Collection of notebooks about quantitative finance, with interactive python code.
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Updated
Oct 22, 2024 - Jupyter Notebook
Collection of notebooks about quantitative finance, with interactive python code.
High-frequency statistical arbitrage
📒 A collection of notes exploring Quantitative Finance concepts with Python
A statistical toolbox for diffusion processes and stochastic differential equations. Named after the Brownian Bridge.
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
Black Scholes Option Pricing calculator with Greeks and implied volatility computations. Geometric Brownian Motion simulator with payoff value diagram and volatility smile plots. Java GUI.
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).
Lorenz attractors, statistical mechanics, nonlinear dynamical systems, computational physics.
An R Package for Monte Carlo Option Pricing Algorithm for Jump Diffusion Models with Correlational Companies
CAAStools is a bioinformatics toolbox that allows the user to identify and validate CAAS on MSA of orthologous proteins.
A python code to calculate the Brownian motion of colloidal particles in a time varying force field.
Fast and slight DLA3D / DLA2D (Diffusion Limited Aggregation)
Case Studies in Finance: Stock Price Valuation using Black-Scholes using Brownian Motions, Investment Project comparing Stocks and Bonds, Determining Pension Fund's Premium. (Case Study Papers and Code)
Resources for Quantitative Finance
Ornstein-Uhlenbeck models for phylogenetic comparative hypotheses
Python solver for the Brownian, Stochastic, or Noisy Differential Equations
Research and programming of various interesting mathematical examples
Oldschool PlasmaFractal revival with Perlin Noise and WebGL
Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.
generate brown noise in python
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