Quant Mashup - Vertox Quant EigenScore is Live: The First Rated Contest Platform for Quants [Vertox Quant]For the past months, almost every free hour I had went into one thing. Today it’s live. eigenscore.com In quant, everyone claims to be good, and there's no arena to settle it. Competitive programming solved that twenty years ago with rated contests. I built the same thing for us. What it is(...) Conformal Prediction in Quantitative Finance [Vertox Quant]In one of our previous articles, we built a neural-network-based volatility forecaster that beats baseline models in all volatility regimes: Volatility Forecasting using Neural Networks Vertox · Jun 22 Volatility Forecasting using Neural Networks Today, we are gonna look at something we’ve never(...) Fast Option Pricing using Fourier Transform [Vertox Quant]Monte-Carlo Simulation is the most straightforward way to price an option, and if you don’t care about speed, it’s a solid choice. The moment you care about speed, like when quoting live, or when calibrating a pricing model where you need to reprice options thousands of times, Monte Carlo(...) 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(...)