I’ve just ended these three books published this year on the intersection of finance and programming:

• [amazon_link id=“0470030089” target=“_blank” ]C# for Financial Markets[/amazon_link]. This recently published book is just the translation to C# of all the previous books by the same author, especially the ones on the intersection between finance and C++. As such, one-third of the book delves into the implementation of basic mathematical finance (bonds, futures, options, swaptions, curve and surface modelling, finite difference method, …) and two-thirds of the book delves into teaching the C# language and its interoperability with Excel/LINQ/C++: note that if you’re already a pro on C#, you’ll better skip these parts since they are far from being the best authoritative source, although the sections on interoperability are really instructive. The best point of this book is that really full of examples to enlighten every concept (850 pages long!), although they never manage to compose a full application of any financial worth (that’s left as an exercise to the reader!): thus, and only on this technology angle, it’s the best book for beginners.
• [amazon_link id=“0470744898” target=“_blank” ]Financial Modelling – Theory, Implementation and Practice (MATLAB)[/amazon_link]. Do you need a book to quickly acquaint yourself with the state-of-the-art in financial mathematics for asset allocations, hedging and derivatives pricing, skipping the study of dozens of papers? Then this book is your definitive shortcut: it encompasses all from the derivation of the models to their implementation in Matlab, demonstrating that this language can also be used for prototyping purposes in the financial industry, efficiency and interoperability aside (if you don’t know Matlab, a third of the book delves into that). My favourite part of the book it’s the first one on models (stochastic, jump-models, multi-dimensional, copulas) that reads lightly and fast in just an afternoon, but the book is also overfocused on the numerical implementation of the models (a third of the book), when most of these details are just left to some library in the real world. Even so, just running over all its examples is worth its full price.
• [amazon_link id=“0470978708” target=“_blank” ]Financial Risk Modelling and Portfolio Optimization with R[/amazon_link]. R is the lingua franca of statistical research, and its under-utilization in the financial industry it’s a real puzzle, the truth being that the sheer number of packages dealing with every imaginable statistical function should be enough to justify a much deeper penetration into daily use. This book is best suited for quantitative risk managers, and it surveys the latest techniques for modelling and measuring financial risk, besides portfolio optimisation techniques. Every topic follows a precisely defined structure: after a brief overview, an explanation is offered and a very interesting synopsis of R packages and their empirical applications ends the discussion. My favourite parts are at the end, on constructing optimal portfolios subject to risk constraints and tactical asset allocation based on variations of the Black-Litterman approach.