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arXiv · 2411.13559

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

Sahand Hassanizorgabad
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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. 2024-11-06. Composing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic Trading. https://arxiv.org/abs/2411.13559

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