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a-share

综合各种因素实现预测A股涨停股票 Based on various factors to predict daily stock price limits

amazon-personalize-samples

Notebooks and examples on how to onboard and use various features of Amazon Personalize

amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

deep_learning

Basic Deep Learning

deepctr

Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

deepfm_keras

A simple DeepFM.

deepinterestnetwork

dnn_ctr

The framework to deal with ctr problem。The project contains FNN,PNN,DEEPFM, NFM etc

eat_tensorflow2_in_30_days

Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋

factorset

factorset: 提供**A股市场因子集合，包含各类常用及特异因子计算方法，持续更新中。提供轻量级因子计算框架，高可扩展。持续更新中。

feature-engineering-made-easy

Feature Engineering Made Easy, published by Packt

keras-wide-n-deep

Reimplementation of Google's Wide & Deep Network in Keras

lagrangianrelaxationqip

Lagrangian Relaxation approach solve QIP

lstm-neural-network-for-time-series-prediction

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

numpywdl

Implement Wide & Deep algorithm by using NumPy

one-quant-workspace

用于分析A股股票特征

prediction-flow

Deep-Learning based CTR models implemented by PyTorch

prml

PRML algorithms implemented in Python

recommend

Python 3.6 下的推荐算法解析，尽量使用简单的语言剖析原理，相似度度量、协同过滤、矩阵分解等

recommend-estimators

Classical RecSys algorithms implemented by using TensorFlow Estimators

recommender-systems-samples

Codes repository for RecSys and DeepLearnig, RecSys and ReinForcement learning and Traditioinal RecSys.

sequoia

A股自动选股程序，实现了海龟交易法则、缠中说禅牛市买点，以及其他若干种技术形态

stock

30天掌握量化交易 (持续更新)

stock-1

stock，股票系统。使用python进行开发。

stock-predict

预测A股所有股票涨跌

stock_a

「A股指数预测」深度学习量化应用

stockanalyse

沪深A股每日纯技术面分析，MACD/KDJ等指标分析计算

stockpred

用RNN-LSTM方法预测A股走势

svd

Python code implementing the power method for Singular Value Decomposition

tensorflow

An Open Source Machine Learning Framework for Everyone