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abm-python

affine

Affine term structure modeling Python package. See LICENSE for terms of use.

altnnpub

Public code for our paper https://ssrn.com/abstract=3958331

aqfed.jl

Julia package for the book "Applied Quantitative Finance for Equity Derivatives"

autograd

Efficiently computes derivatives of numpy code.

code_snippets

compfinlecture

Material for Antoine Savine's Computational Finance Lectures at Copenhagen University & Kings College London

crypto_data

Data

curves2

datascience

Curated list of Python resources for data science.

deep-reinforcement-learning-hands-on

Hands-on Deep Reinforcement Learning, published by Packt

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

economics_etudes

Economics études: books, papers and courses

fdm-book

Resources for "The Craft of Finite Difference Computing with Partial Differential Equations" by H. P. Langtangen

financialderivatives.jl

Financial derivatives modeling and pricing in Julia.

finmath-lib

Mathematical Finance Library: Algorithms and methodologies related to mathematical finance.

fplanck

Numerically solve the Fokker-Planck equation in N dimensions

fypy

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.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

hawkes

Python class for generation and parameter estimation of multivariate Hawkes processes

hawkes-1

a python package for simulation and inference of Hawkes processes.

mesa

Mesa is an agent-based modeling framework in Python

ml_finance_codes

Machine Learning in Finance: From Theory to Practice Book

mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

muzero-general

MuZero

nns_options

notebooks

Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.

notebooks-1

Notebooks on how to use Distributed Evolutionary Algorithm in Python (DEAP)

num-methods-for-pdes

Lecture material on numerical methods for partial differential equations.

numerical-mooc

A collaborative educational initiative in computational science and engineering.