Abstract
The apparent phenomena of commodity price fluctuations significantly affect the cost of living. Most current studies utilize datasets collected before the Russo-Ukrainian War and Covid-19. Moreover, many people are focusing on fund investment, exploring avenues such as commodity trading in addition to stocks and forex investments. However, most research for price prediction in commodities does not cover the periods of Covid-19 and the Russo-Ukrainian war. The aim of this project is to develop trading strategy models to predict whether to buy or sell a commodity, and to evaluate the potential rewards and profits. The dataset used contains daily historical prices of various types of commodities from the year 2000 until March 2022. Furthermore, a real-world dataset, specifically the gold trading dataset from Nasdaq, will be used to validate the performance of the best-performing trading models. The algorithms employed are reinforcement learning-based: Advantage Actor Critic (A2C) and Proximal Policy Optimization (PPO). Evaluation performance across six rounds of experiments has shown that the A2C model in a forex environment, using 80% of the dataset for training and 20% for testing, achieved the best results, with a Sharpe ratio of 0.63, a Sortino ratio of 1.0, an Omega ratio of 1.24, and a Calmar ratio of 0.55. The best-performing trading models in Objective 2 and Objective 3 are similar but employ different window sizes. Window size specifies the timesteps that will serve as reference points for the trading model to determine the next trade. Different datasets may require different window sizes, an issue that necessitates further refinement. This refinement is crucial as it involves tailoring the window size to align with the unique characteristics and volatility patterns of each dataset, thereby ensuring that the model's predictive accuracy is optimized for varied market conditions and historical trends. In conclusion, the best-performing trading model is the Advantage Actor Critic (A2C) model in a forex environment.
Original language | English |
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Title of host publication | Proceedings of the 13th National Technical Seminar on Unmanned System Technology 2023 |
Subtitle of host publication | NUSYS 2023 |
Editors | Zainah Md. Zain, Zool Hilmi Ismail, Huiping Li, Xianbo Xiang, Rama Rao Karri |
Publisher | Springer Singapore |
Pages | 181-191 |
Number of pages | 11 |
Volume | 1184 |
ISBN (Electronic) | 9789819720279 |
ISBN (Print) | 9789819720262, 9789819720293 |
DOIs | |
Publication status | Published - 17 Sep 2024 |
Event | 13th National Technical Symposium on Unmanned System Technology - Penang, Malaysia Duration: 2 Oct 2023 → 3 Oct 2023 Conference number: 13 |
Publication series
Name | Lecture Notes in Electrical Engineering |
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Publisher | Springer |
Volume | 1184 LNEE |
ISSN (Print) | 1876-1100 |
ISSN (Electronic) | 1876-1119 |
Conference
Conference | 13th National Technical Symposium on Unmanned System Technology |
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Abbreviated title | NUSYS 2023 |
Country/Territory | Malaysia |
City | Penang |
Period | 2/10/23 → 3/10/23 |