A benchmark is the reference series used to score a strategy against a defined baseline.

A benchmark is the reference series used to score a strategy against a defined baseline. In Sonar’s fundamentals material, the benchmark is selected so that the strategy is evaluated relative to a specific alternative rather than in isolation. This gives the test a neutral point of comparison and makes the result interpretable as performance versus a chosen reference, including the case where the reference represents doing nothing.

The mechanism is simple. A strategy produces a return series, and the benchmark provides the comparison series. The benchmark can be chosen to reflect the baseline behavior the researcher wants to treat as neutral. When that benchmark is a do nothing reference, the score answers whether the strategy improves on inactivity. The benchmark therefore does not just add context after the fact. It defines what zero relative value means in the experiment.

Sonar’s backtest overfitting audit uses a reference series directly in the scoring step. The tool evaluates candidate backtests relative to that reference so that a neutral score is well defined. This matters because the audit is not only asking whether a backtest has attractive standalone statistics. It is asking whether the backtest shows evidence of value beyond the selected baseline once multiple testing and selection effects are considered. A benchmark that represents doing nothing makes the neutral score explicit and prevents the test from treating raw activity as meaningful by default.

This benchmark choice also affects downstream performance metrics. In Sonar’s glossary, the deflated Sharpe ratio is presented as a Sharpe based measure adjusted for selection bias and multiple trials. Because the Sharpe ratio depends on the return series being evaluated, the definition of that series relative to a benchmark changes the metric’s interpretation. If returns are measured against a benchmark reference series, then the resulting Sharpe style statistic describes risk adjusted performance relative to that baseline, not just absolute fluctuations of the strategy alone. The deflated Sharpe ratio then asks whether that benchmark relative performance remains statistically credible after accounting for the fact that many variants may have been tried.

For quantitative research, the practical point is that a benchmark is part of the model specification. It defines the null comparison, anchors neutral scoring, and shapes the meaning of metrics used in validation. When the benchmark is chosen to represent doing nothing, the researcher gets a clear answer to a precise question: does the strategy add value relative to a defined inactive baseline.

Covered in depth in the Strategy research fundamentals pillar hub.