International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 4 (July-August 2025) Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
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Volume 7 Issue 4
July-August 2025

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Developing A Machine Learning-Based Options Trading Strategy For The Indian Market

Author(s) Mr. Firoz A Sherasiya
Country India
Abstract With the rapid evolution of the Indian financial market and the growing adoption of derivatives trading, especially options, there is an increasing demand for intelligent systems that can enhance decision-making in trading. This paper presents a comprehensive study of developing a machine learning-based options trading strategy for the Indian stock market, specifically focusing on the Nifty 50 index options. Using historical option chain data sourced from the National Stock Exchange (NSE) and Yahoo Finance, we engineer features from option Greeks, implied volatility, and underlying price movements. A range of supervised learning models including Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks are tested to classify buy/sell signals. The trading strategy is back tested for profitability and risk using metrics like Sharpe ratio, win-loss ratio, and cumulative return. Results demonstrate that the ML-based strategy outperforms a basic momentum strategy, especially during high-volatility market periods. The research emphasizes the viability of using machine learning in algorithmic options trading in emerging markets like India.
Keywords Options Trading Strategy, Machine Learning, Nifty Options, NSE India, XGBoost, LSTM, Buy-Sell Signals, Option Greeks, Backtesting, Indian Financial Market
Field Engineering
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-07-11
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.50375
Short DOI https://doi.org/g9s9kn
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