arXiv Open Access 2023

Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading

Vikram Duvvur Aashay Mehta Edward Sun Bo Wu Ken Yew Chan +1 lainnya

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Abstrak

The use of machine learning in algorithmic trading systems is increasingly common. In a typical set-up, supervised learning is used to predict the future prices of assets, and those predictions drive a simple trading and execution strategy. This is quite effective when the predictions have sufficient signal, markets are liquid, and transaction costs are low. However, those conditions often do not hold in thinly traded financial markets and markets for differentiated assets such as real estate or vehicles. In these markets, the trading strategy must consider the long-term effects of taking positions that are relatively more difficult to change. In this work, we propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model and addresses these challenges. We test our algorithm on 20+ years of equity data from Bursa Malaysia.

Topik & Kata Kunci

cs.LG

Penulis (6)

Vikram Duvvur

Aashay Mehta

Edward Sun

Bo Wu

Ken Yew Chan

Jeff Schneider

Format Sitasi

APA MLA BibTeX

Duvvur, V., Mehta, A., Sun, E., Wu, B., Chan, K.Y., Schneider, J. (2023). Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading. https://arxiv.org/abs/2307.09377

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