/BlackLitterman-Implementation

Quantitative Finance Final

Primary LanguageJupyter NotebookMIT LicenseMIT

Black Litterman Model Implementation

Quantitative Finance 2021 final project. This repository contains an implementation of the Black-Litterman model, along with supporting files for backtesting and statistical analysis.

Table of Contents

Introduction

The Black-Litterman model is a popular portfolio optimization technique that combines market equilibrium assumptions with an investor's subjective views on asset returns. This repository provides an implementation of the Black-Litterman model, allowing users to incorporate their own views and perform portfolio allocation based on the model's outputs.

Files

The repository consists of the following files:

  1. Backtesting.ipynb: This Jupyter Notebook file contains code for backtesting the Black-Litterman model on historical data. It provides an example of how the model can be applied to evaluate its performance over a specific time period.

  2. Statistic.py: This Python file contains functions for calculating various statistics related to the Black-Litterman model, such as portfolio returns, risk measures, and performance metrics. It serves as a utility module for analyzing the model's results.

  3. blacklitterman.py: This Python file contains the main implementation of the Black-Litterman model. It includes functions for incorporating investor views, calculating equilibrium returns, and performing portfolio allocation based on the model's outputs.

Usage

To use this implementation of the Black-Litterman model, follow these steps:

  1. Clone the repository:
git clone https://github.com/your-username/black-litterman-model.git

  1. Navigate to the repository directory:

  2. Open the Backtesting.ipynb notebook in Jupyter or your preferred Python environment to see an example of how to apply the Black-Litterman model to historical data.

  3. Explore the Statistic.py module for additional statistical analysis functions related to the Black-Litterman model.

  4. Incorporate the blacklitterman.py module into your own projects to leverage the Black-Litterman model for portfolio optimization.

Contributing

Contributions to this repository are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request. We appreciate your contributions!

License

This project is licensed under the MIT License.