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IASMFT: intelligent agent simulation model for future trading

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Abstract

Investors depend on various sources for decision-making in trading, with maximum profit earning as the primary objective. A predictive model with experience is essential in developing an automated trading system to make wise decisions and avoid risky situations. The present research aims to investigate an artificial agent simulation model that maximizes trading profit. We have designed an innovative IASMFT (Intelligent Agent Simulation Model for Future Trading) for stock trading and profit maximization. IASMFT integrates Fuzzy-c means clustering, GAN (Generative Adversarial Network), and Reinforcement learning. The experimental data consists of historical datasets of six stocks from 8th August 2016 to 31st March 2023. The existing and proposed models are evaluated based on Domain–specific metrics and General regression metrics. The proposed model, IASMFT, maximized the trading profit with an RMSE of 10.14 and MAE of 2.75 and outperformed the models in the recent literature. The findings indicate that the combination of Domain–specific and General regression metrics is a perfect fit to evaluate trading profit and maximization models. IASMFT maximizes the trading profit and is a reliable approach that can be implemented in a real-time scenario.

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Authors and Affiliations

  1. Department of CSE, NIT Tiruchirappalli, Tuvakudi, Tamilnadu, 620015, India

    S. S. S. N. Usha Devi N.

  2. Department of CSE NIT Tiruchirappalli, Tuvakudi, Tamilnadu, 620015, India

    R. Mohan

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Correspondence to S. S. S. N. Usha Devi N..

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Usha Devi N., S.S.S.N., Mohan, R. IASMFT: intelligent agent simulation model for future trading. Int. j. inf. tecnol. 16, 929–938 (2024). https://doi.org/10.1007/s41870-023-01425-1

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