foxhunt/WAVE_D_FEATURE_CONFIG_COMPLETE.md

Wave D Feature Configuration Implementation - COMPLETE

Date: 2025-10-17
Status: ✅ COMPLETE - All 24 Wave D features registered in FeatureConfig

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Summary

Successfully added all 24 Wave D regime detection and adaptive strategy features to the FeatureConfig system in /home/jgrusewski/Work/foxhunt/ml/src/features/config.rs. These features extend Wave C's 201 features to 225 total features (indices 0-224).

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Implementation Details

1. New Feature Definitions

Added comprehensive feature definitions with three new types:

FeatureCategory Enum

pub enum FeatureCategory {
    OHLCV,
    TechnicalIndicators,
    Microstructure,
    RegimeDetection,      // NEW
    AdaptiveStrategy,     // NEW
}

Feature Struct

pub struct Feature {
    pub index: usize,
    pub name: String,
    pub category: FeatureCategory,
}

wave_d_features() Function

Returns all 24 Wave D features with their indices, names, and categories:

• CUSUM Statistics (indices 201-210): 10 features
• ADX & Directional Indicators (indices 211-215): 5 features
• Regime Transition Probabilities (indices 216-220): 5 features
• Adaptive Strategy Metrics (indices 221-224): 4 features

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2. Wave D Feature List (Indices 201-224)

CUSUM Statistics (10 features)

• 201: cusum_s_plus_normalized
• 202: cusum_s_minus_normalized
• 203: cusum_break_indicator
• 204: cusum_direction
• 205: cusum_time_since_break
• 206: cusum_frequency
• 207: cusum_positive_count
• 208: cusum_negative_count
• 209: cusum_intensity
• 210: cusum_drift_ratio

ADX & Directional Indicators (5 features)

• 211: adx
• 212: plus_di
• 213: minus_di
• 214: dx
• 215: trend_classification

Regime Transition Probabilities (5 features)

• 216: regime_stability
• 217: most_likely_next_regime
• 218: regime_entropy
• 219: regime_expected_duration
• 220: regime_change_probability

Adaptive Strategy Metrics (4 features)

• 221: position_multiplier
• 222: stop_loss_multiplier
• 223: regime_conditioned_sharpe
• 224: risk_budget_utilization

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3. Configuration Updates

Added FeaturePhase::WaveD

pub enum FeaturePhase {
    WaveA,  // 26 features
    WaveB,  // 36 features
    WaveC,  // 201 features
    WaveD,  // 225 features (NEW)
}

Added enable_wave_d_regime Flag

pub struct FeatureConfig {
    // ... existing flags ...
    pub enable_wave_d_regime: bool,  // NEW
}

Added FeatureConfig::wave_d() Constructor

pub fn wave_d() -> Self {
    Self {
        phase: FeaturePhase::WaveD,
        enable_ohlcv: true,
        enable_technical_indicators: true,
        enable_microstructure: true,
        enable_alternative_bars: true,
        enable_barrier_optimization: true,
        enable_fractional_diff: true,
        enable_regime_detection: true,
        enable_wave_d_regime: true,  // NEW
    }
}

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4. Feature Count Updates

Updated feature_count() to return correct totals:

• Wave A: 26 features
• Wave B: 36 features
• Wave C: 201 features (39 base + 162 additions)
• Wave D: 225 features (201 + 24 additions)

Updated feature_indices() to include:

pub struct FeatureIndices {
    // ... existing fields ...
    pub wave_d_regime: Option<(usize, usize)>,  // NEW: indices 201-224
}

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5. Feature Group Updates

Added FeatureGroup::WaveDRegime to support feature group queries:

pub enum FeatureGroup {
    // ... existing variants ...
    WaveDRegime,  // NEW
}

Added get_wave_d_features() method:

pub fn get_wave_d_features(&self) -> Vec<Feature> {
    if self.enable_wave_d_regime {
        wave_d_features()
    } else {
        vec![]
    }
}

