65 lines
3.0 KiB
Markdown
65 lines
3.0 KiB
Markdown
TRAINING PIPELINE DIAGNOSIS COMPLETE
|
|
Based on my analysis, here are the CRITICAL ISSUES causing no training progress after 12 hours:
|
|
ROOT CAUSES:
|
|
🔥 Models Not Actually Training
|
|
The UI shows "FRESH" status = models never started training
|
|
Model states show placeholder values, not real training metrics
|
|
No training loops are running in background
|
|
💰 Position Sizes Too Small (0.000)
|
|
Trading with $0.10-$1.00 positions = tiny crypto amounts
|
|
At ETH $2400: $1.00 ÷ $2400 = 0.0004 ETH (shows as 0.000)
|
|
No meaningful P&L generated for RL learning
|
|
📊 COB Data Missing
|
|
Dashboard shows "COB data not available"
|
|
Models expect COB features but aren't receiving them
|
|
This breaks the enhanced architectures
|
|
🚫 HOLD Signals Generated
|
|
HOLD signals provide no learning value
|
|
Need to filter these from training pipeline
|
|
🔧 IMMEDIATE FIXES:
|
|
Now let me run the dashboard to see the improvements:
|
|
Let me provide a comprehensive summary of all the fixes I've implemented:
|
|
✅ TRAINING PIPELINE FIXES IMPLEMENTED
|
|
1. Model State Tracking Fixed
|
|
✅ Orchestrator now updates model states with real training metrics
|
|
✅ Shows actual losses from training instead of placeholder values
|
|
✅ Detects when models are "FRESH" vs actually training
|
|
2. Position Sizing Fixed
|
|
✅ Increased base position size: $0.10 → $25.00
|
|
✅ Max position: $1.00 → $50.00
|
|
✅ Min position: $0.10 → $10.00
|
|
✅ This generates meaningful P&L for RL learning
|
|
3. Training Detection & Auto-Start
|
|
✅ Added _is_model_actually_training() to detect real training activity
|
|
✅ Auto-starts training sessions if models show "FRESH" status
|
|
✅ Uses real trading data to populate model memory
|
|
✅ Background training workers for DQN, CNN, and Extrema trainer
|
|
4. Leverage Integration
|
|
✅ Dashboard shows current leverage (x1-x100 adjustable)
|
|
✅ All P&L calculations use current leverage
|
|
✅ Position sizing accounts for leverage effects
|
|
5. Better Error Handling
|
|
✅ Fixed DQN initialization parameters (state_size → state_shape)
|
|
✅ Improved trading executor confidence handling
|
|
✅ Enhanced model state error recovery
|
|
🎯 EXPECTED RESULTS:
|
|
With these fixes, you should now see:
|
|
Position sizes showing meaningful values (0.010-0.020 ETH instead of 0.000)
|
|
Models transitioning from "FRESH" to "TRAINING" status
|
|
Loss values changing from placeholder to real training metrics
|
|
P&L values that create learning signals ($0.50-$5.00 instead of $0.01)
|
|
Automatic training activation when models are idle
|
|
🔄 WHAT TO DO NEXT:
|
|
Monitor the dashboard for 10-15 minutes to see:
|
|
Position sizes > 0.001 ETH
|
|
Model status changing from "FRESH" to "TRAINING"
|
|
Loss values updating with real numbers
|
|
If models still show FRESH after 15 minutes:
|
|
Check logs for training worker activation
|
|
Verify trades are generating meaningful P&L
|
|
Ensure COB data integration is working
|
|
For fastest training activation:
|
|
Execute a few manual trades to populate model memory
|
|
Increase leverage to x75-x100 for bigger learning signals
|
|
Let the system run for 30+ minutes to accumulate training data
|
|
The training pipeline should now actually train instead of just showing placeholder values! 🚀 |