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Russell 3000 search-demand history

Entity-mapped Google Trends interest for US-listed equities, point-in-time, with a live preview API. Ticker in, weekly 5-year series out.

Ticker-keyedMapped to US-listed equities

Weekly5-year history per series

0–100Native Google interest index

Preview APIJSON, Bearer or key auth

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ProductWhat this is

A single, narrow slice of FlatNine's Google Trends search-demand history (see data overview for the full dataset): the universe is filtered to the Russell 3000 and each series is keyed to a US listing ticker. The keyword-to-entity mapping institutional users normally have to build themselves is already done. A read-only preview API is exposed so a desk can pull a single ticker and validate the data on its own infrastructure before any commercial conversation.

DatasetCoverage and shape

Each entry is a single US-listed equity mapped to one Google Trends search-demand series. Initial preview scope is the S&P 500 seed (~80% of Russell 3000 market cap); full R3000 expansion is delivered against the client universe on engagement.

503

Tickers mapped (preview scope)

~80%

R3000 market cap covered by seed

5 yr

Weekly history per series

Weekly

Native cadence

0–100

Google relative interest index

Initial market scope

Dual-class shares merged (GOOGL/GOOG, FOXA/FOX, NWSA/NWS) Sector & industry tagged Company name normalised

Per-ticker fields

Identifier & mapping

One row per ticker.

Field	Description
`ticker`	US listing symbol
`company_name`	Normalised issuer name
`sector`	GICS-style sector
`industry`	GICS-style industry
`trend.term`	Search query mapped to the issuer
`trend.region`	ISO-3166 country code (US)
`trend.last_refreshed_at`	ISO-8601 last refresh time

Time series

One row per ticker x week.

Field	Description
`series.source`	Constant: `google_trends`
`series.interval`	Constant: `weekly`
`series.history`	Constant: `5_years`
`series.normalization`	Google relative interest, 0–100
`series.point_in_time`	Snapshot delivered from engagement start; no look-ahead
`series.data[].date`	Week-start date (YYYY-MM-DD, UTC)
`series.data[].unix_ts`	Week-start Unix timestamp
`series.data[].interest`	Google interest index, 0–100

APIPreview endpoint

JSON over HTTPS. Two endpoints: list the mapped tickers, or fetch one ticker's full series. Read-only and rate-limited. Production delivery (full R3000, multi-market, bulk extract) is delivered to S3 / SFTP, not through this preview endpoint.

Base URL

BASEhttps://treendly.com/api/r3000

Authentication

Send the preview key as a Bearer token, or as ?api_key= for quick testing:

# Bearer header (preferred)
    Authorization: Bearer YOUR_KEY

    # or query parameter
    ?api_key=YOUR_KEY

Endpoints

GET/api/r3000list ticker → trend mappings

GET/api/r3000/{ticker}single-ticker 5-year weekly series

Example: list mappings

curl -H "Authorization: Bearer $FLATNINE_KEY" \
         https://treendly.com/api/r3000

{
      "collection":   "Russell 3000",
      "scope_note":   "Initial scope: S&P 500 (~500 tickers). