youlxs's repositories
ChinaStockQuotes
yootech
abp
Open Source Web Application Framework for ASP.NET Core
Advanced-Deep-Trading
Mostly experiments based on "Advances in financial machine learning" book
akshare
AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库
awesome-systematic-trading
A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.
blockcerts-verifier
A Blockcerts verifier and viewer
cert-issuer
Issues Blockcerts using either the Bitcoin or Ethereum blockchain
cert-verifier
Python library for verifying Blockcerts
cert-verifier-js
Javascript library for verifying Blockcerts Certificates
ChinaStockImporter
yootech
dnn_house_price_prediction_scratch
build a deep neural network from scratch for boston house price prediction
ETDataset
The Electricity Transformer dataset is collected to support the further investigation on the long sequence forecasting problem.
Informer2020
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
Lean
Lean Algorithmic Trading Engine by QuantConnect (C#, Python, F#)
libonnx
A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.
lstm_stock_price_prediction
LSTM神经网络预测沪深300指数及其涨跌
machinelearning
ML.NET is an open source and cross-platform machine learning framework for .NET.
MagicOnion
Unified Realtime/API Engine for .NET Core and Unity.
onnx-mlir
Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure
root
The official repository for ROOT: analyzing, storing and visualizing big data, scientifically
SGX-Full-OrderBook-Tick-Data-Trading-Strategy
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
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.
wallet-android
An Android app for Blockcerts
wallet-iOS
An iOS wallet for Blockcerts
zhanghuo-resume
zhanghuo resume