1 reply to this topic

[Midas1]

Good luck pal..be warned you could be called all names of day on here..infiltrator..gangster..blah blah..just warning you..but if you young and inspirational don't let the "sours " put you off even if you old old inspirational stuff the negativity ..

[TraderJacques]

Hi All, 

 

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. 

 

In particular: 

1. 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.
   1. Slide show presentation
   2. Research report: Does Meta-Labeling Add to Signal Efficacy?
2. Our hackathon presentation on machine learning used in portfolio management:
   1. Hierarchical Risk Parity Portfolio Optimization
3. 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.