128 lines
4.7 KiB
Markdown
128 lines
4.7 KiB
Markdown
# Current RL Model Input Data Analysis
|
||
|
||
## What RL Model Currently Receives (INSUFFICIENT)
|
||
|
||
### Current State Vector (Only ~100 basic features)
|
||
The current RL implementation in `training/enhanced_rl_trainer.py` line 472-494 shows:
|
||
|
||
```python
|
||
def _market_state_to_rl_state(self, market_state: MarketState) -> np.ndarray:
|
||
# Fallback implementation - VERY LIMITED
|
||
state_components = [
|
||
market_state.volatility, # 1 feature
|
||
market_state.volume, # 1 feature
|
||
market_state.trend_strength # 1 feature
|
||
]
|
||
|
||
# Add price features from different timeframes
|
||
for timeframe in sorted(market_state.prices.keys()):
|
||
state_components.append(market_state.prices[timeframe]) # ~4 features
|
||
|
||
# Pad or truncate to expected state size of 100
|
||
expected_size = self.config.rl.get('state_size', 100)
|
||
# ... padding logic
|
||
```
|
||
|
||
**Total Current Input: ~100 basic features (CRITICALLY INSUFFICIENT)**
|
||
|
||
### What's Missing from Current Implementation:
|
||
- ❌ **300s of raw tick data** (0 features vs required 3000+ features)
|
||
- ❌ **Multi-timeframe OHLCV data** (4 basic prices vs required 9600+ features)
|
||
- ❌ **BTC reference data** (0 features vs required 2400+ features)
|
||
- ❌ **CNN hidden layer features** (0 features vs required 512 features)
|
||
- ❌ **CNN predictions** (0 features vs required 16 features)
|
||
- ❌ **Pivot point data** (0 features vs required 250+ features)
|
||
- ❌ **Momentum detection from ticks** (completely missing)
|
||
- ❌ **Market regime analysis** (basic vs sophisticated analysis)
|
||
|
||
## What Dashboard Currently Shows
|
||
|
||
From your dashboard display:
|
||
```
|
||
Training Data Stream
|
||
Tick Cache: 129 ticks
|
||
1s Bars: 128 bars
|
||
Stream: LIVE
|
||
```
|
||
|
||
This shows the data is being **collected** but **NOT being fed to the RL model** in the required format.
|
||
|
||
## Required RL Input Data (Per Specification)
|
||
|
||
### ETH Data Requirements:
|
||
1. **300s max of raw ticks data** → ~3000 features
|
||
- Important for detecting single big moves and momentum
|
||
- Currently: 0 features ❌
|
||
|
||
2. **300s of 1s OHLCV data (5 min)** → 2400 features
|
||
- 300 bars × 8 features (OHLC + volume + indicators)
|
||
- Currently: 0 features ❌
|
||
|
||
3. **300 OHLCV + indicators bars for each timeframe** → 7200 features
|
||
- 1m: 300 bars × 8 features = 2400
|
||
- 1h: 300 bars × 8 features = 2400
|
||
- 1d: 300 bars × 8 features = 2400
|
||
- Currently: ~4 basic price features ❌
|
||
|
||
### BTC Reference Data:
|
||
4. **BTC data for all timeframes** → 2400 features
|
||
- Same structure as ETH for correlation analysis
|
||
- Currently: 0 features ❌
|
||
|
||
### CNN Integration:
|
||
5. **CNN hidden layer features** → 512 features
|
||
- Last hidden layers where patterns are learned
|
||
- Currently: 0 features ❌
|
||
|
||
6. **CNN predictions for each timeframe** → 16 features
|
||
- 1s, 1m, 1h, 1d predictions (4 timeframes × 4 outputs)
|
||
- Currently: 0 features ❌
|
||
|
||
### Pivot Points:
|
||
7. **Williams Market Structure pivot points** → 250+ features
|
||
- 5-level recursive pivot point calculation
|
||
- Standard pivot points for all timeframes
|
||
- Currently: 0 features ❌
|
||
|
||
## Total Required vs Current
|
||
|
||
| Component | Required Features | Current Features | Gap |
|
||
|-----------|-------------------|------------------|-----|
|
||
| ETH Ticks | 3000 | 0 | -3000 |
|
||
| ETH Multi-timeframe OHLCV | 7200 | 4 | -7196 |
|
||
| BTC Reference | 2400 | 0 | -2400 |
|
||
| CNN Hidden Features | 512 | 0 | -512 |
|
||
| CNN Predictions | 16 | 0 | -16 |
|
||
| Pivot Points | 250 | 0 | -250 |
|
||
| Market Regime | 20 | 3 | -17 |
|
||
| **TOTAL** | **~13,400** | **~100** | **-13,300** |
|
||
|
||
## Critical Impact
|
||
|
||
The current RL model is operating with **less than 1%** of the required input data:
|
||
- **Current**: ~100 basic features
|
||
- **Required**: ~13,400 comprehensive features
|
||
- **Missing**: 99.25% of required data
|
||
|
||
This explains why RL performance may be poor - the model is essentially "blind" to:
|
||
- Tick-level momentum patterns
|
||
- Multi-timeframe market structure
|
||
- CNN-learned patterns
|
||
- Williams pivot point trends
|
||
- BTC correlation signals
|
||
|
||
## Solution Implementation Status
|
||
|
||
✅ **Already Created**:
|
||
- `training/enhanced_rl_state_builder.py` - Implements comprehensive state building
|
||
- `training/williams_market_structure.py` - Williams pivot point system
|
||
- `docs/RL_TRAINING_AUDIT_AND_IMPROVEMENTS.md` - Complete improvement plan
|
||
|
||
⚠️ **Next Steps**:
|
||
1. Integrate the enhanced state builder into the current RL training pipeline
|
||
2. Update MarketState class to include all required data
|
||
3. Connect tick cache and OHLCV data to state builder
|
||
4. Implement CNN-RL bridge for hidden features
|
||
5. Test with the new ~13,400 feature state vector
|
||
|
||
The gap between current and required RL input data is **massive** and explains why the RL model cannot make sophisticated trading decisions based on the rich market data your system is designed to utilize. |