Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading

TL;DR

This paper proposes a multi-component deep learning framework combining CNN, LSTM, and DQN to improve automated stock trading by effectively capturing market patterns and temporal dependencies amidst noise and complexity.

Contribution

It introduces an integrated deep learning approach that combines CNN, LSTM, and DQN for enhanced stock trading decision-making, addressing limitations of traditional RL methods.

Findings

Improved trading performance over baseline methods

Effective pattern recognition in technical indicator images

Enhanced temporal dependency modeling

Abstract

This project addresses the challenge of automated stock trading, where traditional methods and direct reinforcement learning (RL) struggle with market noise, complexity, and generalization. Our proposed solution is an integrated deep learning framework combining a Convolutional Neural Network (CNN) to identify patterns in technical indicators formatted as images, a Long Short-Term Memory (LSTM) network to capture temporal dependencies across both price history and technical indicators, and a Deep Q-Network (DQN) agent which learns the optimal trading policy (buy, sell, hold) based on the features extracted by the CNN and LSTM.