e8a68ee39f
- Created data/examples/download_ml_training_data.rs using reqwest + Databento HTTP API - Downloaded 90 days × 4 symbols (ES.FUT, NQ.FUT, ZN.FUT, 6E.FUT) - Files saved to test_data/real/databento/ml_training/ - Total: 360 files, 15 MB compressed DBN format - Used existing Rust pattern from download_nq_fut.rs - API key loaded from .env file - 100% success rate (360/360 files) - Ready for ML training benchmarks Next: Create simplified training benchmark for RTX 3050 Ti GPU measurements
484 lines
14 KiB
Markdown
484 lines
14 KiB
Markdown
# Real Data Integration - Next Steps Roadmap
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**Date**: 2025-10-13
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**Status**: Real Data Integration Complete ✅
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**Phase**: Trading Strategy Development
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---
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## 🎯 Executive Summary
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With real data integration complete, the focus shifts to **expanding data coverage** and **validating strategies with production-grade market data**.
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**Current Status**:
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- ✅ DBN integration operational (0.70ms, 14x faster than target)
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- ✅ 6 DBN files acquired (ES.FUT, ESH4, NQ.FUT, CL.FUT)
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- ✅ 19/19 tests passing (100%)
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- ✅ 29,000+ lines of documentation
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**Immediate Priority**: **Expand data coverage to 5-10 symbols, 30-90 days**
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---
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## 📋 Phase 1: Data Coverage Expansion (1-2 Weeks)
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### Priority 1: Additional Futures Symbols (HIGH)
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**Target**: 5-10 futures symbols across multiple asset classes
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**Symbols to Acquire**:
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1. **ES.FUT** - E-mini S&P 500 (already have 1 day ✅)
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- Expand to 30-90 days
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- Target: Q4 2024 (bull market) + Q1 2025 (volatile)
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2. **NQ.FUT** - E-mini Nasdaq 100 (already have 1 day ✅)
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- Expand to 30-90 days
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- High correlation with tech sector
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3. **CL.FUT** - Crude Oil (already have 1 day ✅)
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- Expand to 30-90 days
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- Energy sector, geopolitical volatility
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4. **GC.FUT** - Gold Futures (NEW)
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- 30-90 days
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- Safe haven asset, inflation hedge
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5. **ZN.FUT** - 10-Year Treasury Note (NEW)
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- 30-90 days
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- Interest rate sensitivity, bond market
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6. **6E.FUT** - Euro FX (NEW, Optional)
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- 30-90 days
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- Currency markets, forex volatility
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**Data Requirements**:
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- Format: DBN (Databento Binary) 1-minute OHLCV
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- Venue: CME Group (ES, NQ, CL, GC, ZN) + ICE (6E)
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- Dataset: `ohlcv-1m` schema
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- Date Range: 30-90 consecutive days (Jan-Mar 2024 or Oct-Dec 2024)
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**Effort**: 2-3 days
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- Day 1: Databento API setup, credit purchase (~$50-100)
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- Day 2: Download scripts, file organization, validation
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- Day 3: Test integration, documentation updates
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**Success Criteria**:
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- ✅ 5-10 symbols with 30+ days each
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- ✅ All files validated with automatic anomaly correction
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- ✅ Test coverage updated to use new symbols
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- ✅ Documentation updated with new data inventory
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### Priority 2: Multi-Day Integration Tests (MEDIUM)
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**Goal**: Validate multi-day loading and continuity
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**Tasks**:
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1. Create 30-day continuous backtest test
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2. Validate timestamp ordering across days
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3. Test overnight gaps and weekends
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4. Validate data quality across entire range
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**Files to Update**:
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- `services/backtesting_service/tests/dbn_multi_day_tests.rs`
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- `services/integration_tests/tests/backtest_real_data_test.rs` (NEW)
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**Effort**: 1-2 days
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**Success Criteria**:
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- ✅ 30-day continuous backtest passing
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- ✅ Gap handling validated (weekends, holidays)
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- ✅ Cross-day metrics correct (PnL, drawdown)
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### Priority 3: Automated Data Pipeline (MEDIUM)
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**Goal**: Automate data acquisition and validation
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**Components**:
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1. **Download Script** (`scripts/download_dbn_data.sh`)
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- Databento API integration
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- Bulk download support
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- Progress tracking
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2. **Validation Script** (`scripts/validate_dbn_quality.sh`)
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- Automatic anomaly detection
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- OHLCV validation
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- Summary statistics
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3. **Update Script** (`scripts/update_test_data.sh`)
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- Weekly/monthly data refresh
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- Version control (git LFS for large files)
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**Effort**: 2-3 days
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**Success Criteria**:
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- ✅ One-command data acquisition
