Optimizing Algorithmic Trading Strategies Using AI
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Keywords

Optimizing
Algorithmic Trading
AI

How to Cite

[1]
D. V. Wong, “Optimizing Algorithmic Trading Strategies Using AI”, Journal of Bioinformatics and Artificial Intelligence, vol. 4, no. 2, pp. 138–151, Nov. 2024, Accessed: Nov. 15, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/120

Abstract

Trading involves making decisions based on predictions and achieving the highest possible profits. Algorithmic trading is a decision-making mechanism in the trading process that allows executing trades automatically based on predefined conditions. It is calculated with the help of computer algorithms. This means that computers make decisions to trade or invest. AI in trading applications is used to enhance trading strategies and replace those that require human decision-making due to their slow speed. AI can improve trading decisions when compared to those made by humans. With AI, trading decisions are less emotional, more logical, faster, fully automatic, and more optimal. It is no wonder that trading applications are developing today.

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References

Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.

Pasupuleti, Vikram, et al. "Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management." Logistics 8.3 (2024): 73.

Thota, Shashi, et al. "Federated Learning: Privacy-Preserving Collaborative Machine Learning." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 168-190.

J. Singh, “Advancements in AI-Driven Autonomous Robotics: Leveraging Deep Learning for Real-Time Decision Making and Object Recognition”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 657–697, Apr. 2023

Alluri, Venkat Rama Raju, et al. "Serverless Computing for DevOps: Practical Use Cases and Performance Analysis." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 158-180.

Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.

S. Chitta, S. Thota, S. Manoj Yellepeddi, A. Kumar Reddy, and A. K. P. Venkata, “Multimodal Deep Learning: Integrating Vision and Language for Real-World Applications”, Asian J. Multi. Res. Rev., vol. 1, no. 2, pp. 262–282, Nov. 2020

Ahmad, Tanzeem, et al. "Hybrid Project Management: Combining Agile and Traditional Approaches." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 122-145.

Tamanampudi, Venkata Mohit. "CoWPE: Adaptive Context Window Adjustment in LLMs for Complex Input Queries." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 5.1 (2024): 438-450.

Thota, Shashi, et al. "Few-Shot Learning in Computer Vision: Practical Applications and Techniques." Human-Computer Interaction Perspectives 3.1 (2023): 29-59.

Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.

J. Singh, “Autonomous Vehicle Swarm Robotics: Real-Time Coordination Using AI for Urban Traffic and Fleet Management”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 1–44, Aug. 2023

S. Kumari, “Cloud Transformation for Mobile Products: Leveraging AI to Automate Infrastructure Management, Scalability, and Cost Efficiency”, J. Computational Intel. & Robotics, vol. 4, no. 1, pp. 130–151, Jan. 2024.

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