This repository showcases the capabilities and strategies of Direct Indexing, a personalized and efficient investment approach that replicates the performance of an index while allowing for individual customizations.
The primary objective of this project is to demonstrate and explore Direct Indexing through two key scenarios:
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Replicating an Index with Known Constituents
Develop an optimizer to closely follow an index (e.g., S&P 500) by minimizing active risk.- Active Risk: Defined as the standard deviation of the difference between the portfolio return and the benchmark return.
- Goal: Achieve low tracking error while maintaining alignment with the benchmark's risk-return profile.
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Replicating an Index with Unknown Constituents
Design a trading strategy to mimic the returns of a fund, given:- Fund Returns: Historical daily (or other frequency) returns of the target fund.
- Eligible Universe: A specified domain of securities eligible for investment.
For the proof of concept (POC), the S&P 500 will serve as the index, and a subset of its constituents will define the eligible universe.
- Portfolio Optimization: Advanced optimization techniques to minimize tracking error.
- Return Mimicking: Strategies to estimate and follow fund returns even when the exact constituents are unknown.
- Flexibility: Adaptability to various benchmarks, asset classes, and investment universes.
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Install Dependencies
Ensure that all required libraries and tools are installed. Refer to therequirements.txtfile for the list of dependencies. -
Run the Optimizer
Use the provided scripts to:- Optimize a portfolio to closely track a given index.
- Mimic fund returns with incomplete constituent data.
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Customize Your Universe
Experiment with different universes of eligible securities and observe the impact on tracking error and portfolio performance.
- Extend the framework to non-U.S. indices (e.g., MSCI World, FTSE 100).
- Incorporate ESG filters for more tailored portfolio construction.
- Develop a dashboard for real-time performance monitoring and visualizations.
We welcome contributions to enhance the project. Please fork the repository, create a new branch, and submit a pull request with your changes.
This project is open-source and available under the MIT License.