Tag Archives: Backtesting

Pairs Trading Example: The Vegas Pair That Kept Paying for 5 Years

September 13, 2026 1:35 pm |

Want a real pairs trading example instead of theory? Here’s one straight from our platform: Hilton Grand Vacations (HGV) and MGM Resorts (MGM). Two Las Vegas-rooted leisure stocks. One quietly reliable trading relationship.

The numbers: 12 trades, 12 winners

We ran a 5-year end-of-day backtest at $5,000 per leg. The results speak plainly:

  • 12 completed trades — every one profitable
  • Net profit: $3,122 after $258 in commissions
  • Average profit per trade: $260
  • Maximum drawdown: just 4.02%

The equity curve is a staircase, not a rollercoaster.

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Market-Neutral Pair Trading in Action: May 2026 Top 50 USA Stock Pair Stars

May 11, 2026 4:52 pm |
market-neutral pair trading

Most retail trading strategies depend on direction — and direction is the one thing nobody can predict consistently. By contrast, market-neutral pair trading offers an alternative: trades constructed not on whether a market rises or falls, but on the relationship between two fundamentally related assets. As a result, when that relationship temporarily diverges, mean reversion offers a measurable, repeatable edge.

Your Shortcut to Market-Neutral Pair Trading Success

This May, after another quarter of research, recalibration, and statistical validation, we are releasing the May 2026 Vintage of our Top 50 USA Stock Pair Stars.

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Kalman Filters vs. PairTrade Finder®

March 26, 2026 3:38 pm |
Why We Keep It Simple (On Purpose) Kalman filters

From time to time, experienced traders reach out and ask:

“Why doesn’t PairTrade Finder® use a Kalman filter to calculate dynamic hedge ratios?”

It’s a fair question. Kalman filters are widely used in quantitative finance and are often presented as a more advanced way to trade pairs.

So this post explains our thinking clearly.

PairTrade Finder® is designed to capture most of the statistical edge—without the complexity that often reduces real-world performance.

In practice, as a rule we seek to target a platform that delivers 80% of the benefit of institutional grade models with around 20% of the complexity.

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