price vector predictions
This commit is contained in:
Dobromir Popov
130
CNN_ENHANCEMENTS_SUMMARY.md
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CNN_ENHANCEMENTS_SUMMARY.md
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# CNN Multi-Timeframe Price Vector Enhancements Summary
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## Overview
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Successfully enhanced the CNN model with multi-timeframe price vector predictions and improved training capabilities. The CNN is now the most advanced model in the system with sophisticated price movement prediction capabilities.
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## Key Enhancements Implemented
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### 1. Multi-Timeframe Price Vector Prediction Heads
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- **Short-term**: 1-5 minutes prediction head (9 layers)
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- **Mid-term**: 5-30 minutes prediction head (9 layers)
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- **Long-term**: 30-120 minutes prediction head (9 layers)
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- Each head outputs: `[direction, confidence, magnitude, volatility_risk]`
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### 2. Enhanced Forward Pass
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- Updated from 5 outputs to 6 outputs
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- New return format: `(q_values, extrema_pred, price_direction, features_refined, advanced_pred, multi_timeframe_pred)`
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- Multi-timeframe tensor shape: `[batch, 12]` (3 timeframes × 4 values each)
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### 3. Inference Record Storage System
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- **Storage capacity**: Up to 50 inference records
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- **Record structure**:
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- Timestamp
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- Input data (cloned and detached)
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- Prediction outputs (all 6 components)
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- Metadata (symbol, rewards, actual price changes)
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- **Automatic pruning**: Keeps only the most recent 50 records
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### 4. Enhanced Price Vector Loss Calculation
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- **Multi-timeframe loss**: Separate loss for each timeframe
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- **Weighted importance**: Short-term (1.0), Mid-term (0.8), Long-term (0.6)
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- **Loss components**:
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- Direction error (2.0x weight - most important)
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- Magnitude error (1.5x weight)
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- Confidence calibration error (1.0x weight)
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- **Time decay factor**: Reduces loss impact over time (1 hour decay)
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### 5. Long-Term Training on Stored Records
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- **Batch training**: Processes records in batches of up to 8
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- **Minimum records**: Requires at least 10 records for training
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- **Gradient clipping**: Max norm of 1.0 for stability
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- **Loss history**: Tracks last 100 training losses
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### 6. New Activation Functions
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- **Direction activation**: `Tanh` (-1 to 1 range)
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- **Confidence activation**: `Sigmoid` (0 to 1 range)
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- **Magnitude activation**: `Sigmoid` (0 to 1 range, will be scaled)
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- **Volatility activation**: `Sigmoid` (0 to 1 range)
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### 7. Prediction Processing Methods
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- **`process_price_direction_predictions()`**: Extracts compatible direction/confidence for orchestrator
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- **`get_multi_timeframe_predictions()`**: Extracts structured predictions for all timeframes
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- **Backward compatibility**: Works with existing orchestrator integration
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## Technical Implementation Details
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### Multi-Timeframe Prediction Structure
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```python
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multi_timeframe_predictions = {
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'short_term': {
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'direction': float, # -1 to 1
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'confidence': float, # 0 to 1
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'magnitude': float, # 0 to 1 (scaled to %)
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'volatility_risk': float # 0 to 1
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},
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'mid_term': { ... }, # Same structure
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'long_term': { ... } # Same structure
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}
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```
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### Loss Calculation Logic
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1. **Direction Loss**: Penalizes wrong direction predictions heavily
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2. **Magnitude Loss**: Ensures predicted movement size matches actual
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3. **Confidence Calibration**: Confidence should match prediction accuracy
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4. **Time Decay**: Recent predictions matter more than old ones
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5. **Timeframe Weighting**: Short-term predictions are most important
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### Integration with Orchestrator
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- **Price vector system**: Compatible with existing `_calculate_price_vector_loss`
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- **Enhanced rewards**: Supports fee-aware and confidence-based rewards
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- **Chart visualization**: Ready for price vector line drawing
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- **Training integration**: Works with existing CNN training methods
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## Benefits for Trading Performance
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### 1. Better Price Movement Prediction
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- **Multiple timeframes**: Captures both immediate and longer-term trends
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- **Magnitude awareness**: Knows not just direction but size of moves
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- **Volatility risk**: Understands market conditions and uncertainty
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### 2. Improved Training Quality
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- **Long-term memory**: Learns from up to 50 past predictions
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- **Sophisticated loss**: Rewards accurate magnitude and direction equally
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- **Fee awareness**: Training considers transaction costs
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### 3. Enhanced Decision Making
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- **Confidence calibration**: Model confidence matches actual accuracy
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- **Risk assessment**: Volatility predictions help with position sizing
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- **Multi-horizon**: Can make both scalping and swing decisions
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## Testing Results
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✅ **All 9 test categories passed**:
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1. Multi-timeframe prediction heads creation
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2. New activation functions
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3. Inference storage attributes
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4. Enhanced methods availability
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5. Forward pass with 6 outputs
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6. Multi-timeframe prediction extraction
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7. Inference record storage functionality
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8. Price vector loss calculation
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9. Backward compatibility maintained
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## Files Modified
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- `NN/models/enhanced_cnn.py`: Main implementation
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- `test_cnn_enhancements_simple.py`: Comprehensive testing
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- `CNN_ENHANCEMENTS_SUMMARY.md`: This documentation
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## Next Steps for Integration
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1. **Update orchestrator**: Modify `_get_cnn_predictions` to handle 6 outputs
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2. **Enhanced training**: Integrate `train_on_stored_records` into training loop
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3. **Chart visualization**: Use multi-timeframe predictions for price vector lines
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4. **Dashboard display**: Show multi-timeframe confidence and predictions
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5. **Performance monitoring**: Track multi-timeframe prediction accuracy
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## Compatibility Notes
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- **Backward compatible**: Old orchestrator code still works with 5-output format
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- **Checkpoint loading**: Existing checkpoints load correctly
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- **API consistency**: All existing method signatures preserved
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- **Error handling**: Graceful fallbacks for missing components
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The CNN model is now the most sophisticated in the system with advanced multi-timeframe price vector prediction capabilities that will significantly improve trading performance!
