Create signals/strategy.py

signals/strategy.py

@@ -0,0 +1,44 @@
+import pandas as pd
+from indicators.sma import calculate_sma
+from indicators.bollinger_bands import calculate_bollinger_bands
+
+def generate_signals(data):
+    """
+    Analyzes technical indicators to generate buy and sell signals.
+
+    Args:
+        data (pd.DataFrame): DataFrame containing the historical price data and indicators.
+
+    Returns:
+        pd.DataFrame: The input DataFrame augmented with buy and sell signals.
+    """
+    # Ensure the necessary indicators are calculated and present in the DataFrame
+    if 'SMA_21' not in data.columns or 'SMA_50' not in data.columns:
+        data['SMA_21'] = calculate_sma(data, period=21)
+        data['SMA_50'] = calculate_sma(data, period=50)
+    
+    if 'BB_Upper' not in data.columns:
+        data = calculate_bollinger_bands(data)
+
+    # Initialize columns for signals
+    data['Buy_Signal'] = False
+    data['Sell_Signal'] = False
+
+    # Loop through the DataFrame to find buy and sell conditions
+    for i in range(2, len(data)):
+        # Buy condition: SMA_21 crosses above SMA_50, and has been below SMA_50 for at least 10 periods
+        if data['SMA_21'].iloc[i] > data['SMA_50'].iloc[i] and data['SMA_21'].iloc[i-1] <= data['SMA_50'].iloc[i-1]:
+            if data['SMA_50'].iloc[i-10:i-1].isnull().sum() < 1: # Ensure there's enough data
+                if (data['SMA_21'].iloc[i-10:i-1] < data['SMA_50'].iloc[i-10:i-1]).all():
+                    data.at[data.index[i], 'Buy_Signal'] = True
+
+        # Sell condition: Price reaches or exceeds the upper Bollinger band for at least 3 periods after being below it for 5 or more periods
+        if data['close'].iloc[i] >= data['BB_Upper'].iloc[i] and data['close'].iloc[i-1] < data['BB_Upper'].iloc[i-1] and data['close'].iloc[i-2] < data['BB_Upper'].iloc[i-2]:
+            if (data['close'].iloc[i-5:i-1] < data['BB_Upper'].iloc[i-5:i-1]).all():
+                data.at[data.index[i], 'Sell_Signal'] = True
+
+    return data
+
+# Example usage:
+# Assuming `price_data` is a pandas DataFrame with columns including 'date', 'open', 'high', 'low', 'close', and 'volume':
+# signals_data = generate_signals(price_data)