Machine Learning–Based Algorithmic Trading Bots: A Comparative Study of kNN and LVQ Models on Bitcoin Markets
References
- Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long short-term memory. PLoS ONE, 12(7), e0180944.
- Busu, C., & Busu, M. (2021). An application of the Kalman filter recursive algorithm to estimate the Gaussian errors by minimizing the symmetric loss function. Symmetry, 13(2), 240.
- Busu, C., & Busu, M. (2015). The Liberalization Process of the Railway Sector in Romania and European Union Countries. Revista de Management Comparat International, 16(3), 305.
- Busu, C., & Busu, M. (2019). Economic modeling in the management of transition to bioeconomy. Amfiteatru Econ, 21, 24-40.
- Busu, C., Busu, M., Grasu, S., & Ben Yahia, S. (2025). Innovation-Adjusted Dynamics of E-Waste in the European Union: Mathematical Modeling, Stability and Panel EKC Turning Points. Mathematics, 13(24), 3940.
- Chan, S., Chu, J., Nadarajah, S., & Osterrieder, J. (2017). A statistical analysis of cryptocurrencies. Journal of Risk and Financial Management, 10(2), 12.
- Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
- Katsiampa, P. (2019). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3–6.
- Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464–1480.
- Kohonen, T. (2001). Self-organizing maps (3rd ed.). Springer.
- Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705–1765.
- Lopez de Prado, M. (2018). Advances in financial machine learning. Wiley.
- Narayan, P. K., Phan, D. H. B., Liu, G., & Bannigidadmath, D. (2021). Predicting cryptocurrency returns: A critical review. Journal of International Financial Markets, Institutions and Money, 71, 101284.
- Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259–268.
- Phillips, P. C. B., Shi, S., & Yu, J. (2018). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International Economic Review, 56(4), 1043–1078.
- Șerban, F., & Vrînceanu, B.-P. (2023). A comparison between trend following (Tidy Little Robot – TLR) and reversal trading (Dirty Little Robot – DLR) algobot strategies. European Journal of Theoretical and Applied Sciences, 1(5), 805–822.
- Thatikonda, V. K. (2023). Serverless computing: Advantages, limitations and use cases. European Journal of Theoretical and Applied Sciences, 1(5), 341–347.
- Tsai, C.-F., & Hsiao, Y.-C. (2019). Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decision Support Systems, 38(2), 258–269.
- Zhang, Y., & Zhou, L. (2010). Stock market prediction using artificial neural networks. Expert Systems with Applications, 37(4), 3099–3106.
DOI: https://doi.org/10.2478/picbe-2026-0413 | Journal eISSN: 2558-9652
Language: English
Page range: 5600 - 5612
Published on: Jul 22, 2026
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Related subjects:
© 2026 Bogdan-Petru VRINCEANU, Mihai VRISCU, Liana PARASCHIV, Vlad BUJDEI-TEBEICA, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.