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A Deep Reinforcement Learning Trader without Offline Training

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  • Boian Lazov

Abstract

In this paper we pursue the question of a fully online trading algorithm (i.e. one that does not need offline training on previously gathered data). For this task we use Double Deep $Q$-learning in the episodic setting with Fast Learning Networks approximating the expected reward $Q$. Additionally, we define the possible terminal states of an episode in such a way as to introduce a mechanism to conserve some of the money in the trading pool when market conditions are seen as unfavourable. Some of these money are taken as profit and some are reused at a later time according to certain criteria. After describing the algorithm, we test it using the 1-minute-tick data for Cardano's price on Binance. We see that the agent performs better than trading with randomly chosen actions on each timestep. And it does so when tested on the whole dataset as well as on different subsets, capturing different market trends.

Suggested Citation

  • Boian Lazov, 2023. "A Deep Reinforcement Learning Trader without Offline Training," Papers 2303.00356, arXiv.org.
    • Handle: RePEc:arx:papers:2303.00356
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    References listed on IDEAS

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    1. Adrian Millea, 2021. "Deep Reinforcement Learning for Trading—A Critical Survey," Data, MDPI, vol. 6(11), pages 1-25, November.
    2. Oriol Vinyals & Igor Babuschkin & Wojciech M. Czarnecki & Michaël Mathieu & Andrew Dudzik & Junyoung Chung & David H. Choi & Richard Powell & Timo Ewalds & Petko Georgiev & Junhyuk Oh & Dan Horgan & M, 2019. "Grandmaster level in StarCraft II using multi-agent reinforcement learning," Nature, Nature, vol. 575(7782), pages 350-354, November.
    3. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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