foxhunt/ml/src/features/mod.rs

//! Feature Engineering Module
//!
//! This module provides comprehensive feature extraction for ML models:
//! - Progressive feature engineering (Wave A: 26, Wave B: 36, Wave C: 65+)
//! - Technical indicators (RSI, MACD, Bollinger, ATR, EMA)
//! - Price patterns, volume analysis, microstructure proxies
//! - Time-based and statistical features
//! - MinIO integration for feature caching (10x faster loading)

// New feature system
pub mod adx_features; // Wave D: ADX directional indicators (5 features, indices 211-215)
pub mod alternative_bars;
pub mod barrier_optimization;
pub mod config; // Wave C: Feature configuration for progressive engineering
pub mod ewma;
pub mod extraction;
pub mod feature_extraction; // ATR and other technical indicator calculations
pub mod mbp10_loader; // MBP-10 data loader for OFI feature extraction
pub mod microstructure;
pub mod microstructure_features; // Wave C: Additional microstructure features (9 features)
pub mod minio_integration;
pub mod ofi_calculator; // Order Flow Imbalance features (8 features, indices 226-233)
pub mod normalization; // Wave C: Feature normalization pipeline (5 strategies)
pub mod pipeline; // Wave C: 5-stage feature assembly pipeline (orchestrates all extractors)
pub mod price_features; // Wave C: Price-based features (15 features)
pub mod production_adapter; // WAVE 10: Adapter for common::ml_strategy 225-feature injection
pub mod regime_adaptive; // Wave D: Regime-adaptive position sizing & stop-loss (4 features, indices 221-224)
pub mod regime_adx; // Wave D: ADX & directional indicators (5 features, indices 211-215)
pub mod regime_cusum; // Wave D: CUSUM regime detection features (10 features, indices 201-210)
pub mod regime_transition; // Wave D: Regime transition probabilities (5 features, indices 216-220)
pub mod sample_weights;
pub mod statistical_features; // Wave C: Statistical aggregate features (7 features)
pub mod time_features; // Wave C: Time-based features (8 features)
pub mod unified;
pub mod volume_features; // Wave C: Volume-based features (10 features)

// Feature configuration (Wave C)
pub use config::{FeatureConfig, FeatureGroup, FeatureIndices, FeaturePhase};

pub use extraction::{extract_ml_features, FeatureVector, OHLCVBar};
pub use minio_integration::{
    cache_exists, compute_data_hash, download_cache_metadata, download_features_from_minio,
    list_cached_features, upload_cache_metadata, upload_features_to_minio, CacheMetadata,
};

// WAVE 10: Production adapter for common::ml_strategy dependency injection
pub use production_adapter::ProductionFeatureExtractorAdapter;

// Unified feature extraction (production system)
pub use unified::{
    FeatureExtractionConfig, FeatureQualityMetrics, OrderBookLevel, UnifiedFeatureExtractor,
    UnifiedFinancialFeatures,
};

// Alternative bar sampling (tick, volume, dollar, imbalance, run bars)
pub use alternative_bars::{
    DollarBarSampler, ImbalanceBarSampler, OHLCVBar as AltBar, RunBarSampler, TickBarSampler,
    VolumeBarSampler,
};

// Barrier optimization for triple barrier labeling
pub use barrier_optimization::{BarrierOptimizer, BarrierParams, OptimizationResult};

// EWMA for adaptive thresholds
pub use ewma::{AdaptiveThreshold, EWMACalculator};

// Sample weights for label imbalance and temporal decay
pub use sample_weights::{SampleWeightCalculator, WeightingScheme};

// Price features (Wave C)
pub use price_features::PriceFeatureExtractor;

// Volume features (Wave C)
pub use volume_features::VolumeFeatureExtractor;

// ADX features (Wave D)
pub use adx_features::AdxFeatureExtractor;

// Regime ADX features (Wave D)
pub use regime_adx::RegimeADXFeatures;

// Microstructure features (Wave C)
pub use microstructure_features::{
    BuySellImbalance, HighLowSpread, InterArrivalTime, KyleLambda, MicrostructureFeature,
    PriceImpact, TickCount, VarianceRatio, VolumeWeightedSpread,
};

// Time features (Wave C)
pub use time_features::TimeFeatureExtractor;

// Statistical features (Wave C)
pub use statistical_features::StatisticalFeatureExtractor;

// Normalization pipeline (Wave C)
pub use normalization::{FeatureNormalizer, NormalizationStats, RingBuffer};

// Feature assembly pipeline (Wave C)
pub use pipeline::{FeatureExtractionPipeline, PipelinePerformance};

// Regime detection features (Wave D)
pub use regime_adaptive::RegimeAdaptiveFeatures;
pub use regime_cusum::RegimeCUSUMFeatures;
pub use regime_transition::RegimeTransitionFeatures;

// OFI features (Order Flow Imbalance)
pub use ofi_calculator::{OFICalculator, OFIFeatures};

// MBP-10 data loader for OFI integration
pub use mbp10_loader::{
    get_recent_snapshots, get_snapshots_for_timestamp, load_mbp10_snapshots_sync,
};

// Legacy features_old module removed in Wave D Phase 6 cleanup (3,513 lines)
// Add mock features helper to features module

// Test helper function
#[cfg(test)]
pub fn create_mock_features() -> FeatureVector {
    [0.0; 54] // Return 54-dimension feature vector (Updated from Wave D 225)
}