Machine Learning for Options Trading (Paperback)

Alice Schwartz

ISBN 13: 9798268328554

Editore: Independently Published, 2025

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Paperback. Reactive PublishingUnlock the Power of Machine Learning to Gain a Competitive Edge in Options MarketsIn today's hyper-competitive financial landscape, traditional options trading strategies are no longer enough. Machine Learning for Options Trading bridges the gap between theoretical finance and real-world execution by giving you a practical, end-to-end framework to build predictive models, generate trading signals, and optimize execution using Python.This book is your tactical playbook for deploying supervised and unsupervised learning methods to uncover actionable insights buried in options data. From volatility surfaces and skew metrics to time-decay and delta shifts, you'll learn how to engineer features that matter, and turn those features into alpha-generating signals.What You'll LearnFeature Engineering for Derivatives: Moneyness, IV rank, skew, term structure, gamma exposure, and moreSignal Generation with ML Models: Random forests, gradient boosting, and ensemble techniquesTime Series Forecasting for Options: LSTM and sequence modeling for implied volatility and delta reversionRisk-Aware Portfolio Construction: Designing delta/vega/gamma-neutral basketsBacktesting & Execution: Walk-forward validation, slippage modeling, and trade simulationTools and Frameworks CoveredPython (Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow, Keras)OptionMetrics-style datasets and real-time feedsCustom backtesting engines for options-specific performanceWho This Book Is ForQuantitative traders seeking a machine learning edgeData scientists entering derivatives marketsOptions professionals upgrading their tech stackPython developers moving into financeWhether you're a seasoned quant or a self-taught trader, this book will help you transition from back-of-the-envelope models to machine-learned alpha with statistical rigor and automation. Data is the new edge. Machine learning is how you extract it.Build smarter signals. Trade with conviction. Outperform the crowd. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Dati bibliografici

• Titolo: Machine Learning for Options Trading (Paperback)
• Editore: Independently Published
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 13: 9798268328554
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

Reactive Publishing

Unlock the Power of Machine Learning to Gain a Competitive Edge in Options Markets

In today's hyper-competitive financial landscape, traditional options trading strategies are no longer enough. Machine Learning for Options Trading bridges the gap between theoretical finance and real-world execution by giving you a practical, end-to-end framework to build predictive models, generate trading signals, and optimize execution using Python.

This book is your tactical playbook for deploying supervised and unsupervised learning methods to uncover actionable insights buried in options data. From volatility surfaces and skew metrics to time-decay and delta shifts, you’ll learn how to engineer features that matter, and turn those features into alpha-generating signals.

What You’ll Learn

• Feature Engineering for Derivatives: Moneyness, IV rank, skew, term structure, gamma exposure, and more

• Signal Generation with ML Models: Random forests, gradient boosting, and ensemble techniques

• Time Series Forecasting for Options: LSTM and sequence modeling for implied volatility and delta reversion

• Risk-Aware Portfolio Construction: Designing delta/vega/gamma-neutral baskets

• Backtesting & Execution: Walk-forward validation, slippage modeling, and trade simulation

Tools and Frameworks Covered

• Python (Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow, Keras)

• OptionMetrics-style datasets and real-time feeds

• Custom backtesting engines for options-specific performance

Who This Book Is For

• Quantitative traders seeking a machine learning edge

• Data scientists entering derivatives markets

• Options professionals upgrading their tech stack

• Python developers moving into finance

Whether you're a seasoned quant or a self-taught trader, this book will help you transition from back-of-the-envelope models to machine-learned alpha with statistical rigor and automation.

Data is the new edge. Machine learning is how you extract it.
Build smarter signals. Trade with conviction. Outperform the crowd.