CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

automatic differentiation made easier for C++

Powerful automatic differentiation in C++ and Python

Financial Derivatives Calculator with 168+ Models (Options Calculator)

FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.

Platform for backtesting and live-trading intraday Stock/ETF/ELW using recurrent neural networks

Collection of projects oriented around the computational finance domain.

AAD enabled and scripting included derivatives modeling.

Algorithmic differentiation with hyper-dual numbers in C++ and Python

Derivatives pricing in modern C++.

Application for real-time trading in KRX market using Kiwoom Securities OpenAPI

A simple and efficient program for solving second order homogeneous differential equations

Reversed mode second order automatic differentiation for python (WIP)

financial derivatives

Autodifferentiation engine forked from Ceres-Solver

The people's auto-differentiation library.

Application for logging KRX market Level 2 data using Kiwoom Securities OpenAPI

Pricing in a Heston model context, using the QE scheme, the Andersen scheme and Monte-Carlo methods to price vanilla options.

An application for solving fractional differential equations

Program for taking derivative of an expression and optionally generating a pdf file with a step-by-step solution.