Applications of Deep Learning to Cryptocurrency Trading: A Systematic Analysis

Authors

https://doi.org/10.22105/scfa.vi.77

Abstract

This systematic review analyzes 75 papers (2020-2025) applying Deep Learning (DL) techniques to cryptocurrency trading. It evaluates various DL architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Transformers, and finds that DL methods outperform traditional approaches in managing the high volatility and non-linear patterns of crypto markets. Key findings highlight the promise of hybrid and ensemble models, the benefits of integrating blockchain data, sentiment analysis, and macroeconomic factors for improved predictions, and the potential of Deep Reinforcement Learning (DRL) for developing autonomous trading strategies with risk-adjusted returns. However, challenges such as model interpretability, nonstationary data, and real-world deployment persist. The review emphasizes emerging directions like explainable Artificial Intelligence (AI) for transparent decision-making and high-frequency trading applications, providing a critical synthesis of methodologies, empirical results, and research gaps to inform both academic research and practical trading system development.

Keywords:

Cryptocurrency, Deep learning, Artificial intelligence, Cryptocurrency trading, Reinforcement learning, Time series analysis

References

  1. [1] Murray, K., Rossi, A., Carraro, D., & Visentin, A. (2023). On forecasting cryptocurrency prices: A comparison of machine learning, deep learning, and ensembles. Forecasting, 5(1), 196–209. https://doi.org/10.3390/forecast5010010
  2. [2] Khedmati, M., Seifi, F., & Azizi, M. J. (2020). Time series forecasting of bitcoin price based on autoregressive integrated moving average and machine learning approaches. International journal of engineering, 33(7), 1293–1303. https://www.researchgate.net/publication/350007539
  3. [3] Akyildirim, E., Goncu, A., & Sensoy, A. (2021). Prediction of cryptocurrency returns using machine learning. Annals of operations research, 297(1), 3–36. https://doi.org/10.1007/s10479-020-03575-y
  4. [4] Seabe, P. L., Moutsinga, C. R. B., & Pindza, E. (2023). Forecasting cryptocurrency prices using LSTM, GRU, and Bi-Directional LSTM: A deep learning approach. Fractal and fractional, 7(2), 1–18. https://doi.org/10.3390/fractalfract7020203
  5. [5] Hamayel, M. J., & Owda, A. Y. (2021). A novel cryptocurrency price prediction model using GRU, LSTM and bi-LSTM machine learning algorithms. AI, 2(4), 477–496. https://doi.org/10.3390/ai2040030
  6. [6] Livieris, I. E., Pintelas, E., Stavroyiannis, S., & Pintelas, P. (2020). Ensemble deep learning models for forecasting cryptocurrency time-series. Algorithms, 13(5), 1–21. https://doi.org/10.3390/a13050121
  7. [7] Zhang, Z., Dai, H. N., Zhou, J., Mondal, S. K., García, M. M., & Wang, H. (2021). Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels. Expert systems with applications, 183, 115378. https://doi.org/10.1016/j.eswa.2021.115378
  8. [8] Pintelas, E., Livieris, I. E., Stavroyiannis, S., Kotsilieris, T., & Pintelas, P. (2020). Investigating the problem of cryptocurrency price prediction: A deep learning approach. Artificial intelligence applications and innovations (pp. 99–110). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-49186-4_9
  9. [9] John, D. L., Binnewies, S., & Stantic, B. (2024). Cryptocurrency price prediction algorithms: A survey and future directions. Forecasting, 6(3), 637–671. https://doi.org/10.3390/forecast6030034
  10. [10] Mienye, E., Jere, N., Obaido, G., Mienye, I. D., & Aruleba, K. (2024). Deep learning in finance: A survey of applications and techniques. AI, 5(4), 2066–2091. https://doi.org/10.3390/ai5040101
  11. [11] Mohammadshafie, A., Mirzaeinia, A., Jumakhan, H., & Mirzaeinia, A. (2025). Deep reinforcement learning strategies in finance: Insights into asset holding, trading behavior, and purchase diversity. Artificial intelligence and applications (pp. 449–463). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-86623-4_41
  12. [12] Pippas, N., Ludvig, E. A., & Turkay, C. (2025). The evolution of reinforcement learning in quantitative finance: A Survey. ACM computing surveys, 57(11), 1–15. https://doi.org/10.1145/3733714
