65 lines
2.7 KiB
Markdown
65 lines
2.7 KiB
Markdown
# Aggressive Trading Thresholds Summary
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## Overview
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Lowered confidence thresholds across the entire trading system to execute trades more aggressively, generating more training data for the checkpoint-enabled models.
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## Changes Made
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### 1. Clean Dashboard (`web/clean_dashboard.py`)
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- **CLOSE_POSITION_THRESHOLD**: `0.25` → `0.15` (40% reduction)
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- **OPEN_POSITION_THRESHOLD**: `0.60` → `0.35` (42% reduction)
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### 2. DQN Agent (`NN/models/dqn_agent.py`)
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- **entry_confidence_threshold**: `0.7` → `0.35` (50% reduction)
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- **exit_confidence_threshold**: `0.3` → `0.15` (50% reduction)
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### 3. Trading Orchestrator (`core/orchestrator.py`)
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- **confidence_threshold**: `0.20` → `0.15` (25% reduction)
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- **confidence_threshold_close**: `0.10` → `0.08` (20% reduction)
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### 4. Realtime RL COB Trader (`core/realtime_rl_cob_trader.py`)
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- **min_confidence_threshold**: `0.7` → `0.35` (50% reduction)
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### 5. Training Integration (`core/training_integration.py`)
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- **min_confidence_threshold**: `0.3` → `0.15` (50% reduction)
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## Expected Impact
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### More Aggressive Trading
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- **Entry Thresholds**: Now require only 35% confidence to open new positions (vs 60-70% previously)
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- **Exit Thresholds**: Now require only 8-15% confidence to close positions (vs 25-30% previously)
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- **Overall**: System will execute ~2-3x more trades than before
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### Better Training Data Generation
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- **More Executed Actions**: Since we now store training progress, more executed trades = more training data
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- **Faster Learning**: Models will learn from real trading outcomes more frequently
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- **Split-Second Decisions**: With 100ms training intervals, models can adapt quickly to market changes
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### Risk Management
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- **Position Sizing**: Small position sizes (0.005) limit risk per trade
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- **Profit Incentives**: System still has profit-based incentives for closing positions
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- **Leverage Control**: User-controlled leverage settings provide additional risk management
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## Training Frequency
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- **Decision Fusion**: Every 100ms
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- **COB RL**: Every 100ms
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- **DQN**: Every 30 seconds
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- **CNN**: Every 30 seconds
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## Monitoring
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- Training performance metrics are tracked and displayed
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- Average, min, max training times are logged
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- Training frequency and total calls are monitored
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- Real-time performance feedback available in dashboard
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## Next Steps
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1. Monitor trade execution frequency
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2. Track training data generation rate
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3. Observe model learning progress
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4. Adjust thresholds further if needed based on performance
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## Notes
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- All changes maintain the existing profit incentive system
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- Position management logic remains intact
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- Risk controls through position sizing and leverage are preserved
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- Training checkpoint system ensures progress is not lost |