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- ✅ Automatic validation on download
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- ✅ CI/CD integration (weekly data updates)
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---
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## 📋 Phase 2: Strategy Backtesting (1-2 Weeks)
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### Priority 1: Backtest Existing Strategies (HIGH)
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**Strategies to Test**:
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1. **Moving Average Crossover** (`trading_engine/src/strategies/moving_average_crossover.rs`)
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- Test with ES.FUT 30-day dataset
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- Validate signals vs synthetic data
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- Measure Sharpe, drawdown, win rate
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2. **Adaptive Strategy** (`trading_engine/src/strategies/adaptive_strategy.rs`)
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- Test regime detection with real volatility
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- Validate trending/ranging/sideways classification
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- Compare vs synthetic regime transitions
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3. **Mean Reversion** (if implemented)
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- Test with ranging market data
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- Validate entry/exit timing
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4. **Momentum** (if implemented)
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- Test with trending market data
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- Validate breakout detection
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**Effort**: 3-5 days (1-2 days per strategy)
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**Success Criteria**:
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- ✅ All strategies tested with 30+ days of real data
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- ✅ Performance metrics documented (Sharpe, drawdown, PnL, win rate)
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- ✅ Edge cases identified (gaps, outliers, extreme volatility)
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- ✅ Comparison report: synthetic vs real data performance
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### Priority 2: Multi-Symbol Portfolio Backtest (MEDIUM)
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**Goal**: Test portfolio-level strategies with real cross-asset data
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**Scenarios**:
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1. **Sector Rotation**: ES.FUT (equities) vs ZN.FUT (bonds)
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2. **Risk Parity**: ES, NQ, CL, GC diversification
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3. **Hedge Strategies**: Long ES, short NQ during tech sell-off
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**Effort**: 2-3 days
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**Success Criteria**:
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- ✅ Multi-symbol backtest infrastructure operational
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- ✅ Portfolio-level metrics (total PnL, correlation, beta)
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- ✅ Rebalancing logic validated
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### Priority 3: Transaction Cost & Slippage (MEDIUM)
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**Goal**: Model realistic execution costs
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**Implementation**:
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1. Add bid-ask spread simulation
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2. Model slippage based on volume
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3. Include commission structure (CME fees)
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4. Validate impact on strategy profitability
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**Effort**: 2-3 days
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**Success Criteria**:
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- ✅ Realistic cost model implemented
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- ✅ All strategies re-tested with costs
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- ✅ Profitability adjusted for slippage
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---
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## 📋 Phase 3: ML Model Validation (1-2 Weeks)
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### Priority 1: MAMBA-2 Model (HIGH)
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**Goal**: Test state space model with real market data
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**Tasks**:
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1. Load ES.FUT 30-day dataset
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2. Generate features (price, volume, volatility)
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3. Run inference on all bars
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4. Measure prediction accuracy vs real outcomes
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5. Compare vs synthetic data performance
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**Effort**: 2-3 days
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**Success Criteria**:
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- ✅ MAMBA-2 inference operational with real data
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- ✅ Prediction accuracy measured (MAE, RMSE, R²)
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- ✅ Overfitting identified (if any)
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- ✅ Hyperparameter adjustments documented
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### Priority 2: DQN/PPO Reinforcement Learning (HIGH)
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**Goal**: Test RL agents with real market environments
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**Tasks**:
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1. Create environment using real data replay
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2. Train DQN/PPO agents on 30-day datasets
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3. Validate action selection vs real outcomes
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4. Measure reward accumulation
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5. Compare vs synthetic environment performance
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**Effort**: 3-4 days
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**Success Criteria**:
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- ✅ DQN/PPO training operational with real data
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- ✅ Cumulative reward measured
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- ✅ Policy convergence validated
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- ✅ Overfitting identified (if any)
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### Priority 3: TFT Forecasting (MEDIUM)
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**Goal**: Test temporal fusion transformer for price prediction
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**Tasks**:
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1. Load multi-day dataset for time series
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2. Generate temporal features
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3. Train TFT on historical data
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4. Validate forecasting accuracy
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5. Measure inference latency
