cleanup wave after investigating 287 remaining root files. Archived historical reports, organized documentation, removed regeneratable artifacts, and fixed critical security issue. ## Files Cleaned (119 total) - Archived: 78 files (7 WAVE reports + 71 summaries) → docs/archive/ - Archived: 7 build logs → docs/archive/build_logs/ - Organized: 10 markdown files → docs/guides/ + docs/checklists/ - Deleted: 17 test/coverage artifacts (regeneratable) - Deleted: 7 empty/obsolete files (docker override, clippy baselines) - Deleted: 3 large files (119MB - .venv, ppo_hyperopt_output.txt, backup) ## Space Recovered - Total: ~120.7 MB - Large files: 119.25 MB (.venv, ppo_hyperopt_output.txt) - Archives: 1.04 MB (summaries + build logs) - Test artifacts: 980 KB ## Security Fix (CRITICAL) - Fixed: certs/security.env removed from git tracking (contained JWT secrets) - Updated: .gitignore to prevent future tracking of sensitive cert files - Removed: 4 files from git history (security.env, production.env.template, *.serial) ## Documentation Organization - Created: docs/archive/ (wave_reports/, summaries/, build_logs/) - Created: docs/guides/ (7 detailed implementation guides) - Created: docs/checklists/ (3 operational checklists) - Retained: 30 essential .md files in root (quick refs, CLAUDE.md) ## Investigation Reports Created - MARKDOWN_ORGANIZATION_REPORT.md - TXT_FILES_INVENTORY_AND_ARCHIVAL_PLAN.md - ROOT_CONFIG_FILES_ANALYSIS_REPORT.md - DOCKER_ROOT_FILES_ANALYSIS.md - DATABASE_INITIALIZATION_AND_SETUP_ANALYSIS.md - (6 additional investigation/index files) ## Cleanup Wave Progress - Wave 1: 899 files deleted (1,071,884 lines) - Wave 2: 543 files archived/deleted (~34GB) - Wave 3: 119 files archived/deleted/organized (~121MB) - Total: 1,561 files cleaned, ~35.1GB space recovered ## Result Root directory: 287 files → ~180 files (excluding investigation reports) Clean, organized, production-ready structure maintained. Related: Second cleanup wave (previous commit)
244 lines
22 KiB
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244 lines
22 KiB
Plaintext
================================================================================
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BACKTESTING SERVICE FEATURE GAPS SUMMARY
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October 17, 2025
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================================================================================
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ CURRENT STATE (Wave A) │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ DBN OHLCV Data (0.70ms load) │
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│ ↓ │
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│ StrategyEngine │
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│ • MovingAverageCrossover (trigger_price parameter only) │
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│ • BuyAndHold (static allocation) │
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│ • NewsAware (hardcoded sentiment 0.2, momentum 55.0) │
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│ • MLPoweredStrategy (uses SharedMLStrategy + 8 features) │
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│ ↓ │
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│ Portfolio Execution (Commission + Slippage) │
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│ ↓ │
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│ Performance Metrics │
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│ ✅ Sharpe Ratio (252-day annualized) │
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│ ✅ Sortino Ratio (downside risk only) │
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│ ✅ Max Drawdown (peak-to-trough) │
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│ ✅ Win Rate (winning trades %) │
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│ ✅ Profit Factor (gross profit / gross loss) │
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│ ✅ PnL Tracking (per trade + cumulative) │
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│ │
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│ ⚠️ GAPS: │
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│ • 8 local features vs 256 in ML training │
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│ • Only time-based OHLCV (no alternative bars) │
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│ • No fractional differentiation │
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│ • No meta-labeling precision improvement │
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│ • UnifiedFeatureExtractor initialized but never used │
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│ • ML predictions validated but NOT used for trading │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ NEEDED STATE (Wave C - Fractional Differentiation) │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ DBN OHLCV Data │
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│ ↓ │
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│ ┌─ Alternative Bars Generation ─┐ │
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│ │ • Dollar Bars (noise reduction) │ │
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│ │ • Volume Bars (regime detection)│ │
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│ │ • Run Bars (directional trends) │ │
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│ │ • Tick Bars (frequency-based) │ │
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│ │ • Imbalance Bars (micro-trends) │ │
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│ └───────────────────────────────┘ │
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│ ↓ │
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│ Fractional Differentiation (d=0.5) │
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│ • Removes unit root (stationarity) │
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│ • Preserves long-range memory │
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│ • Improves ML convergence │
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│ ↓ │
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│ UnifiedFeatureExtractor (256 features) │
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│ • 18 Wave A technical indicators (RSI, MACD, Bollinger, ATR, etc.) │
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│ • Price features (returns, volatility, microstructure) │
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│ • Volume features (VWAP, OBV, CMF) │
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│ • Temporal features (hour, day, seasonality) │
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│ • Regime features (structural breaks, CUSUM) │
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│ ↓ │
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│ Meta-Labeling Engine │
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│ • Primary Labels: Triple barrier (upper/lower/time) │