  13. [13] Rao, K. R., Prasad, M. L., Kumar, G. R., Natchadalingam, R., Hussain, M. M., & Reddy, P. C. S. (2023). Time-series cryptocurrency forecasting using ensemble deep learning. 2023 international conference on circuit power and computing technologies (ICCPCT) (pp. 1446–1451). IEEE. https://doi.org/10.1109/ICCPCT58313.2023.10245083
  14. [14] Ammer, M. A., & Aldhyani, T. H. H. (2022). Deep learning algorithm to predict cryptocurrency fluctuation prices: Increasing investment awareness. Electronics, 11(15), 1–22. https://doi.org/10.3390/electronics11152349
  15. [15] Bouteska, A., Abedin, M. Z., Hajek, P., & Yuan, K. (2024). Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods. International review of financial analysis, 92, 103055. https://doi.org/10.1016/j.irfa.2023.103055
  16. [16] Liu, M., Li, G., Li, J., Zhu, X., & Yao, Y. (2021). Forecasting the price of Bitcoin using deep learning. Finance research letters, 40, 101755. https://doi.org/10.1016/j.frl.2020.101755
  17. [17] Lamothe-Fernández, P., Alaminos, D., Lamothe-López, P., & Fernández-Gámez, M. A. (2020). Deep learning methods for modeling bitcoin price. Mathematics, 8(8), 1–13. https://doi.org/10.3390/math8081245
  18. [18] Kim, G., Shin, D. H., Choi, J. G., & Lim, S. (2022). A Deep learning-based cryptocurrency price prediction model that uses on-chain data. IEEE access, 10, 56232–56248. https://doi.org/10.1109/ACCESS.2022.3177888
  19. [19] Akyildirim, E., Cepni, O., Corbet, S., & Uddin, G. S. (2023). Forecasting mid-price movement of Bitcoin futures using machine learning. Annals of operations research, 330(1), 553–584. https://doi.org/10.1007/s10479-021-04205-x
  20. [20] Kang, C. Y., Lee, C. P., & Lim, K. M. (2022). Cryptocurrency price prediction with convolutional neural network and stacked gated recurrent unit. Data, 7(11), 1–13. https://doi.org/10.3390/data7110149
  21. [21] Liu, Y., Li, Z., Nekhili, R., & Sultan, J. (2023). Forecasting cryptocurrency returns with machine learning. Research in international business and finance, 64, 101905. https://doi.org/10.1016/j.ribaf.2023.101905
  22. [22] Parvini, N., Abdollahi, M., Seifollahi, S., & Ahmadian, D. (2022). Forecasting Bitcoin returns with long short-term memory networks and wavelet decomposition: A comparison of several market determinants. Applied soft computing, 121, 108707. https://doi.org/10.1016/j.asoc.2022.108707
  23. [23] Zoumpekas, T., Houstis, E., & Vavalis, M. (2020). ETH analysis and predictions utilizing deep learning. Expert systems with applications, 162, 113866. https://doi.org/10.1016/j.eswa.2020.113866
  24. [24] Sebastião, H., & Godinho, P. (2021). Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financial innovation, 7(1), 1–33. https://doi.org/10.1186/s40854-020-00217-x
  25. [25] Uras, N., Marchesi, L., Marchesi, M., & Tonelli, R. (2020). Forecasting Bitcoin closing price series using linear regression and neural networks models. PeerJ computer science, 6, e279. https://doi.org/10.7717/peerj-cs.279
  26. [26] Dutta, A., Kumar, S., & Basu, M. (2020). A gated recurrent unit approach to bitcoin price prediction. Journal of risk and financial management, 13(2), 1–16. https://doi.org/10.3390/jrfm13020023
  27. [27] Borges, T. A., & Neves, R. F. (2020). Ensemble of machine learning algorithms for cryptocurrency investment with different data resampling methods. Applied soft computing, 90, 106187. https://doi.org/10.1016/j.asoc.2020.106187
  28. [28] M., P., Nguyen, T. N., Hamdi, M., & Cengiz, K. (2021). Global cryptocurrency trend prediction using social media. Information processing & management, 58(6), 102708. https://doi.org/10.1016/j.ipm.2021.102708
  29. [29] Shahbazi, Z., & Byun, Y. C. (2021). Improving the cryptocurrency price prediction performance based on reinforcement learning. IEEE access, 9, 162651–162659. https://doi.org/10.1109/ACCESS.2021.3133937
  30. [30] Livieris, I. E., Kiriakidou, N., Stavroyiannis, S., & Pintelas, P. (2021). An advanced CNN-LSTM model for cryptocurrency forecasting. Electronics, 10(3), 1–16. https://doi.org/10.3390/electronics10030287
  31. [31] Jaquart, P., Köpke, S., & Weinhardt, C. (2022). Machine learning for cryptocurrency market prediction and trading. The journal of finance and data science, 8, 331–352. https://doi.org/10.1016/j.jfds.2022.12.001
