Quant Mashup - Vertox Quant

The Effective Number of Tested Strategies [Vertox Quant]

In one of my recent articles, we looked at a paper that proposed a measure of how many strategies you effectively tested in-sample. I found the idea of such a measure really interesting and useful, so I went deeper into it, uncovered problems with existing measures, and ultimately came up with my(...)

Backtests Lie: Building a Stress-Test Framework for ML Trading Signals [Vertox Quant]

One of your first thoughts when looking at a stranger’s backtest is probably that it’s overfit, or that there is some look-ahead somewhere. When you go a step further, you are probably constantly worried about overfitting your own backtests too! In this article, we will introduce a framework(...)

Looking Inside The Black Box [Vertox Quant]

People often criticise how ML models are just black boxes that take in some features and spit out a prediction. While some models (like linear regression) are naturally a lot more interpretable than others (like neural networks), it’s wrong that you can’t figure out why a model made a certain(...)