Quantitative Trading Model using Moving Averages

In this document, we present a Python function that implements a quantitative trading model based on moving averages. Specifically, we will use the 4-day, 9-day, and 18-day moving averages to generate buy and sell signals.

Overview

Objective

• Buy Signal: When the 4-day moving average exceeds the 18-day moving average.
• Sell Signal: When the 4-day moving average goes below the 18-day moving average.

Libraries Needed

We will utilize the following libraries:

• pandas: For handling time series data.
• numpy: For numerical operations.

Implementation

import pandas as pd
import numpy as np

def moving_average_signals(price_data: pd.Series) -> pd.DataFrame:
    """
    Generates buy and sell signals based on moving averages.

    Args:
        price_data (pd.Series): A Pandas Series containing the historical price data.

    Returns:
        pd.DataFrame: A DataFrame with original price data and corresponding buy/sell signals.
        
    Raises:
        ValueError: If the input price_data is not a Pandas Series or is empty.
    """
    # Validate input
    if not isinstance(price_data, pd.Series) or price_data.empty:
        raise ValueError("Input must be a non-empty Pandas Series of price data.")

    # Calculate moving averages
    ma_4 = price_data.rolling(window=4).mean()
    ma_18 = price_data.rolling(window=18).mean()

    # Create buy and sell signals
    signals = pd.DataFrame(index=price_data.index)
    signals['Price'] = price_data
    signals['MA_4'] = ma_4
    signals['MA_18'] = ma_18

    # Generate Buy and Sell signals
    signals['Signal'] = 0
    signals['Signal'][4:] = np.where(signals['MA_4'][4:] > signals['MA_18'][4:], 1, 0) # Buy
    signals['Position'] = signals['Signal'].diff() # 1 for buy, -1 for sell

    return signals

# Example of Usage
if __name__ == "__main__":
    # Sample price data (you can replace this with actual prices)
    price_data = pd.Series([10, 10.5, 10.7, 11, 11.2, 11.5, 11.8, 12, 12.1, 12.3, 
                            12.5, 12.7, 12.9, 13, 13.3, 13.5, 13.7, 14, 14.5,
                            15, 15.5, 15.8, 16, 16.5, 17, 17.5, 17.8, 18, 18.5])
    
    signals = moving_average_signals(price_data)
    print(signals[['Price', 'MA_4', 'MA_18', 'Signal', 'Position']])

Explanation of the Code

1. Function Definition:

   • The function moving_average_signals takes a pd.Series of price data as input.

2. Input Validation:

   • We check if the input is a non-empty pd.Series. If not, a ValueError is raised.

3. Moving Average Calculation:

   • Compute the 4-day and 18-day moving averages using the rolling().mean() method.

4. Signal Generation:

   • A new DataFrame signals is created which contains the original prices, moving averages, and the generated buy/sell signals.
   • We create a signal: a value of 1 indicates a buy signal, while a value of -1 indicates a sell signal.

5. Output:

   • The function returns a DataFrame with the price data and signals for further analysis.

Example Scenario

• To utilize the function, one can input a series of price data. The example provided simulates stock prices, and upon running the code, it outputs the DataFrame showing prices, moving averages, and generated signals.

Conclusion

This implementation provides a straightforward approach to creating buy and sell signals based on moving averages, which can be an essential part of a quantitative trading strategy. For further learning and enhancing data analysis skills, consider exploring the courses on the Enterprise DNA Platform.