🙌 OpenHands: Code Less, Make More

chat with private and local large language models

An AI Data Science Team

A reactive notebook for Python — run reproducible experiments, execute as a script, deploy as an app, and version with git.

Arbitrary-steps Image Super-resolution via Diffusion Inversion

Voice-Enabled Math Tutor Powered by Groq that Calculates and Renders Live Problems and Instruction with LaTeX in Seconds!

Pandas Chaining Ninja is a CheatSheet-like repo that helps you to writing pandas code in a more readable and maintainable way using the "method chaining" approach

[NOT ACTIVELY MAINTAINED] Tulipy - Financial Technical Analysis Indicator Library (Python bindings for Tulip Charts)

Technical Analysis Indicator Function Library in C

Financial AI with Python

Nosia is a platform that allows you to run an AI model on your own data. It is designed to be easy to install and use.

MLX: An array framework for Apple silicon

Minimalist and opinionated feed reader

Source code for Algorithmic Trading with Python (2020) by Chris Conlan

150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data

Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…

Performance analysis of predictive (alpha) stock factors

uBO Lite home (MV3)

Transparent and Efficient Financial Analysis

This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.

Official implementation of "MIMO: Controllable Character Video Synthesis with Spatial Decomposed Modeling"

Code for Machine Learning for Algorithmic Trading, 2nd edition.

ffn - a financial function library for Python

A sentiment analyzer package for financial assets and securities utilizing GPT models.

Open Machine Learning Course

Multiple Pairwise Comparisons (Post Hoc) Tests in Python

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

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