Most options guides teach you how options work. This notebook shows you how to trade them.

7 complete sections from pricing to a live backtested strategy, all in one runnable Jupyter notebook.

,,, WHAT'S INSIDE,,,

1. Black-Scholes Pricing

Price any call or put. Verify put-call parity. No hand-waving, the math is shown.

2. Full Greeks Suite (8 Greeks)

Delta, Gamma, Vega, Theta, Rho, plus the advanced ones:

Vanna (dDelta/dVol), Volga (dVega/dVol), Charm (dDelta/dTime)

These are what options desks actually use for hedging.

3. Implied Volatility Solver

Given a market price, solve for the implied vol using Brent's method.

Round-trip accuracy to 1e-8. Works on any strike/expiry.

4. Implied Volatility Surface

Build and visualize a full 3D vol surface with realistic skew and term structure.

Plug in real option chain data from any broker to go live.

5. Vol Skew & Term Structure Signals

Extract tradeable signals: skew (25d put - 25d call), term structure slope,

butterfly spread. Positive skew = fear. Inverted term structure = hedging demand.

6. Volatility Risk Premium (VRP) Strategy

The most persistent edge in options markets.

IV is almost always higher than subsequent RV, options buyers overpay for insurance.

The strategy: sell vol when IV >> RV, buy when RV >> IV.

Full implementation with Parkinson vol estimator.

7. Performance Metrics

Sharpe, Sortino, Calmar, max drawdown, win rate.

Side-by-side comparison vs buy & hold.

,,, WHO THIS IS FOR,,,

• Quant traders building options strategies

• Python developers who want to understand vol trading

• Anyone who found PyQuant News useful and wants to go deeper

,,, REQUIREMENTS,,,

Python 3.8+, numpy, scipy, pandas, matplotlib, yfinance (all free)

,,, TAGS,,,

options trading, implied volatility, black scholes, greeks, python, jupyter notebook, volatility surface, VRP, quant finance, algorithmic trading