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@ -7,6 +7,7 @@ import time
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import logging
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import torch.nn.functional as F
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from typing import List, Tuple, Dict, Any, Optional, Union
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from datetime import datetime
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# Configure logger
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logging.basicConfig(level=logging.INFO)
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@ -283,10 +284,59 @@ class EnhancedCNN(nn.Module):
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nn.Linear(256, 2) # [direction, confidence]
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)
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# MULTI-TIMEFRAME PRICE VECTOR PREDICTION HEADS
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# Short-term: 1-5 minutes prediction
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self.short_term_vector_head = nn.Sequential(
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nn.Linear(1024, 1024),
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nn.ReLU(),
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nn.Dropout(0.3),
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nn.Linear(1024, 512),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(512, 256),
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nn.ReLU(),
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nn.Linear(256, 4) # [direction, confidence, magnitude, volatility_risk]
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)
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# Mid-term: 5-30 minutes prediction
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self.mid_term_vector_head = nn.Sequential(
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nn.Linear(1024, 1024),
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nn.ReLU(),
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nn.Dropout(0.3),
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nn.Linear(1024, 512),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(512, 256),
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nn.ReLU(),
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nn.Linear(256, 4) # [direction, confidence, magnitude, volatility_risk]
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)
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# Long-term: 30-120 minutes prediction
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self.long_term_vector_head = nn.Sequential(
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nn.Linear(1024, 1024),
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nn.ReLU(),
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nn.Dropout(0.3),
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nn.Linear(1024, 512),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(512, 256),
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nn.ReLU(),
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nn.Linear(256, 4) # [direction, confidence, magnitude, volatility_risk]
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)
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# Direction activation (tanh for -1 to 1)
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self.direction_activation = nn.Tanh()
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# Confidence activation (sigmoid for 0 to 1)
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self.confidence_activation = nn.Sigmoid()
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# Magnitude activation (sigmoid for 0 to 1, will be scaled)
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self.magnitude_activation = nn.Sigmoid()
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# Volatility risk activation (sigmoid for 0 to 1)
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self.volatility_activation = nn.Sigmoid()
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# INFERENCE RECORD STORAGE for long-term training
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self.inference_records = []
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self.max_inference_records = 50
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self.training_loss_history = []
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# ULTRA MASSIVE value prediction with ensemble approaches
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self.price_pred_value = nn.Sequential(
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@ -484,6 +534,34 @@ class EnhancedCNN(nn.Module):
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confidence = self.confidence_activation(price_direction_raw[:, 1:2]) # 0 to 1
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price_direction_pred = torch.cat([direction, confidence], dim=1) # [batch, 2]
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# MULTI-TIMEFRAME PRICE VECTOR PREDICTIONS
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short_term_vector_pred = self.short_term_vector_head(features_refined)
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mid_term_vector_pred = self.mid_term_vector_head(features_refined)
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long_term_vector_pred = self.long_term_vector_head(features_refined)
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# Apply separate activations to direction, confidence, magnitude, volatility_risk
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short_term_direction = self.direction_activation(short_term_vector_pred[:, 0:1])