  32. [32] Ye, Z., Wu, Y., Chen, H., Pan, Y., & Jiang, Q. (2022). A stacking ensemble deep learning model for bitcoin price prediction using twitter comments on Bitcoin. Mathematics, 10(8), 1–21. https://doi.org/10.3390/math10081307
  33. [33] Patel, M. M., Tanwar, S., Gupta, R., & Kumar, N. (2020). A deep learning-based cryptocurrency price prediction scheme for financial institutions. Journal of information security and applications, 55, 102583. https://doi.org/10.1016/j.jisa.2020.102583
  34. [34] Koker, T. E., & Koutmos, D. (2020). Cryptocurrency trading using machine learning: A Technical note. Journal of risk and financial management, 13(8), 1–7. https://doi.org/10.3390/jrfm13080178
  35. [35] Sattarov, O., Muminov, A., Lee, C. W., Kang, H. K., Oh, R., Ahn, J., … ., & Jeon, H. S. (2020). Recommending cryptocurrency trading points with deep reinforcement learning approach. Applied sciences, 10(4), 1–18. https://doi.org/10.3390/app10041506
  36. [36] Liu, X. Y., Yang, H., Gao, J., & Wang, C. D. (2021). FinRL: Deep reinforcement learning framework to automate trading in quantitative finance. Proceedings of the second ACM international conference on AI in finance (pp. 1–9). Association for computing machinery. https://doi.org/10.1145/3490354.3494366
  37. [37] Schnaubelt, M. (2022). Deep reinforcement learning for the optimal placement of cryptocurrency limit orders. European journal of operational research, 296(3), 993–1006. https://doi.org/10.1016/j.ejor.2021.04.050
  38. [38] Liu, F., Li, Y., Li, B., Li, J., & Xie, H. (2021). Bitcoin transaction strategy construction based on deep reinforcement learning. Applied soft computing, 113, 107952. https://doi.org/10.1016/j.asoc.2021.107952
  39. [39] Nasirtafreshi, I. (2022). Forecasting cryptocurrency prices using recurrent neural network and long short-term memory. Data & knowledge engineering, 139, 102009. https://doi.org/10.1016/j.datak.2022.102009
  40. [40] Cui, T., Ding, S., Jin, H., & Zhang, Y. (2023). Portfolio constructions in cryptocurrency market: A CVaR-based deep reinforcement learning approach. Economic modelling, 119, 106078. https://doi.org/10.1016/j.econmod.2022.106078
  41. [41] Babaei, G., Giudici, P., & Raffinetti, E. (2022). Explainable artificial intelligence for crypto asset allocation. Finance research letters, 47, 102941. https://doi.org/10.1016/j.frl.2022.102941
  42. [42] Goodell, J. W., Ben Jabeur, S., Saâdaoui, F., & Nasir, M. A. (2023). Explainable artificial intelligence modeling to forecast bitcoin prices. International review of financial analysis, 88, 102702. https://doi.org/10.1016/j.irfa.2023.102702
  43. [43] Parekh, R., Patel, N. P., Thakkar, N., Gupta, R., Tanwar, S., Sharma, G., … ., & Sharma, R. (2022). DL-GuesS: Deep learning and sentiment analysis-based cryptocurrency price prediction. IEEE access, 10, 35398–35409. https://doi.org/10.1109/ACCESS.2022.3163305
  44. [44] Guo, H., Zhang, D., Liu, S., Wang, L., & Ding, Y. (2021). Bitcoin price forecasting: A perspective of underlying blockchain transactions. Decision support systems, 151, 113650. https://doi.org/10.1016/j.dss.2021.113650
  45. [45] Erfanian, S., Zhou, Y., Razzaq, A., Abbas, A., Safeer, A. A., & Li, T. (2022). Predicting Bitcoin (BTC) Price in the context of economic theories: A machine learning approach. Entropy, 24(10), 1–29. https://doi.org/10.3390/e24101487
  46. [46] Awoke, T., Rout, M., Mohanty, L., & Satapathy, S. C. (2021). Bitcoin price prediction and analysis using deep learning models. Communication software and networks (pp. 631–640). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-5397-4_63
  47. [47] Cocco, L., Tonelli, R., & Marchesi, M. (2021). Predictions of bitcoin prices through machine learning based frameworks. PeerJ computer science, 7, e413. https://doi.org/10.7717/peerj-cs.413
  48. [48] Kim, H. M., Bock, G. W., & Lee, G. (2021). Predicting Ethereum prices with machine learning based on Blockchain information. Expert systems with applications, 184, 115480. https://doi.org/10.1016/j.eswa.2021.115480
  49. [49] Mahdi, E., Leiva, V., Mara’Beh, S., & Martin-Barreiro, C. (2021). A new approach to predicting cryptocurrency returns based on the gold prices with support vector machines during the COVID-19 pandemic using sensor-related data. Sensors, 21(18), 1–16. https://doi.org/10.3390/s21186319
