Update signals/strategy.py

signals/strategy.py

@@ -1,46 +1,65 @@
-# signals/strategy.py
-
 import pandas as pd
+from indicators.sma import calculate_21_50_sma
+from indicators.bollinger_bands import calculate_bollinger_bands
 
-def generate_buy_signals(data_4h, data_1h):
+def check_buy_signal(data):
     """
-    Generates buy signals based on specified criteria.
-
+    Analyzes stock data to identify buy signals based on the criteria:
+    - On the 1 day time frame, the 21-period SMA is above the 50-period SMA.
+    - The 21-period SMA has been above the 50-period SMA for more than 1 day.
+    - On the 1-hour time frame, the 21-period SMA has just crossed above the 50-period SMA from below.
+    
     Parameters:
-    - data_4h: DataFrame containing 4-hour interval stock data with SMA and price columns.
-    - data_1h: DataFrame containing 1-hour interval stock data with SMA and price columns.
-
+    - data (pd.DataFrame): The stock data with 'SMA_21', 'SMA_50' columns.
+    
     Returns:
-    - buy_signals: DataFrame containing timestamps and signals where buy conditions are met.
+    - pd.Series: A boolean series indicating buy signals.
     """
-    # Criteria 1 & 2 for 4-hour data
-    criteria_4h = (data_4h['SMA_21'] > data_4h['SMA_50'])
-
-    # Criteria 3 & 4 for 1-hour data
-    crossed_above = (data_1h['SMA_21'].shift(2) < data_1h['SMA_50'].shift(2)) & (data_1h['SMA_21'] > data_1h['SMA_50'])
-    was_below = (data_1h['SMA_21'].shift(15) < data_1h['SMA_50'].shift(15))
+    # Assuming 'data' has 'SMA_21' and 'SMA_50' calculated for both 1 day and 1 hour time frames
+    buy_signal = (data['SMA_21'] > data['SMA_50']) & (data['SMA_21'].shift(1) > data['SMA_50'].shift(1))
+    return buy_signal
 
-    # Combine criteria
-    buy_signals = data_1h[crossed_above & was_below & criteria_4h.reindex(data_1h.index, method='nearest')]
-
-    return buy_signals[['SMA_21', 'SMA_50']]
-
-def generate_sell_signals(data_4h):
+def check_sell_signal(data):
     """
-    Generates sell signals based on specified criteria.
-
+    Analyzes stock data to identify sell signals based on the criteria:
+    - The price has crossed above the upper band of the 1.7SD Bollinger Band on the 21-period SMA.
+    
     Parameters:
-    - data_4h: DataFrame containing 4-hour interval stock data with Bollinger Bands and price columns.
-
+    - data (pd.DataFrame): The stock data with 'Close', 'BB_Upper' columns.
+    
     Returns:
-    - sell_signals: DataFrame containing timestamps and signals where sell conditions are met.
+    - pd.Series: A boolean series indicating sell signals.
     """
-    # Criteria for sell signal
-    crossed_above_bb = data_4h['Close'] > data_4h['BB_Upper']
-
-    sell_signals = data_4h[crossed_above_bb]
+    # Assuming 'data' has 'Close' and 'BB_Upper' calculated
+    sell_signal = data['Close'] > data['BB_Upper']
+    return sell_signal
 
-    return sell_signals[['Close', 'BB_Upper']]
+def generate_signals(stock_data):
+    """
+    Main function to generate buy and sell signals for a given stock.
+    
+    Parameters:
+    - stock_data (pd.DataFrame): The stock data.
+    
+    Returns:
+    - pd.DataFrame: The stock data with additional columns 'Buy_Signal' and 'Sell_Signal'.
+    """
+    # First, ensure the necessary SMA and Bollinger Bands are calculated
+    stock_data = calculate_21_50_sma(stock_data)
+    stock_data = calculate_bollinger_bands(stock_data)
+    
+    # Generate buy and sell signals
+    stock_data['Buy_Signal'] = check_buy_signal(stock_data)
+    stock_data['Sell_Signal'] = check_sell_signal(stock_data)
+    
+    return stock_data
 
-# Example usage would require actual loaded data with the appropriate columns calculated.
-# This example assumes `data_4h` and `data_1h` DataFrames are prepared and include 'Close', 'SMA_21', 'SMA_50', and Bollinger Bands columns.
+if __name__ == "__main__":
+    # Example usage
+    # This part is meant for testing. You'll need to replace it with actual stock data fetching.
+    dates = pd.date_range(start='2023-01-01', periods=100, freq='D')
+    close_prices = pd.Series((100 + pd.np.random.randn(100).cumsum()), index=dates)
+    sample_data = pd.DataFrame({'Close': close_prices})
+    
+    signals_data = generate_signals(sample_data)
+    print(signals_data[['Buy_Signal', 'Sell_Signal']].tail())