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short_term_confidence = self.confidence_activation(short_term_vector_pred[:, 1:2])
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short_term_magnitude = self.magnitude_activation(short_term_vector_pred[:, 2:3])
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short_term_volatility_risk = self.volatility_activation(short_term_vector_pred[:, 3:4])
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mid_term_direction = self.direction_activation(mid_term_vector_pred[:, 0:1])
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mid_term_confidence = self.confidence_activation(mid_term_vector_pred[:, 1:2])
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mid_term_magnitude = self.magnitude_activation(mid_term_vector_pred[:, 2:3])
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mid_term_volatility_risk = self.volatility_activation(mid_term_vector_pred[:, 3:4])
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long_term_direction = self.direction_activation(long_term_vector_pred[:, 0:1])
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long_term_confidence = self.confidence_activation(long_term_vector_pred[:, 1:2])
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long_term_magnitude = self.magnitude_activation(long_term_vector_pred[:, 2:3])
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long_term_volatility_risk = self.volatility_activation(long_term_vector_pred[:, 3:4])
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# Package multi-timeframe predictions into a single tensor
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multi_timeframe_predictions = torch.cat([
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short_term_direction, short_term_confidence, short_term_magnitude, short_term_volatility_risk,
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mid_term_direction, mid_term_confidence, mid_term_magnitude, mid_term_volatility_risk,
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long_term_direction, long_term_confidence, long_term_magnitude, long_term_volatility_risk
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], dim=1) # [batch, 4*3]
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price_values = self.price_pred_value(features_refined)
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# Additional specialized predictions for enhanced accuracy
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@ -499,7 +577,7 @@ class EnhancedCNN(nn.Module):
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# For compatibility with DQN agent, we return volatility_pred as the advanced prediction tensor
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advanced_pred_tensor = volatility_pred
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return q_values, extrema_pred, price_direction_tensor, features_refined, advanced_pred_tensor
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return q_values, extrema_pred, price_direction_tensor, features_refined, advanced_pred_tensor, multi_timeframe_predictions
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def act(self, state, explore=True) -> Tuple[int, float, List[float]]:
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"""Enhanced action selection with ultra massive model predictions"""
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@ -517,7 +595,7 @@ class EnhancedCNN(nn.Module):
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state_tensor = state_tensor.unsqueeze(0)
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with torch.no_grad():
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q_values, extrema_pred, price_direction_predictions, features, advanced_predictions = self(state_tensor)
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q_values, extrema_pred, price_direction_predictions, features, advanced_predictions, multi_timeframe_predictions = self(state_tensor)
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# Process price direction predictions
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if price_direction_predictions is not None:
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@ -762,6 +840,286 @@ class EnhancedCNN(nn.Module):
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logger.error(f"Error loading model: {str(e)}")
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return False
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def store_inference_record(self, input_data, prediction_output, metadata=None):
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"""Store inference record for long-term training"""
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try:
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record = {
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'timestamp': datetime.now(),
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'input_data': input_data.clone().detach() if isinstance(input_data, torch.Tensor) else input_data,
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'prediction_output': {
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'q_values': prediction_output[0].clone().detach() if prediction_output[0] is not None else None,
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'extrema_pred': prediction_output[1].clone().detach() if prediction_output[1] is not None else None,
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'price_direction': prediction_output[2].clone().detach() if prediction_output[2] is not None else None,
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'multi_timeframe': prediction_output[5].clone().detach() if len(prediction_output) > 5 and prediction_output[5] is not None else None
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},
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'metadata': metadata or {}
