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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.03949 |
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| _version_ | 1866909602990260224 |
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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 |