160 lines
6.4 KiB
Markdown
160 lines
6.4 KiB
Markdown
# Williams Market Structure CNN Integration Summary
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## 🎯 Overview
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The Williams Market Structure has been enhanced with CNN-based pivot prediction capabilities, enabling real-time training and prediction at each detected pivot point using multi-timeframe, multi-symbol data.
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## ✅ Key Features Implemented
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### 🔄 **Recursive Pivot Structure**
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- **Level 0**: Raw OHLCV price data → Swing points using multiple strengths [2, 3, 5, 8, 13]
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- **Level 1**: Level 0 pivot points → Treated as "price bars" for higher-level pivots
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- **Level 2-4**: Recursive application on previous level's pivots
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- **True Recursion**: Each level builds on the previous level's pivot points
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### 🧠 **CNN Integration Architecture**
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```
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Each Pivot Detection Triggers:
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1. Train CNN on previous pivot (features) → current pivot (ground truth)
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2. Predict next pivot using current pivot features
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3. Store current features for next training cycle
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```
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### 📊 **Multi-Timeframe Input Features**
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- **ETH Primary Symbol**:
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- 900 x 1s bars with indicators (10 features)
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- 900 x 1m bars with indicators (10 features)
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- 900 x 1h bars with indicators (10 features)
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- 5 minutes of tick-derived features (10 features)
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- **BTC Reference Symbol**:
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- 5 minutes of tick-derived features (4 features)
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- **Pivot Context**: Recent pivot characteristics (3 features)
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- **Chart Labels**: Symbol/timeframe identification (3 features)
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- **Total**: 900 timesteps × 50 features
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### 🎯 **Multi-Level Output Prediction**
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- **10 Outputs Total**: 5 Williams levels × (type + price)
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- Level 0-4: [swing_type (0=LOW, 1=HIGH), normalized_price]
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- Allows prediction across all recursive levels simultaneously
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### 📐 **Smart Normalization Strategy**
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- **Data Flow**: Keep actual values throughout pipeline for validation
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- **Final Step**: Normalize using 1h timeframe min/max range
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- **Cross-Timeframe Preservation**: Maintains relationships between different timeframes
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- **Price Features**: Normalized with 1h range
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- **Non-Price Features**: Feature-wise normalization (indicators, counts, etc.)
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## 🔧 **Integration with TrainingDataPacket**
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Successfully leverages existing `TrainingDataPacket` from `core/unified_data_stream.py`:
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```python
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@dataclass
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class TrainingDataPacket:
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timestamp: datetime
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symbol: str
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tick_cache: List[Dict[str, Any]] # ✅ Used for tick features
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one_second_bars: List[Dict[str, Any]] # ✅ Used for 1s data
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multi_timeframe_data: Dict[str, List[Dict[str, Any]]] # ✅ Used for 1m, 1h data
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cnn_features: Optional[Dict[str, np.ndarray]] # ✅ Populated by Williams
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cnn_predictions: Optional[Dict[str, np.ndarray]] # ✅ Populated by Williams
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```
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## 🚀 **CNN Training Flow**
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### **At Each Pivot Point Detection:**
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1. **Training Phase** (if previous pivot exists):
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```python
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X_train = previous_pivot_features # (900, 50)
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y_train = current_actual_pivot # (10,) for all levels
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model.fit(X_train, y_train, epochs=1) # Online learning
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```
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2. **Prediction Phase**:
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```python
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X_predict = current_pivot_features # (900, 50)
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y_predict = model.predict(X_predict) # (10,) predictions for all levels
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```
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3. **State Management**:
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```python
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previous_pivot_details = {
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'features': X_predict,
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'pivot': current_pivot_object
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}
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```
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## 🛠 **Implementation Status**
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### ✅ **Completed Components**
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- [x] Recursive Williams pivot calculation (5 levels)
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- [x] CNN integration hooks at each pivot detection
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- [x] Multi-timeframe feature extraction from TrainingDataPacket
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- [x] 1h-based normalization strategy
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- [x] Multi-level output prediction (10 outputs)
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- [x] Online learning with single-step training
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- [x] Dashboard integration with proper diagnostics
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- [x] Comprehensive test suite
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### ⚠ **Current Limitations**
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- CNN disabled due to TensorFlow dependencies not installed
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- Placeholder technical indicators (TODO: Add real SMA, EMA, RSI, MACD, etc.)
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- Higher-level ground truth uses simplified logic (needs full Williams context)
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### 🔄 **Real-Time Dashboard Integration**
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Fixed dashboard Williams integration:
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- **Reduced data requirement**: 20 bars minimum (from 50)
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- **Proper configuration**: Uses swing_strengths=[2, 3, 5]
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- **Enhanced diagnostics**: Data quality validation and pivot detection logging
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- **Consistent timezone handling**: Proper timestamp conversion for pivot display
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## 📈 **Performance Characteristics**
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### **Pivot Detection Performance** (from diagnostics):
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- ✅ Clear test patterns: Successfully detects obvious pivot points
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- ✅ Realistic data: Handles real market volatility and timing
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- ✅ Multi-level recursion: Properly builds higher levels from lower levels
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### **CNN Training Frequency**:
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- **Level 0**: Most frequent (every raw price pivot)
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- **Level 1-4**: Less frequent (requires sufficient lower-level pivots)
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- **Online Learning**: Single epoch per pivot for real-time adaptation
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## 🎓 **Usage Example**
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```python
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# Initialize Williams with CNN integration
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williams = WilliamsMarketStructure(
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swing_strengths=[2, 3, 5, 8, 13],
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cnn_input_shape=(900, 50), # 900 timesteps, 50 features
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cnn_output_size=10, # 5 levels × 2 outputs
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enable_cnn_feature=True,
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training_data_provider=data_stream # TrainingDataPacket provider
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)
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# Calculate pivots (automatically triggers CNN training/prediction)
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structure_levels = williams.calculate_recursive_pivot_points(ohlcv_data)
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# Extract RL features (250 features for reinforcement learning)
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rl_features = williams.extract_features_for_rl(structure_levels)
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```
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## 🔮 **Next Steps**
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1. **Install TensorFlow**: Enable CNN functionality
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2. **Add Real Indicators**: Replace placeholder technical indicators
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3. **Enhanced Ground Truth**: Implement proper multi-level pivot relationships
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4. **Model Persistence**: Save/load trained CNN models
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5. **Performance Metrics**: Track CNN prediction accuracy over time
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## 📊 **Key Benefits**
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- **Real-Time Learning**: CNN adapts to market conditions at each pivot
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- **Multi-Scale Analysis**: Captures patterns across 5 recursive levels
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- **Rich Context**: 50 features per timestep covering multiple timeframes and symbols
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- **Consistent Data Flow**: Leverages existing TrainingDataPacket infrastructure
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- **Market Structure Awareness**: Predictions based on Williams methodology
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This implementation provides a robust foundation for CNN-enhanced pivot prediction while maintaining the proven Williams Market Structure methodology. |