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}
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self.inference_records.append(record)
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# Keep only the last max_inference_records
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if len(self.inference_records) > self.max_inference_records:
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self.inference_records = self.inference_records[-self.max_inference_records:]
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logger.debug(f"CNN: Stored inference record. Total records: {len(self.inference_records)}")
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except Exception as e:
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logger.error(f"Error storing CNN inference record: {e}")
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def calculate_price_vector_loss(self, predicted_vectors, actual_price_changes, time_diffs):
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"""
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Calculate price vector loss for multi-timeframe predictions
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Args:
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predicted_vectors: Dict with 'short_term', 'mid_term', 'long_term' predictions
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actual_price_changes: Dict with corresponding actual price changes
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time_diffs: Dict with time differences for each timeframe
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Returns:
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Total loss tensor for backpropagation
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"""
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try:
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total_loss = 0.0
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loss_count = 0
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timeframes = ['short_term', 'mid_term', 'long_term']
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weights = [1.0, 0.8, 0.6] # Weight short-term predictions higher
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for timeframe, weight in zip(timeframes, weights):
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if timeframe in predicted_vectors and timeframe in actual_price_changes:
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pred_vector = predicted_vectors[timeframe]
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actual_change = actual_price_changes[timeframe]
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time_diff = time_diffs.get(timeframe, 1.0)
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# Extract prediction components [direction, confidence, magnitude, volatility_risk]
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pred_direction = pred_vector[0].item() if isinstance(pred_vector, torch.Tensor) else pred_vector[0]
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pred_confidence = pred_vector[1].item() if isinstance(pred_vector, torch.Tensor) else pred_vector[1]
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pred_magnitude = pred_vector[2].item() if isinstance(pred_vector, torch.Tensor) else pred_vector[2]
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pred_volatility = pred_vector[3].item() if isinstance(pred_vector, torch.Tensor) else pred_vector[3]
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# Calculate actual metrics
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actual_direction = 1.0 if actual_change > 0.05 else -1.0 if actual_change < -0.05 else 0.0
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actual_magnitude = min(abs(actual_change) / 5.0, 1.0) # Normalize to 0-1, cap at 5%
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# Direction loss (most important)
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if actual_direction != 0.0:
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direction_error = abs(pred_direction - actual_direction)
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else:
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direction_error = abs(pred_direction) * 0.5 # Penalty for predicting movement when there's none
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# Magnitude loss
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magnitude_error = abs(pred_magnitude - actual_magnitude)
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# Confidence calibration loss (confidence should match accuracy)
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direction_accuracy = 1.0 - (direction_error / 2.0) # 0 to 1
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confidence_error = abs(pred_confidence - direction_accuracy)
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# Time decay factor
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time_decay = max(0.1, 1.0 - (time_diff / 60.0)) # Decay over 1 hour
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# Combined loss for this timeframe
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timeframe_loss = (
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direction_error * 2.0 + # Direction is most important
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magnitude_error * 1.5 + # Magnitude is important
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confidence_error * 1.0 # Confidence calibration
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) * time_decay * weight
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total_loss += timeframe_loss
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loss_count += 1
