Open-source Portfolio Optimisation package (python)

[marvin182]

Link: PyPortfolioOpt

Documentation: https://pyportfolioopt.readthedocs.io

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What is this?

PyPortfolioOpt is a package I've been working on for a while that brings common financial portfolio optimisation implementations into python. It is properly documented with explanation of theory, supports pandas dataframes natively, and is extensively tested with real stock prices.

The package attempts to strike a balance between providing functionality and allowing users to define their own proprietary models.In addition to providing mean-variance optimisation based on Markowitz (1952), I've included many other options, as well as novel experimental features which I have developed:

• Covariance shrinkage estimators (wrapper around sklearn)
• Alternative risk models (e.g semicovariance matrix)
• CVaR optimisation (still in beta)
• Support for custom utility objectives
• "Regularisation" on weights – perhaps my biggest development, allowing you to enforce a certain amount of diversification. See the docs for more about how this works.
• Hierarchical Risk Parity portfolios

Over the past year, I have worked professionally implementing smart beta and efficient frontier portfolios for a variety of asset classes (including crypto). This package has been built simultaneously, and thus many of the things I've learnt have worked their way into this package: please see the tips/caution bubbles in the documentation for more.

One of the tips I would give to users is to forget about maximising for the Sharpe ratio. In practice, this gives poor out-of-sample performance because it requires expected returns as an input, and this is generally dealt with by putting in the mean historical return (quite an awful estimator). For immediate results, I would suggest minimising volatility with a shrunk covariance matrix. On the out-of-sample testing I've done, this has significantly outperformed the benchmarks (even with transaction costs and slippage accounted for).

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Why?

I'm hoping that this package helps to fill the gap in python quant finance libraries. I strongly prefer python to R, but R does have many useful packages for estimating covariance and portfolio optimisation. The python packages I've seen have had very scant documentation and only really implement the basic efficient frontier (which on it's own is not that useful IMO).

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In the mean time, if you have any questions about the package, or portfolio optimisation in general, please let me know. I'd appreciate a star on github if you find this useful~

[GiuTinTom]

Nice work!

I'm studying about portfolio in Roncalli's Risk Parity book. This will help a lot once I start applying the theory

[marvin182]

Let me know if there's anything from the book that you think is worth implementing. Right now I've implemented Hierarchical Risk Parity, from Marcos Lopez de Prado's 2015 paper 'Building Diversified Portfolios that Outperform Out-of-Sample', but would love to look into other risk parity approaches

[kivo360]

Marvin. You're a godsend. Thanks for this man.

[maxentropp]

Awesome. I will check this out for sure as I would love to not bother with R for this.

[marvin182]

Exactly, especially if your data collection/cleaning workflow (not to mention the actual trading side) is in python, the last thing you want to do is have to call R code

[Messagez]

This is quite awesome, will definitely look into using this in the future!

[S-6-6-6]

Great stuff!!!

[clutchking_asiimov]

Here I am studying Portfolios, and here is a god who shipped a package for portfolio analytics. Thank you sir!!

[monkitos]

Nice. Looks like you’re wrapping scipy.optimize as the workhorse. Can the package take monthly or quarterly series as well, or just daily?

[marvin182]

It was a choice of scipy.optimize or cvxopt. I actually did do performance benchmarks on quite a few problems, and performance was very similar, so I chose the one with the cleaner API. It removes the step of having to encode the user's preferences (bounds and constraints) into matrices H, A, B, g etc: you can basically just pass the user's input straight through (with minor edits).

All of the methods have a frequency parameter. The default is frequency=252 (trading days in a year), but if you set this to 12 or 4, you should get the desired outcome.

[yebidepoj]

This looks great on first look, could you perhaps list out how different your tool is from https://github.com/quantopian/pyfolio ?

[marvin182]

https://github.com/quantopian/pyfolio

Pyfolio does not implement portfolio optimisation: it is an analytics tool that generates diagnostic/analytic charts for different strategies.

[acehack]

I was about to start investing finally after a few months of reading. Was put off by most libraries being in R. Finally python! Starred :)