International Journal on Science and Technology

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Volume 17 Issue 1
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Super Stock Trading: Automation in Reinforcement Learning with Advanced Multi-Indicator Confirmations

Author(s) Mr. Aditya Sharma, Mr. Arup Kadia, Mr. Suryansh Kumar, Mr. Rajraushan Kumar
Country India
Abstract One of the main reasons that stock market trading can be very difficult is the uncertainty that comes with price fluctuations as well as the noise from the price movements, which together often mislead traders when they use technical indicators that are not complemented by others. The proposed research work presents an algorithmic stock trading framework based on Reinforcement Learning (RL) that links the use of Simple Moving Average Crossover (SMAC) for trend identification, along with Average Traded Volume (ATV) and Put-Call Ratio (PCR) for signals confirmations sentiment, respectively. SMAC is the main source of buy and sell signals, while ATV determines the strength of the market, and PCR shows the sentiment from the derivatives market. The framework is built on Proximal Policy Optimization (PPO) which learns trading policies by adapting them from historical market data. The model looks at trend direction, volume dynamics, sentiment conditions, and current position status in order to decide whether to buy, sell, or hold. Instead, a risk-aware reward formulation that incorporates transaction costs and drawdown penalties is implemented to ensure a stable performance. The results of the experiments show that the SMAC-ATV-PCR-PPO strategy suggested by the authors is superior to the traditional SMAC-based trading methods in terms of risk-adjusted returns and also in terms of decreasing to a significant extent the occurrence of false signals and their handling.
Keywords Reinforcement Learning, Algo trading, Market Sentiment, Proximal Policy Optimization (PPO), Simple Moving Average Crossover, Average Traded Volume, Put Call Ratio
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-01-10
DOI https://doi.org/10.71097/IJSAT.v17.i1.10080
Short DOI https://doi.org/hbjmn6
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