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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.

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Quantpedia API for Peer Group Strategy Analysis

August 26, 2026

A single backtest can show that strategy was profitable, but it does not always show whether the strategy was competitive. This is especially true for systematic futures strategies. A trend-following strategy can have a positive Sharpe ratio, a long live-like performance history, and a reasonable drawdown, but those numbers are difficult to interpret without a relevant comparison group.
A broad equity index is often not the right benchmark for this type of strategy. A monthly rebalanced multi-asset futures strategy has a different objective, different risk profile, and different return drivers than a long-only stock index. A more useful question is whether the strategy performs well compared with other systematic trend-following futures strategies.
This case study shows how the Quantpedia API can be used to build a custom peer group benchmark for strategy evaluation. The workflow has two steps. First, Quantpedia strategy metadata is used to screen and define a comparable peer group. Second, the historical equity curves of the selected strategies are downloaded, converted into daily returns, and aggregated into an equal-weighted peer group benchmark.

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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.

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Quantpedia API as an On-Demand Factor Database

July 7, 2026

Investors often face a simple but important problem. They receive a fund equity curve, a strategy track record, or a portfolio performance series, but they do not know what is actually inside. The manager may provide only a broad description, while the realized return stream may in practice be driven by a mix of momentum, tactical allocation, defensive overlays, cross-asset rotation, or other systematic effects.

One way to approach this type of problem is to use a specialized Multi-Factor Analysis report available in Quantpedia Pro. However, this case study focuses on the second approach: building a custom workflow through the Quantpedia API and AI-assisted methodology design. Instead of treating Quantpedia only as a static library of strategy ideas, the workflow uses it as an on-demand database of factor-like return streams. The unknown curve becomes the object to explain, while the Quantpedia strategy universe becomes the set of candidate explanatory building blocks.

In this test, the unknown equity curve was treated as a blind case. The “correct” answer was not used during the analysis. The task was therefore not to confirm a known decomposition, but to test whether an API-based workflow can identify which known systematic strategies best explain the behavior of a black-box curve.

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Building Meta-Strategies with Quantpedia API

June 2, 2026

Quantitative investors usually start their research by analyzing individual trading strategies. They compare performance, risk, implementation complexity, market exposure, and the economic intuition behind each anomaly. However, once historical equity curves of individual strategies are available, a different research question becomes possible. Instead of asking only which individual strategy looks attractive, we can ask how to allocate capital across a broad universe of strategies.

This is where meta-strategies become useful. A meta-strategy does not invest directly in stocks, ETFs, futures, or other financial instruments. Instead, it invests in underlying trading strategies. These strategies become portfolio building blocks, and the researcher can apply allocation rules such as momentum, risk parity, volatility targeting, or mean-variance optimization directly to their return streams.

The Quantpedia API makes this type of analysis practical. It provides access not only to strategy metadata, but also to historical strategy equity curves. Therefore, users can move from strategy discovery to systematic strategy portfolio construction.

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Building an AI Powered Quant Research Assistant with Quantpedia API

May 29, 2026

Artificial intelligence is gradually changing the way quantitative researchers interact with financial data. Instead of manually browsing databases, comparing strategies one by one and filtering spreadsheets, modern research workflows increasingly rely on conversational systems capable of retrieving and summarizing structured information automatically.

One practical application is combining the Quantpedia API with an LLM such as ChatGPT, Claude or Cursor AI to create a lightweight quant research assistant. In this setup, Quantpedia API provides structured access to quantitative trading strategies, performance metrics, classifications, equity curves, trading codes, and related research metadata through the official Quantpedia API, while the LLM acts as a conv

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