diff --git a/PREDICTION_LOOP_INTEGRATION.md b/PREDICTION_LOOP_INTEGRATION.md new file mode 100644 index 0000000..91e6c55 --- /dev/null +++ b/PREDICTION_LOOP_INTEGRATION.md @@ -0,0 +1,242 @@ +# 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! 🎯