Create strategy.py

signals/strategy.py

@@ -0,0 +1,86 @@
+import pandas as pd
+from indicators.sma import calculate_21_50_sma
+from indicators.bollinger_bands import calculate_bollinger_bands
+
+def calculate_standard_deviation(data):
+    """
+    Calculate the standard deviation of the closing prices over a 21-period window.
+    
+    Parameters:
+    - data (pd.DataFrame): The stock data with 'Close' column.
+    
+    Returns:
+    - pd.DataFrame: The stock data with an added 'SD_21' column for the standard deviation.
+    """
+    data['SD_21'] = data['Close'].rolling(window=21).std()
+    return data
+
+def check_buy_signal(data):
+    """
+    Analyzes stock data to identify buy signals based on enhanced 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.
+    - On the 1-day time frame, the price is either below the 21-period SMA or less than 0.25 SD above the 21-period SMA.
+    
+    Parameters:
+    - data (pd.DataFrame): The stock data with 'Close', 'SMA_21', 'SMA_50', 'SD_21' columns.
+    
+    Returns:
+    - pd.Series: A boolean series indicating buy signals.
+    """
+    price_position = data['Close'] - data['SMA_21']
+    within_sd_limit = (price_position > 0) & (price_position <= 0.25 * data['SD_21'])
+    buy_signal = ((data['SMA_21'] > data['SMA_50']) &
+                  (data['SMA_21'].shift(1) > data['SMA_50'].shift(1)) &
+                  ((data['Close'] < data['SMA_21']) | within_sd_limit))
+    return buy_signal
+
+def check_sell_signal(data):
+    """
+    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 (pd.DataFrame): The stock data with 'Close', 'BB_Upper' columns.
+    
+    Returns:
+    - pd.Series: A boolean series indicating sell signals.
+    """
+    sell_signal = data['Close'] > data['BB_Upper']
+    return sell_signal
+
+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'.
+    """
+    # Ensure the necessary SMA, Bollinger Bands, and standard deviation calculations are performed
+    stock_data = calculate_21_50_sma(stock_data)
+    stock_data = calculate_bollinger_bands(stock_data)
+    stock_data = calculate_standard_deviation(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
+
+if __name__ == "__main__":
+    # Example usage
+    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})
+    
+    # Simulating the adding of SMA and SD columns for the example
+    sample_data = calculate_21_50_sma(sample_data)
+    sample_data = calculate_bollinger_bands(sample_data)
+    sample_data = calculate_standard_deviation(sample_data)
+
+    signals_data = generate_signals(sample_data)
+    print(signals_data[['Buy_Signal', 'Sell_Signal']].tail())