Pairs trading using Kalman Filter in Python
(Thanks to Chamundeswari Koppisetti for providing the code.)
Let us start by importing the necessary libraries for Kalman Filter
# Import a Kalman filter and other libraries
!pip install pykalman
!pip install qq-training-wheels auquan_toolbox –upgrade
from pykalman import KalmanFilter
import numpy as np
import pandas as pd
from scipy import poly1d
from datetime import datetime
import matplotlib.pyplot as plt
plt.rcParams[‘figure.figsize’] = (10,7)
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).
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.
# Define path where data file is saved in your system
#path = ‘../data/’
data = pd.read_csv(path +’data.csv’, index_col =’Date’)
data[‘ratio’] = data[‘BAJAJ’]/ data[‘HERO’]
stock_1 = data[‘BAJAJ’]
stock_2 = data[‘HERO’]
# Calculate the hedge ratio for pairs trading
The output will be as follows:
Hyperparameters of Kalman Filter can be changed for instance:
- Multi dimensional transition matrices, to use more of past information for making expected results at each point
- Different values of observation and transition covariance
kf = KalmanFilter(transition_matrices = ,
observation_matrices = ,
initial_state_mean = 0,
initial_state_covariance = 1,
mean, cov = kf.filter(ratio.values)
mean, std = mean.squeeze(), np.std(cov.squeeze())
plt.plot(ratio.values – mean, ‘m’, lw=1)
plt.plot(np.sqrt(cov.squeeze()), ‘y’, lw=1)
plt.plot(-np.sqrt(cov.squeeze()), ‘c’, lw=1)
plt.title(‘Kalman filter estimate’)
plt.legend([‘Error: real_value – mean’, ‘std’, ‘-std’])
Stay tuned for the next installment, in which Rekhit will showcase how to use Python for a Pairs trading strategy script.
Download the full code: https://blog.quantinsti.com/kalman-filter/.
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