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**Effort**: 2-3 days
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**Success Criteria**:
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- ✅ TFT training operational with real data
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- ✅ Forecast accuracy measured (MAPE, MAE)
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- ✅ Multi-step ahead forecasting validated
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### Priority 4: Feature Engineering Validation (MEDIUM)
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**Goal**: Validate feature extraction with real market data
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**Tasks**:
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1. Test technical indicators (SMA, EMA, RSI, MACD)
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2. Validate microstructure features (bid-ask, order imbalance)
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3. Test TLOB (Temporal Limit Order Book) features
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4. Measure feature importance with real data
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**Effort**: 1-2 days
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**Success Criteria**:
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- ✅ All feature extractors operational
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- ✅ Feature distributions analyzed (vs synthetic)
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- ✅ Feature importance ranking documented
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---
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## 📋 Phase 4: Replace Mock Data in Tests (1 Week)
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### Priority 1: E2E Test Conversion (HIGH)
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**Goal**: Replace synthetic data with real DBN data in all E2E tests
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**Files to Update**:
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- `services/integration_tests/tests/backtest_integration_test.rs`
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- `services/integration_tests/tests/ml_pipeline_test.rs`
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- `services/integration_tests/tests/trading_service_test.rs`
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**Effort**: 2-3 days
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**Success Criteria**:
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- ✅ All E2E tests use real DBN data
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- ✅ 22/22 tests still passing (100%)
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- ✅ Test execution time acceptable (<5 min)
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### Priority 2: Unit Test Conversion (MEDIUM)
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**Goal**: Replace mock data generators with real data helpers
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**Files to Update**:
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- `trading_engine/tests/strategy_tests.rs`
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- `ml/tests/model_tests.rs`
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- `data/tests/feature_extraction_tests.rs`
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**Effort**: 2-3 days
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**Success Criteria**:
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- ✅ Unit tests use real data samples
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- ✅ Test coverage maintained (>95%)
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- ✅ Edge cases validated with real anomalies
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---
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## 📋 Phase 5: Performance Optimization (Optional, 1 Week)
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### Priority 1: Metadata Caching (MEDIUM)
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**Goal**: Cache file metadata for faster date range queries
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**Implementation**:
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- Add `first_ts` and `last_ts` to `FileEntry`
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- Parse first/last record on initial load
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- Skip files outside date range
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**Effort**: 1-2 days
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**Success Criteria**:
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- ✅ 3x speedup for single-day queries on multi-day datasets
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- ✅ Backward compatibility maintained
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### Priority 2: Parallel File Loading (LOW)
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**Goal**: Load multiple files concurrently
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**Implementation**:
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- Use `tokio::task::JoinSet`
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- Load files in parallel
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- Merge results after completion
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**Effort**: 1-2 days
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**Success Criteria**:
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- ✅ Near-linear speedup (3 files in ~1ms instead of 2.1ms)
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- ✅ Thread-safe decoder handling
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### Priority 3: LRU Cache Implementation (LOW)
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**Goal**: Avoid reloading same files for repeated queries
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**Implementation**:
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- Replace `HashMap` with `lru::LruCache`
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- Configurable cache size
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- Eviction policy
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**Effort**: 1 day
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**Success Criteria**:
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- ✅ Near-instant repeated queries (cache hit = 0.001ms)
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- ✅ Memory bounded (configurable limit)
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---
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## 📅 Timeline Summary
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| Phase | Duration | Priority | Status |
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|-------|----------|----------|--------|
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| **Phase 1: Data Expansion** | 1-2 weeks | HIGH | 🔜 Next |
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| **Phase 2: Strategy Backtesting** | 1-2 weeks | HIGH | ⏸️ Pending data |
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| **Phase 3: ML Validation** | 1-2 weeks | HIGH | ⏸️ Pending data |
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| **Phase 4: Mock Data Replacement** | 1 week | MEDIUM | ⏸️ Pending validation |
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| **Phase 5: Performance Optimization** | 1 week | LOW | ⏸️ Optional |
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**Total Duration**: 4-7 weeks (depending on parallel execution)
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---
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## 🎯 Success Metrics
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### Data Coverage
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- **Target**: 5-10 symbols, 30-90 days each
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- **Current**: 4 symbols, 1-4 days each
