andrewcztrack

qlib

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

FinRL

FinRL: Financial Reinforcement Learning. 🔥

tf-quant-finance

High-performance TensorFlow library for quantitative finance.

neural_prophet

NeuralProphet: A simple forecasting package

Riskfolio-Lib

Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

pmdarima

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

FinanceOps

Research in investment finance with Python Notebooks

yahooquery

Python wrapper for an unofficial Yahoo Finance API

mystic

constrained nonlinear optimization for scientific machine learning, UQ, and AI

LaMCTS

The release codes of LA-MCTS with its application to Neural Architecture Search.

torchcde

Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.

timeseers

Time should be taken seer-iously

aiif

Jupyter Notebooks and code for the book Artificial Intelligence in Finance (O'Reilly) by Yves Hilpisch.

pyFTS

An open source library for Fuzzy Time Series in Python

fullstack-trading-app

A full stack Python app for trading using the Alpaca API

simdkalman

Python Kalman filters vectorized as Single Instruction, Multiple Data

boost-histogram

Python bindings for the C++14 Boost::Histogram library

neuralRDEs

Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)

cdml-neurips2020

This repository captures source code and data sets for our paper at the Causal Discovery & Causality-Inspired Machine Learning Workshop at Neural Information Processing Systems (NeurIPS) 2020.

Gradient_Starvation

Gradient Starvation: A Learning Proclivity in Neural Networks

qc_portfolio_optimization

A program that implements the portfolio optimization experiments using a hybrid quantum computing algorithm from arXiv:1911.05296. The code was developed as part of the 2020 Quantum mentorship program. Many thanks to my mentor Guoming Wang from Zapata Computing!

meta_learning_pacoh

Meta-learning Gaussian process (GP) priors via PAC-Bayes bounds

NetworkDOS

Network Density of States (https://arxiv.org/abs/1905.09758) (KDD 2019)

mkalgo

Mueen-Keogh Algorithm for finding timeseries motifs

SharpeR

R package for inference on the Sharpe ratio.

RecyclableGP

Recyclable Gaussian Processes

Dynamic_Local_Regret_for_Non-convex_Online_Forecasting_NeurIPS2019

low_rank_forecasting_code

Code for "Low Rank Forecasting" paper.

Binary_classification_phase_separation

Python code for the paper "Binary classification as a phase separation process", by Rafael Monteiro. Further information can be found in the tutorial website below.

IDY

Code and data for author's article A New Stock Market Valuation Measure with Applications in Equity-Linked Annuities