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Fundamental data

As explained in Financial AI in Practice

Company- and economy-related information derived from financial reports and other measures of underlying financial condition.

Fundamental data is company- and economy-related information derived from financial reports and other measures of underlying financial condition. In quantitative trading, it is one of three broad data categories, alongside market data and alternative data. Each can provide predictive signals. section 10.2.1

Treat fundamental data as a candidate data source, not an automatic trading signal. Data categories have distinct structural challenges, biases, and engineering pitfalls, so assess a source before investing in a strategy built on it. Adjusted prices and survivorship bias illustrate pitfalls in market data; they are not examples of fundamental data. section 10.2.1

How Financial AI in Practice covers it

How deep it goes
Identifies fundamental data as a data category but stops short of explaining its fields, processing, or modeling.
Examples
Uses stock splits and survivorship bias to illustrate market-data pitfalls, without giving a fundamental-data example.
What you can do after
Evaluate/understand where fundamental data fits among quantitative data sources and why candidate sources need scrutiny.
How it connects
Relates fundamental data to market data, alternative data, predictive signals, and data-source due diligence.

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This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.