Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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AI-Powered Bot for Trading

Authors
Akshat More1, *, Ananya Shrivastava1, Muhammad Rishan1, Sridevi Sridhar1
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
*Corresponding author. Email: am1769@srmist.edu.in
Corresponding Author
Akshat More
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_56How to use a DOI?
Keywords
Algorithmic trading; AI trading bot; Long short-term memory models; Integrated Broker API’s; Deep Neural Networks; Machine Learning
Abstract

The integration of artificial intelligence and machine learning into trading is significantly transforming financial markets, often surpassing traditional methods in terms of efficiency and accuracy. This paper presents a comprehensive study that encompasses a systematic literature review of algorithmic trading techniques and the development of a cutting-edge AI trading bot. The proposed system employs advanced models—including Random Forest Regressors, Genetic Algorithms paired with Deep Neural Networks, and Long Short-Term Memory networks—to craft accurate trading signals and adapt to complex market scenarios. Our architectural design bifurcates the system into distinct roles, empowering traders with account management and market analytics while the bot autonomously validates and executes trade orders through integrated broker APIs. Data pre-processing techniques, including lag adjustments and strategic dataset segmentation, underpin the model training and enhance predictive accuracy. Extensive evaluation—highlighted by back-testing on multiple trading strategies across varying durations—demonstrates the bot’s proficiency in reducing trade execution times, minimizing human error, and outperforming traditional benchmarks.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_56How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Akshat More
AU  - Ananya Shrivastava
AU  - Muhammad Rishan
AU  - Sridevi Sridhar
PY  - 2025
DA  - 2025/10/31
TI  - AI-Powered Bot for Trading
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 682
EP  - 692
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_56
DO  - 10.2991/978-94-6463-866-0_56
ID  - More2025
ER  -