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│ • Secondary Labels: ML model predictions (DQN, PPO, MAMBA-2) │
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│ • Precision Improvement: Filter low-confidence predictions │
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│ ↓ │
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│ StrategyEngine (with 256 features + meta-labels) │
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│ • Adaptive strategy (detects regime switches) │
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│ • ML signal generation with confidence thresholds │
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│ • Dynamic position sizing (Kelly criterion based on Sharpe) │
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│ ↓ │
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│ Portfolio Execution │
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│ ↓ │
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│ Enhanced Performance Metrics │
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│ ✅ All Wave A metrics │
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│ ✅ Feature-level performance attribution │
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│ ✅ Regime-specific Sharpe ratios │
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│ ✅ Prediction accuracy (ML signals vs actual returns) │
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│ ✅ Meta-label precision/recall │
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│ │
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│ 📊 EXPECTED IMPROVEMENTS: │
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│ • Win Rate: 41.8% → 48-52% (+10-24%) │
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│ • Sharpe Ratio: -6.52 → 0.5-1.0 (+7 points) │
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│ • Max Drawdown: Lower due to regime detection │
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│ • Feature coverage: 8 → 256 features (32x increase) │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ FEATURE EXTRACTION DISCONNECTS │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ Location 1: StrategyEngine (strategy_engine.rs:311) │
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│ ───────────────────────────────────────────────── │
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│ feature_extractor: Arc<UnifiedFeatureExtractor>, │
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│ │
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│ Status: ❌ INITIALIZED BUT NEVER CALLED │
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│ Used: 0x across entire codebase │
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│ Expected: 1x per market data point │
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│ │
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│ Location 2: MLStrategyEngine (ml_strategy_engine.rs:74-172) │
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│ ───────────────────────────────────────────────────────── │
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│ Local MLFeatureExtractor with 8 features: │
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│ 1. Price return (6-period) │
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│ 2. MA ratio (5-period SMA) │
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│ 3. Price volatility (10-period std dev) │
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│ 4. Volume ratio (2-period) │
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│ 5. Volume MA ratio (5-period) │
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│ 6. Hour of day (normalized 0-1) │
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│ 7. Day of week (normalized 0-1) │
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│ 8. All normalized via tanh() │
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│ │
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│ Status: ⚠️ OUTDATED (old architecture) │
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│ Should: Delegate to UnifiedFeatureExtractor + alternative bars │
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│ │
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│ Location 3: NewsAwareStrategy (strategy_engine.rs:462-464) │
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│ ───────────────────────────────────────────────────────── │
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│ Comment: "In reality, the strategy would use UnifiedFeatureExtractor" │
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│ Status: ❌ TODO - NOT IMPLEMENTED │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ AVAILABLE WAVE C COMPONENTS (Already Implemented) │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ Component Location Status │
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│ ───────────────────────────────────────────────────────────────────── │
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│ Alternative Bars ml/src/features/ ✅ 19/19 │
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│ (Dollar, Volume, Run, Tick, Imbalance) alternative_bars.rs tests │
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│ │
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│ Barrier Labeling (Triple Barrier) ml/src/labeling/ ✅ Tests │
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│ barrier_backtest.rs passing │
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│ │
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│ Meta-Labeling Engine ml/src/labeling/ ✅ Tests │
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│ meta_labeling_engine.rs passing │
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│ │
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│ Barrier Optimization ml/src/features/ ✅ Tests │
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│ barrier_optimization.rs passing │
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│ │
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│ UnifiedFeatureExtractor (256 features) data/src/unified_ ✅ 100% │
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│ feature_extractor.rs complete │
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│ │
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│ Technical Indicators (18) ml/src/features/ ✅ All 18 │
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│ (RSI, MACD, Bollinger, ATR, ADX, technical_indicators.rs integrated │
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│ CCI, Stochastic, EWMA, etc.) │
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│ │
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│ 🔴 MISSING: Fractional Differentiation (NOT YET IMPLEMENTED) │
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│ Purpose: Remove unit root while preserving long-range memory │
