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

Financial Derivatives Calculator with 168+ Models (Options Calculator)

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)

Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston

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.

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

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).

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

Quantitative finance and derivative pricing

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

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

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

📚SDE research and modelling in Finance📚

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

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.