#Option-pricing
Showing 42 of 42 repositories tagged #option-pricing, ranked by stars
Collection of notebooks about quantitative finance, with interactive python code.
Rust library for quantitative finance.
A nimble options research and backtesting library for Python
Courses, Articles and many more which can help beginners or professionals.
C++ 17 based library (with sample applications) for testing equities, futures, currencies, etfs & options based automated trading ideas using DTN IQFeed real time data feed and Interactive Brokers (IB TWS API) for trade execution. libtorch/lstm/cuda demo. Support for Alpaca & Phemex. Notifications via Telegram.
Quantitative Finance tools
A Python library for mathematical finance
OptionStratLib is a comprehensive Rust library for options trading and strategy development across multiple asset classes.
Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston
Quant Option Pricing - Exotic/Vanilla: Barrier, Asian, European, American, Parisian, Lookback, Cliquet, Variance Swap, Swing, Forward Starting, Step, Fader
Python Financial ENGineering (PyFENG package in PyPI.org)
High-performance quantitative finance in Rust — 120+ stochastic processes, option pricing, calibration, fixed income, risk & copulas, with SIMD/GPU acceleration and Python bindings.
Vanilla and exotic option pricing library to support quantitative R&D. Focus on pricing interesting/useful models and contracts (including and beyond Black-Scholes), as well as calibration of financial models to market data.
Vanilla option pricing and visualisation using Black-Scholes model in pure Python
A python program to implement the discrete binomial option pricing model
I have been deeply interested in algorithmic trading and systematic trading algorithms. This Repository contains the code of what I have learnt on the way. It starts form some basic simple statistics and will lead up to complex machine learning algorithms.
A collection of educational notebooks covering key mathematical concepts and their applications in quantitative finance
Quantitative finance and derivative pricing
C Bayer, B Stemper (2018). Deep calibration of rough stochastic volatility models.
A simple real-time Open Interest & Strategy Profit and Loss Visualizer for Indian Benchmark Indices and F&O Stocks inspired by Sensibull. The app is built with React, Material UI, D3 and Node.
Simulated GBM using MC simulation, estimated option' Greeks using numerical methods such as finite difference, pathwise derivative estimate and likelihood ratio methods. Lastly, implemented binomial tree option pricing to price American option.
Solving High Dimensional Partial Differential Equations with Deep Neural Networks
using the Inverse-Transform method to speed up options pricing simulations in R
C++ implementation of rBergomi model
A high-performance Rust library for options market making infrastructure, providing a complete Option Chain Order Book system built on top of OrderBook-rs, PriceLevel, and OptionStratLib.
Stochastic models to price financial options
A (very) fast Rust library for quantitative finance.
Implementation of option pricing models using Numba that performs better. This entire project has utilized as little libraries as possible, even though certain models have their own Machine Learning Model with assessment and performance.
Option pricing using Black-Scholes model, Bachelier model, Binomial Trees and Monte Carlo simulation under different stochastic processes
Formally verified mathematical finance in Lean 4. Black–Scholes/Greeks/PDE, Itô calculus, FTAP/Girsanov, CRR→BS convergence, Merton jump-diffusion.
Resources for Quantitative Finance
Financial Engineering in R
DeFiMath is open-source, high-performance Solidity library for Ethereum smart contract development
Piecewise quadratic approximation to the Black-Scholes value of a straddle vs. stock price
Bayer, Friz, Gulisashvili, Horvath, Stemper (2017). Short-time near-the-money skew in rough fractional volatility models.
Dynamic Hedging Backtesting Engine
R Finance packages not listed in the Empirical Finance Task View
Neural network framework for volatility surface approximation and calibration. Supports rough Heston/Bergomi, random grids, multi-regime architectures.
Small projects for Quantitative Financial Applications. Beginner level python codes
Package for deploying lattice models for option pricing
Quantitative Finance Library & Option Trading Tool
Q-Variance Challenge: Can any continuous-time stochastic-volatility model reproduce q-variance?