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import pandas as pd
def calculate_rsi(prices, length=14, oversold=30, overbought=70):
"""
Calculates the Relative Strength Index (RSI) and marks the oversold and overbought conditions.
Parameters:
- prices (pd.Series): A pandas Series containing the stock's closing prices.
- length (int): The length of the RSI period. Defaults to 14.
- oversold (int): The level at which the asset is considered oversold. Defaults to 30.
- overbought (int): The level at which the asset is considered overbought. Defaults to 70.
Returns:
- pd.DataFrame: A DataFrame containing the RSI values, and flags for oversold and overbought conditions.
"""
delta = prices.diff()
gain = (delta.where(delta > 0, 0)).rolling(window=length).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=length).mean()
rs = gain / loss
rsi = 100 - (100 / (1 + rs))
rsi_df = pd.DataFrame(data={'RSI': rsi})
rsi_df['Oversold'] = rsi_df['RSI'] < oversold
rsi_df['Overbought'] = rsi_df['RSI'] > overbought
return rsi_df
if __name__ == "__main__":
# Example usage
data = {'Close': [22, 24, 23, 25, 26, 28, 27, 29, 30, 32, 31, 33]}
prices = pd.Series(data['Close'])
# User-defined parameters for RSI
length = 14 # RSI period
oversold = 30 # Oversold threshold
overbought = 70 # Overbought threshold
# Calculate RSI
rsi_df = calculate_rsi(prices, length, oversold, overbought)
print(rsi_df)
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