96 lines
4.2 KiB
Markdown
96 lines
4.2 KiB
Markdown
# Prediction Data Optimization Summary
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## Problem Identified
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In the `_get_all_predictions` method, data was being fetched redundantly:
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1. **First fetch**: `_collect_model_input_data(symbol)` was called to get standardized input data
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2. **Second fetch**: Each individual prediction method (`_get_rl_prediction`, `_get_cnn_predictions`, `_get_generic_prediction`) called `build_base_data_input(symbol)` again
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3. **Third fetch**: Some methods like `_get_rl_state` also called `build_base_data_input(symbol)`
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This resulted in the same underlying data (technical indicators, COB data, OHLCV data) being fetched multiple times per prediction cycle.
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## Solution Implemented
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### 1. Centralized Data Fetching
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- Modified `_get_all_predictions` to fetch `BaseDataInput` once using `self.data_provider.build_base_data_input(symbol)`
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- Removed the redundant `_collect_model_input_data` method entirely
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### 2. Updated Method Signatures
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All prediction methods now accept an optional `base_data` parameter:
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- `_get_rl_prediction(model, symbol, base_data=None)`
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- `_get_cnn_predictions(model, symbol, base_data=None)`
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- `_get_generic_prediction(model, symbol, base_data=None)`
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- `_get_rl_state(symbol, base_data=None)`
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### 3. Backward Compatibility
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Each method maintains backward compatibility by building `BaseDataInput` if `base_data` is not provided, ensuring existing code continues to work.
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### 4. Removed Redundant Code
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- Eliminated the `_collect_model_input_data` method (60+ lines of redundant code)
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- Removed duplicate `build_base_data_input` calls within prediction methods
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- Simplified the data flow architecture
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## Benefits
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### Performance Improvements
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- **Reduced API calls**: No more duplicate data fetching per prediction cycle
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- **Faster inference**: Single data fetch instead of 3-4 separate fetches
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- **Lower latency**: Predictions are generated faster due to reduced data overhead
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- **Memory efficiency**: Less temporary data structures created
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### Code Quality
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- **DRY principle**: Eliminated code duplication
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- **Cleaner architecture**: Single source of truth for model input data
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- **Maintainability**: Easier to modify data fetching logic in one place
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- **Consistency**: All models now use the same data structure
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### System Reliability
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- **Consistent data**: All models use exactly the same input data
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- **Reduced race conditions**: Single data fetch eliminates timing inconsistencies
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- **Error handling**: Centralized error handling for data fetching
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## Technical Details
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### Before Optimization
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```python
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async def _get_all_predictions(self, symbol: str):
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# First data fetch
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input_data = await self._collect_model_input_data(symbol)
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for model in models:
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if isinstance(model, RLAgentInterface):
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# Second data fetch inside _get_rl_prediction
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rl_prediction = await self._get_rl_prediction(model, symbol)
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elif isinstance(model, CNNModelInterface):
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# Third data fetch inside _get_cnn_predictions
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cnn_predictions = await self._get_cnn_predictions(model, symbol)
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```
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### After Optimization
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```python
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async def _get_all_predictions(self, symbol: str):
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# Single data fetch for all models
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base_data = self.data_provider.build_base_data_input(symbol)
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for model in models:
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if isinstance(model, RLAgentInterface):
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# Pass pre-built data, no additional fetch
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rl_prediction = await self._get_rl_prediction(model, symbol, base_data)
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elif isinstance(model, CNNModelInterface):
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# Pass pre-built data, no additional fetch
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cnn_predictions = await self._get_cnn_predictions(model, symbol, base_data)
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```
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## Testing Results
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- ✅ Orchestrator initializes successfully
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- ✅ All prediction methods work without errors
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- ✅ Generated 3 predictions in test run
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- ✅ No performance degradation observed
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- ✅ Backward compatibility maintained
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## Future Considerations
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- Consider caching `BaseDataInput` objects for even better performance
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- Monitor memory usage to ensure the optimization doesn't increase memory footprint
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- Add metrics to measure the performance improvement quantitatively
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This optimization significantly improves the efficiency of the prediction system while maintaining full functionality and backward compatibility. |