heston-model

Collection of notebooks about quantitative finance, with interactive python code.

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)

A model free Monte Carlo approach to price and hedge American options equiped with Heston model, OHMC, and LSM

Option pricing with various models (Black-Scholes, Heston, Merton jump diffusion, etc) and methods (Monte Carlo, finite difference, Fourier).

Quantitative finance and derivative pricing

Option pricing function for the Heston model based on the implementation by Christian Kahl, Peter Jäckel and Roger Lord. Includes Black-Scholes-Merton option pricing and implied volatility estimation. No Financial Toolbox required.

A UI-friendly program calculating Black-Scholes options pricing with advanced algorithms incorporating option Greeks, IV, Heston model, etc. Reads input from users, files, databases, and real-time, external market feeds (e.g. APIs).

Modelling the implicit volatility, using multi-factor statistical models.

Vollab (Volatility Laboratory) is a python package for testing out different approaches to volatility modelling within the field of mathematical 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.

Custom Neuron Decision-Making and Visual Workflow Orchestration Quantitative

Stochastic volatility models and their application to Deribit crypro-options exchange

Low-latency options pricing engine in Rust. BSM, Black-76, Heston, Bates (jumps), Local Vol (Dupire), Monte Carlo (Euler/Andersen QE). Adaptive Gauss-Kronrod CF pricers, full analytic Greeks, forward-mode AD (incl. jump sensitivities), Halley IV solver, LM/DE global calibration, no-arbitrage repair, Rayon parallelism. CI + clippy, 0 warnings.

Closed-form solutions and fast calibration & simulation for SABR-based models with mean-reverting stochastic volatility

Arbitrage-free volatility surface construction with SVI & Heston calibration. Python toolkit for options pricing and risk management.

We apply Finite Element Method (FEM) for option pricing problem under Heston's Model.

Institutional-grade quantitative finance terminal: C++20 engine + Python. Options pricing (Black-Scholes, Heston, Merton, Monte Carlo, PDE), Kalman filtering, GARCH forecasting, 3D volatility surfaces, backtesting, walk-forward optimization, Markowitz portfolios - on live market data.