andrewcztrack's starred repositories
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.
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
FinanceOps
Research in investment finance with Python Notebooks
yahooquery
Python wrapper for an unofficial Yahoo Finance API
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)
RecyclableGP
Recyclable Gaussian Processes
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.