# Financial Vision Based Reinforcement Learning Trading Strategy

arXiv: https://arxiv.org/abs/2202.04115 · HTML: https://mlsanity.com/paper/2202.04115/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Imaging Time-Series to Improve Classification and Imputation](https://mlsanity.com/paper/1506.00327/) (2015)
- [Deep Reinforcement Learning for Trading](https://mlsanity.com/paper/1911.10107/) (2019)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [Explainable Deep Convolutional Candlestick Learner](https://mlsanity.com/paper/2001.02767/) (2020)

## Built on by (most cited first, 1 of 1)

- [Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network](https://mlsanity.com/paper/2206.14267/) (2022)

## 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.