foxhunt/AGENT_F12_REGIME_BACKTEST_STATUS_REPORT.md

Agent F12: Regime-Adaptive vs Baseline Backtesting Comparison - Status Report

Date: 2025-10-18 Agent: F12 Objective: Execute backtesting comparison between regime-adaptive strategy and baseline strategy to validate +25-50% Sharpe improvement hypothesis Status: ⚠️ BLOCKED - Infrastructure Not Ready

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Executive Summary

The requested regime-adaptive vs baseline backtesting comparison CANNOT be executed at this time because the required test infrastructure has not been implemented. The test file /home/jgrusewski/Work/foxhunt/services/backtesting_service/tests/wave_d_regime_backtest_test.rs exists but has 6 compilation errors that prevent execution.

Critical Finding: This is a TDD "RED phase" test file that was created as part of Wave D development but the underlying implementation is incomplete.

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Compilation Errors Analysis

Error Summary

• Total Errors: 6 compilation errors
• Root Causes:
  1. Missing BacktestStatus enum (private/not exported)
  2. Missing BacktestingDatabaseConfig::default() implementation
  3. Test infrastructure scaffolding incomplete

Detailed Errors

error[E0603]: enum `BacktestStatus` is private
  --> services/backtesting_service/tests/wave_d_regime_backtest_test.rs:19:53
   |
19 | use backtesting_service::service::{BacktestContext, BacktestStatus};
   |                                                     ^^^^^^^^^^^^^^ private enum

error[E0599]: no function or associated item named `default` found for struct `BacktestingDatabaseConfig`
  --> services/backtesting_service/tests/wave_d_regime_backtest_test.rs:117:104
   |
117 |     let storage_manager = Arc::new(StorageManager::new(&config::structures::BacktestingDatabaseConfig::default()).await?);
    |                                                                                                        ^^^^^^^ function or associated item not found

Impact: All 5 test functions in the file fail to compile:

1. test_red_regime_adaptive_backtest_basic ❌
2. test_red_regime_vs_baseline_comparison ❌ (Primary target test)
3. test_red_regime_conditioned_performance ❌
4. test_red_regime_attribution_analysis ❌
5. test_red_regime_performance_targets ❌

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Wave D Implementation Status

Phase 1 (Agents D1-D8): ✅ COMPLETE (81% test pass rate)

• Implemented: CUSUM, PAGES Test, Bayesian Changepoint, Multi-CUSUM, Trending, Ranging, Volatile, Transition Matrix
• Code: 3,759 lines implementation + 4,411 lines tests
• Performance: 467x better than targets (0.01μs CUSUM vs 50μs target)
• Real Data Validation: ES.FUT (93 breaks/1,679 bars), 6E.FUT (52 breaks/1,877 bars)

Phase 2 (Agents D9-D12): ✅ DESIGN COMPLETE (87% code reuse)

• Components: Position Sizer, Dynamic Stops, Performance Tracker, Ensemble Aggregator
• Infrastructure Reuse: 8,073 existing lines, 1,250 new lines planned
• Status: Design approved, implementation pending

Phase 3 (Agents D13-D16): ⏳ IN PROGRESS (40% complete)

• Target: 24 Wave D features (indices 201-225)
• Implemented:
  • D13: CUSUM Statistics (indices 201-210, 10 features) ✅
  • D14: ADX & Directional Indicators (indices 211-215, 5 features) ✅
  • D15: Regime Transition Probabilities (indices 216-220, 5 features) ⚠️ PARTIAL
  • D16: Adaptive Strategy Metrics (indices 221-224, 4 features) ❌ NOT STARTED

Phase 4 (Agents D17-D20): ❌ NOT STARTED

• Objective: Integration & validation with real Databento data
• Missing: Backtesting integration layer (current blocker)

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Root Cause Analysis

Why the Test Cannot Run

1. TDD "RED Phase" Test File

   • The test file wave_d_regime_backtest_test.rs follows strict TDD methodology
   • It was created before the implementation exists (by design)
   • All 5 tests are marked with test_red_* prefix indicating RED phase
   • Expected behavior: Tests fail until GREEN phase implementation

2. Missing Integration Layer

   • The MLStrategyEngine::execute_ml_backtest() method exists but doesn't yet support Wave D regime features
   • Parameter parsing for enable_regime_features, regime_position_sizing, regime_stop_loss not implemented
   • No regime-adaptive strategy switching logic integrated into backtesting engine

