Machine Learning for Options Trading: Building Alpha with Data-Driven Signals: Predictive Modeling, Feature Engineering, and Risk-Aware Execution for ... Markets (Comprehensive Options 2025, Band 3) - Softcover
Buch 2 von 4: Comprehensive Options 2025
Inhaltsangabe
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.
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Machine Learning for Options Trading: Building Alpha with Data-Driven Signals: Predictive Modeling, Feature Engineering, and Risk-Aware Execution for
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Machine Learning for Options Trading (Paperback)
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Paperback. Zustand: new. 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 multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798268328554
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Machine Learning for Options Trading: Building Alpha with Data-Driven Signals: Predictive Modeling, Feature Engineering, and Risk-Aware Execution for Derivatives Markets
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Machine Learning for Options Trading: Building Alpha with Data-Driven Signals: Predictive Modeling, Feature Engineering, and Risk-Aware Execution for
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Machine Learning for Options Trading (Paperback)
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Paperback. Zustand: new. 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. Bestandsnummer des Verkäufers 9798268328554
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