Algorithmic Trading — Pairs Trading
In this article we will describes a detailed pairs trading strategy illustrated with code snippets.
The article provides a step-by-step guide to pairs trading, using code and real-world examples.
Let’s start coding:
# Import Dependencies
from cointegration_analysis import estimate_long_run_short_run_relationships, engle_granger_two_step_cointegration_test
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
This code imports two functions, estimate_long_run_short_run_relationships and engle_granger_two_step_cointegration_test, from a module called cointegration_analysis. It also imports some other modules like pandas, numpy, and matplotlib.pyplot.
The estimate_long_run_short_run_relationships function is used to estimate the long-run and short-run relationships between two time series data, which is important for pairs trading. The engle_granger_two_step_cointegration_test function is used to perform a two-step cointegration test to check if two time series are cointegrated, meaning they move together in the long run.
The pandas and numpy modules are used for data manipulation and analysis, while the matplotlib.pyplot module is used to plot the results of the analysis. The %matplotlib inline line is a Jupyter Notebook magic command that allows plots to be displayed directly in the notebook.
Data Analysis
In this section we visualise our data which helps us further understand what strategies to adapt.