• Stars
    star
    148
  • Rank 248,464 (Top 5 %)
  • Language
  • License
    Other
  • Created over 3 years ago
  • Updated almost 3 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.

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.

  • 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: [email protected].

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.

More Repositories

1

mlfinlab

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
Python
3,845
star
2

arbitragelab

ArbitrageLab is a python library that enables traders who want to exploit mean-reverting portfolios by providing a complete set of algorithms from the best academic journals.
Python
444
star
3

backtest_tutorial

Jupyter Notebook
111
star
4

meta-labeling

Code base for the meta-labeling papers published with the Journal of Financial Data Science
Jupyter Notebook
70
star
5

arbitrage_research

Jupyter Notebook examples on how to use the ArbitrageLab - pairs trading - python library.
Jupyter Notebook
65
star
6

SecondBrain

JavaScript
43
star
7

oct_applications

Applications to the apprenticeship program, October 2020.
Jupyter Notebook
27
star
8

march_applications_21

Skillset Challenge for the Apprenticeship Program
Jupyter Notebook
20
star
9

example-notebooks

Jupyter Notebook
13
star
10

research_public

Notebooks based on financial machine learning.
11
star
11

june_applications_21

Skillset Challenge for the Apprenticeship Program, June 2021.
Jupyter Notebook
10
star
12

definitive_guide_to_pairs_trading

4
star
13

guide_to_modern_portfolio_optimization

4
star
14

interview_april

Interview question for the jr Data Science / Machine Learning Engineer.
4
star
15

hudsonthames-sphinx-theme

Sphinx theme for Hudson and Thames documentation
CSS
2
star
16

a-practitioners-guide-to-the-ONC-algorithm

Code base for the practitioner's guide to the ONC algorithm paper published with the Journal of Financial Data Science
Jupyter Notebook
2
star
17

oct_applications_21

Applications to the apprenticeship program, October 2021.
1
star
18

mlfinlab-quickstart

Python
1
star