Financial Vision Based Reinforcement Learning Trading Strategy
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.
Code (0)
등록된 구현이 없습니다.
Tasks
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
DDPG based on multi-scale strokes for financial time series trading strategy
With the development of artificial intelligence,more and more financial practitioners apply deep reinforcement learning to financial trading strategies.However,It is difficult to extract accurate features due to the char…
Deep Reinforcement LearningQ-Learningreinforcement-learningReinforcement Learning+3Financial Trading with Feature Preprocessing and Recurrent Reinforcement Learning
Financial trading aims to build profitable strategies to make wise investment decisions in the financial market. It has attracted interests in the machine learning community for a long time. This paper proposes to trade …
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Financial News-Driven LLM Reinforcement Learning for Portfolio Management
Reinforcement learning (RL) has emerged as a transformative approach for financial trading, enabling dynamic strategy optimization in complex markets. This study explores the integration of sentiment analysis, derived fr…
Decision MakingManagementreinforcement-learningReinforcement Learning+2From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information
The problem of how to take the right actions to make profits in sequential process continues to be difficult due to the quick dynamics and a significant amount of uncertainty in many application scenarios. In such compli…
Decision Makingreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Deep reinforcement learning on a multi-asset environment for trading
Financial trading has been widely analyzed for decades with market participants and academics always looking for advanced methods to improve trading performance. Deep reinforcement learning (DRL), a recently reinvigorate…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)