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Zhen Liu's Projects

arctic icon arctic

High performance datastore for time series and tick data

awesome-quant icon awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

backtrader icon backtrader

Python Backtesting library for trading strategies

bike-share-data-best-practices icon bike-share-data-best-practices

This repo is designed to gather bike share data best practices AND socialize a list of open and free tools to hack on bike share data. This grows from Council Member Brad Lander introducing Int. No 1117-2013 on 24 July 2013. This is a local law to amend the administrative code of the City of New York, in relation to requiring the complication of Citi Bike usage data.

bootcamp2018 icon bootcamp2018

Repository of syllabi, lecture notes, Jupyter notebooks, code, and problem sets for OSM Lab Boot Camp 2018

bt icon bt

bt - flexible backtesting for Python

colorfill icon colorfill

Takes an image, generates a coloring page, and allows you to color the image

data-science-ipython-notebooks icon data-science-ipython-notebooks

Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

docker-stacks icon docker-stacks

Opinionated stacks of ready-to-run Jupyter applications in Docker.

ib_insync icon ib_insync

Python sync/async framework for Interactive Brokers API

ibc icon ibc

Automation of Interactive Brokers TWS. You can download the latest release here: https://github.com/ibcalpha/ibc/releases/latest

interpy-zh icon interpy-zh

📘《Python进阶》（Intermediate Python 中文版）

jekyll-bootstrap icon jekyll-bootstrap

The quickest way to start and publish your Jekyll powered blog. 100% compatible with GitHub pages.

jupyter icon jupyter

Jupyter metapackage for installation, docs and chat

kaggle-titanic icon kaggle-titanic

A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstartes basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

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