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>
134 lines
4.4 KiB
Rust
134 lines
4.4 KiB
Rust
//! VALIDATION 1/8: Test that SharedMLStrategy extracts 225 features
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//!
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//! This test validates that the Wave D implementation correctly extracts
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//! all 225 features (201 Wave C + 24 Wave D regime detection features).
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use chrono::Utc;
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use common::ml_strategy::SharedMLStrategy;
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use ml::features::ProductionFeatureExtractorAdapter;
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#[tokio::test]
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async fn test_sharedml_extracts_225_features() {
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// Create SharedMLStrategy with production feature extractor (225 features)
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let extractor = Box::new(ProductionFeatureExtractorAdapter::new());
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let strategy = SharedMLStrategy::new_with_production_extractor(extractor, 0.5);
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// Warm up the feature extractor with some historical data
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// (needed to properly compute indicators like EMAs, RSI, etc.)
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for i in 0..100 {
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let price = 100.0 + (i as f64 * 0.1);
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let volume = 1000.0 + (i as f64 * 10.0);
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let _ = strategy
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.get_ensemble_prediction(price, volume, Utc::now())
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.await;
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}
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// Extract features from the strategy
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let predictions = strategy
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.get_ensemble_prediction(100.0, 1000.0, Utc::now())
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.await
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.expect("Should extract features successfully");
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// Get features from the first prediction (all models use same features)
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assert!(
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!predictions.is_empty(),
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"Should have at least one prediction"
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);
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let features = &predictions[0].features;
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// VALIDATION 1: Verify feature count is exactly 225
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assert_eq!(
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features.len(),
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225,
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"SharedMLStrategy must extract exactly 225 features (201 Wave C + 24 Wave D), but got {}",
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features.len()
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);
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// VALIDATION 2: Verify no NaN values
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for (i, f) in features.iter().enumerate() {
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assert!(
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!f.is_nan(),
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"Feature at index {} is NaN (value: {})",
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i,
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f
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);
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}
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// VALIDATION 3: Verify no Inf values
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for (i, f) in features.iter().enumerate() {
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assert!(
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f.is_finite(),
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"Feature at index {} is not finite (value: {}). All features must be finite numbers.",
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i,
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f
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);
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}
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println!("✅ VALIDATION 1/8 PASSED");
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println!(" - Feature count: {} (expected 225)", features.len());
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println!(" - All features are finite");
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println!(" - No NaN or Inf values detected");
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}
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#[tokio::test]
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async fn test_feature_extraction_wave_d_breakdown() {
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// Create SharedMLStrategy with production feature extractor (225 features)
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let extractor = Box::new(ProductionFeatureExtractorAdapter::new());
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let strategy = SharedMLStrategy::new_with_production_extractor(extractor, 0.5);
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// Warm up the feature extractor
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for i in 0..100 {
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let price = 100.0 + (i as f64 * 0.1);
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let volume = 1000.0 + (i as f64 * 10.0);
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let _ = strategy
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.get_ensemble_prediction(price, volume, Utc::now())
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.await;
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}
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// Extract features
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let predictions = strategy
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.get_ensemble_prediction(100.0, 1000.0, Utc::now())
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.await
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.expect("Should extract features successfully");
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let features = &predictions[0].features;
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// Verify feature breakdown (expected from Wave D documentation):
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// - Wave A: 26 features (indices 0-25)
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// - Wave B: 10 features (indices 26-35) [alternative bar sampling]
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// - Wave C: 165 features (indices 36-200) [advanced feature engineering]
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// - Wave D: 24 features (indices 201-224) [regime detection]
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// Total: 225 features
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assert_eq!(
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features.len(),
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225,
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"Expected 225 total features (26 Wave A + 10 Wave B + 165 Wave C + 24 Wave D)"
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);
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// Verify Wave D features (indices 201-224) are present
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for i in 201..225 {
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let feature_value = features.get(i);
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assert!(
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feature_value.is_some(),
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"Wave D feature at index {} is missing",
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i
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);
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let value = feature_value.unwrap();
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assert!(
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value.is_finite(),
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"Wave D feature at index {} is not finite: {}",
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i,
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value
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);
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}
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println!("✅ Wave D feature breakdown validated");
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println!(" - Wave A features (0-25): present");
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println!(" - Wave B features (26-35): present");
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println!(" - Wave C features (36-200): present");
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println!(" - Wave D features (201-224): present");
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}
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