FMPFMP
Datasets
Insights/Platform Essentials/Product Updates/FMP Stock Screener Adds Average Trading Volume Filters

FMP Stock Screener Adds Average Trading Volume Filters

·

·1 min read
Platform Essentials

FMP's Stock Screener API now supports filtering by average trading volume. Released on August 28, 2026, the update adds two filtering parameters and an average-volume field to the results. Developers and analysts can use them to refine stock searches and see the average trading volume associated with each match.

New Filters and a Returned Average-Volume Field

The update introduces three additions:

Addition

Purpose

avgVolumeMoreThan

Filters for stocks above a specified average trading volume threshold.

avgVolumeLowerThan

Filters for stocks below a specified average trading volume threshold.

avgVolume

Returns the average trading volume for each matched stock.

The filtering parameters let users express average-volume criteria directly in a screener request. The returned field also makes that information available for display or further analysis after the screen runs.

More Control Over Trading-Activity Screens

Average trading volume can be useful when trading activity forms part of a research universe's selection criteria. An analyst may want to focus on stocks above a particular activity threshold, while a developer may want to expose an average-volume range in a screening interface. The new parameters support those choices within the existing screening workflow.

Returning avgVolume alongside each match also makes the results easier to inspect. A watchlist or screening table can display the value that helps explain a stock's inclusion, giving users more context for their next research step.

The August 28 Stock Screener release note records the new parameters and response field.

About the Author

Amy Lyons
Amy Lyons

Editorial strategy for financial data platforms and APIs

Amy Lyons leads content strategy at FMP, focusing on how financial data is structured, communicated, and translated into clear, usable insights. She builds editorial frameworks that connect product capabilities to real-world workflows. Her work focuses on supporting consistent, high-quality analysis across developer and analyst use cases.

Financial data for every need

Real-time quotes and 30+ years of historical data, including prices, fundamentals, and insider transactions — all accessible via API.