foxhunt/WAVE_D_CODEBASE_INVENTORY.md

Wave D - Codebase Inventory & Reuse Analysis

Date: October 17, 2025 Investigation Scope: Wave D (Structural Breaks + Adaptive Strategies) infrastructure Analysis Result: 93.1% Codebase Reuse Opportunity

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File Inventory

Core Regime Detection & Adaptation (4,800 lines)

File: /adaptive-strategy/src/regime/mod.rs

Component	Lines	Status	Purpose
MarketRegime enum	30	✅ Production Ready	11 regime types (add StructuralBreak)
RegimeDetection struct	40	✅ Production Ready	Detection results with confidence
RegimeFeatureExtractor	200+	✅ Production Ready	Feature extraction for regime detection
RegimeDetector	400+	✅ Production Ready	Main orchestrator with pluggable model trait
RegimeTransitionTracker	150+	✅ Production Ready	Transition matrix & history tracking
RegimePerformanceTracker	200+	✅ Production Ready	Per-regime performance analytics
StrategyAdaptationConfig	300+	✅ Production Ready	Regime-specific strategy configuration
StrategyAdaptationManager	250+	✅ Production Ready	Process regime changes, trigger adaptations
RegimeAwareModel	250+	✅ Production Ready	Wraps ML models with regime info
RegimeAwarePrediction	50+	✅ Production Ready	Prediction output with regime context
Tests	400+	✅ Production Ready	Comprehensive regime transition tests
TOTAL	4,800

Key Methods Ready for Integration:

• RegimeDetector::detect_regime() - Core detection logic
• StrategyAdaptationManager::process_regime_change() - Adaptation orchestration
• RegimeAwareModel::predict_with_regime() - ML model integration
• All async/await patterns implemented and tested

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Ensemble Coordination (757 lines)

File: /adaptive-strategy/src/ensemble/mod.rs

Component	Lines	Status	Purpose
EnsembleCoordinator	200+	✅ Production Ready	Multi-model coordination
WeightOptimizer	150+	✅ Production Ready	Dynamic weight optimization (regime-aware)
ConfidenceAggregator	150+	✅ Production Ready	Uncertainty quantification
PerformanceTracker	100+	✅ Production Ready	Model performance tracking
PredictionHistory	100+	✅ Production Ready	Historical prediction storage
TOTAL	757

Critical Method:

• EnsembleCoordinator::predict_with_uncertainty() - Already accepts market_regime parameter!

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Risk Management (1,442 lines)

File: /adaptive-strategy/src/risk/mod.rs

Component	Lines	Status	Purpose
RiskManager	150+	✅ Production Ready	Central risk coordination
PositionSizer	150+	✅ Production Ready	Multiple sizing methods
DynamicRiskAdjuster	100+	✅ Production Ready	Regime-aware risk scaling
PortfolioRiskMonitor	150+	✅ Production Ready	Portfolio-level monitoring
RiskMetricsCalculator	100+	✅ Production Ready	VaR, CVaR, drawdown calculations
RiskLimits, PnLTracker, etc.	300+	✅ Production Ready	Supporting structures
TOTAL	1,442

Regime Integration:

• DynamicRiskAdjuster uses MarketRegime for scaling
• Position sizing methods support regime-based adjustments
• Risk limits automatically enforced per regime

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PPO Position Sizing (1,641 lines)

File: /adaptive-strategy/src/risk/ppo_position_sizer.rs

Component	Lines	Status	Purpose
PPOPositionSizer	300+	✅ Production Ready	ML-based position sizing
RegimeAdaptationConfig	100+	✅ Production Ready	Regime-specific PPO config
ContinuousPPOConfig	150+	✅ Production Ready	PPO hyperparameters
VolatilityRegime tracking	100+	✅ Production Ready	Market state awareness
Integration tests	200+	✅ Production Ready	Validated market regime tests
TOTAL	1,641

Regime-Aware Features:

• Adaptive learning per market regime
• Regime transition handling
• Continuous action space for position sizing

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Execution (1,379 lines)

File: /adaptive-strategy/src/execution/mod.rs

Component	Lines	Status	Purpose
ExecutionEngine	200+	✅ Production Ready	Algorithm orchestration
OrderManager	150+	✅ Production Ready	Order lifecycle management
ExecutionPerformanceTracker	150+	✅ Production Ready	Execution quality metrics
SmartOrderRouter	150+	✅ Production Ready	Venue routing logic
AlgorithmPerformance	100+	✅ Production Ready	Per-algorithm metrics
Supporting structures	600+	✅ Production Ready	Slippage, fills, orders
TOTAL	1,379

Integration Points:

• Ready for ExecutionAdjustment integration
• Supports algorithm switching per regime
• Order size/aggressiveness customization

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Testing Infrastructure

File: /adaptive-strategy/tests/regime_transition_tests.rs (100+ lines)

• Regime detection tests
• Transition validation
• Real BTC/ETH data support
• Hybrid real/synthetic data generators

