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, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

Quant/Algorithm trading resources with an emphasis on Machine Learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

Notes/links on math and science, including statistics, bayes, cmpsc, quant trading, machine learning, etc

:books: Quant 教程整理

This code is for the book

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This is the code for "Quantum Machine Learning" By Siraj Raval on Youtube

Companion code for the "Self Learning Quant" blog post

This repository contains curated roadmaps, case studies, and learning resources for finance careers involving programming and data. Each file offers a structured guide to mastering the tools, languages, and concepts needed in that field. Ideal for students, aspiring quants, and finance professionals exploring the intersection of tech and finance

Study resources for learning quantum computing

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This repository contains the source code used to produce the results presented in the paper "Machine learning method for state preparation and gate synthesis on photonic quantum computers".

A Practical Guide to Quantum Machine Learning and Quantum Optimization, published by Packt

The code used for the free quants@dev Webinar series on Reinforcement Learning in Finance

使用Python进行量化投资的学习报告

This project studies the intrinsic relationship between the stocks’ multiple factors and the investment value of the stocks listed in China Securities Index 800 Index through the machine method. The investment system pipeline has been implemented including data acquirement, data preprocessing, model tuning and selection based on the XGBoost boosted tree model.

A transfer learning approach applied to hybrid neural networks composed of classical and quantum elements.

Implementation of "Advances in Financial Machine Learning" quant trading strategies in python (building upon Freqtrade / FreqAI open source project)

This repo is to contain material from the University of Toronto Quantum Machine Learning Course by Prof. Peter Wittek on edX

This is the code for "Quantum Machine Learning LIVE" By Siraj Raval on Youtube

Learn Quantum Machine Learning

Quantum Machine Learning is Quantum Computing + Machine Learning 🧠. There are various benefits associated with using QML: It helps to speed up the training process, working on high dimensional data is possible and more 📊.

聚宽网量化投资策略编写的学习文档

[iewoai]量化投资学习，多因子、三因子、五因子、索罗斯投资策略

This project is one of the Qiskit mentorship programs to replicate two papers arXiv:1905.10876 and arXiv:2003.09887 using the Qiskit environment. We evaluate the parameterized quantum circuit, reproduce the expressibility and entangling capability of the 19 circuits, and the classification accuracy.

Work on Evidential Deep Learning to Quantify Classification Uncertainty

💰Step by steps to explore and analysis the data, build model by utilizing machine learning model or deep learning model, the tools include numpy, pandas,scipy,sklearn,tensorflow etc, or big data analysis with Apache Spark.

Detecting anomalies in quant finance using Quantum machine learning