Supervised machine learning classification for short straddles on the S&P500

TL;DR

This paper explores using supervised machine learning to decide daily whether to execute short straddle options on the S&P500, aiming to improve trading strategies through classification models.

Contribution

It introduces a framework for applying supervised classification to short straddle decisions and evaluates standard models without hyperparameter tuning.

Findings

No significant outperformance over a 'trade always' baseline.

Provides insights for future model improvements.

Framework for classification-based options trading.

Abstract

In this working paper we present our current progress in the training of machine learning models to execute short option strategies on the S&P500. As a first step, this paper is breaking this problem down to a supervised classification task to decide if a short straddle on the S&P500 should be executed or not on a daily basis. We describe our used framework and present an overview over our evaluation metrics on different classification models. In this preliminary work, using standard machine learning techniques and without hyperparameter search, we find no statistically significant outperformance to a simple "trade always" strategy, but gain additional insights on how we could proceed in further experiments.