Financial Modelling: Theory, Implementation and Practice with MATLAB Source:
Daniel Wetterau - Jörg Kienitz
A unique contribution to the application of quantitative techniques to financial problems and programming using Matlab.
Implementing QuantLib: Quantitative finance in C++: An inside look at the architecture of the QuantLib library
- Luigi Ballabio
This book is a report on the design and implementation of the QuantLib Library for C++
Practical C++20 Financial Programming: Problem Solving for Quantitative Finance, Financial Engineering, Business, and Economics
Carlos Oliveira -
Updated for C++20, this book covers those aspects of the language that are more frequently used in writing financial software.
Programming Clojure: The Pragmatic Programmers Series.
Chas Emerick, Brian Carper & Christophe Grand -
Written by members of the Clojure core team, this book is the essential, definitive guide to Clojure.
Quantitative Trading – 2nd Ed.: How to Build Your Own Algorithmic Trading Business
- Ernest P. Chan
In this newly revised Second Edition, quant trading expert Dr. Ernest P. Chan shows you how to apply both time-tested and novel quantitative trading strategies to develop or improve your own trading firm.
QuantLib Python Cookbook: Quantitative finance in Python.
Goutham Balaraman and - Luigi Ballabio
A hands-on, interactive look at the QuantLib library through the use of Jupyter notebooks as working examples.
Way back in 2013 we spoke with Daniel J. Duffy whose co-authored C# for Financial Markets with Andrea Germani. We talk about what makes C# a great choice for programming financial applications and how it contrasts with C++ in terms of productivity and flexibility.
In this extended interview Jacob Bettany talks to Jörg Kienitz, co-author with Daniel Wetterau of Financial Modelling: Theory, Implementation and Practice with MATLAB Source. They discuss the book and talk about the state of Computational Finance as it was back in 2013.
In 2015 Jacob Bettany talked with Yves Hilpisch about his book Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging. In a wide-ranging conversation, they covered the state-of-the-art on Python, and why institutions are increasingly choosing the language for financial applications.