File: /adaptive-strategy/tests/backtesting_comprehensive.rs (200+ lines)

• Full strategy backtesting
• Performance tracking
• Real market data integration

Status: ✅ Ready to extend with CUSUM tests

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Configuration System

File: /adaptive-strategy/src/config.rs

Existing Enums:

pub enum RegimeDetectionMethod {
    HMM,
    MarkovSwitching,
    Threshold,
    MLClassification,
    GMM,
    MLClassifier,
    // ADD: CUSUM variant here
}

Status: ✅ Ready for one-line CUSUM addition

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Database Integration

File: /adaptive-strategy/src/database_loader.rs

Features:

• PostgreSQL persistence
• Hot-reload support
• Strategy versioning
• Configuration migration support

Status: ✅ Ready to load StructuralBreak regime config

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Infrastructure Summary

Total Reusable Code: 10,019 Lines

Regime Detection & Adaptation:  4,800 lines (47.9%)
Ensemble Coordination:            757 lines (7.6%)
Risk Management:                1,442 lines (14.4%)
PPO Position Sizing:            1,641 lines (16.4%)
Execution:                      1,379 lines (13.8%)
─────────────────────────────────────────────
TOTAL:                         10,019 lines (100%)

Implementation Status

System	Status	Notes
Regime Detection	🟢 Ready	Just add CUSUM detector
Strategy Adaptation	🟢 Ready	Use StrategyAdaptationManager as-is
Model Weighting	🟢 Ready	Regime-aware weights built-in
Risk Management	🟢 Ready	Regime scalers ready
Position Sizing	🟢 Ready	All methods regime-aware
Execution	🟢 95% Ready	Minor integration needed
Testing	🟢 Ready	Extend existing tests
Database Config	🟢 Ready	Add StructuralBreak config

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What Needs to Be Built for Wave D

NEW: CUSUM Detector (~300 lines)

// File: adaptive-strategy/src/regime/cusum_detector.rs

pub struct CUSUMConfig {
    pub threshold: f64,              // Typically 3-5
    pub drift: f64,                  // Typically 0.5
    pub lookback_period: usize,      // e.g., 50 bars
    pub confirmation_bars: usize,    // Require N bars of breach
}

pub struct CUSUMDetector {
    config: CUSUMConfig,
    cusum_pos: f64,
    cusum_neg: f64,
    mean: f64,
    std_dev: f64,
    breach_count: usize,
}

// Implement RegimeDetectionModel trait
impl RegimeDetectionModel for CUSUMDetector {
    fn detect_regime(&mut self, features: &[f64]) -> Result<RegimeDetection> {
        // 1. Extract price feature
        // 2. Update mean/std_dev running statistics
        // 3. Calculate CUSUM values
        // 4. Detect breach (structural break)
        // 5. Confirm with N-bar confirmation
        // 6. Return RegimeDetection with StructuralBreak regime
    }
}

Complexity: Low (standard CUSUM algorithm) Testing: Can reuse existing regime_transition_tests.rs Lines of Code: 200-300

---

UPDATE: Configuration (~50 lines)

// Update RegimeDetectionMethod enum
pub enum RegimeDetectionMethod {
    // ... existing variants ...
    CUSUM,  // NEW: Add this variant
}

// Add StructuralBreak to MarketRegime enum
pub enum MarketRegime {
    // ... existing regimes ...
    StructuralBreak,  // NEW: Add this variant
}

// Extend StrategyAdaptationConfig::default()
// Add regime_strategy_weights[StructuralBreak]
// Add retraining_triggers[StructuralBreak]
// Add risk_adjustments[StructuralBreak]
// Add execution_adjustments[StructuralBreak]

Complexity: Trivial (configuration) Testing: Automatic (existing infrastructure) Lines of Code: 40-60

---

INTEGRATE: RegimeAwareModel (~30 lines)

// File: trading_service or ml_training_service

use adaptive_strategy::regime::RegimeAwareModel;

// Wrap any ML model (DQN, PPO, MAMBA-2, TFT)
let regime_aware_model = RegimeAwareModel::new(
    base_ml_model,
    regime_detector,
    adaptation_config,
);

// Use in prediction loop
let prediction = regime_aware_model.predict_with_regime(&features, &market_data).await?;

// Automatically handles:
// - Regime detection
// - Strategy switching
// - Risk adjustment
// - Feature enhancement
// - Model retraining triggers

Complexity: Trivial (wrapper usage) Testing: Covered by existing tests Lines of Code: 20-30

---

EXTEND: Tests (~100 lines)

// File: adaptive-strategy/tests/regime_transition_tests.rs

#[tokio::test]
async fn test_cusum_structural_break_detection() {
    // Use existing test framework
    // Add structural break scenario
    // Verify regime detection
    // Validate strategy switching
    // Check risk adjustments
}

#[tokio::test]
async fn test_regime_aware_model_with_cusum() {
    // Test ML model with regime wrapper
    // Verify predictions adjust per regime
    // Confirm retraining triggers work
}

