docs: Add WAVE 12.5.2 quick reference guide
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WAVE_12_5_2_QUICK_REFERENCE.md
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WAVE_12_5_2_QUICK_REFERENCE.md
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# WAVE 12.5.2 - ML Pipeline Integration Tests - Quick Reference
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**Status**: ✅ **COMPLETE** (11/11 tests, 100% pass rate)
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**Test Time**: 0.08 seconds
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**Commit**: c96a1533, 79c6fb20
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---
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## 🚀 Run Tests
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```bash
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# Run all ML pipeline integration tests
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cargo test -p foxhunt_e2e --test ml_pipeline_integration_test
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# Run with output
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cargo test -p foxhunt_e2e --test ml_pipeline_integration_test -- --nocapture
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# Run single test
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cargo test -p foxhunt_e2e --test ml_pipeline_integration_test test_full_ml_pipeline_end_to_end
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```
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---
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## 📝 Test Summary (11 Tests)
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| # | Test Name | Purpose | Status |
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|---|-----------|---------|--------|
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| 1 | test_full_ml_pipeline_end_to_end | Complete pipeline (7 stages) | ✅ |
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| 2 | test_real_time_prediction_pipeline | Streaming predictions | ✅ |
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| 3 | test_multi_symbol_pipeline | ES.FUT + ZN.FUT | ✅ |
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| 4 | test_dbn_to_ml_features | DBN → 16 features | ✅ |
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| 5 | test_ml_predictions_to_trading_decisions | Predictions → Orders | ✅ |
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| 6 | test_trading_decisions_to_orders | Decisions → Executable | ✅ |
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| 7 | test_adaptive_ensemble_real_data | Ensemble validation | ✅ |
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| 8 | test_shared_ml_strategy_integration | ONE SINGLE SYSTEM | ✅ |
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| 9 | test_regime_detection_accuracy | Bull/Bear/Sideways | ✅ |
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| 10 | test_ml_inference_latency | <100ms target | ✅ |
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| 11 | test_backtesting_throughput | >100 bars/s target | ✅ |
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---
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## 📁 Key Files
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```
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tests/e2e/
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├── tests/
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│ └── ml_pipeline_integration_test.rs # 850+ lines, 11 tests
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└── Cargo.toml # Added dbn + candle-core deps
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test_data/real/databento/ml_training/
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└── ES.FUT_ohlcv-1m_2024-03-25.dbn # 1,674+ bars
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```
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---
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## 🔧 Pipeline Stages
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```
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1. Data Ingestion → Load DBN binary files
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2. Feature Engineering → Extract 16 technical indicators
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3. ML Prediction → Generate ensemble predictions (mock)
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4. Trading Agent → Universe/Asset/Allocation decisions
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5. Order Generation → Create executable orders
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6. Trading Execution → Execute orders (simulated)
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7. Backtesting → Calculate performance metrics
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```
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---
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## 📊 Features Extracted (16 Total)
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| Feature | Type | Lookback |
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|---------|------|----------|
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| Open, High, Low, Close, Volume | OHLCV | Current bar |
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| Returns | Momentum | 1 bar |
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| MA5 | Moving Avg | 5 bars |
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| Volatility | Std Dev | 10 bars |
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| RSI | Oscillator | 14 bars |
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| MACD, Signal | Trend | 12/26 bars |
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| Bollinger Upper/Lower | Volatility | 20 bars |
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| ATR | Volatility | 14 bars |
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| EMA12, EMA26 | Moving Avg | 12/26 bars |
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---
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## ⚡ Performance Targets
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| Metric | Target | Actual | Status |
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|--------|--------|--------|--------|
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| Full pipeline time | <30s | 0.08s | ✅ 375x faster |
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| Inference latency | <100ms | <1ms | ✅ 100x faster (mock) |
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| Backtest throughput | >100 bars/s | >10K bars/s | ✅ 100x faster |
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---
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## 🔄 Mock vs Real
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### Current (Mock)
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- **Strategy**: Moving Average Crossover (5 vs 20 period)
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- **Purpose**: Validate infrastructure
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- **Performance**: <1ms per prediction
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- **Status**: ✅ All tests passing
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### Next Wave (Real)
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- **Models**: MAMBA-2, DQN, PPO, TFT (6 models total)
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- **Purpose**: Production predictions
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- **Expected**: 10-50ms per prediction (GPU)
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- **Status**: ⏳ Pending trained checkpoints
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---
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## 📈 Usage Examples
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### Load DBN Data
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```rust
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let bars = load_dbn_data("test_data/real/databento/ml_training/ES.FUT_ohlcv-1m_2024-03-25.dbn").await?;
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// Returns: Vec<OhlcvBar> with timestamp, OHLCV, symbol
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```
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### Extract Features
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```rust
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let features = extract_features(&bars)?;
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// Returns: Vec<Vec<f64>> - 16 features per bar
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```
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### Generate Predictions
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```rust
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let predictions = mock_ensemble_predictions(&bars, &features)?;
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// Returns: Vec<f64> - prediction scores (0.0-1.0)
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```
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### Create Trading Decisions
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```rust
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let decisions = generate_trading_decisions(&predictions)?;
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// Returns: Vec<TradingDecision> with action (Buy/Sell/Hold)
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```
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---
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## 🐛 Known Issues
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None! All 11 tests passing.
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---
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## 🎯 Next Steps
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1. **Load Real Models** (Priority 1)
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- Replace mock with MAMBA-2, DQN, PPO, TFT
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- Test with trained checkpoints
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- Validate GPU inference
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2. **Expand Symbols** (Priority 2)
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- Add NQ.FUT, 6E.FUT, GC.FUT
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- Test multi-symbol coordination
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- Validate cross-symbol strategies
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3. **Real Trading Integration** (Priority 3)
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- Connect to Trading Service gRPC
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- Test paper trading execution
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- Real-time market data streaming
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4. **Production Deployment** (Priority 4)
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- Add monitoring and alerts
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- Production logging
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- Error handling and recovery
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---
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## 📚 Documentation
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- **Full Report**: `WAVE_12_5_2_ML_PIPELINE_INTEGRATION_COMPLETE.md`
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- **Test File**: `tests/e2e/tests/ml_pipeline_integration_test.rs`
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- **CLAUDE.md**: Section on ML Pipeline Testing (updated)
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---
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## 🔗 Related Waves
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- **Wave 12.4.1**: Trading Service ML Migration (SharedMLStrategy)
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- **Wave 12.4.2**: Backtesting E2E Migration (Real implementations)
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- **Wave 206**: MAMBA-2 Shape Bug Fix (Ready for training)
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---
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**Quick Command**:
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```bash
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cargo test -p foxhunt_e2e --test ml_pipeline_integration_test -- --nocapture
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```
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**Expected Output**: `test result: ok. 11 passed; 0 failed; 0 ignored`
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---
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🤖 Generated with [Claude Code](https://claude.com/claude-code)
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