Book

Python Fundamentals for Finance: A survey of Algorithmic Options trading with Python

What this book is

A first read that introduces Python and options trading together. The audience is a programmer who knows little finance, or a finance-curious person who has written a bit of Python — Van Der Post writes for both. The book gets you from "what is a call option" to a runnable Python implementation of Black-Scholes pricing in five compact topics.

It is not a substitute for a quant-finance textbook (the math is sketched, not derived) and not a substitute for Python for Algorithmic Trading Cookbook (which assumes you already have the foundation this book provides). It sits one rung below: vocabulary, basic implementations, and the shape of an options strategy you can actually run.

The shape of the argument

Executive summary

The book makes three load-bearing claims.

Options pricing is reducible to inputs and a closed-form formula

Black-Scholes (1973) takes five inputs — spot price, strike, time to expiry, risk-free rate, volatility — and returns a price. The math behind the derivation is heavy (stochastic calculus, Itô's lemma, geometric Brownian motion); the implementation is a few lines of Python. Most working options traders never re-derive the formula but use it daily — same as most engineers use FFT without deriving it.

Volatility is the only unknown that matters

Spot, strike, expiry, and rate are observable. Volatility is the one you have to estimate (historical vol) or back out from market prices (implied vol). Almost every options edge comes from a better volatility estimate; almost every options loss comes from a worse one.

Algorithmic options trading is mostly risk management dressed as code

The pricing math is the easy half. The hard half is sizing, position management, hedging the Greeks, and not blowing up on the first 3-sigma move. The book's final topic gestures at this — a full treatment is what the Algorithmic Trading Cookbook provides.

Who this is for

Topic index

Topic 1 — Introduction to Options in FinanceThe vocabulary: calls, puts, strikes, expiry, in-the-money vs out-of-the-money, the four basic positions and their payoff shapes.Topic 2 — Python Programming Fundamentals for FinanceThe longest topic. numpy, pandas, control flow, functions — enough Python to handle market data without writing slow loops.Topic 3 — Python-based Market Data AnalysisPulling free data with yfinance, plotting prices and returns, computing rolling statistics — the lightweight version.Topic 4 — Enforcing Black-Scholes in PythonThe formula in code, the Greeks (delta, gamma, theta, vega, rho), implied volatility extraction by Newton-Raphson root-finding.Topic 5 — Advanced Concepts in Trading and PythonPutting the pieces together — a simple algorithmic options strategy, sizing, and the risk-management gestures the book has time for.

How to read these summaries

The topics build linearly. Topic 2 is the largest and the foundation for 3-5. If you already know Python, skim topic 2 and dwell on topics 4-5 where the finance gets specific.

Concept companions

Options contractThe right (not obligation) to buy or sell an underlying asset at a specified price by a specified date.Black-ScholesThe 1973 closed-form formula for European-option pricing under geometric Brownian motion.Implied volatilityThe volatility input that, plugged into Black-Scholes, reproduces the market price — i.e. what the market is pricing volatility at.Algorithmic tradingTrading executed by rules-based software rather than human discretion.Quantitative financeThe broader discipline this book introduces — mathematics, statistics, and computation applied to markets.