Update app.py

app.py

@@ -44,7 +44,7 @@ def calculate_indicators(df, lengthEMA=3, lengthRSI=14, momentumLength=3, trendL
     # Calculate Momentum
     df['Momentum'] = df['Close'].diff(momentumLength)
 
-    # Composite SIGNAL Calculation
+    # Composite SIGNAL Calculation with Dynamic Adjustments
     df['SignalComposite'] = (0.5 * df['SignalEMA']) + (0.3 * (df['RSI'] - 50) / 100) + (0.2 * (df['Momentum'] / df['Close'].rolling(window=lengthRSI).mean()))
 
     # Smooth the Composite SIGNAL with EMA
@@ -53,11 +53,17 @@ def calculate_indicators(df, lengthEMA=3, lengthRSI=14, momentumLength=3, trendL
     # Trend Filter (SMA)
     df['TrendSMA'] = df['Close'].rolling(window=trendLength).mean()
 
-    # Buy and Sell Signals
-    buyThreshold = 0.75
-    sellThreshold = -0.75
-    df['BuySignal'] = (df['SmoothedSignal'] > buyThreshold) & (df['Close'] > df['TrendSMA'])
-    df['SellSignal'] = (df['SmoothedSignal'] < sellThreshold) & (df['Close'] < df['TrendSMA'])
+    # Adjusted Buy and Sell Thresholds
+    buyThreshold = 1.5  # Increased threshold for stronger signals
+    sellThreshold = -1.5
+
+    # Buy and Sell Signals with Momentum Condition
+    df['BuySignal'] = (df['SmoothedSignal'] > buyThreshold) & (df['Close'] > df['TrendSMA']) & (df['Momentum'] > 0)
+    df['SellSignal'] = (df['SmoothedSignal'] < sellThreshold) & (df['Close'] < df['TrendSMA']) & (df['Momentum'] < 0)
+
+    # Add Cooldown Logic to Reduce Repeated Signals
+    df['BuySignal'] = df['BuySignal'] & ~df['BuySignal'].shift(1).fillna(False)
+    df['SellSignal'] = df['SellSignal'] & ~df['SellSignal'].shift(1).fillna(False)
 
     return df