PortfolioBuilder

Overview

PortfolioBuilder is a powerful tool designed to help investors construct optimal portfolios by analyzing stock information. Utilizing data from the yfinance package, this tool enables users to gather stock data for a specified range and compute returns, ultimately assisting in identifying companies that are suitable for portfolio inclusion based on correlation analysis.

Features

• Data Gathering: Retrieve stock data for selected companies using the yfinance package.
• Customizable Time Range: Specify a date range with a daily time interval for data collection.
• Return Calculation: Compute the daily returns for each company based on closing values.
• Correlation Analysis: Generate a correlation matrix to identify relationships between companies.
• Portfolio Optimization: Exclude highly correlated companies to minimize risk and enhance diversification.

Installation

To use PortfolioBuilder, follow these simple steps:

1. Clone the repository: Navigate to your desired directory in your terminal and run the following command:

   https://github.com/MauroAndretta/PortfolioBuilder.git

   Then, change into the directory by executing:

   cd PortfolioBuilder

2. Install the required packages: Ensure you have Python 3.7+ installed. Then, install the necessary packages by running:

   pip install -r requirements.txt

or using the dedicated conda environment going first the appropiate conda folder:

conda env create --name notebooks_portfolio_builder --file=notebooks_portfolio_builder.yml

Usage

Here's a step-by-step guide to using PortfolioBuilder:

1. Prepare your list of companies: Create a list in the PortfolioBuilder.ipynb the stock ticker symbols.

2. Run the tool: Execute the main PortfolioBuilder.ipynb with your desired parameters.

   • start-date and end-date: Define the time range for data collection.

   • range_analysis : Define the time range for data analysis. The valid options are: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max

   • companies: List the ticker symbols of companies you wish to include.

tickers = ["TCEHY", "HSBC", "RTX", "ROG.SW", "SSNLF", "TM", "UL", "NVDA", 
           "QBE.  AX", "RIO", "MHR.SG", "RMS.PA", "ESLOY", "SPOT", "WMT", 
           "ENR.DE", "ABNB", "BAYN.DE", "MMM", "EL.PA", "AMZN"]

3. Analyze the results: Review the generated correlation matrix and identify suitable companies for your portfolio.

Sample Output

• Correlation Matrix:

Contributing

Contributions are welcome!

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

If you encounter any issues or have questions, feel free to open an issue on GitHub or contact me at [email protected].

Acknowledgments

• Special thanks to the contributors of the yfinance package.
• Inspired by various portfolio optimization techniques and financial analysis tools.

Follow Us

Stay updated with our latest developments:

• LinkedIn
• GitHub

---