An Introduction to Computational Finance Without Agonizing Pain

A comprehensive guide to portfolio design, from Markowitz mean-variance to risk parity, robust and drawdown portfolios, index tracking and pairs trading. It connects every formulation with practical numerical algorithms.

Daniel P. Palomar

Learn Python for economic and financial modeling with NumPy, SciPy, pandas and Matplotlib. Written by Thomas Sargent and John Stachurski, with runnable code in every lecture.

Thomas J. Sargent, John Stachurski

A collection of C++ algorithms for finance: present value, bonds and term structure, mean-variance portfolios, binomial trees, Black-Scholes and Monte Carlo pricing. Each method comes with the formula and working code.

Bernt Arne Ødegaard

Explains the mathematics behind portfolio analysis, from matrices and orthogonal projection to Markowitz efficient portfolios and the CAPM. Proofs and worked examples make each model easy to follow.

Farida Kachapova

Lecture notes on Monte Carlo simulation, from random number generation to estimating probabilities and expected values, with R code. The last chapters price European, Asian and binary options by simulation.

Johan van Leeuwaarden, Jorn van der Pol