All Projects → hudson-and-thames → portfoliolab

hudson-and-thames / portfoliolab

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

Projects that are alternatives of or similar to portfoliolab

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.
Stars: ✭ 2,676 (+2473.08%)
Mutual labels:  finance, research, trading, investing, portfolio-optimization, quantitative-finance, algorithmic-trading, portfolio-management
Pyportfolioopt
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Stars: ✭ 2,502 (+2305.77%)
Mutual labels:  finance, investing, portfolio-optimization, quantitative-finance, algorithmic-trading, portfolio-management
Research
Notebooks based on financial machine learning.
Stars: ✭ 714 (+586.54%)
Mutual labels:  finance, research, trading, quantitative-finance, algorithmic-trading
AutoTrader
A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.
Stars: ✭ 227 (+118.27%)
Mutual labels:  finance, trading, investing, quantitative-finance, algorithmic-trading
Qlib
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.
Stars: ✭ 7,582 (+7190.38%)
Mutual labels:  finance, research, quantitative-finance, algorithmic-trading
Introneuralnetworks
Introducing neural networks to predict stock prices
Stars: ✭ 486 (+367.31%)
Mutual labels:  finance, trading, quantitative-finance, algorithmic-trading
Turingtrader
The Open-Source Backtesting Engine/ Market Simulator by Bertram Solutions.
Stars: ✭ 132 (+26.92%)
Mutual labels:  finance, trading, quantitative-finance, algorithmic-trading
Quantdom
Python-based framework for backtesting trading strategies & analyzing financial markets [GUI ]
Stars: ✭ 449 (+331.73%)
Mutual labels:  finance, trading, quantitative-finance, algorithmic-trading
trading-rules-using-machine-learning
A financial trading method using machine learning.
Stars: ✭ 16 (-84.62%)
Mutual labels:  finance, trading, quantitative-finance, algorithmic-trading
cira
Cira algorithmic trading made easy. A Façade library for simpler interaction with alpaca-trade-API from Alpaca Markets.
Stars: ✭ 21 (-79.81%)
Mutual labels:  finance, trading, quantitative-finance
Financial Machine Learning
A curated list of practical financial machine learning tools and applications.
Stars: ✭ 2,172 (+1988.46%)
Mutual labels:  finance, quantitative-finance, algorithmic-trading
Trading-Algorithms
This repository contains the customized trading algorithms that I have created using the Quantopian IDE.
Stars: ✭ 86 (-17.31%)
Mutual labels:  portfolio-optimization, quantitative-finance, portfolio-management
Riskfolio Lib
Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
Stars: ✭ 305 (+193.27%)
Mutual labels:  finance, trading, quantitative-finance
Machine Learning And Ai In Trading
Applying Machine Learning and AI Algorithms applied to Trading for better performance and low Std.
Stars: ✭ 258 (+148.08%)
Mutual labels:  finance, trading, quantitative-finance
piker
#nontina, #paperhands,, #pwnzebotz, #tradezbyguille
Stars: ✭ 63 (-39.42%)
Mutual labels:  finance, trading, algorithmic-trading
Stocksharp
Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
Stars: ✭ 4,601 (+4324.04%)
Mutual labels:  finance, trading, quantitative-finance
Strategems.jl
Quantitative systematic trading strategy development and backtesting in Julia
Stars: ✭ 106 (+1.92%)
Mutual labels:  finance, trading, quantitative-finance
QuantResearch
Quantitative analysis, strategies and backtests
Stars: ✭ 1,013 (+874.04%)
Mutual labels:  quantitative-finance, algorithmic-trading, portfolio-management
Tradingstrategies
Algorithmic trading strategies
Stars: ✭ 120 (+15.38%)
Mutual labels:  finance, trading, quantitative-finance
Mixture model trading public
Stars: ✭ 166 (+59.62%)
Mutual labels:  finance, trading, algorithmic-trading

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.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].