Get Started

Why Average Strategy Performance Can Mislead Portfolio Research

September 2, 2026

Average strategy performance is one of the most common shortcuts in portfolio research. It gives the researcher a clean benchmark, a single reference line, and a simple way to compare one strategy against a broader group of similar strategies. In many cases, this is useful. But it can also be misleading.

The problem is that an average hides dispersion. Two peer groups can have the same average return, but the internal structure of those groups can be completely different. In one year, nearly all strategies may behave similarly and cluster around the median. In another year, the same average may hide a wide spread between winners and losers. For portfolio construction, this distinction matters.

This case study shows how Quantpedia API can be used to go beyond the average peer group return and measure yearly performance dispersion across a group of trend-following strategies. The goal is not only to ask how the average strategy performed, but also how different the individual strategies were from each other.

Continue reading »

From Backtest to Benchmark: Validating New Strategies with Quantpedia API

July 27, 2026

A profitable backtest is rarely the end of a research process. In professional quantitative research, the more important question often comes after the first positive result: is the strategy genuinely new, or is it simply another version of an already known factor, timing rule, or anomaly?

This is especially relevant when a researcher develops a new systematic strategy with a clean historical equity curve. The strategy may have acceptable risk-adjusted performance, stable drawdowns, and a logical trading rule, but those statistics alone do not prove that the idea is unique. A silver strategy, for example, may look different on the surface while still behaving like a known commodity timing model, a trend-following strategy, a volatility filter, or a broader macro factor exposure.

This article shows how Quantpedia API can be used as a benchmark dataset for validating new research. Instead of evaluating a new backtest in isolation, the strategy is compared against the Quantpedia universe of documented quantitative strategies. The workflow identifies nearest neighbours, assigns the strategy to a factor cluster, calculates a uniqueness score, and produces a research robustness report that can be used for deeper validation.

The goal is not to replace human research judgment. The goal is to create a structured robustness checker that helps researchers understand whether a new strategy is truly differentiated, redundant with known effects, or simply a variation of an existing Quantpedia strategy profile.

Continue reading »