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Optimize stock portfolios using mean-variance optimization and other strategies.

Project description

Portfolio Optimize

A simple Python package for optimizing investment portfolios using historical return data from Yahoo Finance. Users can easily determine the optimal portfolio allocation among a given set of tickers based on the mean-variance optimization method or other algorithms.

Features

  • Easy-to-use interface for defining a portfolio of tickers.
  • Supports customization of the data window (in years) for historical data analysis.
  • Allows choosing between mean-variance optimization and other optimization algorithms.
  • Includes functionality to plot the efficient frontier for the selected portfolio.

Installation

pip install portfolio-optimize

Usage

Portfolio Optimization

from portfolio_optimize import portfolio_optimize

# Optimize portfolio
optimal_weights = portfolio_optimize(tickers=["MSFT", "AAPL", "GOOG"], window=5, optimization="MV")

print(optimal_weights)

Plotting the Efficient Frontier

from portfolio_optimize import portfolio_optimize

# Plot the efficient frontier for a set of tickers
portfolio_optimize.graph(tickers=["MSFT", "AAPL", "GOOG"], window=5)

License

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

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