gogo2/SYNTHETIC_DATA_REMOVAL_SUMMARY.md

Synthetic Data Removal Summary

This document summarizes all changes made to eliminate the use of synthetic data throughout the trading system.

Files Modified

1. NN/train_rl.py

   • Removed _create_synthetic_1s_data method
   • Removed _create_synthetic_hourly_data method
   • Removed _create_synthetic_daily_data method
   • Modified RLTradingEnvironment class to require all timeframes as real data
   • Removed fallback to synthetic data when real data is unavailable
   • Eliminated generate_price_prediction_training_data function
   • Removed pretrain_price_prediction function that used synthetic data
   • Updated train_rl function to load all required timeframes

2. train_rl_with_realtime.py

   • Updated EnhancedRLTradingEnvironment class to require all timeframes
   • Modified create_enhanced_env function to load all required timeframes
   • Added prominent warning logs about requiring real market data
   • Fixed imports to accommodate the changes

3. README_enhanced_trading_model.md

   • Updated to emphasize that only real market data is supported
   • Listed all required timeframes and their importance
   • Added clear warnings against using synthetic data
   • Updated usage instructions

4. New files created

   • REAL_MARKET_DATA_POLICY.md: Comprehensive policy document explaining why we only use real market data

Key Changes in Implementation

1. Data Requirements

   • Now explicitly require all timeframes (1m, 5m, 15m, 1h, 1d) as real data
   • Removed all synthetic data generation functionalities
   • Added validation to ensure all required timeframes are available

2. Error Handling

   • Improved error messages when required data is missing
   • Eliminated synthetic data fallbacks when real data is unavailable
   • Added clear logging to indicate when real data is required

3. Training Process

   • Removed pre-training functions that used synthetic data
   • Updated the main training loop to work exclusively with real data
   • Disabled options related to synthetic data generation

Benefits of These Changes

1. More Realistic Training

   • Models now train exclusively on real market patterns and behaviors
   • No risk of learning artificial patterns that don't exist in real markets

2. Better Performance

   • Trading strategies more likely to work in live markets
   • Models develop more realistic expectations about market behavior

3. Simplified Codebase

   • Removal of synthetic data generation code reduces complexity
   • Clearer data requirements make the system easier to understand and use

Conclusion

These changes ensure our trading system works exclusively with real market data, providing more realistic training and better performance in live trading environments. The system now requires all timeframes to be available as real data and will not fall back to synthetic data under any circumstances.