Building a Crypto Pairs Strategy with Cointegration and Z-Scores

Summary

This guide outlines a crypto pairs mean-reversion strategy built around cointegration rather than correlation alone. It proposes testing logged price series with the Engle–Granger method, estimating a regression hedge ratio, and checking the resulting spread for stationarity with the Augmented Dickey–Fuller test. A Hurst exponent below 0.5 is offered as further evidence of mean-reverting behavior. The spread’s rolling mean and standard deviation then supply Z-scores for trade signals: unusually positive or negative readings prompt opposite positions in the paired assets, with exits as the spread moves back toward its mean.

The discussion also recommends screening for adequate data history and plausible hedge ratios, monitoring relationships for structural change, and including fees, slippage, funding charges, stop losses, and take profits in backtests. It describes possible evaluation measures such as returns, Sharpe ratio, and drawdown, but provides no actual empirical results in this installment. Thresholds and safeguards are examples rather than validated universal settings; cointegration can weaken, and historical tests do not establish live profitability.

Key ideas

• The strategy screens crypto pairs for cointegration and tests whether their constructed spread is stationary.
• A regression hedge ratio is used to define the spread between paired assets.
• Rolling spread Z-scores provide entry signals, while movement toward zero prompts exits.
• The guide recommends retesting pairs because market regime changes can undermine previously stable relationships.
• Backtests should account for transaction costs and risk controls, though this document reports no empirical performance results.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.