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Financial trading using learning-based approach

Financial trading has been widely studied and many algorithms and approaches have been applied to gain higher profit. In this work, deep reinforcement learning algorithms were applied to automate the trading process. The data used in this work were 1-minute, 5-minute, and 30-minute candlesticks f...

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Main Author: Tan, Li Xue
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
Q Science (General)
T Technology (General)
Online Access:http://eprints.utar.edu.my/4667/1/fyp_CS_2022_TLX.pdf
http://eprints.utar.edu.my/4667/
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spelling my-utar-eprints.46672023-01-15T13:31:10Z Financial trading using learning-based approach Tan, Li Xue Q Science (General) T Technology (General) Financial trading has been widely studied and many algorithms and approaches have been applied to gain higher profit. In this work, deep reinforcement learning algorithms were applied to automate the trading process. The data used in this work were 1-minute, 5-minute, and 30-minute candlesticks from different asset classes including Foreign Exchange markets (FOREX), equity indexes, and commodities. The proposed framework utilised data from different time intervals to make a trading decision. For each time interval, an autoencoder consisting of InceptionTime and Long Short-Term Memory (LSTM) was trained to perform feature extraction. The reinforcement learning algorithms applied include Advantage Actor-Critic (A2C), Proximal Policy Optimisation (PPO), and Twin Delayed Deep Deterministic Policy Gradient (TD3). Both discrete and continuous action spaces were studied. The performance of the models was evaluated by using expected return and risk-adjusted return such as the Sharpe ratio. Furthermore, the models were trained under different transaction cost settings to identify the effect of transaction cost on the performance of the models. The results showed that the most consistent model is PPO and SAC performs the worst in this setting. Furthermore, the results also showed that the best transaction cost setting should be equal to or higher than the actual transaction cost. Bachelor 2022-04-22 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4667/1/fyp_CS_2022_TLX.pdf Tan, Li Xue (2022) Financial trading using learning-based approach. Final Year Project, UTAR. http://eprints.utar.edu.my/4667/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Tan, Li Xue
Financial trading using learning-based approach
description Financial trading has been widely studied and many algorithms and approaches have been applied to gain higher profit. In this work, deep reinforcement learning algorithms were applied to automate the trading process. The data used in this work were 1-minute, 5-minute, and 30-minute candlesticks from different asset classes including Foreign Exchange markets (FOREX), equity indexes, and commodities. The proposed framework utilised data from different time intervals to make a trading decision. For each time interval, an autoencoder consisting of InceptionTime and Long Short-Term Memory (LSTM) was trained to perform feature extraction. The reinforcement learning algorithms applied include Advantage Actor-Critic (A2C), Proximal Policy Optimisation (PPO), and Twin Delayed Deep Deterministic Policy Gradient (TD3). Both discrete and continuous action spaces were studied. The performance of the models was evaluated by using expected return and risk-adjusted return such as the Sharpe ratio. Furthermore, the models were trained under different transaction cost settings to identify the effect of transaction cost on the performance of the models. The results showed that the most consistent model is PPO and SAC performs the worst in this setting. Furthermore, the results also showed that the best transaction cost setting should be equal to or higher than the actual transaction cost. Bachelor
format Final Year Project / Dissertation / Thesis
author Tan, Li Xue
author_facet Tan, Li Xue
author_sort Tan, Li Xue
title Financial trading using learning-based approach
title_short Financial trading using learning-based approach
title_full Financial trading using learning-based approach
title_fullStr Financial trading using learning-based approach
title_full_unstemmed Financial trading using learning-based approach
title_sort financial trading using learning-based approach
publishDate 2022
url http://eprints.utar.edu.my/4667/1/fyp_CS_2022_TLX.pdf
http://eprints.utar.edu.my/4667/
_version_ 1755876966164070400
score 13.211869

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