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Paper ID:IJSREM68240

TradIQ An AI-Assisted Paper-Trading and Portfolio Analysis Platform

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68240

Volume:

10 |

10 |

Oct 2026

Pages:

1-14

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68240

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Ravsaheb Khairnar

Ravsaheb Khairnar

Department of computer science

Chikitsak Samuha's Patkar-Varde College

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Abstract

Today, digital trading applications make it easier for users to access financial-market information, check their portfolio, view charts, and perform trading activities from web and mobile applications. However, users often need to use different sections or applications for trading, portfolio tracking, risk analysis, financial research, and performance analysis. Beginners may also find some of the financial information difficult to understand. This paper presents an Intelligent Paper-Trading and Portfolio Analysis Platform that combines paper trading, portfolio management, risk analysis, transaction-cost calculation, and AI-assisted financial analysis in one application. The proposed system provides three main modes: Invest, Trade, and Analyze. The Invest mode focuses on portfolio and investment information, the Trade mode provides simulated trading functionality, and the Analyze mode provides portfolio analysis, risk information, research, and AI-based explanations. The proposed system uses a secure authentication architecture with separate authentication for the main application and socket communication. The application authentication handles login, registration, OTP verification, OAuth authentication, PIN verification, and token refresh. The socket authentication is used for controlled access to real-time stock activity and WebSocket communication. The paper-trading system uses virtual money and simulates orders, transactions, holdings, and portfolio values without performing real financial transactions. The analysis layer calculates portfolio value, gross and net profit and loss, transaction charges, sector exposure, portfolio concentration, and the possible effect of a new order on the user's portfolio. An AI Financial Copilot is also proposed to provide explanations using verified portfolio and market information. Important financial calculations are performed by the application itself instead of depending on the AI model for numerical results. The evaluation of the proposed system can include functional testing, financial calculation accuracy, order-processing performance, portfolio-risk calculations, AI response grounding, and comparison of different system configurations. Since the complete experimental evaluation is still part of the development process, this paper describes the proposed evaluation method instead of claiming results that have not yet been measured.

KEYWORDS

Paper Trading FinTech Portfolio Analysis Risk Analysis Artificial Intelligence Financial Technology WebSocket Trading Simulation Explainable AI Portfolio

Cite This Article

Ravsaheb Khairnar, "TradIQ An AI-Assisted Paper-Trading and Portfolio Analysis Platform," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-14, Oct 2026. https://doi.org/10.55041/IJSREM68240

Full Article/Conclusion

This paper presented an Intelligent Paper-Trading and Portfolio Analysis Platform that combines simulated trading, portfolio management, risk analysis, transaction-cost calculation, and AI-assisted financial analysis in one system.   The proposed platform is divided into three main modes: Invest, Trade, and Analyze. Invest mode provides basic investment and portfolio features. Trade mode provides simulated market and order functionality. Analyze mode provides portfolio analysis, risk analysis, financial research, and AI-assisted explanations.   The system also separates application authentication from real-time socket activity. The application handles login and token-related operations, while a separate socket authentication mechanism is used for WebSocket-based activity.   The paper-trading engine maintains the relationship between orders, transactions, holdings, and portfolio values. The portfolio engine calculates average cost, portfolio value, gross P&L, and net P&L. The risk engine checks how a proposed order can change portfolio exposure before the simulated order is executed.   One important part of the proposed system is the separation of financial calculations from AI-generated explanations. Values such as portfolio value, holdings, transaction values, and P&L should be calculated by the application services. The AI layer can then use these verified values to generate explanations in natural language.   The proposed evaluation includes functional testing, financial calculation accuracy, portfolio-risk accuracy, order-processing latency, AI grounding, and ablation testing of the major components.   At the current stage, the complete experimental evaluation is still part of the development process. Therefore, this paper does not claim specific improvements in trading performance or AI accuracy without actual experimental results. The next step is to complete the implementation, prepare the evaluation dataset, perform the planned experiments, and analyze the obtained results.

References

[1]   F. Allen, J. McAndrews, and P. Strahan, “E-Finance: An Introduction,” Journal of Financial Services Research, vol. 22, 2002.   [2]  R. C. Merton, “The Theory of Rational Option Pricing,” The Bell Journal of Economics and Management Science, vol. 4, no. 1, 1973.   [3]  H. Markowitz, “Portfolio Selection,” The Journal of Finance, vol. 7, no. 1, 1952.   [4]   W. F. Sharpe, “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk,” The Journal of Finance, vol. 19, no. 3, 1964.   [5]  A. López de Prado, Advances in Financial Machine Learning. Wiley, 2018.   [6]  T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer, 2009.   [7]  T. Brown et al., “Language Models are Few-Shot Learners,” Advances in Neural Information Processing Systems, 2020.   [8]    P. Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” Advances in Neural Information Processing Systems, 2020.   [9]   N. Reimers and I. Gurevych, “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,” Proceedings of EMNLP-IJCNLP, 2019.   [10]   Y. Liu et al., “RoBERTa: A Robustly Optimized BERT Pretraining Approach,” arXiv:1907.11692, 2019.

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Publication Date

10/23/2026

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Double-Blind Peer Review

Review Rounds: 2

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