heston

Financial Derivatives Calculator with 171+ Models (Options Calculator)

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.

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.

Quant. Research project - Cutting-Edge project (In collaboration with Milliman & University of Paris-Saclay)

The project aims to compare the effectiveness of the Heston model and WGAN-GP in modeling financial time series data.

自研随机过程驱动的交易终端(QMT-like)：每个品种的价格过程可插拔，用于随机过程与金融数学定价研究。

Differentiable quantitative finance library in PyTorch: classical and stochastic-vol pricing, autodiff Greeks, calibration, GPU Monte Carlo (CUDA), arbitrage-penalized neural volatility surfaces

Heston model implementation with Monte Carlo simulation for stochastic volatility pricing.Implementation of the Heston Stochastic Volatility Model in Python along with it's Monte Carlo Simulation

Sixteen option pricers over six stochastic models including Heston, Bates, SABR and rough Bergomi, spanning analytic, lattice, finite-difference, COS Fourier and Monte Carlo methods.

Derivatives pricing library: Black-Scholes-Merton (16 Greeks), Monte Carlo with variance reduction, Asian/Barrier/Lookback/Digital exotics, Heston (Fourier pricing, QE simulation, SPX calibration), rough Bergomi (hybrid scheme). 1,013 tests, QuantLib-validated, 97% coverage.

SPX option pricing as imaginary-time quantum mechanics: arbitrage-free local vol in C++, backtested against SVI, Heston and the practitioner smile on 1.2M quotes

Numerical derivatives-pricing and model-validation core: Monte Carlo, finite-difference PDEs, Greeks, and SABR/Heston calibration, each challenged against closed forms and QuantLib, with an SR 26-2-style validation report.

Heston stochastic-volatility calibration for option pricing, comparing classical optimization and deep-learning methods on SPX implied-volatility data.

Crypto options pricing and volatility-surface engine: Black-Scholes/Heston/Merton/SABR, Gatheral SVI calibration, model-free no-arbitrage checks, and mispricing signals netted against costs.

Reproduction of J.P. Morgan's Deep Hedging papers (2019 pathwise, 2022 risk-averse actor-critic), checked number by number

Rust library for option pricing: Binomial, Black-Scholes, Heston and intrinsic value, with Least Squares Monte Carlo and random-forest regression for American exercise.

High-performance options Greeks engine — Black-Scholes/Heston, AVX2-vectorised pricing, Google Benchmark suite

Dynamic Collar Hedging — PFE ECE Paris × EY Paris. Multi-models pricing (Black-Scholes, Heston, Hull-White, SABR), Greeks engine, backtesting, FastAPI + React.

FFT, Monte Carlo, and finite difference