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

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)

OptionStratLib is a comprehensive Rust library for options trading and strategy development across multiple asset classes.

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).

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).

Quantitative finance and derivative pricing

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

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.

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

Custom Neuron Decision-Making and Visual Workflow Orchestration Quantitative

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

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

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

📚SDE research and modelling in Finance📚

No description provided.

Machine Learning for Finance (FIN-418 EPFL) final project: Comparison of different option pricers for the Heston model

Determine implied volatility according to Black-Scholes dynamics.

Demonstrates how to price derivatives in a Heston framework, using successive approximations of the invariant distribution of a Markov ergodic diffusion with decreasing time discretization steps. The framework is that of G. Pagès & F. Panloup.

a use of the Heston model and BS model part of the paper "沪深300股指期权定价实证研究——基于BS、 CEV、Heston模型的对比分析" (An Empirical Study of CSI 300 Index Option Pricing Based on the BS, CEV and Heston Models). An English README.md in the files, if you need to read English text, click the README_en.md file and read the information about this project.

This is a simulation project for the seconder order discretization schemes for the CIR process.

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

Some applications in Financial Mathematics.

A professional-grade quantitative finance platform combining classic financial models and advanced AI for option pricing, risk analysis, portfolio management, and crypto derivatives. Features include Black-Scholes, Heston, Monte Carlo, GARCH, exotic options, real-time risk monitoring, AI-enhanced trading, and interactive visualizations

High-fidelity synthetic financial data generator using Heston Stochastic Volatility and Jump Diffusion.

Volatility arbitrage trading system with delta-neutral options, backtesting, live trading, and comprehensive Greeks management.

Stochastic Valuation Processes for stock prices and bond rates