{"id":81875,"date":"2021-03-31T12:15:00","date_gmt":"2021-03-31T16:15:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=81875"},"modified":"2023-02-24T14:51:34","modified_gmt":"2023-02-24T19:51:34","slug":"how-to-create-kalman-filter-in-python-part-vi","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/how-to-create-kalman-filter-in-python-part-vi\/","title":{"rendered":"How to Create Kalman Filter in Python \u2013 Part VI"},"content":{"rendered":"\n

See Part I<\/a>, Part II<\/a> , Part III<\/a>,  Part IV<\/a> and Part V<\/a> of this series to get started with the statistical terms and concepts used in Kalman Filter.<\/em><\/p>\n\n\n\n

Pairs trading using Kalman Filter in Python<\/h2>\n\n\n\n

(Thanks to Chamundeswari Koppisetti<\/a> for providing the code.)<\/p>\n\n\n\n

Let us start by importing the necessary libraries for Kalman Filter<\/p>\n\n\n\n

\n# Import a Kalman filter and other libraries
\n!pip install pykalman
\n!pip install qq-training-wheels auquan_toolbox –upgrade
\nfrom pykalman import KalmanFilter
\nimport numpy as np
\nimport pandas as pd
\nfrom scipy import poly1d
\nfrom datetime import datetime

\n\nimport matplotlib.pyplot as plt
\n%matplotlib inline
\nplt.style.use(‘seaborn-darkgrid’)
\nplt.rcParams[‘figure.figsize’] = (10,7)\n<\/p>\n\n\n\n

We will consider the 4 year (Aug 2015 – Aug 2019) Adjusted Close price data for Bajaj Auto Limited (BAJAJ-AUTO.NS) and Hero MotoCorp Limited (HEROMOTOCO.NS).<\/p>\n\n\n\n

We have included the data file in the zip file along with the code for you to run on your system later. The link to download the files can be found at the end of the blog<\/a>.<\/p>\n\n\n\n

\n# Define path where data file is saved in your system
\n#path = ‘..\/data\/’
\ndata = pd.read_csv(path +’data.csv’, index_col =’Date’)
\ndata[‘ratio’] = data[‘BAJAJ’]\/ data[‘HERO’]
\nstock_1 = data[‘BAJAJ’]
\nstock_2 = data[‘HERO’]

\n\n# Calculate the hedge ratio for pairs trading
\nratio =stock_1\/stock_2
\ndata.head()
\n<\/p>\n\n\n\n

The output will be as follows:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Hyperparameters of Kalman Filter can be changed for instance:<\/p>\n\n\n\n