gogo2/reports/AGGRESSIVE_TRADING_THRESHOLDS_SUMMARY.md

Aggressive Trading Thresholds Summary

Overview

Lowered confidence thresholds across the entire trading system to execute trades more aggressively, generating more training data for the checkpoint-enabled models.

Changes Made

1. Clean Dashboard (web/clean_dashboard.py)

• CLOSE_POSITION_THRESHOLD: 0.25 → 0.15 (40% reduction)
• OPEN_POSITION_THRESHOLD: 0.60 → 0.35 (42% reduction)

2. DQN Agent (NN/models/dqn_agent.py)

• entry_confidence_threshold: 0.7 → 0.35 (50% reduction)
• exit_confidence_threshold: 0.3 → 0.15 (50% reduction)

3. Trading Orchestrator (core/orchestrator.py)

• confidence_threshold: 0.20 → 0.15 (25% reduction)
• confidence_threshold_close: 0.10 → 0.08 (20% reduction)

4. Realtime RL COB Trader (core/realtime_rl_cob_trader.py)

• min_confidence_threshold: 0.7 → 0.35 (50% reduction)

5. Training Integration (core/training_integration.py)

• min_confidence_threshold: 0.3 → 0.15 (50% reduction)

Expected Impact

More Aggressive Trading

• Entry Thresholds: Now require only 35% confidence to open new positions (vs 60-70% previously)
• Exit Thresholds: Now require only 8-15% confidence to close positions (vs 25-30% previously)
• Overall: System will execute ~2-3x more trades than before

Better Training Data Generation

• More Executed Actions: Since we now store training progress, more executed trades = more training data
• Faster Learning: Models will learn from real trading outcomes more frequently
• Split-Second Decisions: With 100ms training intervals, models can adapt quickly to market changes

Risk Management

• Position Sizing: Small position sizes (0.005) limit risk per trade
• Profit Incentives: System still has profit-based incentives for closing positions
• Leverage Control: User-controlled leverage settings provide additional risk management

Training Frequency

• Decision Fusion: Every 100ms
• COB RL: Every 100ms
• DQN: Every 30 seconds
• CNN: Every 30 seconds

Monitoring

• Training performance metrics are tracked and displayed
• Average, min, max training times are logged
• Training frequency and total calls are monitored
• Real-time performance feedback available in dashboard

Next Steps

1. Monitor trade execution frequency
2. Track training data generation rate
3. Observe model learning progress
4. Adjust thresholds further if needed based on performance

Notes

• All changes maintain the existing profit incentive system
• Position management logic remains intact
• Risk controls through position sizing and leverage are preserved
• Training checkpoint system ensures progress is not lost