3. Configuration Infrastructure Gap

   • BacktestingDatabaseConfig missing Default trait implementation
   • BacktestStatus enum not exported from backtesting service public API
   • Storage manager initialization pattern incompatible with test structure

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What Would Need to Be Implemented

Minimum Viable Implementation (8-12 hours)

1. Fix Configuration Issues (1 hour)

   // In config/src/structures.rs
   impl Default for BacktestingDatabaseConfig {
       fn default() -> Self {
           Self {
               database_url: "postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt".to_string(),
               max_connections: Some(10),
               min_connections: Some(2),
               acquire_timeout_ms: Some(5000),
               statement_cache_capacity: Some(100),
           }
       }
   }
   

2. Export BacktestStatus (15 minutes)

   // In services/backtesting_service/src/service.rs
   pub use crate::foxhunt::tli::BacktestStatus;
   

3. Integrate Regime Features into Backtesting Engine (6-8 hours)

   • Parse enable_regime_features, regime_position_sizing, regime_stop_loss parameters
   • Instantiate RegimeDetector (CUSUM/PAGES/Bayesian)
   • Instantiate TrendingClassifier, RangingClassifier, VolatileClassifier
   • Apply position multipliers based on current regime (1.0x, 1.5x, 0.5x, 0.2x)
   • Adjust stop-loss levels using DynamicStopLossManager
   • Track regime-conditioned performance metrics

4. Implement Regime-Conditioned Metrics Tracking (2-3 hours)

   • Store regime type in trade metadata
   • Aggregate PnL by regime (Trending, Ranging, Volatile, Crisis)
   • Calculate per-regime Sharpe ratio
   • Generate regime attribution report

Complete Implementation (Wave D Phase 4: 3-4 days)

Agent D17: Backtesting Integration Layer (1 day)

• Full regime feature parameter parsing
• Regime detector initialization and management
• Position sizing multiplier application
• Dynamic stop-loss integration

Agent D18: Performance Tracking Infrastructure (1 day)

• Regime-conditioned metrics storage
• Per-regime PnL attribution
• Regime transition analysis
• Performance dashboard integration

Agent D19: Real Data Validation (1 day)

• ES.FUT full-day backtest
• Multi-symbol validation (NQ.FUT, 6E.FUT, ZN.FUT)
• Performance benchmarking (<50μs per feature target)
• Sharpe improvement validation

Agent D20: Production Readiness (1 day)

• Final test coverage (target: >85%)
• Documentation updates
• Performance optimization
• Production deployment checklist

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Alternative: Simplified Validation Approach

Quick Validation Path (4-6 hours)

Instead of fixing the full backtesting integration, we could validate the hypothesis using a simplified standalone script:

1. Create Standalone Validation Script (ml/examples/validate_regime_adaptive.rs)

   • Load ES.FUT data directly via fixtures::get_es_fut_bars()
   • Run two parallel simulations:
     • Baseline: Fixed 1.0x position sizing, fixed 2.0x ATR stop-loss
     • Regime-Adaptive: Dynamic position sizing (1.5x trending, 0.5x volatile), dynamic stops
   • Calculate comparative metrics (Sharpe, drawdown, win rate)
   • Generate report with improvement percentages

2. Advantages:

   • Bypasses backtesting service integration complexity
   • Faster to implement (4-6 hours vs 8-12 hours)
   • Still provides actionable validation of hypothesis
   • Can be completed in single development session

3. Limitations:

   • Not integrated into main backtesting infrastructure
   • Requires manual execution (not automated test suite)
   • Less representative of production trading environment

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Current Wave D Capabilities

What IS Working (Can Be Demonstrated)

1. Regime Detection (Wave D Phase 1)

   # CUSUM structural break detection
   cargo test -p ml --test cusum_test --release -- --nocapture
   # Output: 93 breaks detected in ES.FUT (1,679 bars)
   

2. Regime Classification (Wave D Phase 1)

   # Trending regime classification
   cargo test -p ml --test trending_test --release -- --nocapture
   # Ranging regime classification
   cargo test -p ml --test ranging_test --release -- --nocapture
   # Volatile regime classification
   cargo test -p ml --test volatile_test --release -- --nocapture
   

3. Feature Extraction (Wave D Phase 3 - Partial)

   # CUSUM statistics features (indices 201-210)
   cargo test -p ml regime_cusum -- --nocapture
   # ADX directional indicators (indices 211-215)
   cargo test -p ml regime_adx -- --nocapture
   

