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Options Pricing with Python: Master option pricing and apply Python to quantitative finance, trading, and risk management
Paperback
Investing & FinanceGeneral ComputersProgramming
ISBN13: 9781807301996
Publisher: Packt Publishing
Published: Sep 18 2026
Pages: 656
Weight: 2.45
Height: 1.32 Width: 7.50 Depth: 9.25
Language: English
Begin your professional options trading journey by learning the principles, creating methods, and using Python to thrive in the volatile trading market
Key Features:
- Master option pricing with Python using Black-Scholes, binomial and trinomial trees, and Monte Carlo simulation
- Decode implied volatility and option Greeks to analyze sensitivities, valuation, and risk
- Apply option pricing across asset classes through real-world case studies, machine learning applications, portfolio optimization, and risk management
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description:
Master option pricing with Python by turning financial theory into practical pricing models, analysis, and real-world applications.
Learn options trading fundamentals and prepare financial data before implementing Black-Scholes, binomial and trinomial trees, Monte Carlo simulation, implied volatility models, and option Greeks. Advance to exotic options, risk-neutral valuation, and numerical pricing methods while learning how to test and evaluate your models.
Practice what you learn through real-world options pricing across asset classes and machine learning applications. Understand trading strategies, portfolio optimization, hedging, and risk management, and discover how option pricing models fit into quantitative finance and trading workflows.
By the end, you will be able to build, test, and apply Python option pricing models and understand how AI/ML and emerging techniques are shaping the future of quantitative finance.
What You Will Learn:
- Master Black-Scholes option pricing with Python
- Build binomial, trinomial, and Monte Carlo models
- Apply option pricing across FX, equity, rates, commodities, and other asset classes
- Decode implied volatility and volatility models
- Understand option Greeks and risk sensitivities
- Practice trading strategies, hedging, and portfolio optimization
- Price exotic options using numerical methods
- Apply machine learning techniques to options pricing and risk analysis
Who this book is for:
This book is for capital markets professionals, quantitative and algorithmic traders, researchers, developers, and finance students who want to master option pricing with Python. Readers will learn to build and test pricing models, analyze implied volatility and Greeks, and apply Python to quantitative finance, trading, portfolio optimization, and risk management.
Table of Contents
- Introduction to Options Trading
- Options Types and Trading Fundamentals
- Gathering and Preparing Data
- Black-Scholes Closed-Form Pricing
- Binomial and Trinomial Trees
- Monte Carlo Simulation
- Implied Volatility & Volatility Models
- Greeks and Sensitivity Analysis
- Exotic Options Pricing Models
- Risk-Neutral Valuation & Numerical Methods
- Testing & Evaluating Pricing Models
- Real-World Case Study: FX Options
- Real-World Case Study: Equity Options
- Options Strategies, Portfolio Optimization & Risk Management
- Conclusion: Best Practices AI/ML & Future Trends
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