derivatives

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, Matlab/Octave, Julia or Javascript

automatic differentiation made easier for C++

Financial Derivatives Calculator with 171+ 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.

Derivatives pricing in modern C++.

options market making engine in C++20 — SVI vol surface, Black-Scholes pricing, lock-free SPSC queues, delta hedging, and real-time PnL attribution.

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

An options trading bot

A high-performance quantitative finance compute engine for derivatives pricing

AutoNeuroNet is a fully implemented automatic differentiation engine with custom matrices, a full neural network architecture, and a training pipeline. It comes with Python bindings via PyBind11, enabling quick, easy network development in Python, backed by C++ for enhanced speed and performance.

A C++ implementation of Black-Scholes options pricing model with Monte Carlo simulation with a Python backtesting interface that prices live options strips using real market data.

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

Options volatility models

Reversed mode second order automatic differentiation for python (WIP)

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

The people's auto-differentiation library.

Autodifferentiation engine forked from Ceres-Solver