AI-Enhanced Cryptocurrency Trading Algorithm for Optimal Investment Strategies

Authors

  • Samuel Koehler College of Engineering and Computer Science, University of Central Florida, USA
  • Niravkumar Dhameliya PLC Programmer, Innovative Electronics Corporation, Pittsburgh, PA, USA
  • Bhavik Patel PCB Design Engineer, Innovative Electronics Corporation, Pittsburgh, PA, USA
  • Sunil Kumar Reddy Anumandla Java Technical Lead, American Family Insurance, Madison, Wisconsin, USA

Keywords:

Cryptocurrency, Trading Algorithm, Investment Strategies, Optimization, Artificial Intelligence, Financial Markets

Abstract

This study aims to understand better how to use AI-enhanced bitcoin trading algorithms in the dynamic and turbulent cryptocurrency market to optimize investing strategies. The primary goals are to determine future trends and policy implications, assess the impact on investment strategies and risk management, and investigate the efficacy of AI techniques. Methodologically, a thorough analysis of the body of research on AI-enhanced trading algorithms is carried out, emphasizing machine learning, deep learning, and natural language processing approaches. Important discoveries demonstrate how AI can improve automation, efficiency, and decision-making in bitcoin trading. However, there are restrictions, including poor data quality, unclear regulations, and market manipulation, that need to be considered by regulators and policymakers. The policy implications underscore the necessity of unambiguous regulatory frameworks, risk management protocols, and investor education campaigns to guarantee responsibility, equity, and openness in algorithmic trading operations. Overall, this study emphasizes how AI-enhanced cryptocurrency trading algorithms have the potential to completely transform risk management and investment strategies in the cryptocurrency market. It also emphasizes how crucial it is for stakeholders to work together to address related issues.

Downloads

Download data is not yet available.

References

Alcantara, C., Dick, C. (2017). Decolonization in a Digital Age: Cryptocurrencies and Indigenous Self-Determination in Canada. Canadian Journal of Law and Society, 32(1), 19-35. https://doi.org/10.1017/cls.2017.1

Alcarria, R., Bordel, B., Robles, T., Martín, D., Manso-Callejo, M-Á. (2018). A Blockchain-Based Authorization System for Trustworthy Resource Monitoring and Trading in Smart Communities. Sensors, 18(10). https://doi.org/10.3390/s18103561

Alessandretti, L., ElBahrawy, A., Aiello, L. M., Baronchelli, A. (2018). Anticipating Cryptocurrency Prices Using Machine Learning. Complexity, 2018. https://doi.org/10.1155/2018/8983590

Anumandla, S. K. R. (2018). AI-enabled Decision Support Systems and Reciprocal Symmetry: Empowering Managers for Better Business Outcomes. International Journal of Reciprocal Symmetry and Theoretical Physics, 5, 33-41. https://upright.pub/index.php/ijrstp/article/view/129

Chuen, K., Lee, D., Guo, L., Wang, Y. (2018). Cryptocurrency: A New Investment Opportunity?. The Journal of Alternative Investments, 20(3), 16-40. https://doi.org/10.3905/jai.2018.20.3.016

Hassani, H., Huang, X., Silva, E. (2018). Big-Crypto: Big Data, Blockchain and Cryptocurrency. Big Data and Cognitive Computing, 2(4), 34. https://doi.org/10.3390/bdcc2040034

Khair, M. A. (2018). Security-Centric Software Development: Integrating Secure Coding Practices into the Software Development Lifecycle. Technology & Management Review, 3, 12-26. https://upright.pub/index.php/tmr/article/view/124

Lotti, L. (2018). Fundamentals of Algorithmic Markets: Liquidity, Contingency, and the Incomputability of Exchange. Philosophy & Technology, 31(1), 43-58. https://doi.org/10.1007/s13347-016-0249-8

Mullangi, K. (2017). Enhancing Financial Performance through AI-driven Predictive Analytics and Reciprocal Symmetry. Asian Accounting and Auditing Advancement, 8(1), 57–66. https://4ajournal.com/article/view/89

Sandu, A. K., Surarapu, P., Khair, M. A., & Mahadasa, R. (2018). Massive MIMO: Revolutionizing Wireless Communication through Massive Antenna Arrays and Beamforming. International Journal of Reciprocal Symmetry and Theoretical Physics, 5, 22-32. https://upright.pub/index.php/ijrstp/article/view/125

Shajahan, M. A. (2018). Fault Tolerance and Reliability in AUTOSAR Stack Development: Redundancy and Error Handling Strategies. Technology & Management Review, 3, 27-45. https://upright.pub/index.php/tmr/article/view/126

Tejani, J. G. (2017). Thermoplastic Elastomers: Emerging Trends and Applications in Rubber Manufacturing. Global Disclosure of Economics and Business, 6(2), 133-144. https://doi.org/10.18034/gdeb.v6i2.737

Ying, D., Patel, B., & Dhameliya, N. (2017). Managing Digital Transformation: The Role of Artificial Intelligence and Reciprocal Symmetry in Business. ABC Research Alert, 5(3), 67–77. https://doi.org/10.18034/ra.v5i3.659

Downloads

Published

2018-12-31

How to Cite

Koehler, S., Dhameliya, N., Patel, B., & Anumandla, S. K. R. (2018). AI-Enhanced Cryptocurrency Trading Algorithm for Optimal Investment Strategies. Asian Accounting and Auditing Advancement, 9(1), 101–114. Retrieved from https://4ajournal.com/article/view/91

Issue

Vol. 9 No. 1 (2018): January - December

Section

Articles

License

Copyright (c) 2018 Asian Accounting and Auditing Advancement

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Most read articles by the same author(s)