Financial Vision Based Reinforcement Learning Trading Strategy
Not on the map. Why?
Abstract (arXiv)
Recent advances in artificial intelligence (AI) for quantitative trading have led to its general superhuman performance in significant trading performance. However, the potential risk of AI trading is a "black box" decision. Some AI computing mechanisms are complex and challenging to understand. If we use AI without proper supervision, AI may lead to wrong choices and make huge losses. Hence, we need to ask about the AI "black box", including why did AI decide to do this or not? Why can people trust AI or not? How can people fix their mistakes? These problems also highlight the challenges that AI technology can explain in the trading field.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 3 Feb 2022.
Builds on
5 of 17 references.
References · 5
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2015Imaging Time-Series to Improve Classification and Imputationcited by 85
- 2019Deep Reinforcement Learning for Tradingcited by 73
- 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
- 2020Explainable Deep Convolutional Candlestick Learnercited by 2
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Citing papers per year: 2022: 1.
1 paper.