This project is for Financial Computing Service course which is hosted by Shanghai Jiao Tong University with Morgan Stanley.

From wikipedia

Algorithmic trading is a method of executing a large order(too large to fill all at once) using automated pro-programmed trading instructions accounting for variables such as time, price, and volume to send small slices of the order (child orders) out to the market overtime.

You need all these third-party python library to run the whole program. (Note that we just test the project under python 3.5.2, but it should work with any python version that support all these library)

aiohttp
requests
pandas
tushare
html5lib
pymongo
mongoengine
arrow
flask
flask-mongoengine
flask-bcrypt
flask-debugtoolbar
flask-login
redis

libs.policy holds policy of slicing orders.(including a trival twap, vwap by n days mean volume and vwap with prediction)

libs.back_test.py for backtest the performance of one of the three policy.

libs.data_persist_mongodb.py persists historical tick data to mongodb.

libs.data_persisit_redis.py persists realtime quotes into redis server.

libs.OrderMatch does order match inside our system with the real market.

predict contains machine learning algorithm of predicting the trend and volume of a security

static hosts the static resources needed by flask web framework.

template hosts html files need by flask web framework.

app.py auth.py forms.py models.py user.py are all python files for run flask web framework.

presentation keynotes for presentation our project.

def function_name(param1, param2):
    '''describe what this functino does in one line
    
    multipline to describle what this function does
    
    :param param1: first arg
    :type param1: first arg type
    :param param2: second arg
    :type param2: second arg type
    :return return_value
    :rtype: return type of the return value
    '''