A Deep Reinforcement Learning Trader without Offline Training
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 39 references that are in the map. First posted on arXiv 1 Mar 2023.
Builds on
6 of 39 references.
References · 6
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
- 2015Cyclical Learning Rates for Training Neural Networkscited by 627
- 2018Deep Reinforcement Learning and the Deadly Triadcited by 170
- 2018A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillationcited by 110
- 2020An Application of Deep Reinforcement Learning to Algorithmic Tradingcited by 30
- 2020Deep Reinforcement Learning using Cyclical Learning Ratescited by 6
Built on by
Not cited yet.