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The KPIs you care about; Unparalleled data integrity

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Whether you’re a data scientist running scenario analysis or an analyst looking to scale your workflow, Candas offers the flexibility of both in an easy-to-use Python library.


Canalyst Candas 
Data Science Library

Idea generation and risk management with KPI search, KPI relevance analysis, comp screens, and multi-security scenario analysis in Python - all based on Canalyst’s 4000+ global equity model database and 10 years of clean fundamental data.

Official PyPI project page

Perfect. Puts functionality into users hands and keeps it simple.

Research Analyst, hedge fund, 
$22B AUM

Candas includes the adjusted fundamental data critical to quality analysis straight off our industry-leading models, but also the calculating engine of the models themselves, both in original form and also allowing for formula re-wiring. 

Just want our basic model dataframes - either for one ticker at a time or many in a group? Then model_frame is an easy to use core function of the library.

Search for one or many KPI across our entire database of models


Rank order one stock’s KPI by post-earnings stock price and 
revenue movement

Visualize and rank order KPI key drivers of revenue by importance

Ran the comp sheet this weekend. The pull works BEAUTIFULLY and runs in like 10-15 seconds. Amazing.

Director of Research, Public Equities

This kicks ass.

Data Scientist, Quantamental Research Team, 
$400B AUM

Canalyst serves 1000+ investment professionals at hedge funds, family offices, and 8 of the top 10 asset managers. We empower Analysts and Portfolio Managers to improve idea generation and enable CIOs and Directors to maximize the efficiency of their research teams.