Machine Learning for Short-Term Options Trading: Real-time ML models, Greeks-aware prediction engines, earnings-driven IV forecasting.

By Sterling Whitmore

Reactive Publishing

In the modern derivatives market, speed alone is no longer an edge, intelligence is. Machine learning has quietly become the backbone of the most profitable short-term options strategies. Now, for the first time, this hidden layer of quantitative practice is revealed with clarity, precision, and institutional depth.

Machine Learning for Short-Term Options Trading is a definitive guide to building predictive systems capable of navigating the microstructure, volatility shifts, and order-flow dynamics that shape intraday and multi-day options performance. Designed for traders who demand more than indicators and heuristics, this book shows you how to engineer real, testable edge using advanced modeling techniques.

Inside, you will learn how to:

Build and train ML models for directional, volatility, and skew forecasting

Predict short-term IV changes, delta drift, and gamma-linked price behavior

Construct features that capture volatility clustering, flow pressure, and sentiment

Identify structural short-term patterns around earnings, macro releases, and liquidity cycles

Design robust pipelines for real-time decision making and automated execution

Evaluate edge durability using regime filters, cross-validation, and walk-forward analysis

Integrate ML signals into spreads, verticals, straddles, and short-term directional plays

From steel-tight data engineering to battle-tested execution logic, every chapter is built for the trader who wants to operate at the level of a modern quant desk, without the institutional walls.

Whether you're an options trader seeking systematic structure or a quantitative researcher exploring short-horizon prediction, this book gives you the roadmap to turn machine learning into a repeatable, scalable edge.

This is the new standard for short-term options trading in the age of intelligent markets.

• Format: Paperback
• Language: English
• ISBN: B0G2B9SYSX
• ISBN13: 9798274420655
• Release Date: November 2025
• Publisher: Independently Published
• Length: 456 Pages
• Weight: 1.33 lbs.
• Dimensions: 0.9" x 6.0" x 9.0"