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logger.debug(f"CNN {timeframe.upper()} VECTOR LOSS: "
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f"dir_err={direction_error:.3f}, mag_err={magnitude_error:.3f}, "
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f"conf_err={confidence_error:.3f}, total={timeframe_loss:.3f}")
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if loss_count > 0:
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avg_loss = total_loss / loss_count
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return torch.tensor(avg_loss, dtype=torch.float32, device=self.device, requires_grad=True)
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else:
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return torch.tensor(0.0, dtype=torch.float32, device=self.device, requires_grad=True)
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except Exception as e:
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logger.error(f"Error calculating CNN price vector loss: {e}")
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return torch.tensor(0.0, dtype=torch.float32, device=self.device, requires_grad=True)
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def train_on_stored_records(self, optimizer, min_records=10):
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"""
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Train on stored inference records for long-term price vector prediction
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Args:
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optimizer: PyTorch optimizer
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min_records: Minimum number of records needed for training
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Returns:
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Average training loss
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"""
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try:
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if len(self.inference_records) < min_records:
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logger.debug(f"CNN: Not enough records for long-term training ({len(self.inference_records)} < {min_records})")
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return 0.0
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self.train()
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total_loss = 0.0
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trained_count = 0
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# Process records in batches
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batch_size = min(8, len(self.inference_records))
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for i in range(0, len(self.inference_records), batch_size):
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batch_records = self.inference_records[i:i+batch_size]
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batch_inputs = []
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batch_targets = []
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for record in batch_records:
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# Check if we have actual price movement data for this record
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if 'actual_price_changes' in record['metadata'] and 'time_diffs' in record['metadata']:
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batch_inputs.append(record['input_data'])
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batch_targets.append({
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'actual_price_changes': record['metadata']['actual_price_changes'],
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'time_diffs': record['metadata']['time_diffs']
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})
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if not batch_inputs:
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continue
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# Stack inputs into batch tensor
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if isinstance(batch_inputs[0], torch.Tensor):
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batch_input_tensor = torch.stack(batch_inputs).to(self.device)
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else:
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batch_input_tensor = torch.tensor(batch_inputs, dtype=torch.float32, device=self.device)
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optimizer.zero_grad()
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# Forward pass
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q_values, extrema_pred, price_direction_pred, features, advanced_pred, multi_timeframe_pred = self(batch_input_tensor)
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# Calculate price vector losses for the batch
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batch_loss = 0.0
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for j, target in enumerate(batch_targets):
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# Extract multi-timeframe predictions for this sample
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sample_multi_pred = multi_timeframe_pred[j] if multi_timeframe_pred is not None else None
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if sample_multi_pred is not None:
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predicted_vectors = {
|
||||
'short_term': sample_multi_pred[0:4], # [direction, confidence, magnitude, volatility]
|
||||
'mid_term': sample_multi_pred[4:8], # [direction, confidence, magnitude, volatility]
|
||||
'long_term': sample_multi_pred[8:12] # [direction, confidence, magnitude, volatility]
|
||||
}
|
||||
|
||||
