International Journal on Science and Technology

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Volume 17 Issue 1
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DRLMMF: Deep Reinforcement Learning-based Autonomous Money-Management Framework

Author(s) Prashansa Bharti, Kamal Narayan, Arup Kadia, Bidya Bharti
Country India
Abstract The growing complexity and volatility of the modern financial markets require smart and adaptable money management systems that should be able to make decisions on their own. This paper presents a Proximal Policy Optimization (PPO), based self-directed money management system that is capable of portfolio allocation optimization and risk control under changing market conditions. The system is integrated in reinforcement learning with financial time series representations calculated from a series of stock market returns, volatility measures, and capital exposure constraints to find the best investment policies. Due to its reliability, sample efficiency, and robustness in continuous action spaces, PPO is chosen as a method of operation, making it fit for the financial environments of the real world. The proposed system autonomously modifies the position sizing, capital allocation, and risk exposure while observing the set drawdown and leverage constraints. An extensive experimental study has been done on the historical market data to show how the PPO-based system has done better than the conventional rule, based and static allocation strategies in terms of cumulative returns, risk, adjusted performance, and capital preservation using the market situations. The evidenced results have shown the increased capability of the model to market regime shifts and the consequences of lowered risk during times of high volatility. The research has pointed out the efficiency of the reinforcement learning driven by PPO in the creation of scalable, data-driven, and resilient money management systems, which are most suitable for intelligent financial decision-making.
Keywords Automation, Technical Analysis, Money Management, Reinforcement Learning, Proximal Policy Optimization, Algorithmic Trading.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-02-25
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