Quantpedia now lets API subscribers download staff-authored research papers directly. The July update also added five new studies on trend-following, return prediction, and AI research. The database holds over 700 strategies.
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Quantpedia, the quantitative strategy research platform, has expanded its API to let subscribers download full research papers written by its own team. The change, announced in a July update, means users can pull both strategy data and supporting documentation through automated workflows – a shift for researchers who previously had to source papers separately.
The API already gave Pro subscribers access to in-sample and out-of-sample statistics, source code snippets, and links to academic repositories. The new feature adds direct PDF retrieval for any strategy authored by Quantpedia staff. The company holds the rights to those publications, so it can distribute them freely. For third-party academic papers, the API continues to provide links to open-access repositories, as copyright restrictions prevent direct distribution.
The update follows a period of infrastructure work. Quantpedia said it reorganized its underlying database to prepare for a “significant expansion” of the API’s capabilities in the coming months. The company did not specify what those capabilities would be.
Separately, Quantpedia published five new research reviews on its blog during July. The topics cover short-term trend-following, return prediction, commodity portfolio shocks, and AI-generated financial research. Two of the reviews were authored by Quantpedia staff (David Mesicek wrote one on validating strategies with the API and another on commodity crisis analysis); the remaining three come from academic researchers.
One paper, “Is Trend Still Your Friend? A Microstructural Account of the Demise of Short-Term Trend-Following” by Kurth, Eisler, Rej, and Bouchaud, examines why short-term trend strategies have lost effectiveness. Another, “Can AI Do Financial Research? LLM-Guided Hypothesis Discovery in Asset Pricing” by Liu, Liu, Liu, and Mei, tests whether large language models can generate testable asset-pricing hypotheses. A third, “Getting the Target Right in Return Prediction” by Cakici and Zaremba, looks at how to improve forecast accuracy.
Quantpedia’s database now holds more than 700 trading strategies and hundreds of related academic papers, the company said. The platform is used by quantitative researchers and systematic traders.
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