Hivemind: Trends

What Are Hivemind Trends?

Hivemind trends are point-in-time measurements of how frequently and how strongly specific macro, thematic, or narrative concepts appear in aggregated discussion.

They quantify evolving focus on ideas such as inflation, AI, geopolitics, regulations, etc.

Trends update continuously and are designed to capture drivers of risk and return that are not well explained by traditional style or factor models.

UI Access

Hivemind Trends can be explored directly in the ForecastOS UI, where users can browse trend histories, compare trends over time, and inspect how narratives evolve across market regimes.

API Access

Hivemind trends are available via the ForecastOS API for programmatic access and integration into research and portfolio workflows.

Example: Fetching Trends via API

curl -X GET "https://app.forecastos.com/api/v1/trends" \
-H "Authorization: Bearer YOUR_API_TOKEN" \
-H "Content-Type: application/json"

Query Parameters

Parameter	Type	Default	Required	Description
page	integer	1	No	Page number for pagination.
market_relevant	boolean	false	No	Only return trends that are flagged as market relevant or not.
identified_on_start	string	-	No	Only return trends that were identified after or on this date. Must be in YYYY-MM-DD format.
identified_on_end	string	-	No	Only return trends that were identified before or on this date. Must be in YYYY-MM-DD format.

Response

{
  "data": [
    {
      "id": 21141,
      "start_date": "2025-05-24T00:00:00.000Z",
      "end_date": "2025-06-23T00:00:00.000Z",
      "title": "debate, argument, arguments, debates",
      "market_relevant": false,
      "trend_rank": 72,
      "topic_size_rank": 117,
      "short_term_growth_rank": 211,
      "long_term_growth_rank": 116,
      "short_term_growth": 1.0320599421203407,
      "long_term_growth": 1.1348288716421182
    }
  ],
  "meta": {
    "page": 1,
    "per_page": 2500,
    "total_count": 54600,
    "total_pages": 22
  }
}

Open-Source Access

The open-source ForecastOS Python library provides helpers to fetch, normalize, transform, and align Hivemind trend time series data for research and modeling workflows.

Core trend construction and source ingestion remain managed services.

Example: Fetching Trends via OS

import forecastos as fos

df_trend = fos.Trend.get_df(
  market_relevant=True,
  identified_on_start='2025-11-09',
  identified_on_end='2025-11-10'
)

Parameters

Parameter	Type	Default	Required	Description
market_relevant	boolean	false	No	Only return trends that are flagged as market relevant or not.
identified_on_start	string	-	No	Only return trends that were identified after or on this date. Must be in YYYY-MM-DD format.
identified_on_end	string	-	No	Only return trends that were identified before or on this date. Must be in YYYY-MM-DD format.

This returns a time-series DataFrame for all the trends matching the filter parameters.

Next: Hivemind Persistent Trends

Let's explore Hivemind Persistent Trends next.