International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Stock-Crypto-App – Recommendation System for Stock and Cryptocurrency Market Using Cutting Edge Machine Learning Technology

Abstract

In the dynamic world of financial markets, the prediction of stock performance and bitcoin trading is undergoing a significant transformation due to the integration of advanced technologies and novel methodologies. The incorporation of Transformer models alongside Time Embeddings significantly improves the precision of stock market predictions by effectively capturing intricate temporal relationships and mitigating the presence of overly simplistic assumptions. The integration of real-time social media data with sentiment analysis based on BERT provides significant value in understanding investor sentiment. Additionally, the application of language model pre-training, as exemplified by BERT, brings about a transformative impact on text classification for predicting stock prices. Within the domain of cryptocurrency, sophisticated algorithms such as Transformers, Long Short-Term Memory (LSTM), Deep Convolutional LSTM (DC-LSTM), and Neural Networks (NN) have demonstrated enhanced capabilities in predicting price movements. These algorithms are further bolstered by the implementation of a comprehensive trading strategy. Automated systems for bitcoin trading introduce elements of personalization and adaptability to the trading process, thereby facilitating broader access to a diverse group of traders. The progress highlights the significant importance of the integration of technology and methodologies in the field of financial analysis. This integration enables investors and traders to possess the necessary resources for making well-informed choices within the ever-changing landscape of financial markets.

Country : Sri Lanka

1 Thushantha Sanju2 Hasindu Liyanage3 Keshara Bandara4 Dilini Kandakkulama5 Dilshan De Silva6 Jeewaka Perera

  1. Department of Computer Science and Software Engineering, Sri Lanka Institute of Technology, Sri Lanka
  2. Department of Computer Science and Software Engineering, Sri Lanka Institute of Technology, Sri Lanka
  3. Department of Computer Science and Software Engineering, Sri Lanka Institute of Technology, Sri Lanka
  4. Department of Computer Science and Software Engineering, Sri Lanka Institute of Technology, Sri Lanka
  5. Department of Computer Science and Software Engineering, Sri Lanka Institute of Technology, Sri Lanka
  6. Department of Computer Science and Software Engineering, Sri Lanka Institute of Technology, Sri Lanka

IRJIET, Volume 7, Issue 9, September 2023 pp. 110-117

doi.org/10.47001/IRJIET/2023.709012

Full Paper
Download

References

  1. C. Li and G. Qian, “Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network,” Applied Sciences, vol. 13, no. 1, p. 222, Dec. 2022, doi: 10.3390/app13010222.
  2. C. Wang, Y. Chen, S. Zhang, and Q. Zhang, “Stock market index prediction using deep Transformer model,” Expert Systems With Applications, vol. 208, p. 118128, Dec. 2022, doi: 10.1016/j.eswa.2022.118128.
  3. Q. Zhang, C. Qin, Y. Zhang, F. Bao, C. Zhang, and P. Liu, “Transformer-based attention network for stock movement prediction,” Expert Systems With Applications, vol. 202, p. 117239, Sep. 2022, doi: 10.1016/j.eswa.2022.117239.
  4. V. Le Guen and N. Thome, "Deep Time Series Forecasting With Shape and Temporal Criteria," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 342-355, 1 Jan. 2023, doi: 10.1109/TPAMI.2022.3152862.
  5. T. Muhammad et al., “Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market,” International Journal of Computational Intelligence and Applications, Apr. 2023, doi: 10.1142/s146902682350013x.
  6. Kate Murray, Andrea Rossi, Deigo Cararro and Andrea Visentin “On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles” 2023.
  7. Huali Zhao, Martin Crane and Marija Bezbradica “Transformer with Sentiment on Cryptocurrencies Price Prediction” 2022.
  8. Dr. Laayouni Lahcen, Dr. Azzouz Mohamed, Dr. Assaidi Abdelouahid “CRYPTOCURRENCY PRICE FORECASTING USING TRANSFORMER MODEL” 2023.
  9. Y. Chen, M. Dai, X. Hu, and J. Liu, "Forecasting Cryptocurrency Prices with Deep Learning Neural Networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 4847-4860, 2018.
  10. C. M. Ma, Y. H. Lu, C. M. Yeh, and S. T. Liang, "Cryptocurrency Price Prediction with Multiple Deep Learning Techniques," Proceedings of the 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 334-341, 2018.
  11. A.Giuseppe, C. Luca, G. Paolo and B. Elena. “Combining News Sentiment and Technical Analysis to Predict Stock Trend Reversal,” 2019 International Conference on Data Mining Workshops (ICDMW), November 2019.
  12. H. Ziniu, L. Weiqing, B. Jiang, L. Xuanzhe and L. Tie-Yan. Listening to Chaotic Whispers: “A Deep Learning Framework for News-oriented Stock Trend Prediction,” WSDM 2018, 261-269, December 2018.
  13. T. Sun, J.Wang, P. Zhang, Y. Cao, B. Liu and D. Wang, “Predicting Stock Price Returns using Microblog Sentiment for Chinese Stock Market,” 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM), Chengdu, 2017, pp. 87-96, August 2017.
  14. V. Bruce, G. Adrian, H. Geoff. “Do news and sentiment play a role in stock price prediction?” Applied Intelligence, April 2019.
  15. B. Rakhi and D. Sher. “Integrating StockTwits with sentiment analysis for better prediction of stock price movement,” 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), March 2018.
  16. S. Zengcai, X. Hua, Z. Dongwen and X. Yunfeng. “Chinese sentiment classification using a neural network tool-Word2vec,” 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), September 2014.
  17. K. Zeynep, O. Derya and U. Mitat. “Financial Sentiment Analysis for Predicting Direction of Stocks using Bidirectional Encoder Representations from Transformers (BERT) and Deep Learning Models,” International Conference on Innovative Technologies (ICIIT-19), December 2019.
  18. J. Schulman et al., "Proximal Policy Optimization Algorithms," IEEE Transactions on Robotics, vol. 36, no. 5, pp. 131-145, 2019.
  19. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G.,... & Petersen, S. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
  20. Lim, S., Park, J. H., & Kim, B. (2018). Deep reinforcement learning in portfolio management. Applied Sciences, 8(10), 1736.
  21. Lim, S., et al. (2018). Deep reinforcement learning in portfolio management. Applied Sciences, 8(10), 1736.
  22. Schulman, J., et al. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
  23. Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3-4), 279-292.
  24. J. Doe, "Cryptocurrency Data Collection from Reliable Sources," IEEE Journal of Financial Technology, vol. 10, no. 3, pp. 45-52, 2022.
  25. A.Smith, B. Johnson, and C. Brown, "Measuring Risk Preferences: A Survey Approach," IEEE Transactions on Behavioral and Social Sciences, vol. 5, no. 1, pp. 20-32, 2021.
  26. X. Wang, Y. Li, and Z. Zhang, "Data Preprocessing Techniques for Reinforcement Learning in Financial Applications," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, pp. 3541-3554, 2018.
  27. J. Schulman et al., "Proximal Policy Optimization Algorithms," IEEE Transactions on Robotics, vol. 36, no. 5, pp. 131-145, 2019.
  28. C. Watkins and P. Dayan, "Q-learning," IEEE Transactions on Neural Networks, vol. 5, no. 3, pp. 567-573, 1992.
  29. S. Kumar and R. Rai, "Performance Evaluation Metrics for Trading Systems," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 42, no. 6, pp. 1392-1403, 2012.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies