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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 check_buy_signal(data):
"""
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 (pd.DataFrame): The stock data with 'SMA_21', 'SMA_50' columns.
Returns:
- pd.Series: A boolean series indicating buy signals.
"""
# 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
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.
"""
# Assuming 'data' has 'Close' and 'BB_Upper' calculated
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'.
"""
# 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
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())
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