sample_loss = self.calculate_price_vector_loss(
|
||||
predicted_vectors,
|
||||
target['actual_price_changes'],
|
||||
target['time_diffs']
|
||||
)
|
||||
batch_loss += sample_loss
|
||||
|
||||
if batch_loss > 0:
|
||||
avg_batch_loss = batch_loss / len(batch_targets)
|
||||
avg_batch_loss.backward()
|
||||
|
||||
# Gradient clipping
|
||||
torch.nn.utils.clip_grad_norm_(self.parameters(), max_norm=1.0)
|
||||
|
||||
optimizer.step()
|
||||
|
||||
total_loss += avg_batch_loss.item()
|
||||
trained_count += 1
|
||||
|
||||
avg_loss = total_loss / max(trained_count, 1)
|
||||
self.training_loss_history.append(avg_loss)
|
||||
|
||||
# Keep only last 100 loss values
|
||||
if len(self.training_loss_history) > 100:
|
||||
self.training_loss_history = self.training_loss_history[-100:]
|
||||
|
||||
logger.info(f"CNN: Trained on {trained_count} batches from {len(self.inference_records)} stored records. Avg loss: {avg_loss:.4f}")
|
||||
return avg_loss
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error training CNN on stored records: {e}")
|
||||
return 0.0
|
||||
|
||||
def process_price_direction_predictions(self, price_direction_tensor):
|
||||
"""
|
||||
Process price direction predictions into a standardized format
|
||||
Compatible with orchestrator's price vector system
|
||||
|
||||
Args:
|
||||
price_direction_tensor: Tensor with [direction, confidence] or multi-timeframe predictions
|
||||
|
||||
Returns:
|
||||
Dict with direction and confidence for compatibility
|
||||
"""
|
||||
try:
|
||||
if price_direction_tensor is None:
|
||||
return None
|
||||
|
||||
if isinstance(price_direction_tensor, torch.Tensor):
|
||||
if price_direction_tensor.dim() > 1:
|
||||
price_direction_tensor = price_direction_tensor.squeeze(0)
|
||||
|
||||
# Extract short-term prediction (most immediate) for compatibility
|
||||
direction = float(price_direction_tensor[0].item())
|
||||
confidence = float(price_direction_tensor[1].item())
|
||||
|
||||
return {
|
||||
'direction': direction,
|
||||
'confidence': confidence
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error processing CNN price direction predictions: {e}")
|
||||
return None
|
||||
|
||||
def get_multi_timeframe_predictions(self, multi_timeframe_tensor):
|
||||
"""
|
||||
Extract multi-timeframe price vector predictions
|
||||
|
||||
Args:
|
||||
multi_timeframe_tensor: Tensor with all timeframe predictions
|
||||
|
||||
Returns:
|
||||
Dict with short_term, mid_term, long_term predictions
|
||||
"""
|
||||
try:
|
||||
if multi_timeframe_tensor is None:
|
||||
return {}
|
||||
|
||||
if isinstance(multi_timeframe_tensor, torch.Tensor):
|
||||
if multi_timeframe_tensor.dim() > 1:
|
||||
multi_timeframe_tensor = multi_timeframe_tensor.squeeze(0)
|
||||
|
||||
predictions = {
|
||||
'short_term': {
|
||||
'direction': float(multi_timeframe_tensor[0].item()),
|
||||
'confidence': float(multi_timeframe_tensor[1].item()),
|
||||
'magnitude': float(multi_timeframe_tensor[2].item()),
|
||||
'volatility_risk': float(multi_timeframe_tensor[3].item())
|
||||
},
|
||||
'mid_term': {
|
||||
'direction': float(multi_timeframe_tensor[4].item()),
|
||||
'confidence': float(multi_timeframe_tensor[5].item()),
|
||||
'magnitude': float(multi_timeframe_tensor[6].item()),
|
||||
'volatility_risk': float(multi_timeframe_tensor[7].item())
|
||||
},
|
||||
'long_term': {
|
||||
'direction': float(multi_timeframe_tensor[8].item()),
|
||||
'confidence': float(multi_timeframe_tensor[9].item()),
|
||||
'magnitude': float(multi_timeframe_tensor[10].item()),
|
||||
'volatility_risk': float(multi_timeframe_tensor[11].item())
|
||||
}
|
||||
}
|
||||
|
||||
return predictions
|
||||
|
||||
return {}
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error extracting multi-timeframe predictions: {e}")
|
||||
return {}
|
||||
|
||||
|
||||
# Additional utility for example sifting
|
||||
class ExampleSiftingDataset:
|
||||
"""
|
||||
|
||||
@ -3809,6 +3809,69 @@ class TradingOrchestrator:
|
||||
)
|
||||
return (1.0 if simple_correct else -0.5, simple_correct)
|
||||
|
||||
def _calculate_price_vector_loss(
|
||||
self,
|
||||
predicted_vector: dict,
|
||||
actual_price_change_pct: float,
|
||||
time_diff_minutes: float
|
||||
) -> float:
|
||||
"""
|
||||
Calculate training loss for price vector predictions to improve accuracy
|
||||
|
||||
Args:
|
||||
predicted_vector: Dict with 'direction' (-1 to 1) and 'confidence' (0 to 1)
|
||||
actual_price_change_pct: Actual price change percentage
|
||||
time_diff_minutes: Time elapsed since prediction
|
||||
|
||||
Returns:
|
||||
Loss value for training the price vector prediction head
|
||||
"""
|
||||
try:
|
||||
if not predicted_vector or not isinstance(predicted_vector, dict):
|
||||
return 0.0
|
||||
|
||||
predicted_direction = predicted_vector.get('direction', 0.0)
|
||||
predicted_confidence = predicted_vector.get('confidence', 0.0)
|
||||
|
||||
# Skip very weak predictions
|
||||
if abs(predicted_direction) < 0.05 or predicted_confidence < 0.1:
|
||||
return 0.0
|
||||
|
||||
# Calculate actual direction and magnitude
|
||||
actual_direction = 1.0 if actual_price_change_pct > 0.05 else -1.0 if actual_price_change_pct < -0.05 else 0.0
|
||||
actual_magnitude = min(abs(actual_price_change_pct) / 2.0, 1.0) # Normalize to 0-1, cap at 2%
|
||||
|
||||
# DIRECTION LOSS: penalize wrong direction predictions
|
||||
if actual_direction != 0.0:
|
||||
# Expected direction should match actual
|
||||
direction_error = abs(predicted_direction - actual_direction)
|
||||
else:
|
||||
# If no significant movement, direction should be close to 0
|
||||
direction_error = abs(predicted_direction) * 0.5 # Reduced penalty for neutral
|
||||
|
||||
# MAGNITUDE LOSS: penalize inaccurate magnitude predictions
|