  50. [50] Saad, M., Choi, J., Nyang, D., Kim, J., & Mohaisen, A. (2020). Toward characterizing blockchain-based cryptocurrencies for highly accurate predictions. IEEE systems journal, 14(1), 321–332. https://doi.org/10.1109/JSYST.2019.2927707
  51. [51] Chen, Z., Li, C., & Sun, W. (2020). Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal of computational and applied mathematics, 365, 112395. https://doi.org/10.1016/j.cam.2019.112395
  52. [52] Singh, H. J., & Hafid, A. S. (2020). Prediction of transaction confirmation time in ethereum blockchain using machine learning. Blockchain and applications (pp. 126–133). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-23813-1_16
  53. [53] Ortu, M., Uras, N., Conversano, C., Bartolucci, S., & Destefanis, G. (2022). On technical trading and social media indicators for cryptocurrency price classification through deep learning. Expert systems with applications, 198, 116804. https://doi.org/10.1016/j.eswa.2022.116804
  54. [54] Gurrib, I., & Kamalov, F. (2021). Predicting bitcoin price movements using sentiment analysis: a machine learning approach. Studies in economics and finance, 39(3), 347–364. https://doi.org/10.1108/SEF-07-2021-0293
  55. [55] Serafini, G., Yi, P., Zhang, Q., Brambilla, M., Wang, J., Hu, Y., & Li, B. (2020). Sentiment-driven price prediction of the bitcoin based on statistical and deep learning approaches. 2020 international joint conference on neural networks (IJCNN) (pp. 1–8). IEEE. https://doi.org/10.1109/IJCNN48605.2020.9206704
  56. [56] Shang, D., Yan, Z., Zhang, L., & Cui, Z. (2023). Digital financial asset price fluctuation forecasting in digital economy era using blockchain information: A reconstructed dynamic-bound Levenberg–Marquardt neural-network approach. Expert systems with applications, 228, 120329. https://doi.org/10.1016/j.eswa.2023.120329
  57. [57] Wang, C., Shen, D., & Li, Y. (2022). Aggregate investor attention and bitcoin return: The long short-term memory networks perspective. Finance research letters, 49, 103143. https://doi.org/10.1016/j.frl.2022.103143
  58. [58] Politis, A., Doka, K., & Koziris, N. (2021). Ether price prediction using advanced deep learning models. 2021 ieee international conference on blockchain and cryptocurrency (ICBC) (pp. 1–3). IEEE. https://doi.org/10.1109/ICBC51069.2021.9461061
  59. [59] Tanwar, S., Patel, N. P., Patel, S. N., Patel, J. R., Sharma, G., & Davidson, I. E. (2021). Deep learning-based cryptocurrency price prediction scheme with inter-dependent relations. IEEE access, 9, 138633–138646. https://doi.org/10.1109/ACCESS.2021.3117848
  60. [60] Oyedele, A. A., Ajayi, A. O., Oyedele, L. O., Bello, S. A., & Jimoh, K. O. (2023). Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction. Expert systems with applications, 213, 119233. https://doi.org/10.1016/j.eswa.2022.119233
  61. [61] Gurdgiev, C., & O’Loughlin, D. (2020). Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty. Journal of behavioral and experimental finance, 25, 100271. https://doi.org/10.1016/j.jbef.2020.100271
  62. [62] Lahmiri, S., & Bekiros, S. (2020). Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market. Chaos, solitons & fractals, 133, 109641. https://doi.org/10.1016/j.chaos.2020.109641
  63. [63] Vo, A., & Yost-Bremm, C. (2020). A high-frequency algorithmic trading strategy for cryptocurrency. Journal of computer information systems, 60(6), 555–568. https://doi.org/10.1080/08874417.2018.1552090
  64. [64] Aras, S. (2021). Stacking hybrid GARCH models for forecasting Bitcoin volatility. Expert systems with applications, 174, 114747. https://doi.org/10.1016/j.eswa.2021.114747

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Published

2025-12-19

Issue

Vol. 2 No. 4 (2025): Soft Computing Fusion with Applications

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Articles

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How to Cite

Ataei, S., Ataei, S. T., Omidmand, P., Hajian Karahroodi, H. ., & Nikzat, P. (2025). Applications of Deep Learning to Cryptocurrency Trading: A Systematic Analysis. Soft Computing Fusion With Applications , 2(4), 255-268. https://doi.org/10.22105/scfa.vi.77

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