Stars
This is a repo with links to everything you'd ever want to learn about data engineering
Practical Guide to Applied Conformal Prediction, published by Packt
This repository helps teach people how to correctly define and create cumulative tables!
An introduction to data science in Python, for people with no programming experience.
Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, i…
A Python package for aggregating and normalizing historical data from popular and free financial APIs.
Python implementation of binary and multi-class Venn-ABERS calibration
Extra blocks for scikit-learn pipelines.
This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.
Code implementation of the Quantigic 101 Formulaic Alphas
A Python Library of Curated Disparity Testing Metrics for Use in Real-World Settings
Generate relevant synthetic data quickly for your projects. The Databricks Labs synthetic data generator (aka `dbldatagen`) may be used to generate large simulated / synthetic data sets for test, P…
Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications
A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Common financial technical indicators implemented in Pandas.
Investment Research for Everyone, Everywhere.
This repository is to host template for calculating ROI on Artificial Intelligence projects
A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.