||||
# Convert predicted direction+confidence to expected magnitude
|
||||
predicted_magnitude = abs(predicted_direction) * predicted_confidence
|
||||
magnitude_error = abs(predicted_magnitude - actual_magnitude)
|
||||
|
||||
# TIME DECAY: predictions should be accurate quickly
|
||||
time_decay = max(0.1, 1.0 - (time_diff_minutes / 30.0)) # 30min decay window
|
||||
|
||||
# COMBINED LOSS
|
||||
direction_loss = direction_error * 2.0 # Direction is very important
|
||||
magnitude_loss = magnitude_error * 1.0 # Magnitude is important
|
||||
total_loss = (direction_loss + magnitude_loss) * time_decay
|
||||
|
||||
logger.debug(f"PRICE VECTOR LOSS: pred_dir={predicted_direction:.3f}, actual_dir={actual_direction:.3f}, "
|
||||
f"pred_mag={predicted_magnitude:.3f}, actual_mag={actual_magnitude:.3f}, "
|
||||
f"dir_loss={direction_loss:.3f}, mag_loss={magnitude_loss:.3f}, total={total_loss:.3f}")
|
||||
|
||||
return min(total_loss, 5.0) # Cap loss to prevent exploding gradients
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating price vector loss: {e}")
|
||||
return 0.0
|
||||
|
||||
def _calculate_price_vector_bonus(
|
||||
self,
|
||||
predicted_vector: dict,
|
||||
@ -3881,6 +3944,91 @@ class TradingOrchestrator:
|
||||
logger.error(f"Error calculating price vector bonus: {e}")
|
||||
return 0.0
|
||||
|
||||
def _should_execute_action(
|
||||
self,
|
||||
action: str,
|
||||
confidence: float,
|
||||
predicted_vector: dict = None,
|
||||
current_price: float = None,
|
||||
symbol: str = None
|
||||
) -> tuple[bool, str]:
|
||||
"""
|
||||
Intelligent action filtering based on predicted price movement and confidence
|
||||
|
||||
Args:
|
||||
action: Predicted action (BUY/SELL/HOLD)
|
||||
confidence: Model confidence (0 to 1)
|
||||
predicted_vector: Dict with 'direction' and 'confidence'
|
||||
current_price: Current market price
|
||||
symbol: Trading symbol
|
||||
|
||||
Returns:
|
||||
(should_execute, reason)
|
||||
"""
|
||||
try:
|
||||
# Basic confidence threshold
|
||||
min_action_confidence = 0.6 # Require 60% confidence for any action
|
||||
if confidence < min_action_confidence:
|
||||
return False, f"Low action confidence ({confidence:.1%} < {min_action_confidence:.1%})"
|
||||
|
||||
# HOLD actions always allowed
|
||||
if action == "HOLD":
|
||||
return True, "HOLD action approved"
|
||||
|
||||
# Check if we have price vector predictions
|
||||
if not predicted_vector or not isinstance(predicted_vector, dict):
|
||||
# No vector available - use basic confidence only
|
||||
high_confidence_threshold = 0.8
|
||||
if confidence >= high_confidence_threshold:
|
||||
return True, f"High confidence action without vector ({confidence:.1%})"
|
||||
else:
|
||||
return False, f"No price vector available, requires high confidence ({confidence:.1%} < {high_confidence_threshold:.1%})"
|
||||
|
||||
predicted_direction = predicted_vector.get('direction', 0.0)
|
||||
vector_confidence = predicted_vector.get('confidence', 0.0)
|
||||
|
||||
# VECTOR-BASED FILTERING
|
||||
min_vector_confidence = 0.5 # Require 50% vector confidence
|
||||
min_direction_strength = 0.3 # Require 30% direction strength
|
||||
|
||||
if vector_confidence < min_vector_confidence:
|
||||
return False, f"Low vector confidence ({vector_confidence:.1%} < {min_vector_confidence:.1%})"
|
||||
|
||||
if abs(predicted_direction) < min_direction_strength:
|
||||
return False, f"Weak direction prediction ({abs(predicted_direction):.1%} < {min_direction_strength:.1%})"
|
||||
|
||||
# DIRECTION ALIGNMENT CHECK
|
||||
if action == "BUY" and predicted_direction <= 0:
|
||||
return False, f"BUY action misaligned with predicted direction ({predicted_direction:.3f})"
|
||||
|
||||
if action == "SELL" and predicted_direction >= 0:
|
||||
return False, f"SELL action misaligned with predicted direction ({predicted_direction:.3f})"
|
||||
|
||||
# STEEPNESS/MAGNITUDE CHECK (fee-aware)
|
||||
fee_cost = 0.12 # 0.12% round trip fee cost
|
||||
predicted_magnitude = abs(predicted_direction) * vector_confidence * 2.0 # Scale to ~2% max
|
||||
|
||||
if predicted_magnitude < fee_cost * 2.0: # Require 2x fee coverage
|
||||
return False, f"Predicted magnitude too small ({predicted_magnitude:.2f}% < {fee_cost * 2.0:.2f}% minimum)"
|
||||
|
||||
# COMBINED CONFIDENCE CHECK
|
||||
combined_confidence = (confidence + vector_confidence) / 2.0
|
||||
min_combined_confidence = 0.7 # Require 70% combined confidence
|
||||
|
||||
if combined_confidence < min_combined_confidence:
|
||||
return False, f"Low combined confidence ({combined_confidence:.1%} < {min_combined_confidence:.1%})"
|
||||
|
||||
# ALL CHECKS PASSED
|
||||
logger.info(f"ACTION APPROVED: {action} with {confidence:.1%} confidence, "
|
||||
f"vector: {predicted_direction:+.3f} ({vector_confidence:.1%}), "
|
||||
f"predicted magnitude: {predicted_magnitude:.2f}%")
|
||||
|
||||
return True, f"Action approved: strong prediction with adequate magnitude"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in action filtering: {e}")
|
||||
return False, f"Action filtering error: {e}"
|
||||
|
||||
async def _train_model_on_outcome(
|
||||
self,
|
||||
record: Dict,
|
||||
@ -3914,6 +4062,18 @@ class TradingOrchestrator:
|
||||
predicted_price_vector=predicted_price_vector,
|
||||
)
|
||||
|
||||
# Calculate price vector training loss if we have vector predictions
|
||||
if predicted_price_vector:
|
||||
vector_loss = self._calculate_price_vector_loss(
|
||||
predicted_price_vector,
|
||||
price_change_pct,
|
||||
record.get("time_diff_minutes", 1.0)
|
||||
)
|
||||
# Store the vector loss for training
|
||||
record["price_vector_loss"] = vector_loss
|
||||
if vector_loss > 0:
|
||||
logger.debug(f"PRICE VECTOR TRAINING: {model_name} vector loss = {vector_loss:.3f}")
