Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model
Distributed ledger technology (DLT) and cryptocurrency have revolutionized the financial landscape and relevant applications, particularly in investment opportunities. Despite its growth, the market’s volatility and technical complexities hinder widespread adoption. This study proposes a cryptocurre...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/13/1/17 |
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| author | Zhi Zhan Lua Chee Kiat Seow Raymond Ching Bon Chan Yiyu Cai Qi Cao |
| author_facet | Zhi Zhan Lua Chee Kiat Seow Raymond Ching Bon Chan Yiyu Cai Qi Cao |
| author_sort | Zhi Zhan Lua |
| collection | DOAJ |
| description | Distributed ledger technology (DLT) and cryptocurrency have revolutionized the financial landscape and relevant applications, particularly in investment opportunities. Despite its growth, the market’s volatility and technical complexities hinder widespread adoption. This study proposes a cryptocurrency trading system powered by advanced machine learning (ML) models to address these challenges. By leveraging random forest (RF), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM) models, the cryptocurrency trading system is equipped with strong predictive capacity and is able to optimize trading strategies for Bitcoin. The up-to-date price prediction information obtained by the machine learning model is incorporated by custom oracle contracts and is transmitted to portfolio smart contracts. The integration of smart contracts and on-chain oracles ensures transparency and security, allowing real-time verification of portfolio management. The deployed cryptocurrency trading system performs these actions automatically without human intervention, which greatly reduces barriers to entry for ordinary users and investors. The results demonstrate the feasibility of creating a cryptocurrency trading system, with the LSTM model achieving a return on investment (ROI) of 488.74% for portfolio management during the duration of 9 December 2022 to 23 May 2024. The ROI obtained by the LSTM model is higher than the performance of Bitcoin at 234.68% and that of other benchmarking models with RF and Bi-LSTM over the same timeframe. This approach offers significant cost savings, transparent portfolio management, and a trust-free platform for investors, paving the way for broader cryptocurrency adoption. Future work will focus on enhancing prediction accuracy and achieving greater decentralization. |
| format | Article |
| id | doaj-art-434a081a34a2462db12a0bbd57efbf4c |
| institution | Kabale University |
| issn | 2227-9091 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj-art-434a081a34a2462db12a0bbd57efbf4c2025-01-24T13:48:21ZengMDPI AGRisks2227-90912025-01-011311710.3390/risks13010017Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning ModelZhi Zhan Lua0Chee Kiat Seow1Raymond Ching Bon Chan2Yiyu Cai3Qi Cao4School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UKSchool of Computing Science, University of Glasgow, Glasgow G12 8QQ, UKInfoComm Technology Cluster, Singapore Institute of Technology, Singapore 138683, SingaporeSchool of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore 639798, SingaporeSchool of Computing Science, University of Glasgow, Glasgow G12 8QQ, UKDistributed ledger technology (DLT) and cryptocurrency have revolutionized the financial landscape and relevant applications, particularly in investment opportunities. Despite its growth, the market’s volatility and technical complexities hinder widespread adoption. This study proposes a cryptocurrency trading system powered by advanced machine learning (ML) models to address these challenges. By leveraging random forest (RF), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM) models, the cryptocurrency trading system is equipped with strong predictive capacity and is able to optimize trading strategies for Bitcoin. The up-to-date price prediction information obtained by the machine learning model is incorporated by custom oracle contracts and is transmitted to portfolio smart contracts. The integration of smart contracts and on-chain oracles ensures transparency and security, allowing real-time verification of portfolio management. The deployed cryptocurrency trading system performs these actions automatically without human intervention, which greatly reduces barriers to entry for ordinary users and investors. The results demonstrate the feasibility of creating a cryptocurrency trading system, with the LSTM model achieving a return on investment (ROI) of 488.74% for portfolio management during the duration of 9 December 2022 to 23 May 2024. The ROI obtained by the LSTM model is higher than the performance of Bitcoin at 234.68% and that of other benchmarking models with RF and Bi-LSTM over the same timeframe. This approach offers significant cost savings, transparent portfolio management, and a trust-free platform for investors, paving the way for broader cryptocurrency adoption. Future work will focus on enhancing prediction accuracy and achieving greater decentralization.https://www.mdpi.com/2227-9091/13/1/17decentralized application (dApp)blockchain oraclecryptocurrency price predictionportfolio management |
| spellingShingle | Zhi Zhan Lua Chee Kiat Seow Raymond Ching Bon Chan Yiyu Cai Qi Cao Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model Risks decentralized application (dApp) blockchain oracle cryptocurrency price prediction portfolio management |
| title | Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model |
| title_full | Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model |
| title_fullStr | Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model |
| title_full_unstemmed | Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model |
| title_short | Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model |
| title_sort | automated bitcoin trading dapp using price prediction from a deep learning model |
| topic | decentralized application (dApp) blockchain oracle cryptocurrency price prediction portfolio management |
| url | https://www.mdpi.com/2227-9091/13/1/17 |
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