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())