As the datasets are too large, we do not upload them on github, u can find a sample of datafile what we use under the ./dataset/training_data/600000.csv.

split_train_data.py helps extract the original dataset file traing_data.zip and split them with respect to stock codes, saving them under ./dataset/training_data

dbloader.py helps load stocks data via its stock code and assigned date interval

Runing for day price prediction: day_model.py

Seasonal Components of the day model

Runing for minute price prediction: minute_model.py

Model Structure

Search for high correlated stock pairs, saved under ./top_corr/ runing: find_corr_top.py

Search for significant stock pairs, saved under ./rule/trade_rule.csv runing: cointegration.py
Spread price of pair(600015,600016) with high significant

OLS fit on Spread price of pair(600015,600016)

A pvalue(1-pvalue) test matrix of 600000 with some other stocks

numpy,pandas,matplotlib

day_model.py: fbprophet==0.3.post2
minute_model.py: keras, tensorflow, sklearn

find_corr_top.py: None except Public packages
cointegration.py: statsmodels, seaborn