kumnikhil

FinMathematics

Books

Object-Oriented-Data-Structures-Cpp

Computational-Finance-Course

Here you will find materials for the course of Computational Finance

DeepBSDE

Python code for solving partial differential equations (PDEs) using deep learning. Specifically, we provide implementations for solving the following PDEs

Deep-PPDE

Deep Learning methods to solve path-dependent PDEs / to price path-dependent derivatives like exotic options

Option-Pricing

European/American/Asian option pricing module. BSM/Monte Carlo/Binomial

Bard-API

The unofficial python package that returns response of Google Bard through cookie value.

pandas-ai

Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.

QuantLib-with-Python-Blog-Examples

Financial security modelling with Python and QuantLib

MonteCarlo

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

pymc-examples

Examples of PyMC models, including a library of Jupyter notebooks.

streamlit-multiapps

A simple framework in python to create multi page web application using streamlit

pymle

Maximum Likelihood estimation and Simulation for Stochastic Differential Equations (Diffusions)

Advanced-Term-Structures

Our project extends the classical models such as Vasicek and CIR to incorporate the effects of jump-risks in the market. We explore modern methods to price and calibrate such models and evaluate their pricing performance with respect to classical models and the observed market prices.

Financial-Derivative-Analysis-and-Simulation

Pricing and Analysis of Financial Derivative by Credit Suisse using Monte Carlo, Geometric Brownian Motion, Heston Model, CIR model, estimating greeks such as delta, gamma etc, Local volatility model incorporated with variance reduction.(For MH4518 Project)

deep_rough_calibration

C Bayer, B Stemper (2018). Deep calibration of rough stochastic volatility models.

Quant

pfhedge

PyTorch-based framework for Deep Hedging

deephedging

Implementation of the vanilla Deep Hedging engine

fin-ml

This github repository of "Machine Learning and Data Science Blueprints for Finance". Please star.

rl-trader

This MLOps project productionizes a Deep Reinforcement Learning agent with a scalable, distributed data streaming infrastructure using Kafka and Ray. A thorough walkthrough of the code is described in this article on medium: https://ryanraymartin.medium.com/deep-reinforcement-learning-for-stock-trading-with-kafka-and-rllib-d738b9634675

gym-anytrading

The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)

FinRL_Podracer

Cloud-native Financial Reinforcement Learning

Momentum-Trading-Example

An example algorithm for a momentum-based day trading strategy.

gs-quant

Python toolkit for quantitative finance

tf-quant-finance

High-performance TensorFlow library for quantitative finance.

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

torchsde

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

gwp

An MCMC implementation of the generalized Wishart process

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)