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│ Estimated effort: 2-3 days │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ INTEGRATION ROADMAP (3 Weeks) │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ Week 1: Feature Extraction Consolidation │
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│ ─────────────────────────────────────── │
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│ Day 1-2: Create DbnAlternativeBarsConverter │
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│ • Wrap DbnDataSource with alternative bar generation │
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│ • Support dollar/volume/run/tick/imbalance bars │
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│ Day 3-4: Update MarketData struct │
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│ • Add bar_type enum │
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│ • Add bar metadata (cumulative $, volume, runs, etc.) │
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│ Day 5: Integrate UnifiedFeatureExtractor │
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│ • Replace 8-feature local extraction │
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│ • Call during execute_backtest() │
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│ │
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│ Week 2: Strategy Enhancements │
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│ ─────────────────────────── │
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│ Day 1-2: Implement fractional differentiation │
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│ • d=0.5 (0-1 range for stationarity) │
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│ • Preserve memory (vs full differencing d=1) │
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│ Day 3-4: Implement meta-labeling in backtesting │
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│ • Primary labels: Triple barrier │
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│ • Secondary labels: ML predictions │
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│ Day 5: Update strategies with 256 features │
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│ • Adaptive strategy (regime detection) │
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│ • Dynamic position sizing │
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│ │
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│ Week 3: Validation & Testing │
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│ ──────────────────────────── │
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│ Day 1-2: Implement prediction-to-trade mapping │
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│ • Generate ML signals with confidence thresholds │
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│ • Validate predictions vs actual returns │
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│ Day 3-4: Create Wave A/B/C comparison suite │
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│ • Side-by-side backtest results │
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│ • Feature performance attribution │
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│ Day 5: Comprehensive testing (50+ test cases) │
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│ • Unit tests for each component │
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│ • E2E pipeline tests │
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│ • Real data validation (ES.FUT, NQ.FUT, ZN.FUT) │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ KEY METRICS TO TRACK │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ Performance: │
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│ • Sharpe Ratio (currently: -6.52 → target: 0.5-1.0) │
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│ • Win Rate (currently: 41.8% → target: 48-52%) │
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│ • Max Drawdown (lower with regime detection) │
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│ • Profit Factor (gross profit / gross loss) │
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│ │
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│ Feature Quality: │
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│ • Feature importance ranking (via SHAP or permutation) │
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│ • Feature correlation (remove redundant features) │
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│ • Feature coverage (8 → 256 features) │
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│ │
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│ ML Integration: │
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│ • Prediction accuracy vs actual returns │
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│ • Meta-label precision (filters ~30% low-confidence signals) │
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│ • Model latency (<100μs target) │
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│ • Confidence calibration (predicted vs realized) │
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│ │
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│ Regime Detection: │
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│ • Sharpe ratio by regime (up/down/sideways) │
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│ • Strategy switching frequency │
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│ • Adaptation lag (days to detect regime change) │
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│ │
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│ Data Quality: │
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│ • Alternative bar validity (no NaNs, monotonicity) │
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│ • Fractional differentiation stationarity (ADF test) │
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│ • Feature scaling consistency │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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CRITICAL SUCCESS FACTORS:
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1. ✅ Consolidate on ONE feature extractor (UnifiedFeatureExtractor)
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2. ✅ Validate features during backtesting (not just predictions)
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3. ✅ Use same features as live trading (SharedMLStrategy)
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4. ✅ Compare Wave A/B/C sequentially (not isolation)
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5. ✅ Test on real market data (DBN + real edge cases)
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TIMELINE: 3 weeks (5 engineers working in parallel)
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STATUS: READY TO IMPLEMENT (all components exist, just need integration)
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================================================================================
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