Pair Trading Sounds Smart—But How Do You Actually Find Two Related Stocks That Work?

How to find two “related” stocks or crypto that behave like a pair? If you’ve opened up a trading platform, stared at endless tickers, and felt paralyzed, this article is for you.

What Is Pair Trading?

1. Finding two assets that move together over time.
2. Waiting for that relationship to temporarily break.
3. Taking a long position in the underpriced one and a short position in the overpriced one.
4. Profiting when they snap back to equilibrium.

Think of it like two rubber-band-connected runners—one surges ahead, one lags. Eventually, they’ll move back toward each other.

But how do you find these “magic” pairs?

Step 1: Start With Companies in the Same Industry

Look for stocks that:

• Share the same sector
• Have similar fundamentals
• Face the same macro trends

Example:

• Coca-Cola & Pepsi
• Mastercard & Visa
• Ford & General Motors
• Shell & BP

These are natural pair candidates—they operate under the same business dynamics and are often held by the same types of investors.

Step 2: Check Correlation

Use basic tools (like Excel, TradingView, or Python) to calculate Pearson correlation. You’re looking for a high correlation (above 0.8), but here’s the kicker:

High correlation ≠ profitable pair.

It just means they move similarly. But you want to know if they mean to revert.

Step 3: Test for Cointegration

Cointegration means the pair doesn’t just move similarly—they stay anchored in a statistically consistent way. Even if both stocks go up or down over time, the spread between them remains mean-reverting.

Use tools like

• Python (statsmodels, Engle-Granger test)
• QuantConnect
• Backtesting libraries like bt or zipline

If a pair passes the cointegration test, congrats—you have a statistically valid trading candidate.

Real Example: Coca-Cola vs. Pepsi

• Observation: High correlation over 10 years
• Cointegration: Confirmed via Engle-Granger test
• Spread = KO — β * PEP (calculated via linear regression)

Now you monitor that spread. When it widens beyond historical limits (say, 2 standard deviations), you:

• Buy KO (undervalued).
• Short PEP (overvalued)

Wait for reversion, close both legs, and lock in your delta.

But what could go wrong?

• The spread might never revert.
• Structural shifts (M&A, scandals) can break the pair.
• Trading costs and slippage kill profitability.
• Market-wide crashes throw all correlations into the trash.

That’s why this isn’t just plug-and-play. You need constant recalibration and discipline.

Tools to Make It Easier

• QuantConnect—Free Python-based backtesting with brokerage integration
• Pairs Trader Pro (desktop)—Visual interface for identifying spreads
• Kibot or Quandl—Historical data
• TradingView—Fast correlation visualization
• Python Libraries—pandas, statsmodels, matplotlib, scikit-learn

Even Google Sheets can get you started with correlation heatmaps.