Financial Trading with Feature Preprocessing and Recurrent Reinforcement Learning

2021-09-11 · Lin Li

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 financial assets automatically using feature preprocessing skills and Recurrent Reinforcement Learning (RRL) algorithm. The strategy starts from technical indicators extracted from assets' market information. Then these technical indicators are preprocessed by Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) and eventually inputted to the RRL algorithm to do the trading. The extensive empirical evidence shows that the proposed strategy is not only effective and robust in its performance, but also can mitigate the drawbacks underlying the initial trading using RRL.

📄 PDF Abstract BibTeX arXiv:2109.05283

Code (0)

등록된 구현이 없습니다.

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

An Automated Portfolio Trading System with Feature Preprocessing and Recurrent Reinforcement Learning

2021-10-11 · Lin Li

We propose a novel portfolio trading system, which contains a feature preprocessing module and a trading module. The feature preprocessing module consists of various data processing operations, while in the trading part,…

reinforcement-learning Reinforcement Learning (RL)

Financial Trading as a Game: A Deep Reinforcement Learning Approach

2018-07-08 · Chien Yi Huang

An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of s…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

News-Aware Direct Reinforcement Trading for Financial Markets

2025-10-22 · Qing-Yu Lan, Zhan-He Wang, Jun-Qian Jiang, Yu-Tong Wang 외 arxiv

The financial market is known to be highly sensitive to news. Therefore, effectively incorporating news data into quantitative trading remains an important challenge. Existing approaches typically rely on manually design…

Reinforcement Learning

DDPG based on multi-scale strokes for financial time series trading strategy

2022-06-05 · Jun-Cheng Chen, Cong-Xiao Chen, Li-Juan Duan, Zhi Cai

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 Learning Q-Learning reinforcement-learning Reinforcement Learning+3

Using Reinforcement Learning in the Algorithmic Trading Problem

2020-02-26 · Evgeny Ponomarev, Ivan Oseledets, Andrzej Cichocki

The development of reinforced learning methods has extended application to many areas including algorithmic trading. In this paper trading on the stock exchange is interpreted into a game with a Markov property consistin…

Algorithmic Trading reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)