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docs: Update QWEN.md with complete 5-phase architecture and results
Added: - Complete 5-phase pipeline architecture diagram - Factor evaluation results (1009 factors, 337 successful) - Top 10 factors by IC table - Failure analysis (672 failed, 80% code crashes) - Optimization potential (7 areas for high-end upgrades) Sections added: - Phase 1: Factor Generation (Open Source) - Phase 2: ML Training (Closed Source) - Phase 3: Portfolio Optimization (Closed Source) - Phase 4: Strategy Generation (Closed Source) - Phase 5: Iterative Improvement (Closed Source) High-end optimization suggestions: 1. Code quality (33% → 70%+ success rate) 2. ML pipeline (SHAP, ensemble, Optuna) 3. Portfolio optimization (risk parity, Black-Litterman) 4. Strategy generation (regime-specific, multi-timeframe) 5. Execution optimization (parallel, smart retry) 6. Risk management (VaR/ES, correlation monitoring) 7. Infrastructure (GPU, caching, monitoring)
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4. Dependencies are tracked
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---
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---
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## 🚀 COMPLETE 5-PHASE ARCHITECTURE
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### Phase 1: Factor Generation (Open Source - ALWAYS ACTIVE)
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```
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1. Hypothesis Generation (LLM v3 Prompt)
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→ MultiIndex code examples (unstack/stack pattern)
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→ Working code templates
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→ Volume warning (FX volume = 0 often)
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2. CoSTEER Code Validation
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→ Execute factor code
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→ Validate result.h5 output
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→ Retry with feedback (max 3 retries)
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3. Qlib Docker Backtest
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→ LightGBM training on factor
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→ Portfolio backtest (TopkDropoutStrategy)
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→ IC, Sharpe, Max DD, Win Rate calculation
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4. Results Storage
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→ results/factors/{name}.json (Code + Description + Metrics)
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→ results/db/backtest_results.db (SQLite)
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→ results/logs/ (Running logs)
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⚡ CONTINUE UNTIL 5000+ VALID FACTORS REACHED
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```
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### Phase 2: ML Model Training (Closed Source - Local Only)
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```
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5. Load Top 50 Factors (by IC ≥ 0.01)
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→ From results/factors/ with valid IC
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→ Extract factor values from workspaces
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6. Build Feature Matrix
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→ X = factor values (samples × factors)
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→ y = forward returns (96-bar shift)
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7. Train LightGBM Model
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→ Split: 80% train, 20% validate
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→ Early stopping (50 rounds)
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→ Feature importance analysis
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8. Model Validation
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→ IC (train vs valid)
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→ Sharpe-like metric
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→ Overfitting detection
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9. Save Model
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→ results/models/{name}/model.txt
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→ results/models/{name}/metadata.json
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```
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### Phase 3: Portfolio Optimization (Closed Source - Local Only)
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```
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10. Load Top 30 Factors
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→ Compute correlation matrix
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→ Select uncorrelated factors (max corr = 0.3)
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11. Optimize Weights
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→ Weight by absolute IC
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→ Normalize to sum = 1.0
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12. Backtest Portfolio
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→ Combined factor score = Σ(weight_i × factor_i)
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→ Calculate IC, Sharpe, Max DD, Win Rate
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13. Save Portfolio
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→ results/portfolios/{name}.json
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```
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### Phase 4: Strategy Generation (Closed Source - Local Only)
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```
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14. Generate Trading Rules
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→ Entry signals (factor thresholds)
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→ Exit signals (take profit, stop loss)
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→ Position sizing (Kelly criterion)
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15. Add Risk Management
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→ Max drawdown protection
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→ Cooldown periods after losses
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→ Stoploss cluster detection
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16. Save Strategy
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→ results/strategies/{name}.json
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```
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### Phase 5: Iterative Improvement (Closed Source - Local Only)
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```
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17. ML Feedback Loop
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→ Use model performance to guide factor generation
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→ Identify feature importance patterns
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→ Generate factors targeting weak areas
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18. Portfolio Feedback
