Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/138942


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
黃冠棋
Huang, Kuan-Chi
Keywords: 深度強化學習
神經網路
Q學習
深度雙Q網路
股票交易
Deep reinforcement learning
Neural network
Q-learning
DDQN
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。

DDQN是一種深度強化學習模型,透過建立目標網路和調整誤差函數使得他能夠避免DQN的過估計問題,並得到更好的效能,在我們的實驗中,我們得到了一個良好的效果,證明DDQN在自動交易系統上是有效的。
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
國立政治大學
應用數學系
107751007
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107751007
Data Type: thesis
Appears in Collections:[應用數學系] 學位論文

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