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import pandas as pd
def calculate_sma(data, window):
"""
Calculate the Simple Moving Average (SMA) for the given data.
Parameters:
- data (pd.Series): The stock data (typically closing prices).
- window (int): The period over which to calculate the SMA.
Returns:
- pd.Series: The calculated SMA values.
"""
return data.rolling(window=window, min_periods=1).mean()
def calculate_21_50_sma(data):
"""
Calculate both the 21-period and 50-period SMAs for the given stock data.
Parameters:
- data (pd.DataFrame): The stock data, expected to have a 'Close' column.
Returns:
- pd.DataFrame: The input data frame with added columns for the 21-period and 50-period SMAs.
"""
if 'Close' not in data.columns:
raise ValueError("Data frame must contain a 'Close' column.")
# Calculate the SMAs
data['SMA_21'] = calculate_sma(data['Close'], 21)
data['SMA_50'] = calculate_sma(data['Close'], 50)
return data
if __name__ == "__main__":
# Example usage
# Generate a sample DataFrame with 'Close' prices
dates = pd.date_range(start='2023-01-01', periods=100, freq='D')
close_prices = pd.Series((100 + np.random.randn(100).cumsum()), index=dates)
sample_data = pd.DataFrame({'Close': close_prices})
# Calculate the 21 and 50 period SMAs
sma_data = calculate_21_50_sma(sample_data)
print(sma_data.head()) # Print the first few rows to verify
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