From Stationarity to Pairs Trading: A Practical Time-Series Playbook for Stock Market Forecasting

A hands-on walkthrough of stock-market forecasting with log returns, ARIMA and SARIMAX models, GARCH volatility, VAR macro signals, cointegration tests, and pairs-trading backtests.

Use the url at the end of this article to download the entire code!

import warnings
warnings.filterwarnings('ignore')

The purpose here is simply to quiet down warning messages for the rest of the notebook. Python libraries often emit warnings when they want to flag something that is technically valid but worth noticing, such as a deprecated feature or a numerical issue that may affect results. By telling the warnings system to ignore them, the notebook keeps the screen cleaner so the focus stays on the plots and analysis rather than on extra messages. Nothing visible appears in the saved output because this step only changes how future warnings are handled; it does not compute a result or display anything on its own.

%matplotlib inline

import pandas_datareader.data as web
import numpy as np

import statsmodels.tsa.api as tsa
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.stattools import acf, q_stat, adfuller
from scipy.stats import probplot, moment

import matplotlib.pyplot as plt
import seaborn as sns

The purpose here is to set up the notebook’s working environment for time series analysis and plotting. First, inline plotting is enabled so that any charts created later will appear directly inside the notebook rather than in separate windows. That makes it easier to inspect time series plots and diagnostics as the analysis unfolds.

After that, the main libraries needed for the rest of the notebook are imported. One package is used to pull financial and economic data from online sources, another provides numerical tools, and a group of statsmodels functions supplies the time series methods that will be used later for things like autocorrelation plots, partial autocorrelation plots, the autocorrelation function itself, Ljung-Box style Q statistics, and the Augmented Dickey-Fuller stationarity test. The SciPy imports add statistical helpers for Q-Q plots and moment calculations, which are useful for checking whether transformed data looks more like a stable, well-behaved series. Finally, Matplotlib and Seaborn are brought in for plotting and styling.