If you plan on using this code along with the equity database, please reference the following link before moving forward: https://github.com/aconstandinou/data-warehouse-build

Otherwise, you will need to change all mean reversion tests to satisfy where you plan on pulling your data.

You need to have PostgreSQL and Python installed.

Here are the python libraries used in the mean reversion tests. Version numbers are included as well.

• psycopg2 version 2.7.5 (dt dec pq3)
• numpy 1.14.3
• pandas 0.23.0
• statsmodels 0.9.0
• statsmodels.api
• statsmodels.tsa.stattools
• matplotlib 2.2.2
• math
• os
• datetime

1. All tests perform mean reversion analysis on data held in a pandas dataframe. Therefore if you plan on using the methods within each python script for your own use, ensure that the input variable to the method is a pandas dataframe.
2. Stock data used in these tests were from date range '2004-12-30' to '2010-12-30' which equates to around half of our data samples.

1. Performs the ADF test with lags of 2 to 20.
2. Output two files: one file for t-test < 1% critical level, and one file for t-test < 5% critical level

1. Calculates the Hurst Exponent with range 2 to 100.
2. Output a file of tickers that passed the HE test where HE < 0.5. This file is used in the half_life.py script for further analysis.

1. Output file from he_test.py is needed to run this python script.
2. Performs the Half-Life calculation on stocks that passed HE criteria of < 0.5.
3. Output two files: one file of tickers where Half-Life <= 50.0, second file of tickers where Half-Life > 50.0.

• Spyder IDE version 3.2.8
• Python 3.6.5
• PostgreSQL 9.5.9

Antonio Constandinou