Numin: Weighted-Majority Ensembles for Intraday Trading

TL;DR

This paper introduces Numin, an ensemble method that dynamically weights multiple machine learning models for intraday stock trading, improving prediction accuracy and profitability by adapting to real-time performance.

Contribution

The paper presents a novel weighted-majority ensemble approach for short-term trading that updates model weights based on recent performance metrics, including profitability proxies.

Findings

Ensemble improves accuracy over individual models.

Dynamic weighting enhances utility and profitability.

Real-time adaptation outperforms static models.

Abstract

We consider the application of machine learning models for short-term intra-day trading in equities. We envisage a scenario wherein machine learning models are submitted by independent data scientists to predict discretised ten-candle returns every five minutes, in response to five-minute candlestick data provided to them in near real-time. An ensemble model combines these multiple models via a weighted-majority algorithm. The weights of each model are dynamically updated based on the performance of each model, and can also be used to reward model owners. Each model's performance is evaluated according to two different metrics over a recent time window: In addition to accuracy, we also consider a `utility' metric that is a proxy for a model's potential profitability under a particular trading strategy. We present experimental results on real intra-day data that show that our weighted-majority ensemble techniques show improved accuracy as well as utility over any of the individual models, especially using the utility metric to dynamically re-weight models over shorter time-windows.