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TPFOpt: Portfolio Optimization for Tracking Stock Indices

Overview

This repository contains a Python implementation of a portfolio optimization framework for tracking stock indices. It is designed to construct tracking portfolios, such as those underlying ETFs, that replicate the performance of a target index.

Features

  • Construction of index-tracking portfolios via optimized sampling
  • Performance evaluation metrics, including concentration measures, risk measures, and tracking statistics
  • Sector allocation analysis
  • Turnover calculation and reporting

Results

The optimized portfolio weights and performance metrics are stored in a ResultsStore object, which can be saved to a file for further analysis.

Contributing

Contributions are welcome — please open an issue or submit a pull request to improve the code or the documentation.

Acknowledgments

This code was developed in the course of the research published as Feature selection based index tracking: A two-stage approach for optimized sampling.