RL training

NN/utils/data_interface.py

@@ -13,6 +13,7 @@ import json
 import pickle
 from sklearn.preprocessing import MinMaxScaler
 import sys
+import ta
 
 # Add project root to sys.path
 project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -534,3 +535,77 @@ class DataInterface:
         timestamp = df['timestamp'].iloc[-1]
         
         return X, timestamp
+
+    def get_training_data(self, timeframe='1m', n_candles=5000):
+        """
+        Get a consolidated dataframe for RL training with OHLCV and technical indicators
+        
+        Args:
+            timeframe (str): Timeframe to use
+            n_candles (int): Number of candles to fetch
+            
+        Returns:
+            DataFrame: Combined dataframe with price data and technical indicators
+        """
+        # Get historical data
+        df = self.get_historical_data(timeframe=timeframe, n_candles=n_candles, use_cache=True)
+        
+        if df is None or len(df) < 100:  # Minimum required for indicators
+            logger.error(f"Not enough data for RL training (need at least 100 candles)")
+            return None
+        
+        # Calculate technical indicators
+        try:
+            # Add RSI (14)
+            df['rsi'] = ta.rsi(df['close'], length=14)
+            
+            # Add MACD
+            macd = ta.macd(df['close'])
+            df['macd'] = macd['MACD_12_26_9']
+            df['macd_signal'] = macd['MACDs_12_26_9']
+            df['macd_hist'] = macd['MACDh_12_26_9']
+            
+            # Add Bollinger Bands
+            bbands = ta.bbands(df['close'], length=20)
+            df['bb_upper'] = bbands['BBU_20_2.0']
+            df['bb_middle'] = bbands['BBM_20_2.0']
+            df['bb_lower'] = bbands['BBL_20_2.0']
+            
+            # Add ATR (Average True Range)
+            df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)
+            
+            # Add moving averages
+            df['sma_20'] = ta.sma(df['close'], length=20)
+            df['sma_50'] = ta.sma(df['close'], length=50)
+            df['ema_20'] = ta.ema(df['close'], length=20)
+            
+            # Add OBV (On-Balance Volume)
+            df['obv'] = ta.obv(df['close'], df['volume'])
+            
+            # Add momentum indicators
+            df['mom'] = ta.mom(df['close'], length=10)
+            
+            # Normalize price to previous close
+            df['close_norm'] = df['close'] / df['close'].shift(1) - 1
+            df['high_norm'] = df['high'] / df['close'].shift(1) - 1
+            df['low_norm'] = df['low'] / df['close'].shift(1) - 1
+            
+            # Volatility features
+            df['volatility'] = df['high'] / df['low'] - 1
+            
+            # Volume features
+            df['volume_norm'] = df['volume'] / df['volume'].rolling(20).mean()
+            
+            # Calculate returns
+            df['returns_1'] = df['close'].pct_change(1)
+            df['returns_5'] = df['close'].pct_change(5)
+            df['returns_10'] = df['close'].pct_change(10)
+            
+        except Exception as e:
+            logger.error(f"Error calculating technical indicators: {str(e)}")
+            return None
+        
+        # Drop NaN values
+        df = df.dropna()
+        
+        return df