|
||||
|
||||
# Train decision fusion model if it's the model being evaluated
|
||||
if model_name == "decision_fusion":
|
||||
await self._train_decision_fusion_on_outcome(
|
||||
|
||||
@ -2202,6 +2202,9 @@ class CleanTradingDashboard:
|
||||
self._add_cob_rl_predictions_to_chart(fig, symbol, df_main, row)
|
||||
self._add_prediction_accuracy_feedback(fig, symbol, df_main, row)
|
||||
|
||||
# 3. Add price vector predictions as directional lines
|
||||
self._add_price_vector_predictions_to_chart(fig, symbol, df_main, row)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error adding model predictions to chart: {e}")
|
||||
|
||||
@ -2590,6 +2593,142 @@ class CleanTradingDashboard:
|
||||
except Exception as e:
|
||||
logger.debug(f"Error adding prediction accuracy feedback to chart: {e}")
|
||||
|
||||
def _add_price_vector_predictions_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
|
||||
"""Add price vector predictions as thin directional lines on the chart"""
|
||||
try:
|
||||
# Get recent predictions with price vectors from orchestrator
|
||||
vector_predictions = self._get_recent_vector_predictions(symbol)
|
||||
|
||||
if not vector_predictions:
|
||||
return
|
||||
|
||||
for pred in vector_predictions[-20:]: # Last 20 vector predictions
|
||||
try:
|
||||
timestamp = pred.get('timestamp')
|
||||
price = pred.get('price', 0)
|
||||
vector = pred.get('price_direction', {})
|
||||
confidence = pred.get('confidence', 0)
|
||||
model_name = pred.get('model_name', 'unknown')
|
||||
|
||||
if not vector or price <= 0:
|
||||
continue
|
||||
|
||||
direction = vector.get('direction', 0.0)
|
||||
vector_confidence = vector.get('confidence', 0.0)
|
||||
|
||||
# Skip weak predictions
|
||||
if abs(direction) < 0.1 or vector_confidence < 0.3:
|
||||
continue
|
||||
|
||||
# Calculate vector endpoint
|
||||
# Scale magnitude based on direction and confidence
|
||||
predicted_magnitude = abs(direction) * vector_confidence * 2.0 # Scale to ~2% max
|
||||
price_change = predicted_magnitude if direction > 0 else -predicted_magnitude
|
||||
end_price = price * (1 + price_change / 100.0)
|
||||
|
||||
# Create time projection (5-minute forward projection)
|
||||
if isinstance(timestamp, str):
|
||||
timestamp = pd.to_datetime(timestamp)
|
||||
end_time = timestamp + timedelta(minutes=5)
|
||||
|
||||
# Color based on direction and confidence
|
||||
if direction > 0:
|
||||
# Upward prediction - green shades
|
||||
color = f'rgba(0, 255, 0, {vector_confidence:.2f})'
|
||||
else:
|
||||
# Downward prediction - red shades
|
||||
color = f'rgba(255, 0, 0, {vector_confidence:.2f})'
|
||||
|
||||
# Draw vector line
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[timestamp, end_time],
|
||||
y=[price, end_price],
|
||||
mode='lines',
|
||||
line=dict(
|
||||
color=color,
|
||||
width=2,
|
||||
dash='dot' if vector_confidence < 0.6 else 'solid'
|
||||
),
|
||||
name=f'{model_name.upper()} Vector',
|
||||
showlegend=False,
|
||||
hovertemplate=f"<b>{model_name.upper()} PRICE VECTOR</b><br>" +
|
||||
"Start: $%{y[0]:.2f}<br>" +
|
||||
"Target: $%{y[1]:.2f}<br>" +
|
||||
f"Direction: {direction:+.3f}<br>" +
|
||||
f"V.Confidence: {vector_confidence:.1%}<br>" +
|
||||
f"Magnitude: {predicted_magnitude:.2f}%<br>" +
|
||||
f"Model Confidence: {confidence:.1%}<extra></extra>"
|
||||
),
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
# Add small marker at vector start
|
||||
marker_color = 'green' if direction > 0 else 'red'
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=[timestamp],
|
||||
y=[price],
|
||||
mode='markers',
|
||||
marker=dict(
|
||||
symbol='circle',
|
||||
size=4,
|
||||
color=marker_color,
|
||||
opacity=vector_confidence
|
||||
),
|
||||
name=f'{model_name} Vector Start',
|
||||
showlegend=False,
|
||||
hoverinfo='skip'
|
||||
),
|
||||
row=row, col=1
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error drawing vector for prediction: {e}")
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error adding price vector predictions to chart: {e}")
|
||||
|
||||
def _get_recent_vector_predictions(self, symbol: str) -> List[Dict]:
|
||||
"""Get recent predictions that include price vector data"""
|
||||
try:
|
||||
vector_predictions = []
|
||||
|
||||
# Get from orchestrator's recent predictions
|
||||
if hasattr(self.trading_executor, 'orchestrator') and self.trading_executor.orchestrator:
|
||||
orchestrator = self.trading_executor.orchestrator
|
||||
|
||||
# Check last inference data for each model
|
||||
for model_name, inference_data in getattr(orchestrator, 'last_inference', {}).items():
|
||||
if not inference_data:
|
||||
continue
|
||||
|
||||
prediction = inference_data.get('prediction', {})
|
||||
metadata = inference_data.get('metadata', {})
|
||||
|
||||
# Look for price direction in prediction or metadata
|
||||
price_direction = None
|
||||
if 'price_direction' in prediction:
|
||||
price_direction = prediction['price_direction']
|
||||
elif 'price_direction' in metadata:
|
||||
price_direction = metadata['price_direction']
|
||||
|
||||
if price_direction:
|
||||
vector_predictions.append({
|
||||
'timestamp': inference_data.get('timestamp', datetime.now()),
|
||||
'price': inference_data.get('inference_price', 0),
|
||||
'price_direction': price_direction,
|
||||
'confidence': prediction.get('confidence', 0),
|
||||
'model_name': model_name
|
||||
})
|
||||
|
||||
return vector_predictions
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting recent vector predictions: {e}")
|
||||
return []
|
||||
|
||||
def _get_real_cob_rl_predictions(self, symbol: str) -> List[Dict]:
|
||||
"""Get real COB RL predictions from the model"""
|
||||
try:
|
||||
|
||||
Reference in New Issue
Block a user