The 46-Page Ultimate Guide to Pricing Options and Implied Volatility With Python (PDF + code)
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This 46-page ultimate guide teaches you everything you need to start analyzing equity options with Python.
The guide contains both a PDF and a Jupyter Notebook file.
What you'll be able to do with this ultimate guide:
- Understand the Important Jargon
- Answer What Are Options?
- Answer What Is the Black-Scholes Option Pricing Model?
- Understand (Some of) the Math
- Code Black-Scholes Formula in Python
- Understand The Greeks
- Code the Greeks in Python
- Code Realized Volatility
- Code Implied Volatility
- Get Real Options Market Data
- Compute Implied Volatility
- Interpolate Missing and Bad Implied Volatility Values
- Compute Black-Scholes and the Greeks
- Analyze the Model Error
- Analyze Implied Volatility
Build the famous hockey stick charts for any position.
Compute the value of an option using Black-Scholes.
Compute the rolling historical volatility of an underlying.
Compute and analyze model error.
Build the implied volatility skew and term structure.
Plot the 3D volatility surface chart.
Includes 46-page PDF, Jupyter Notebook file, cached data file
Python libraries used
Pandas, NumPy, SciPy, Matplotlib, Jupyter NotebookWhat you'll learn
How to calculate and plot option payoffs, code the Black-Scholes pricing formula and greeks, compute implied volatility, analyze implied volatility and more...Support
@pyquantnews
- Includes 46-page PDF, Jupyter Notebook file, cached data file
- Python libraries usedPandas, NumPy, SciPy, Matplotlib, Jupyter Notebook
- What you'll learnHow to calculate and plot option payoffs, code the Black-Scholes pricing formula and greeks, compute implied volatility, analyze implied volatility and more...
- Support@pyquantnews