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Composing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic Trading

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  • Sahand Hassanizorgabad

Abstract

Financial markets are nonlinear with complexity, where different types of assets are traded between buyers and sellers, each having a view to maximize their Return on Investment (ROI). Forecasting market trends is a challenging task since various factors like stock-specific news, company profiles, public sentiments, and global economic conditions influence them. This paper describes a daily price directional predictive system of financial instruments, addressing the difficulty of predicting short-term price movements. This paper will introduce the development of a novel trading system methodology by proposing a two-layer Composing Ensembles architecture, optimized through grid search, to predict whether the price will rise or fall the next day. This strategy was back-tested on a wide range of financial instruments and time frames, demonstrating an improvement of 20% over the benchmark, representing a standard investment strategy.

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  • Sahand Hassanizorgabad, 2024. "Composing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic Trading," Papers 2411.13559, arXiv.org.
    • Handle: RePEc:arx:papers:2411.13559
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    References listed on IDEAS

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    1. Cooper, Ricky & Ong, Michael & Van Vliet, Ben, 2015. "Multi-scale capability: A better approach to performance measurement for algorithmic trading," Algorithmic Finance, IOS Press, vol. 4(1-2), pages 53-68.
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