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)