derivatives

Investment Research for Everyone, Everywhere.

Python toolkit for quantitative finance

A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit 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 or Matlab/Octave.

automatic differentiation made easier for C++

Synthetix Solidity smart contracts

Open source analytics and market risk library from OpenGamma

A hopefully comprehensive guide to the defi derivative landscape

A library for financial options pricing written in Python.

Multi-asset, multi-strategy, event-driven trading platform for running low to medium freq strategies at many venues simultaneously with portfolio-based risk management and %-per-strategy capital allocation. Supports event-driven backtesting across all desired instruments, venues and strategies under a single parameterized portfolio.

Quantitative Finance tools

A composable, real time, market data and trade execution toolkit. Built with Elixir, runs on the Erlang virtual machine

The NSE has a website which displays the option chain in near real-time. This program retrieves this data from the NSE site and then generates useful analysis of the Option Chain for the specified Index or Stock. It also continuously refreshes the Option Chain and visually displays the trend in various indicators useful for Technical Analysis.

☕ Symja - computer algebra language & symbolic math library. A collection of popular algorithms implemented in pure Java.

Powerful automatic differentiation in C++ and Python

Option pricing based on Black-Scholes processes, Monte-Carlo simulations with Geometric Brownian Motion, historical volatility, implied volatility, Greeks hedging

Оценка эффективности инвестиций с учетом комиссий, налогов (удержанных и ожидающихся), дивидендов и купонов.

Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

Machine Learning with Symbolic Tensors

Automated, smooth, N'th order derivatives of non-uniformly sampled time series data