Refactor RSI calculation in RsiMonitor to use pandas/numpy

- Removed dependency on TA-Lib for RSI calculations to simplify installation and avoid import errors.
- Implemented custom RSI and SMA calculation methods using pandas, enhancing reliability and maintainability.
- Updated the RsiMonitor to utilize the new methods, ensuring consistent performance in RSI monitoring.

requirements.txt

@@ -5,5 +5,4 @@ pandas
 psutil
 requests
 aiohttp
-hyperliquid
-talib 
+hyperliquid

src/monitoring/rsi_monitor.py

@@ -12,11 +12,8 @@ from src.config.config import Config
 from src.clients.hyperliquid_client import HyperliquidClient
 from src.notifications.notification_manager import NotificationManager
 
-try:
-    import talib
-except ImportError:
-    logging.error("talib is required for RSI calculations. Install with: pip install talib")
-    raise
+# Using pandas/numpy for RSI calculation instead of TA-Lib to avoid installation issues
+import pandas as pd
 
 logger = logging.getLogger(__name__)
 
@@ -52,6 +49,68 @@ class RsiMonitor:
         
         logger.info(f"🔧 RSI Monitor initialized: {self.timeframe} timeframe, RSI({self.rsi_period}), SMA({self.rsi_sma_period})")
     
+    def _calculate_rsi(self, prices: np.ndarray, period: int) -> np.ndarray:
+        """
+        Calculate RSI using pandas/numpy implementation.
+        
+        Args:
+            prices: Array of price values
+            period: RSI calculation period
+            
+        Returns:
+            Array of RSI values
+        """
+        try:
+            # Convert to pandas Series for easier calculation
+            prices_series = pd.Series(prices)
+            
+            # Calculate price changes
+            delta = prices_series.diff()
+            
+            # Separate gains and losses
+            gains = delta.where(delta > 0, 0)
+            losses = -delta.where(delta < 0, 0)
+            
+            # Calculate rolling averages of gains and losses
+            avg_gains = gains.rolling(window=period, min_periods=period).mean()
+            avg_losses = losses.rolling(window=period, min_periods=period).mean()
+            
+            # Calculate RS (Relative Strength)
+            rs = avg_gains / avg_losses
+            
+            # Calculate RSI
+            rsi = 100 - (100 / (1 + rs))
+            
+            return rsi.values
+            
+        except Exception as e:
+            logger.error(f"❌ Error calculating RSI: {e}")
+            return np.full(len(prices), np.nan)
+    
+    def _calculate_sma(self, values: np.ndarray, period: int) -> np.ndarray:
+        """
+        Calculate Simple Moving Average using pandas.
+        
+        Args:
+            values: Array of values
+            period: SMA calculation period
+            
+        Returns:
+            Array of SMA values
+        """
+        try:
+            # Convert to pandas Series
+            values_series = pd.Series(values)
+            
+            # Calculate rolling mean
+            sma = values_series.rolling(window=period, min_periods=period).mean()
+            
+            return sma.values
+            
+        except Exception as e:
+            logger.error(f"❌ Error calculating SMA: {e}")
+            return np.full(len(values), np.nan)
+    
     async def has_new_candle(self, symbol: str) -> bool:
         """
         Check if there's a new candle for the given symbol and timeframe.
@@ -122,11 +181,11 @@ class RsiMonitor:
             # Extract close prices
             close_prices = np.array([float(candle[4]) for candle in candles])  # candle[4] is close price
             
-            # Calculate RSI using talib
-            rsi_values = talib.RSI(close_prices, timeperiod=self.rsi_period)
+            # Calculate RSI using pandas/numpy implementation
+            rsi_values = self._calculate_rsi(close_prices, self.rsi_period)
             
             # Calculate RSI SMA (Simple Moving Average of RSI)
-            rsi_sma_values = talib.SMA(rsi_values, timeperiod=self.rsi_sma_period)
+            rsi_sma_values = self._calculate_sma(rsi_values, self.rsi_sma_period)
             
             # Get the latest values (skip NaN values)
             valid_indices = ~(np.isnan(rsi_values) | np.isnan(rsi_sma_values))

trading_bot.py

@@ -14,7 +14,7 @@ from datetime import datetime
 from pathlib import Path
 
 # Bot version
-BOT_VERSION = "2.5.273"
+BOT_VERSION = "2.5.274"
 
 # Add src directory to Python path
 sys.path.insert(0, str(Path(__file__).parent / "src"))