165 lines
4.6 KiB
Markdown
165 lines
4.6 KiB
Markdown
# Trading System Enhancements Summary
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## 🎯 **Issues Fixed**
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### 1. **Position Sizing Issues**
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- **Problem**: Tiny position sizes (0.000 quantity) with meaningless P&L
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- **Solution**: Implemented percentage-based position sizing with leverage
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- **Result**: Meaningful position sizes based on account balance percentage
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### 2. **Symbol Restrictions**
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- **Problem**: Both BTC and ETH trades were executing
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- **Solution**: Added `allowed_symbols: ["ETH/USDT"]` restriction
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- **Result**: Only ETH/USDT trades are now allowed
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### 3. **Win Rate Calculation**
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- **Problem**: Incorrect win rate (50% instead of 69.2% for 9W/4L)
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- **Solution**: Fixed rounding issues in win/loss counting logic
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- **Result**: Accurate win rate calculations
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### 4. **Missing Hold Time**
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- **Problem**: No way to debug model behavior timing
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- **Solution**: Added hold time tracking in seconds
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- **Result**: Each trade now shows exact hold duration
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## 🚀 **New Features Implemented**
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### 1. **Percentage-Based Position Sizing**
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```yaml
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# config.yaml
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base_position_percent: 5.0 # 5% base position of account
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max_position_percent: 20.0 # 20% max position of account
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min_position_percent: 2.0 # 2% min position of account
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leverage: 50.0 # 50x leverage (adjustable in UI)
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simulation_account_usd: 100.0 # $100 simulation account
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```
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**How it works:**
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- Base position = Account Balance × Base % × Confidence
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- Effective position = Base position × Leverage
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- Example: $100 account × 5% × 0.8 confidence × 50x = $200 effective position
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### 2. **Hold Time Tracking**
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```python
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@dataclass
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class TradeRecord:
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# ... existing fields ...
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hold_time_seconds: float = 0.0 # NEW: Hold time in seconds
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```
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**Benefits:**
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- Debug model behavior patterns
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- Identify optimal hold times
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- Analyze trade timing efficiency
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### 3. **Enhanced Trading Statistics**
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```python
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# Now includes:
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- Total fees paid
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- Hold time per trade
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- Percentage-based position info
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- Leverage settings
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```
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### 4. **UI-Adjustable Leverage**
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```python
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def get_leverage(self) -> float:
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"""Get current leverage setting"""
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def set_leverage(self, leverage: float) -> bool:
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"""Set leverage (for UI control)"""
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def get_account_info(self) -> Dict[str, Any]:
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"""Get account information for UI display"""
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```
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## 📊 **Dashboard Improvements**
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### 1. **Enhanced Closed Trades Table**
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```
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Time | Side | Size | Entry | Exit | Hold (s) | P&L | Fees
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02:33:44 | LONG | 0.080 | $2588.33 | $2588.11 | 30 | $50.00 | $1.00
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```
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### 2. **Improved Trading Statistics**
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```
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Win Rate: 60.0% (3W/2L) | Avg Win: $50.00 | Avg Loss: $25.00 | Total Fees: $5.00
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```
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## 🔧 **Configuration Changes**
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### Before:
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```yaml
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max_position_value_usd: 50.0 # Fixed USD amounts
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min_position_value_usd: 10.0
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leverage: 10.0
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```
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### After:
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```yaml
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base_position_percent: 5.0 # Percentage of account
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max_position_percent: 20.0 # Scales with account size
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min_position_percent: 2.0
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leverage: 50.0 # Higher leverage for significant P&L
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simulation_account_usd: 100.0 # Clear simulation balance
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allowed_symbols: ["ETH/USDT"] # ETH-only trading
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```
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## 📈 **Expected Results**
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With these changes, you should now see:
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1. **Meaningful Position Sizes**:
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- 2-20% of account balance
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- With 50x leverage = $100-$1000 effective positions
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2. **Significant P&L Values**:
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- Instead of $0.01 profits, expect $10-$100+ moves
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- Proportional to leverage and position size
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3. **Accurate Statistics**:
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- Correct win rate calculations
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- Hold time analysis capabilities
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- Total fees tracking
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4. **ETH-Only Trading**:
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- No more BTC trades
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- Focused on ETH/USDT pairs only
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5. **Better Debugging**:
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- Hold time shows model behavior patterns
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- Percentage-based sizing scales with account
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- UI-adjustable leverage for testing
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## 🧪 **Test Results**
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All tests passing:
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- ✅ Position Sizing: Updated with percentage-based leverage
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- ✅ ETH-Only Trading: Configured in config
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- ✅ Win Rate Calculation: FIXED
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- ✅ New Features: WORKING
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## 🎮 **UI Controls Available**
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The trading executor now supports:
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- `get_leverage()` - Get current leverage
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- `set_leverage(value)` - Adjust leverage from UI
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- `get_account_info()` - Get account status for display
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- Enhanced position and trade information
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## 🔍 **Debugging Capabilities**
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With hold time tracking, you can now:
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- Identify if model holds positions too long/short
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- Correlate hold time with P&L success
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- Optimize entry/exit timing
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- Debug model behavior patterns
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Example analysis:
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```
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Short holds (< 30s): 70% win rate
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Medium holds (30-60s): 60% win rate
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Long holds (> 60s): 40% win rate
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```
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This data helps optimize the model's decision timing! |