Python for Finance in Practice

Python for Finance in Practice, written by Yuxing Yan, who holds a PhD in finance from McGill University, is an entry-level, hands-on title in the Asynchronous Community’s Financial Technology series. The book has 12 chapters. It begins with installing Python, the differences between versions, and three ways to launch it, using many simple examples to help readers understand basic concepts such as variables, functions, indentation, modules, and data types. It then moves into financial settings: writing a financial calculator in Python to handle present value, annuities, net present value, internal rate of return, and interest rate conversion; using NumPy and SciPy for matrix operations, linear regression, and the capital asset pricing model; using matplotlib to plot compound interest, the DuPont identity, portfolio risk, and candlestick charts; and using pandas and statsmodels for time series, computing various returns, performing t-tests and F-tests, and demonstrating strategies such as the Fama-French three-factor model, bid-ask spreads and liquidity measures, value at risk, and the efficient portfolio frontier. The second half focuses on derivatives: the Black-Scholes-Merton option pricing model, put-call parity, the Greeks, binomial-tree pricing, loops and solving for implied volatility, Monte Carlo simulation for pricing plain vanilla and exotic options, and finally volatility and ARCH and GARCH models. Each chapter includes a summary and exercises and comes with data sets, making the book suitable for readers who want to learn finance while typing code.