Here's a README for your code:

This Python script is designed to implement a momentum-based stock selection strategy using historical stock price data obtained from Yahoo Finance. The strategy selects stocks with high momentum by considering their price returns over different time periods.

• Introduction
• Dependencies
• Usage
• Strategy Overview
• Code Structure

The script uses the yfinance library to fetch historical stock price data for a list of S&P 500 stocks. It calculates various price returns, including 1-year,6-months price returns, and assigns a percentile score to each stock based on its returns. The stocks are then ranked by their percentile scores to identify high-quality momentum stocks.

The script relies on the following Python libraries:

• numpy
• pandas
• scipy.stats
• math
• yfinance
• threading
• concurrent.futures
• datetime

You can install these libraries using pip if you haven't already:

pip install numpy pandas scipy yfinance

1. Clone or download the repository to your local machine.

2. Make sure you have the required dependencies installed (see the Dependencies section).

3. Prepare a CSV file containing the list of S&P 500 stock tickers (e.g., "sp_500_stocks.csv").

4. Run the script by executing the momentum_strategy.py file.

python Quant_Momentum_Strategy.py

5. Follow the on-screen instructions to enter the size of your investment portfolio.

6. The script will generate a CSV file named "Final_Strategy.csv" containing the selected stocks and the number of shares to buy for each stock based on your portfolio size.

The strategy ranks stocks based on their percentile scores for different time periods, including 1-year price returns. High percentile scores indicate strong momentum. The selected stocks are then allocated to your portfolio based on your specified portfolio size.

• Data Extraction: The script uses yfinance to extract historical stock price data for the S&P 500 stocks in parallel using multi-threading.

• Returns Calculation: It calculates various price returns (e.g., 1-year returns) for each stock.

• Percentile Ranking: Stocks are ranked by their percentile scores for different time periods, and the scipy.stats library is used for percentile calculations.

• Portfolio Allocation: The script determines the number of shares to buy for each selected stock based on your portfolio size.

• Output: The final list of selected stocks and their allocation details are saved in a CSV file for your reference.

This script provides a simple yet effective way to identify high-quality momentum stocks based on historical price returns.