# Real-Time Prediction Loop - Integration Guide

## Problem Identified

**Your models are NEVER called for real-time predictions!**

- `model.predict()` only called ONCE at startup in `chain_inference()`
- NO continuous prediction loop exists
- ALL signals come from COB imbalance only
- CNN/DQN models sit idle despite being loaded

## Solution Created

**`core/realtime_prediction_loop.py`** - Continuous prediction system

### How It Works:

1. **Monitors Market Data**
   - Detects new 1s candles → triggers prediction
   - Detects new 1m candles → triggers prediction
   - Detects pivot points → triggers prediction
   - Runs periodic predictions every 5s

2. **Calls ALL Models**
   ```python
   #  CNN Model
   cnn_prediction = self.orchestrator.cnn_model.predict(cnn_features)
   
   #  DQN Agent
   action = self.orchestrator.dqn_agent.act(dqn_state)
   
   #  COB RL
   cob_prediction = self.orchestrator.cob_rl_model.predict(cob_features)
   ```

3. **Combines Predictions**
   - Voting system: each model contributes
   - Confidence-weighted decisions
   - Generates trading signals

---

## Integration Steps

### Step 1: Add to Orchestrator

```python
# File: core/orchestrator.py

from .realtime_prediction_loop import RealtimePredictionLoop

class TradingOrchestrator:
    def __init__(self, ...):
        # ... existing init ...
        
        # Initialize prediction loop
        self.prediction_loop = RealtimePredictionLoop(
            orchestrator=self,
            data_provider=self.data_provider
        )
        logger.info("Real-time prediction loop initialized")
    
    async def start_prediction_loop(self):
        """Start continuous model predictions"""
        if self.prediction_loop:
            await self.prediction_loop.start()
    
    def stop_prediction_loop(self):
        """Stop prediction loop"""
        if self.prediction_loop:
            self.prediction_loop.stop()
    
    async def process_signal(self, signal: Dict):
        """Process trading signal from prediction loop"""
        try:
            logger.info(f"📥 Received prediction signal: {signal['action']} for {signal['symbol']} "
                       f"(conf: {signal['confidence']:.2f})")
            
            # Execute through trading executor
            if hasattr(self, 'trading_executor') and self.trading_executor:
                result = self.trading_executor.execute_signal(
                    symbol=signal['symbol'],
                    action=signal['action'],
                    confidence=signal['confidence'],
                    current_price=signal['price']
                )
                
                if result:
                    logger.info(f" Signal executed successfully")
                else:
                    logger.warning(f" Signal execution failed or blocked")
                    
        except Exception as e:
            logger.error(f"Error processing prediction signal: {e}")
```

### Step 2: Add to Dashboard

```python
# File: web/clean_dashboard.py

class CleanTradingDashboard:
    def __init__(self, ...):
        # ... existing init ...
        
        # Start prediction loop in background
        if self.orchestrator:
            import threading
            def run_prediction_loop():
                import asyncio
                asyncio.run(self.orchestrator.start_prediction_loop())
            
            self.prediction_thread = threading.Thread(
                target=run_prediction_loop,
                daemon=True,
                name="PredictionLoop"
            )
            self.prediction_thread.start()
            logger.info(" Real-time prediction loop started in background")
```

### Step 3: Update Data Provider (if needed)

```python
# File: core/data_provider.py

async def get_latest_candles(self, symbol: str, timeframe: str, limit: int = 100):
    """Get latest candles for a symbol and timeframe"""
    try:
        # Use existing cache or fetch from API
        if hasattr(self, 'candle_cache'):
            key = f"{symbol}_{timeframe}"
            if key in self.candle_cache:
                return self.candle_cache[key][-limit:]
        
        # Fetch from Binance/exchange
        candles = await self.fetch_candles(symbol, timeframe, limit)
        return candles
        
    except Exception as e:
        logger.error(f"Error getting latest candles: {e}")
        return []
```

---

## Expected Behavior After Integration

### Before (Current):
```
INFO:web.clean_dashboard:🔗 Running initial chained inference...
INFO:web.clean_dashboard:🔗 Running chained inference for ETH/USDT with 10 steps
WARNING:core.orchestrator:No model predictions available for step 0
WARNING:web.clean_dashboard: Chained inference returned no predictions

# Then nothing... models never called again
```

### After (Expected):
```
INFO:core.realtime_prediction_loop: Starting Real-Time Prediction Loop
INFO:core.realtime_prediction_loop: Prediction loop started for ETH/USDT
INFO:core.realtime_prediction_loop: Prediction loop started for BTC/USDT

# Every 1-5 seconds:
INFO:core.realtime_prediction_loop:📊 New 1s candle detected for ETH/USDT - running predictions
INFO:core.realtime_prediction_loop: CNN prediction for ETH/USDT: {'action': 'BUY', 'confidence': 0.78}
INFO:core.realtime_prediction_loop: DQN prediction for ETH/USDT: BUY
INFO:core.realtime_prediction_loop: COB RL prediction for ETH/USDT: {'action': 'BUY', 'confidence': 0.82}
INFO:core.realtime_prediction_loop:📤 Trading signal sent for ETH/USDT: BUY (confidence: 0.80, trigger: new_1s_candle)

INFO:core.orchestrator:📥 Received prediction signal: BUY for ETH/USDT (conf: 0.80)
INFO:core.trading_executor:Executing BUY: 0.050000 ETH/USDT at $4191.25
```

---

## Monitoring & Validation

### Check if Prediction Loop is Running:
```python
# In dashboard or orchestrator logs, look for:
- " Real-time prediction loop started"
- "📊 New 1s candle detected" (every second)
- " CNN prediction" (model being called!)
- " DQN prediction" (model being called!)
- "📤 Trading signal sent"
```

### Your Breakpoint Should Now Trigger:
- **Before**: `predict()` called once at startup
- **After**: `predict()` called every 1-5 seconds!

### Model Usage Stats:
```python
# Track how often models are called
cnn_predictions_count = 0
dqn_predictions_count = 0
cob_predictions_count = 0

# Should see:
# CNN: ~100-200 predictions/minute
# DQN: ~100-200 predictions/minute
# COB: ~100-200 predictions/minute
```

---

## Files Modified

1.  **core/realtime_prediction_loop.py** (NEW)
   - Created prediction loop

2. ⏳ **core/orchestrator.py** (TO MODIFY)
   - Add prediction_loop initialization
   - Add start_prediction_loop() method
   - Add process_signal() method

3. ⏳ **web/clean_dashboard.py** (TO MODIFY)
   - Start prediction loop in background thread

4. ⏳ **core/data_provider.py** (MAY NEED)
   - Ensure get_latest_candles() works properly

---

## Next Steps

1. Integrate prediction loop into orchestrator (5 min)
2. Start loop from dashboard (5 min)
3. Test and verify models being called (10 min)
4. Monitor logs for continuous predictions (ongoing)

---

## Summary

**Root Cause**: No continuous prediction loop - models loaded but never used  
**Solution**: Created `RealtimePredictionLoop` to call `model.predict()` continuously  
**Impact**: Models will now make 100-200 predictions per minute instead of 1 at startup  

Your breakpoint on `predict()` should now trigger every 1-5 seconds! 🎯