用于记录我找到的好的学习资源，以便厘清学习的优先度
一、量化平台&框架
1.BackTrader
QuantWorld2022/backtrader
(github.com)
2.Vnpy
vnpy/vnpy_algotrading:
VeighNa框架的算法交易模块 (github.com)
3.Qlib
[microsoft/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.
(github.com)](https://github.com/microsoft/qlib)
二、Python库
1.基于机器学习的凸优化库
cvxgrp/cvxpylayers:
Differentiable convex optimization layers (github.com)
2.mlflow
mlflow 用于学习如何管理machine
learning项目周期，并以此作为突破口加深对qlib work flow的认知
三、机器学习
1.Qlib
[microsoft/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.
(github.com)](https://github.com/microsoft/qlib)
2.深度学习
d2l-ai/d2l-zh:
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
(github.com)
3.AAAMLP
https://ytzfhqs.github.io/AAAMLP-CN/
https://github.com/abhishekkrthakur/approachingalmost/tree/master
四、协方差学习
shit我居然看不懂一点
正确理解
Barra 的纯因子模型
Ledoit
and Wolf 的协方差矩阵收缩之旅
协方差矩阵的估计和评价方法【天风金工因子选股系列之七】
Machine Learning for
Asset Managers 笔记-Chapter2 - 知乎 (zhihu.com)
四、协方差学习
