Composing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic Trading
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915028690534400 |
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| author | Hassanizorgabad, Sahand |
| author_facet | Hassanizorgabad, Sahand |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_13559 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Composing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic Trading Hassanizorgabad, Sahand Trading and Market Microstructure Artificial Intelligence Machine Learning 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. |
| title | Composing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic Trading |
| topic | Trading and Market Microstructure Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.13559 |