Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Computing | en_US |
| dc.contributor.advisor | Guo, Song (COMP) | en_US |
| dc.creator | Wang, Qi | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/11376 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Trend adaptive deep reinforcement learning for quantitative trading | en_US |
| dcterms.abstract | Quantitative trading is a crucial investment method in the field of financial investment. Traditional quantitative trading methods usually analyze the price and volume on handcrafted technical indicators. With the rapid development of machine learning and deep learning, quantitative trading based on artificial intelligence has received more and more attention from institutional and individual investors. Reinforcement learning learns through repeated trial and error and can be used to build automated trading strategies without explicit predictions about market trends. Existing works usually use market data and technical indicators of a single stock or futures as environment for reinforcement learning training. However, these methods has several shortcomings which seriously affects the performance such like weak generalization performance and overfitting problem. This paper presents a new trend adaptive reinforcement learning model based on a generative environment. The environment provides real historical market data and simulated market data generated by the artificial neural network as the model's state to improve the generalization performance of the model. At the same time, Trend adaptive agents based on Dueling DQN are proposed in this work. and can have a relatively stable effect for different types of market conditions. The experimental results show that compared with the previous work, the algorithm in this paper effectively increases the profit and reduces the maximum drawdown. | en_US |
| dcterms.extent | viii, 41 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2021 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Machine learning | en_US |
| dcterms.LCSH | Finance -- Data processing | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5862.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.4 MB | Adobe PDF | View/Open |
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