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aicloudmaster

量化投资云大师

aicm_doc

AI Cloud Master

awesome-python

A curated list of awesome Python frameworks, libraries and software

caffe

Caffe: a fast framework for deep learning. For the most recent version checkout the dev branch. For the latest stable release checkout the master branch.

chinesecloud

Chinese Cloud

darts

A python library for user-friendly forecasting and anomaly detection on time series.

deep-trading

Algorithmic trading with deep learning experiments

demo-electron-notepad

easyloggingpp

Single header only C++ logging library. It is extremely light-weight, robust, fast performing, thread and type safe and consists of many built-in features. It provides ability to write logs in your own customized format. It also provide support for logging your classes, third-party libraries, STL and third-party containers etc.

electron-quick-start

Clone to try a simple Electron app

exprtk

C++ Mathematical Expression Parsing And Evaluation Library

financial-machine-learning

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

lean

Lean Algorithmic Trading Engine by QuantConnect

libsvm

markdays

markdays.github.io

Mark days blog

mlpack

mlpack: a scalable C++ machine learning library

mongo

The Mongo Database

montecarlomethodsinfinance

iversity course -- python implementation

mootdx

通达信数据读取的一个简便使用封装

openmc

OpenMC Monte Carlo Code

pangu-weather

An official implementation of Pangu-Weather

pyalgotrade

Python Algorithmic Trading Library

python_reference

Useful functions, tutorials, and other Python-related things

quantlib

The QuantLib C++ library and extensions

robogpt

scrapy

Scrapy, a fast high-level screen scraping and web crawling framework for Python.

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,

ta-lib

Python wrapper for TA-Lib (http://ta-lib.org/).