Create rsi.py

indicators/rsi.py

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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)