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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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/4667/1/fyp_CS_2022_TLX.pdf http://eprints.utar.edu.my/4667/ |
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| Summary: | 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 |
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