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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())
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