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- **Gap**: 1-6 additional symbols, 26-89 additional days per symbol
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### Strategy Performance
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- **Target**: All strategies validated with real data
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- **Current**: Zero strategies tested with real data
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- **Gap**: 3-5 strategies need validation
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### ML Model Accuracy
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- **Target**: Accuracy measured on real data, overfitting identified
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- **Current**: No ML models tested with real data
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- **Gap**: 4 models (MAMBA-2, DQN, PPO, TFT) need validation
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### Test Coverage
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- **Target**: 100% real data in tests
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- **Current**: 19/19 backtesting tests use real data, 0/22 E2E tests
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- **Gap**: 22 E2E tests need conversion
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---
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## 📝 Deliverables by Phase
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### Phase 1 Deliverables
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- [ ] 5-10 DBN files (30-90 days each)
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- [ ] Updated data inventory documentation
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- [ ] Automated download/validation scripts
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- [ ] Multi-day integration tests (30+ days)
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### Phase 2 Deliverables
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- [ ] Strategy backtest reports (Sharpe, drawdown, PnL)
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- [ ] Synthetic vs real data comparison
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- [ ] Multi-symbol portfolio backtest results
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- [ ] Transaction cost model implementation
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### Phase 3 Deliverables
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- [ ] MAMBA-2 accuracy report (real data)
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- [ ] DQN/PPO training results (real data)
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- [ ] TFT forecasting accuracy report
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- [ ] Feature importance analysis (real vs synthetic)
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### Phase 4 Deliverables
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- [ ] E2E tests converted to real data
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- [ ] Unit tests converted to real data
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- [ ] 22/22 E2E tests passing (100%)
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- [ ] Test execution time optimized
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### Phase 5 Deliverables (Optional)
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- [ ] Metadata caching implemented
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- [ ] Parallel file loading implemented
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- [ ] LRU cache implemented
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- [ ] Performance benchmark report
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---
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## 🚀 Getting Started
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### Immediate Next Step: Expand Data Coverage
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**Action**: Acquire 5-10 symbols with 30-90 days each
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**Execution Plan**:
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```bash
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# Step 1: Set up Databento API credentials
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export DATABENTO_API_KEY="your_api_key_here"
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# Step 2: Run download script (to be created)
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./scripts/download_dbn_data.sh --symbols ES.FUT,NQ.FUT,CL.FUT,GC.FUT,ZN.FUT --start 2024-01-01 --end 2024-03-31
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# Step 3: Validate data quality
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./scripts/validate_dbn_quality.sh test_data/real/databento/*.dbn
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# Step 4: Run tests
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cargo test -p backtesting_service --test dbn_multi_day_tests
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cargo test -p backtesting_service --test dbn_integration_tests
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# Step 5: Update documentation
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# - Update CLAUDE.md with new data inventory
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# - Update DBN_INTEGRATION_GUIDE.md with new examples
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```
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**Expected Outcome**:
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- ✅ 5-10 symbols with 30-90 days each acquired
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- ✅ All files validated with automatic anomaly correction
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- ✅ Tests passing with expanded data coverage
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- ✅ Documentation updated
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**Timeline**: 2-3 days
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---
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## 📞 Questions & Support
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**For Data Acquisition**:
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- See `docs/DATABENTO_GUIDELINES.md`
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- See `docs/DBN_INTEGRATION_GUIDE.md`
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- Contact: Databento support (support@databento.com)
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**For Strategy Backtesting**:
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- See `docs/DBN_INTEGRATION_GUIDE.md` (Integration Examples)
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- See `services/backtesting_service/DBN_REPOSITORY_USAGE.md`
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- Review `docs/examples/dbn_backtesting_integration.rs`
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**For ML Model Validation**:
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- See `TESTING_PLAN.md`
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- See `ml/README.md` (if exists)
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- Review `docs/examples/dbn_basic_loading.rs`
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**For Performance Optimization**:
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- See `services/backtesting_service/tests/fixtures/PERFORMANCE.md`
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- See `docs/DBN_TROUBLESHOOTING.md`
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---
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**Roadmap Created**: 2025-10-13
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**Status**: Real Data Integration Complete ✅
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**Next Milestone**: Data Coverage Expansion (5-10 symbols, 30-90 days)
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**Expected Completion**: 1-2 weeks
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