989ad8485c
Wave 9: Feature Integration (20 agents) - Wire Wave D features into extraction pipeline (ml/src/features/extraction.rs:197-204) - Reduce statistical features from 50 to 26 to make room for Wave D - Update method signature to &mut self for stateful extractors - Fix 7 division-by-zero bugs in feature extraction - Train all 4 models (DQN, PPO, MAMBA-2, TFT) with 225 features - Test pass rate: 99.2% (2,061/2,074 tests) Wave 10: Production Feature Extractor Fix (1 agent) - Create ProductionFeatureExtractor225 trait - Implement ProductionFeatureExtractorAdapter - Fix production code using only 66 features + 159 zeros - Use dependency injection to avoid circular dependencies Wave 11: Service Migration (20 agents) - Migrate Trading Service to use ProductionFeatureExtractorAdapter - Migrate Backtesting Service to use production extractor - Update all integration tests and E2E tests - Performance: 3.98μs/bar (22% faster than Wave 9) - Test pass rate: 99.84% (1,239/1,241 tests) Key Achievements: - All 225 features (201 Wave C + 24 Wave D) fully integrated - All services using production feature extractor - Zero NaN/Inf errors after division-by-zero fixes - 922x average performance improvement vs targets - System 100% ready for extended training data download Files Modified: - ml/src/features/extraction.rs (Wave D wiring) - ml/src/features/production_adapter.rs (NEW - adapter pattern) - common/src/ml_strategy.rs (trait + dependency injection) - services/trading_service/src/paper_trading_executor.rs - services/backtesting_service/src/ml_strategy_engine.rs - 18+ test files updated for &mut self pattern Next Steps: - Wave 12: Download 180 days Databento data (~$3.50) - Wave 13: Retrain all models with extended datasets - Wave 14: Run Wave Comparison Backtest - Wave 15-16: Production deployment 🤖 Generated with Claude Code (Waves 9-11: 41 agents, 153 total) Co-Authored-By: Claude <noreply@anthropic.com>
57 lines
2.3 KiB
Rust
57 lines
2.3 KiB
Rust
//! Verify 225-feature extraction with Wave D integration
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use ml::features::extraction::{extract_ml_features, OHLCVBar};
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use chrono::Utc;
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fn main() {
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// Create 100 test bars
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let bars: Vec<OHLCVBar> = (0..100)
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.map(|i| OHLCVBar {
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timestamp: Utc::now() + chrono::Duration::hours(i),
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open: 100.0 + i as f64 * 0.1,
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high: 101.0 + i as f64 * 0.1,
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low: 99.0 + i as f64 * 0.1,
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close: 100.5 + i as f64 * 0.1,
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volume: 1000.0 + i as f64 * 10.0,
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})
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.collect();
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let features = extract_ml_features(&bars).expect("Feature extraction failed");
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println!("✓ Feature extraction successful");
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println!(" - Input bars: {}", bars.len());
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println!(" - Output vectors: {}", features.len());
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println!(" - Features per vector: {}", features[0].len());
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// Verify dimensions
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assert_eq!(features[0].len(), 225, "Expected 225 features, got {}", features[0].len());
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// Verify no NaN/Inf in Wave D features (indices 201-224)
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for (i, feature_vec) in features.iter().enumerate() {
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for (j, &val) in feature_vec.iter().enumerate() {
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assert!(val.is_finite(), "Non-finite value at bar {}, feature {}: {}", i, j, val);
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}
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// Wave D features are at indices 201-224
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let wave_d_features = &feature_vec[201..225];
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println!("Bar {} Wave D features (201-224): min={:.4}, max={:.4}, avg={:.4}",
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i,
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wave_d_features.iter().fold(f64::INFINITY, |a, &b| a.min(b)),
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wave_d_features.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b)),
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wave_d_features.iter().sum::<f64>() / wave_d_features.len() as f64
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);
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if i >= 5 { break; } // Only show first 5 bars
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}
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println!("\n✓ All 225 features extracted successfully!");
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println!(" - Features 0-4: OHLCV (5)");
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println!(" - Features 5-14: Technical indicators (10)");
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println!(" - Features 15-74: Price patterns (60)");
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println!(" - Features 75-114: Volume patterns (40)");
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println!(" - Features 115-164: Microstructure proxies (50)");
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println!(" - Features 165-174: Time-based (10)");
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println!(" - Features 175-200: Statistical (26)");
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println!(" - Features 201-224: Wave D regime detection (24)");
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}
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