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Title: 應用深度雙Q網路於股票自動交易系統
Double Deep Q-Network in Automated Stock Trading
Authors: 黃冠棋
Huang, Kuan-Chi
Contributors: 蔡炎龍
Huang, Kuan-Chi
Keywords: 深度強化學習
Deep reinforcement learning
Neural network
Stocks trading
Date: 2021
Issue Date: 2022-02-10 13:07:06 (UTC+8)
Abstract: 本篇文章使用了強化學習結合深度學習的技術去訓練自動交易系統,我們分別建立了深度卷積網路和全連接網路去預測動作的Q值,並使用DDQN的模型去更新我們的動作價值。我們的交易系統每天採用10天前的股票資訊,去預測股票的趨勢,並最大化我們的利益。

In this paper, we use the artificial neural network combined with reinforcement learning to train the automated trading system. We construct the CNN model and the fully-connected model to predict the Q-values of the actions and use the algorithm of DDQN to correct the TD error. According to past 10 days data, the system predicts the trend of the stocks and maximize our profit.

DDQN is a deep reinforcement model, which is an improvement of DQN, build the target network and modify loss function to avoid overestimation and get better performance. In our experiment, we get a good result that DDQN is feasible on automated trading systems.
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Description: 碩士
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Data Type: thesis
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