Capture Market Trends through Multi-Indicator Confirmations using Reinforcement Learning Models

Rajraushan Kumar; Arup Kadia; Suryansh Kumar; Aditya Sharma

doi:10.71097/IJSAT.v17.i1.10096

Capture Market Trends through Multi-Indicator Confirmations using Reinforcement Learning Models

Author(s)	Mr. Rajraushan Kumar, Mr. Arup Kadia, Mr. Suryansh Kumar, Mr. Aditya Sharma
Country	India
Abstract	Trading on the stock market is a very complicated activity, and the main reasons for this are: high volatility, a lot of market noise, and changes in the behaviour of the investors, which are taking place at a very fast pace, and all these factors together are making the trading signals less reliable when the single technical indicators are used one by one. To solve these problems, the authors of this paper come up with a super resilient multi-indicator trading system that combines Simple Moving Average Crossover (SMAC) for trend detection with Average Traded Volume (ATV), Money Flow Index (MFI), and Put–Call Ratio (PCR) as supporting indicators. SMAC is the main method that provides buy and sell signals; at the same time, ATV approves market participation, MFI states the strength of both momentum and money flow, and PCR reflects the sentiment of the market, which is derived from the derivatives market. The new system is tested on large-cap stocks through considerable backtesting and using various time frames, which include short-term, medium-term, and long-term trading horizons. The evaluation is conducted with the help of important metrics like cumulative returns, trade accuracy, Sharpe ratio, and Sortino ratio. The experimental results show that the strategy of combining SMAC with ATV, MFI, and PCR has significantly surpassed the traditional SMAC-only methods, as it has reduced false signals, overtrading and risk-adjusted returns have been improved. One major point that the research has made is that by merging trend, volume, momentum, and sentiment data into a single trading model, a more reliable and understandable automated trading system is created, which is fit for the real-world market conditions.
Keywords	Simple Moving Average Crossover (SMAC), Average Traded Volume (ATV), Money Flow Index (MFI), Put–Call Ratio (PCR), Algorithmic Trading, Technical Indicators, Market Sentiment, Stock Market Analysis
Field	Computer > Data / Information
Published In	Volume 17, Issue 1, January-March 2026
Published On	2026-01-11
DOI	https://doi.org/10.71097/IJSAT.v17.i1.10096
Short DOI	https://doi.org/hbjmn5

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Capture Market Trends through Multi-Indicator Confirmations using Reinforcement Learning Models

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