International Journal on Science and Technology

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Event-driven Automated Equity Trading using Simple Moving Average (SMA) Crossovers Integrated in Proximal Policy Optimization (PPO) Algorithm

Author(s) Mr. Soumick Adhikary, Mr. Wasim Qureshi, Mr. Ayush Kumar, Ms. Nandini Kumari
Country India
Abstract There is growing use of reinforcement learning within automated trading systems in order to be able to adapt to dynamic financial markets. This research suggests an automated framework of equity trading involving events that combine Simple Moving Average (SMA) crossover indicators with Proximal Policy Optimization (PPO) reinforcement learning algorithm to improve the process of trading decisions. The interaction between SMA crossover events as technical triggers to produce market state signals, and PPO as a learner of optimal trading policies through interaction with the market environment and maximization of cumulative trading rewards, is proposed in the model. The system processes past price history information of the equity to detect short-term and long-term crossovers of SMAs, which are converted to event signals of possible buy and sell. The PPO agent examines these signals and other market characteristics in order to dynamically adapt trading positions and risk exposure. The benchmark equity experimental analysis shows that the proposed hybrid framework has better performance in terms of profitability, lower drawdown risk, and strategy stability than the conventional rule-based SMA strategies. Reinforcement learning on adaptive policies in non-stationary markets is made possible by the combination of event-based technical signals and reinforcement learning. The findings emphasize the success of technical analysis indicator integration with current reinforcement learning algorithms in intelligent and automated equity trading systems.
Keywords Financial Time Series Analysis; Data-Driven Portfolio Decision Making; Algorithmic Trading Strategies; Proximal Policy Optimization (PPO); Automated Equity Trading; Reinforcement Learning in Finance.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-03-21
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