4. Performance Validation

   # Wave D Phase 1 achieved 467x better than targets
   # CUSUM: 0.01μs (target: 50μs) = 5000x improvement
   # Trending: 0.02μs (target: 50μs) = 2500x improvement
   # Ranging: 0.03μs (target: 50μs) = 1667x improvement
   

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Recommendations

Option 1: Complete Wave D Phase 4 (Recommended for Production)

• Time: 3-4 days
• Outcome: Full production-ready regime-adaptive backtesting
• Validates: +25-50% Sharpe improvement hypothesis with real ES.FUT data
• Next Steps: Agents D17-D20 implementation

Option 2: Simplified Validation Script (Recommended for Hypothesis Validation)

• Time: 4-6 hours
• Outcome: Quick validation of regime-adaptive performance improvement
• Validates: Hypothesis with high confidence (not production-ready)
• Next Steps: Create ml/examples/validate_regime_adaptive.rs

Option 3: Manual Component Testing (Immediate Option)

• Time: 1-2 hours
• Outcome: Validate individual Wave D components work correctly
• Validates: Technical correctness (not performance improvement)
• Next Steps: Run existing Wave D Phase 1 tests + document results

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Conclusion

Primary Finding: The requested regime-adaptive vs baseline backtesting comparison cannot be executed because Wave D Phase 4 (integration & validation) has not been implemented yet.

Current Status:

• Wave D is 60% complete (Phases 1-2 done, Phase 3 40% complete)
• Regime detection and classification ARE working and exceed performance targets by 467x
• Backtesting integration layer IS NOT working (missing implementation)

Recommendation:

1. Immediate (Today): Execute Option 3 - Run existing Wave D component tests to demonstrate technical correctness
2. Near-Term (Next 1-2 days): Execute Option 2 - Create simplified validation script to validate +25-50% Sharpe hypothesis
3. Long-Term (Next 3-4 days): Execute Option 1 - Complete Wave D Phase 4 for production-ready regime-adaptive backtesting

Impact on Production Timeline:

• If we proceed with Option 2 (simplified validation), we can validate the hypothesis TODAY
• If we proceed with Option 1 (full implementation), production deployment pushed back 3-4 days but gains full integration

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Next Steps

Immediate Actions (Today)

1. Run Existing Wave D Component Tests (1 hour)

   # Validate CUSUM detection
   cargo test -p ml --test cusum_test --release -- --nocapture > /tmp/cusum_validation.txt
   
   # Validate regime classification
   cargo test -p ml trending_test ranging_test volatile_test --release -- --nocapture > /tmp/regime_validation.txt
   
   # Validate feature extraction
   cargo test -p ml regime_cusum regime_adx --release -- --nocapture > /tmp/feature_validation.txt
   

2. Document Current Capabilities (30 minutes)

   • Create summary of Wave D Phase 1 performance metrics
   • Create summary of Wave D Phase 2 design (87% code reuse)
   • Create summary of Wave D Phase 3 progress (40% complete)

3. Decide on Validation Approach (15 minutes)

   • Option 2 (Simplified): Start implementation of validate_regime_adaptive.rs
   • Option 1 (Complete): Start Agent D17 implementation (backtesting integration)

Follow-Up (Next Session)

• If Option 2 chosen: Execute validation script, analyze results, generate report
• If Option 1 chosen: Implement Agent D17 (1 day), then Agent D18 (1 day), validate with real data

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Appendix: Test File Structure

The test file that was requested to run (wave_d_regime_backtest_test.rs) contains 5 tests:

1. test_red_regime_adaptive_backtest_basic (Lines 107-170)

   • Tests basic regime-adaptive backtest execution
   • Validates trades were executed with regime features enabled
   • Checks Sharpe >0.0, win rate >40%

2. test_red_regime_vs_baseline_comparison (Lines 173-285) ⭐ PRIMARY TARGET

   • Compares regime-adaptive vs baseline (no adaptation)
   • Validates Sharpe improvement, drawdown reduction
   • This is the test that was requested to run

3. test_red_regime_conditioned_performance (Lines 288-381)

   • Tests per-regime performance tracking
   • Validates trending regime (1.5x multiplier)
   • Validates volatile regime (0.5x multiplier)

4. test_red_regime_attribution_analysis (Lines 384-433)

   • Tests PnL attribution by regime type
   • Validates regime metadata stored in trades

5. test_red_regime_performance_targets (Lines 436-520)

   • Validates production targets (Sharpe >1.5, win rate >55%, drawdown <20%)
   • Confirms model performance tracking integration

All tests follow TDD RED phase pattern and will fail until implementation is complete.