https://github.com/hudson-and-thames/portfoliolab
PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.
https://github.com/hudson-and-thames/portfoliolab
algorithmic-trading finance investing machine-learning portfolio-management portfolio-optimization python quantitative-finance research trading
Last synced: 6 months ago
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PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.
- Host: GitHub
- URL: https://github.com/hudson-and-thames/portfoliolab
- Owner: hudson-and-thames
- License: other
- Created: 2021-01-12T15:32:42.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-12-02T09:23:54.000Z (almost 4 years ago)
- Last Synced: 2025-04-05T14:41:55.641Z (7 months ago)
- Topics: algorithmic-trading, finance, investing, machine-learning, portfolio-management, portfolio-optimization, python, quantitative-finance, research, trading
- Homepage: https://hudson-and-thames-portfoliolab.readthedocs-hosted.com/en/latest/
- Size: 35.2 KB
- Stars: 164
- Watchers: 14
- Forks: 45
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Welcome to Portfolio Optimisation Laboratory!
>This repo is public facing and exists for the sole purpose of providing users with an easy way to raise bugs, feature requests, and other issues.
## What is PortfolioLab?
PortfolioLab python library includes both various end-to-end portfolio optimization strategies
and strategy creation tools that cover the whole range of techniques you would need to create your own top-earning strategy.
We pride ourselves in the robustness of our codebase - every line of code existing in the modules is extensively tested and
documented.
## Documentation, Example Notebooks and Lecture Videos
For every technique present in the library we not only provide extensive documentation, with both theoretical explanations
and detailed descriptions of available functions, but also supplement the modules with ever-growing array of lecture videos and slides
on the implemented methods.
We want you to be able to use the tools right away. To achieve that, every module comes with a number of example notebooks
which include detailed examples of the usage of the algorithms. Our goal is to show you the whole pipeline, starting from
importing the libraries and ending with strategy performance metrics so you can get the added value from the get-go.
### Included modules:
- Bayesian Models
- Black-Litterman Model
- Entropy Pooling
- Robust Bayesian Allocation
- Clustering Models
- Hierarchical Risk Parity (HRP)
- Hierarchical Equal Risk Contribution (HERC)
- Nested Clustered Optimization (NCO)
- Risk and Return Estimators
- Modern Portfolio Theory
- Critical Line Algorithm (CLA)
- Mean-Variance Optimisation
- Online Portfolio Selection
- Benchmarks
- Momentum
- Mean Reversion
- Pattern Matching
## Licensing options
This project is licensed under an all rights reserved [license](https://github.com/hudson-and-thames/portfoliolab/blob/master/LICENSE.txt).
* Business
* Enterprise
## Community
With the purchase of the library, our clients get access to the Hudson & Thames Slack community, where our engineers and other quants
are always ready to answer your questions.
Alternatively, you can email us at: research@hudsonthames.org.
## Who is Hudson & Thames?
Hudson and Thames Quantitative Research is a company with the goal of bridging the gap between the advanced research developed in
quantitative finance and its practical application. We have created three premium python libraries so you can effortlessly access the
latest techniques and focus on what matters most: **creating your own winning strategy**.
### What was only possible with the help of huge R&D teams is now at your disposal, anywhere, anytime.