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Original Article
Machine Learning-Driven Options Analytics: A Production Framework for BTC and ETH Derivatives
Anirudh Khajuria1
Indian Institute of Forest Management, Bhopal, Madhya Pradesh, India.
Published Online: January-April 2026
Pages: 146-150
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501020
References
1. Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637–654.[1]
2. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD '16.[2]
3. Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.[3]
4. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
5. Cont, R. (2001). Empirical Properties of Asset Returns: Stylized Facts. Quantitative Finance, 1(2), 223–236.
2. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD '16.[2]
3. Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.[3]
4. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
5. Cont, R. (2001). Empirical Properties of Asset Returns: Stylized Facts. Quantitative Finance, 1(2), 223–236.
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