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Test Results

All 11 configuration tests pass:

test features::config::tests::test_default_is_wave_a ... ok
test features::config::tests::test_feature_indices_wave_a ... ok
test features::config::tests::test_feature_indices_wave_b ... ok
test features::config::tests::test_feature_indices_wave_d ... ok
test features::config::tests::test_get_wave_d_features ... ok
test features::config::tests::test_is_enabled ... ok
test features::config::tests::test_wave_a_config ... ok
test features::config::tests::test_wave_b_config ... ok
test features::config::tests::test_wave_c_config ... ok
test features::config::tests::test_wave_d_config ... ok
test features::config::tests::test_wave_d_features ... ok

Test Coverage

• ✅ Wave D configuration returns 225 features
• ✅ Wave D features start at index 201
• ✅ Wave D feature definitions contain all 24 features
• ✅ Feature categories are correctly assigned
• ✅ Feature indices are properly calculated
• ✅ get_wave_d_features() returns correct feature list

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Usage Example

use ml::features::config::{FeatureConfig, wave_d_features};

// Get Wave D configuration
let config = FeatureConfig::wave_d();
assert_eq!(config.feature_count(), 225);

// Get feature indices
let indices = config.feature_indices();
assert_eq!(indices.wave_d_regime, Some((201, 225)));

// Get Wave D feature definitions
let features = config.get_wave_d_features();
assert_eq!(features.len(), 24);

// Check specific feature
assert_eq!(features[0].index, 201);
assert_eq!(features[0].name, "cusum_s_plus_normalized");
assert_eq!(features[0].category, FeatureCategory::RegimeDetection);

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Integration Points

This configuration update integrates with:

1. DbnSequenceLoader (ml/src/data_loaders/dbn_sequence_loader.rs)

   • Uses FeatureConfig to determine which features to extract during training

2. MLFeatureExtractor (common/src/ml_strategy.rs)

   • Uses FeatureConfig to determine which features to extract during inference

3. Feature Extraction Pipeline (ml/src/features/pipeline.rs)

   • Can use get_wave_d_features() to understand which Wave D features to compute

4. ML Model Training (ml/examples/train_*.rs)

   • Models can now be trained with 225-dimensional input (Wave D)

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Next Steps

1. Implement Feature Extractors (Agents D13-D16)

   • Agent D13: CUSUM Statistics extractor (10 features)
   • Agent D14: ADX & Directional Indicators extractor (5 features)
   • Agent D15: Regime Transition Probabilities extractor (5 features)
   • Agent D16: Adaptive Strategy Metrics extractor (4 features)

2. Update Data Loaders

   • Modify DbnSequenceLoader to extract Wave D features when enable_wave_d_regime = true
   • Modify MLFeatureExtractor to compute Wave D features in real-time

3. Integration Testing

   • Test Wave D feature extraction with real DBN data (ES.FUT, NQ.FUT)
   • Validate feature values are computed correctly
   • Benchmark performance (<50μs per feature target)

4. Model Retraining

   • Retrain DQN, PPO, MAMBA-2, TFT with 225-dimensional input
   • Evaluate regime-adaptive strategy performance
   • Validate +25-50% Sharpe ratio improvement hypothesis

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Files Modified

• /home/jgrusewski/Work/foxhunt/ml/src/features/config.rs
  • Added FeatureCategory enum
  • Added Feature struct
  • Added wave_d_features() function
  • Added FeaturePhase::WaveD variant
  • Added enable_wave_d_regime field
  • Added FeatureConfig::wave_d() constructor
  • Added FeatureGroup::WaveDRegime variant
  • Added FeatureIndices::wave_d_regime field
  • Added get_wave_d_features() method
  • Updated documentation for Wave C and D
  • Added 3 new tests for Wave D features

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Success Criteria

✅ All 24 features registered
✅ Indices 201-224 configured
✅ All tests passing (11/11)
✅ Zero compilation errors
✅ Documentation updated
✅ Integration points identified

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Conclusion

Wave D feature configuration is 100% complete. The FeatureConfig system now supports 225 total features across 4 waves (A, B, C, D), with all 24 Wave D regime detection and adaptive strategy features properly registered and ready for implementation in the feature extraction pipeline.

Estimated Time: 45 minutes
Actual Time: 45 minutes
Test Coverage: 11/11 tests passing (100%)