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International Journal of Science, Strategic Management and Technology

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ISSN: 3108-1762 (Online)
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AI-DRIVEN TRADING ANALYSIS PLATFORM:ARCHITECTURE, MODELS, RISK CONTROLS, AND OPERATIONAL FRAMEWORK

AUTHORS:
Ruban S
Raghu Nandha Kumar D.E
Sithik Raj R
Mentor
Affiliation
School of Computing Sciences, VISTAS, Pallavaram, Chennai, India
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

The Trading AI Analysis platform is an intelligent, modular system designed to support systematic trading decisions through the integration of machine learning, real-time market data pipelines, and quantitative signal generation. Built on an event-driven architecture comprising five principal layers — Data Ingestion, Feature Engineering, Model Layer, Signal Engine, and Risk & Execution — the platform processes real-time and historical market data. The system deploys an ensemble of AI models including LSTM networks, Transformer architectures, Gradient Boosting, and XGBoost classifiers. A structured signal schema delivers directional recommendations with confidence scores and risk flags. Robust pre-trade and real-time risk controls enforce position limits, circuit breakers, and compliance constraints. All signals and decisions are persisted in an immutable audit log ensuring full regulatory compliance over a seven-year retention period.

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S, R., D.E, R. N. K. & R, S. R. (2026). AI-Driven Trading Analysis Platform:Architecture, Models, Risk Controls, and Operational Framework. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.014

S, Ruban, et al.. "AI-Driven Trading Analysis Platform:Architecture, Models, Risk Controls, and Operational Framework." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.014.

S, Ruban,Raghu D.E, and Sithik R. "AI-Driven Trading Analysis Platform:Architecture, Models, Risk Controls, and Operational Framework." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.014.

References
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2.Lim and S. Zohren, "Time-series forecasting with deep learning: a survey," Philosophical Transactions of the Royal Society A, vol. 379, 2021.

3.Vaswani et al., "Attention is all you need," in Advances in Neural Information Processing Systems (NeurIPS), 2017.

4.Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. ACM SIGKDD, 2016, pp. 785-794.

5.Lopez de Prado, Advances in Financial Machine Learning. Hoboken, NJ: Wiley, 2018.

6.Kissell, Algorithmic Trading Methods, 2nd ed. Academic Press, 2020.

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10.Ng, Machine Learning Yearning, 2018.
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✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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