lemon234071

HowToLiveLonger

程序员延寿指南 | A programmer's guide to live longer

machine-learning-for-trading

Code for Machine Learning for Algorithmic Trading, 2nd edition.

tsfresh

Automatic extraction of relevant features from time series:

financial-machine-learning

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

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

awesome-quant

**的Quant相关资源索引

shareOI

算法竞赛课件分享

QuantsPlaybook

量化研究-券商金工研报复现

Machine-Learning-for-Algorithmic-Trading-Second-Edition

Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.

pmdarima

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

Hands-On-Machine-Learning-for-Algorithmic-Trading

Hands-On Machine Learning for Algorithmic Trading, published by Packt

Undergraduate

Collection of undergraduate course homework and projects

deepdow

Portfolio optimization with deep learning.

Temporal_Relational_Stock_Ranking

Code for paper "Temporal Relational Ranking for Stock Prediction"

learn2018-autodown

清华大学新版网络学堂课程自动下载脚本 / A python script to clone all files from learn.tsinghua.edu.cn

Barra_CNE6

Barra CNE6 因子构建

DL4Stock

This is the project for deep learning in stock market prediction.

Barra_CNE5

Provide risk forecasts by Barra China Equity Model

QuantStudio

Deep-Portfolio-Theory

Autoencoder framework for portfolio selection (paper published by J. B. Heaton, N. G. Polson, J. H. Witte.)

Quant-AI-OR-Math-Statistics

stock

stock

Barra-Model

An internship project: Implement Barra model to take risk or style factor attribution based on multi-factor model.

wavelet-denoising

Some algorithms of wavelet denoising based on PyWavelets.

Models-for-Intraday-Trading-Volume-Prediction

Having effective intraday forecast for the level of trading volume is of vital importance to algorithmic trading and portfolio management since it attempts to minimize transaction costs by optimally scheduling and placing. The purpose of this project is to create dynamic statistical models of intraday trading volume prediction (in Python). By assuming the stable U shape distribution of intraday trading volume, we apply Deterministic blend, Lognormal Bayesian, Kalman filter and ARIMA model to estimate and generate out of sample forecast on 12 US equity sector ETFs. Results show that some of the proposed methods are able to obviously outperform common volume forecasting methods.

ridgecvtest

量化交易股票预测系统

shuffle

Python下shuffle几百G文件

Machine-Learning-Case-Studies

机器学习项目

Store-Turnover-Forecast

turnover-forecasting-ETS-ARIMA

Experimenting with ETS and ARIMA to model retail turnover