Optimizing Signal Research Workflows Using FactSet’s Python-Based QRE

Evaluating the performance of individual signals for downstream investment process development

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In recent years, systematic techniques have grown in popularity due to the main drawbacks of more traditional discretionary investment approaches. To address these roadblocks, investors need an automated, end-to-end solution that seamlessly takes them through the key steps of the investment process.


In this paper, we provide a practical example of a sample of modules and functionalities that a quant researcher can utilize within FactSet’s web browser-based, Quantitative Research Environment (QRE), for the purpose of standalone and composite signal performance analysis.

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