Financial Vision Based Reinforcement Learning Trading Strategy
Artificial Intelligence
2022-02-10 v1 Computational Engineering, Finance, and Science
Machine Learning
Abstract
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.
Cite
@article{arxiv.2202.04115,
title = {Financial Vision Based Reinforcement Learning Trading Strategy},
author = {Yun-Cheng Tsai and Fu-Min Szu and Jun-Hao Chen and Samuel Yen-Chi Chen},
journal= {arXiv preprint arXiv:2202.04115},
year = {2022}
}
Comments
arXiv admin note: text overlap with arXiv:2104.07715