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Algorithmic Trading Project with Python

In this GitHub repository, I present an Algorithmic Trading Project with Python where we can apply 3 different strategies to the S&P 500 index. Each strategy is showcased in a Jupyter Notebook and the output of the code is an Excel File with the symbols, stock prices, relevant columns, and the target number of shares to buy for each symbol in the strategy.

Strategies

1. Equal-Weighted S&P 500 Portfolio

This strategy involves creating a portfolio where each stock in the S&P 500 index is equally weighted.

2. Momentum Strategy

The momentum strategy involves analyzing the S&P 500 index and selecting stocks that have shown upward momentum in their stock prices.

3. Value Investing Strategy

The value investing strategy involves identifying undervalued stocks within the S&P 500 index based on certain fundamental metrics.

Output

The code will prompt the user for the portfolio size or the amount of money to invest, and it will output the Excel file containing the symbols, stock prices, relevant columns, and the target number of shares to buy for each symbol in the strategy.

Data Source

This project requires an API key from IEX Cloud in order to access the data. The free trial for the API key lasts for 7 days.

Disclaimer:

This GitHub Repository is intended for educational and skill showcasing purposes only. None of the projects included should be considered as investment recommendations. The content provided is for informational and educational purposes, and should not be construed as financial advice. Visitors are encouraged to seek professional financial advice before making any investment decisions.

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