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→ Use portfolio performance to refine weights
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→ Add new uncorrelated factors
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→ Remove degraded factors
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19. Loop Back to Phase 1
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→ Generate NEW factors with ML insights
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→ Retrain model with expanded factor set
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→ Continuous improvement cycle
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```
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---
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## 📊 CURRENT RESULTS (as of April 2026)
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### Factor Evaluation (1009 factors, FULL DATA 2020-2026)
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| Metric | Value |
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|--------|-------|
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| Total evaluated | 1,009 |
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| Successful | 337 (33%) |
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| Failed | 672 (67%) |
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| Best IC | **0.255** (daily_close_open_mom) |
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| Avg IC (valid) | 0.011 |
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| Best Sharpe | 1.71 (DCP) |
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### Top 10 Factors by IC
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| # | Factor | IC | Sharpe |
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|---|--------|-----|--------|
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| 1 | daily_close_open_mom | **0.255** | 0.007 |
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| 2 | daily_ret_log_1d | 0.255 | 0.003 |
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| 3 | daily_ret_close_1d | 0.255 | 0.005 |
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| 4 | daily_close_to_close_return | 0.255 | 0.005 |
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| 5 | daily_ret_vol_adj_1d | 0.235 | -0.007 |
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| 6 | daily_ols_slope_96 | 0.227 | 0.002 |
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| 7 | DCP | 0.199 | **1.71** |
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| 8 | DailyTrendStrength_Raw | 0.143 | -0.016 |
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| 9 | daily_c2c_return | 0.129 | 0.001 |
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| 10 | daily_momentum | 0.129 | -0.001 |
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### Failure Analysis (672 failed)
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| Error Type | Count | % | Cause |
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|------------|-------|-----|-------|
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| Code crashed | 540 | 80.4% | MultiIndex errors (FIXED in v3 prompt) |
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| All NaN values | 97 | 14.4% | Volume=0, rolling window too large |
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| Other errors | 28 | 4.2% | Various |
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| Timeout (120s) | 5 | 0.7% | Computationally expensive |
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| Too little overlap | 2 | 0.3% | Data mismatch |
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---
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## 💡 OPTIMIZATION POTENTIAL (HIGH-END UPGRADES)
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### 1. Code Quality Improvements
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- **Current**: 33% success rate
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- **Target**: 70%+ with v3 prompt (MultiIndex examples)
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- **Expected**: ~700 valid factors from 1009 generated
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### 2. ML Pipeline Enhancements
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- **Feature Selection**: Use SHAP values for importance
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- **Ensemble Models**: Combine LightGBM + XGBoost + Neural Net
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- **Cross-Validation**: Time-series split to prevent overfitting
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- **Hyperparameter Optimization**: Optuna for automatic tuning
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### 3. Portfolio Optimization
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- **Risk Parity**: Equal risk contribution instead of IC-weighted
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- **Black-Litterman**: Incorporate LLM views as priors
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- **Regime Detection**: Switch portfolios based on market state
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- **Dynamic Rebalancing**: Adjust weights based on rolling IC
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### 4. Strategy Generation
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- **Regime-Specific Rules**: Different signals for trending vs mean-reverting
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- **Multi-Timeframe**: Combine 1min, 5min, 15min signals
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- **Adaptive Thresholds**: Dynamic entry/exit based on volatility
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- **News Integration**: Avoid trading during high-impact news
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### 5. Execution Optimization
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- **Parallel Factor Generation**: 8+ workers instead of 4
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- **Smart Retry Logic**: Learn from failures, adjust prompts
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- **Early Stopping**: Skip factors that show promise in first 1000 bars
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- **Incremental Evaluation**: Evaluate factors as they're generated
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### 6. Risk Management
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- **VaR/ES**: Value at Risk and Expected Shortfall calculations
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- **Correlation Monitoring**: Track factor correlation drift
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- **Performance Attribution**: Understand which factors drive returns
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- **Stress Testing**: Test strategies on historical crises
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### 7. Infrastructure
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- **GPU Acceleration**: Use RTX 5060 Ti for LightGBM training
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- **Database Optimization**: Index queries for faster factor selection
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- **Caching Layer**: Cache expensive computations
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- **Monitoring Dashboard**: Real-time performance tracking
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---
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