Mastering Pairs Trading: A Complete Guide to Cointegration, Spreads, and Mean Reversion

From stationarity and integration order to hedge ratios, z-scores, rolling windows, and out-of-sample validation for real-world statistical arbitrage

import math
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels import regression
from statsmodels.tsa.stattools import adfuller, coint
import seaborn
import matplotlib.pyplot as plt
plt.style.use(’seaborn-whitegrid’)
default_size = (10, 6)
plt.rcParams[”figure.figsize”] = default_size
from quantopian import get_pricing
seed_value = 107
np.random.seed(seed_value)

The code initializes a computational environment tailored for quantitative finance analysis, specifically pairs trading, by importing libraries that enable numerical array operations (numpy), data frame manipulation (pandas), linear regression and time series diagnostics (statsmodels), and financial data retrieval (quantopian’s get_pricing). It also sets up visualization tools with seaborn and matplotlib, applying a clean grid style and standard figure dimensions to facilitate plotting of asset price relationships, spreads, or test statistics without visual distractions.