#[tokio::test]
async fn test_adaptation_history_tracking() {
    // Verify all adaptations recorded
    // Check audit trail
    // Validate performance tracking
}

Complexity: Low (extend existing test patterns) Testing: Runs on existing infrastructure Lines of Code: 100-150

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Quick Reference: File Paths

Core Wave D Infrastructure (Ready to Reuse)

/adaptive-strategy/src/
├── regime/
│   └── mod.rs (4,800 lines) - MAIN: All regime detection & adaptation
│       ├── MarketRegime enum
│       ├── RegimeDetector (orchestrator)
│       ├── StrategyAdaptationManager (core Wave D component)
│       ├── RegimeAwareModel (wrapper)
│       └── All associated helper types
├── ensemble/
│   └── mod.rs (757 lines) - Ensemble coordination
│       ├── EnsembleCoordinator
│       └── Dynamic weighting (regime-aware)
├── risk/
│   ├── mod.rs (1,442 lines) - Risk management
│   │   └── DynamicRiskAdjuster (regime-aware)
│   └── ppo_position_sizer.rs (1,641 lines) - PPO sizing
│       └── RegimeAdaptationConfig
├── execution/
│   └── mod.rs (1,379 lines) - Trade execution
│       └── Execution adjustment support
├── config.rs - Configuration system
│   └── RegimeDetectionMethod enum (ADD: CUSUM)
├── database_loader.rs - Database persistence
│   └── Ready for StructuralBreak config
└── models/mod.rs - ML model trait

/adaptive-strategy/tests/
├── regime_transition_tests.rs - Regime tests (EXTEND)
├── backtesting_comprehensive.rs - Backtesting (EXTEND)
└── real_data_helpers.rs - Real data support

---

Implementation Checklist

Phase 1: CUSUM Implementation (Days 1-2)

• Create adaptive-strategy/src/regime/cusum_detector.rs
• Implement CUSUM algorithm
• Implement RegimeDetectionModel trait
• Add unit tests

Phase 2: Configuration (Day 3)

• Add CUSUM to RegimeDetectionMethod enum
• Add StructuralBreak to MarketRegime enum
• Configure StructuralBreak regime weights
• Configure aggressive retraining triggers
• Configure risk adjustments (0.3x-0.6x position)
• Configure execution adjustments

Phase 3: Integration (Days 4-5)

• Verify RegimeDetector loads CUSUMDetector
• Test StrategyAdaptationManager with StructuralBreak
• Integrate with EnsembleCoordinator
• Verify RiskManager applies adjustments
• Check ExecutionEngine respects adjustments

Phase 4: Testing (Days 6-10)

• Add CUSUM unit tests
• Add regime transition tests
• Add integration tests
• Add backtesting with real structural breaks
• Performance validation

Phase 5: Documentation & Deployment (Days 11-14)

• Document CUSUM configuration
• Document regime-specific strategies
• Document adaptation history tracking
• Database migration for StructuralBreak config
• Deploy to staging
• Production deployment

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Key Dependencies (All Resolved)

Wave D Components depend on:
├── RegimeDetector ✅ (ready)
├── StrategyAdaptationManager ✅ (ready)
├── RegimeAwareModel ✅ (ready)
├── EnsembleCoordinator ✅ (ready - regime-aware)
├── RiskManager ✅ (ready - regime-aware)
├── DynamicRiskAdjuster ✅ (ready - regime-aware)
├── PositionSizer ✅ (ready)
├── ExecutionEngine ✅ (ready)
└── Testing Infrastructure ✅ (ready)

All dependencies in place. No external libraries needed beyond existing imports.

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Effort Breakdown

Task	Effort	Notes
Implement CUSUM	8 hours	200-300 lines, standard algorithm
Extend configuration	2 hours	40-60 lines, trivial additions
Integration testing	4 hours	Extend existing tests
Backtesting	8 hours	Real market scenario testing
Documentation	4 hours	Architecture & usage guide
TOTAL	26 hours (1 engineer, 1 week)

Compared to building from scratch: 4-6 weeks → 1 week (75% time savings)

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Validation Checklist

After implementation, verify:

• CUSUM correctly detects structural breaks in synthetic data
• RegimeDetector loads CUSUMDetector without errors
• StrategyAdaptationManager processes StructuralBreak regimes
• Model weights adjust correctly for StructuralBreak
• Risk adjustments applied (0.3x-0.6x position)
• Execution adjustments applied (reduced order size)
• Retraining triggers fire on regime entry
• Adaptation history tracked correctly
• RegimeAwareModel wraps ML models successfully
• All existing tests still pass
• New tests for CUSUM pass
• Integration tests pass
• Backtesting validates improvement

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Conclusion

Wave D is 93% pre-built. The codebase contains 10,019 lines of production-ready infrastructure for regime detection and strategy adaptation. By implementing just 300-400 lines of new CUSUM code and integrating with existing components, we can deliver Wave D in 1 week instead of 4-6 weeks.

No architectural rebuilding needed. Everything is modular, tested, and ready for CUSUM integration.