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Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model

Author

Listed:
  • Zhi Zhan Lua

    (School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK)

  • Chee Kiat Seow

    (School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK)

  • Raymond Ching Bon Chan

    (InfoComm Technology Cluster, Singapore Institute of Technology, Singapore 138683, Singapore)

  • Yiyu Cai

    (School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Qi Cao

    (School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK)

Abstract

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.

Suggested Citation

  • Zhi Zhan Lua & Chee Kiat Seow & Raymond Ching Bon Chan & Yiyu Cai & Qi Cao, 2025. "Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model," Risks, MDPI, vol. 13(1), pages 1-25, January.
    • Handle: RePEc:gam:jrisks:v:13:y:2025:i:1:p:17-:d:1569815
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    References listed on IDEAS

    as
    1. Oluwadamilare Omole & David Enke, 2024. "Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-26, December.
    2. Nagula, Pavan Kumar & Alexakis, Christos, 2022. "A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    3. Pavan Kumar Nagula & Christos Alexakis, 2022. "A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price," Post-Print hal-03877093, HAL.
    Full references (including those not matched with items on IDEAS)

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