new__training

core/data_provider.py

@@ -500,9 +500,38 @@ class DataProvider:
             return pd.DataFrame()
     
     def get_current_price(self, symbol: str) -> Optional[float]:
-        """Get current price for a symbol"""
-        with self.data_lock:
-            return self.current_prices.get(symbol)
+        """Get current price for a symbol from latest candle"""
+        try:
+            # Try to get from 1s candle first (most recent)
+            for tf in ['1s', '1m', '5m', '1h']:
+                df = self.get_latest_candles(symbol, tf, limit=1)
+                if df is not None and not df.empty:
+                    return float(df.iloc[-1]['close'])
+            
+            # Fallback to any available data
+            key = f"{symbol}_{self.timeframes[0]}"
+            if key in self.historical_data and not self.historical_data[key].empty:
+                return float(self.historical_data[key].iloc[-1]['close'])
+                
+            logger.warning(f"No price data available for {symbol}")
+            return None
+            
+        except Exception as e:
+            logger.error(f"Error getting current price for {symbol}: {e}")
+            return None
+    
+    def get_price_at_index(self, symbol: str, index: int, timeframe: str = '1m') -> Optional[float]:
+        """Get price at specific index for backtesting"""
+        try:
+            key = f"{symbol}_{timeframe}"
+            if key in self.historical_data:
+                df = self.historical_data[key]
+                if 0 <= index < len(df):
+                    return float(df.iloc[index]['close'])
+            return None
+        except Exception as e:
+            logger.error(f"Error getting price at index {index}: {e}")
+            return None
     
     def get_feature_matrix(self, symbol: str, timeframes: List[str] = None, 
                           window_size: int = 20) -> Optional[np.ndarray]: