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Bibliographic Details
Main Authors: Tidwell, John Christopher, Tidwell, John Storm
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2505.03949
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author Tidwell, John Christopher
Tidwell, John Storm
author_facet Tidwell, John Christopher
Tidwell, John Storm
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2505_03949
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading
Tidwell, John Christopher
Tidwell, John Storm
Machine Learning
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.
title Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading
topic Machine Learning
url https://arxiv.org/abs/2505.03949