Popular GitHub Repositories for Backtesting Frameworks

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Pocket Flow Tutorial Project: Turns GitHub repo into Easy Tutorial with AI

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including OpenAI Agents SDK, CrewAI, Langchain, Autogen, AG2, and CamelAI

? AgentVerse ? is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation

The convertor/conversion of deep learning models for different deep learning frameworks/softwares.

Algorithmic Trading in Python with Machine Learning

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

? Everything you need to create an LLM Agent—tools, prompts, frameworks, and models—all in one place.

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

Open source platform for AI Engineering: OpenTelemetry-native LLM Observability, GPU Monitoring, Guardrails, Evaluations, Prompt Management, Vault, Playground. ?? Integrates with 50+ LLM Providers, VectorDBs, Agent Frameworks and GPUs.

Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

fastquant — Backtest and optimize your ML trading strategies with only 3 lines of code!

Python AutoML for Trading Systems and Sports Betting

A curated list of tools, frameworks, and platforms to build powerful LLM applications through effective context engineering.

A curated list of resources about AI agents for Computer Use, including research papers, projects, frameworks, and tools.

This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.

Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.