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alphalens

Performance analysis of predictive (alpha) stock factors

alphasickle

多因子指数增强策略/多因子全流程实现

alphatrading

An workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implement APT model, BARRA's risk model and dynamic multi-factor model in this project.

barra-multiple-factor-risk-model

Barra-Multiple-factor-risk-model

caliper

Micro-benchmarking library for Java

data-science-for-beginners

10 Weeks, 20 Lessons, Data Science for All!

easyquotation

实时获取新浪 / 腾讯 的免费股票行情 / 集思路的分级基金行情

financial-machine-learning

A curated list of practical financial machine learning tools and applications.

git-tips

:trollface:Git的奇技淫巧

god-of-bigdata

专注大数据学习面试，大数据成神之路开启。Flink/Spark/Hadoop/Hbase/Hive...

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

java-matrix-benchmark

Java Matrix Benchmark is a tool for evaluating Java linear algebra libraries for speed, stability, and memory usage.

jqfactor_analyzer

聚宽单因子分析工具

lectures

machine_learning_examples

A collection of machine learning examples and tutorials.

numpy-100

100 numpy exercises (with solutions)

pyportfolioopt

Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

python-guide

Python best practices guidebook, written for humans.

questdb

An open source SQL database designed to process time series data, faster

spectre

GPU-accelerated Factors analysis library and Backtester

sql-parser

The Akiban SQL Parser provides a complete, production-quality Java parser for the SQL language. It defines the SQL grammar as implemented by Akiban, but can be used independently. It is derived from the Apache Derby parser.

stock-prediction-models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

teiid

Teiid is a data virtualization system that allows applications to use data from multiple, heterogenous data stores.

tips

Most commonly used git tips and tricks.

ustc-cs-courses-resource

:heart:**科学技术大学计算机学院课程资源(https://mbinary.xyz/ustc-cs/)