Financial Vision Based Reinforcement Learning Trading Strategy

TL;DR

This paper discusses the development of a reinforcement learning trading strategy that incorporates financial vision to enhance decision transparency and reduce risks associated with AI black box models in trading.

Contribution

It introduces a novel reinforcement learning approach utilizing financial vision to improve interpretability and trustworthiness in AI-driven trading systems.

Findings

Enhanced transparency in AI trading decisions

Reduced risk of large losses due to better understanding of AI actions

Improved trust in AI trading strategies

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.