Posted 30 March 2019 - 08:43 PM
Posted 30 March 2019 - 05:49 PM
My lab partner and I have been working on some advanced implementations of machine learning in finance (for our MSFE), particularly the work of Dr Lopez Marcos de Prado, and have built a couple of open source implementations that we think the community in general will benefit from.
- An implementation of Meta-Labeling: This is a secondary model that determines if a primary model such as a discretionary portfolio manager, or a technical trading strategy, is correct or not. In short it helps to improve all performance metrics such as drawdowns, sharpe ratio, calmar and so on. It is also very useful when paired with bet sizing algorithms where the primary model determines the side of the trade and the secondary model the size.
- Our hackathon presentation on machine learning used in portfolio management:
- We are busy implementing an open source python package called mlfinlab, which contains the source code for various implementations of the ideas in the text book: Advances in Financial Machine Learning.
We find the research fascinating as it incorporates several skill sets, from HFT techniques, market microstructure, portfolio optimization, to machine learning algorithms and ensembles.
The following links to a post regarding where we are currently in the open source package.
I would like to stress that we are not selling anything. Its all open source and a labor of love. It would be great to open up a dialog around these topics.
Edited by TraderJacques, 30 March 2019 - 05:50 PM.
Also tagged with one or more of these keywords: machine learning, open source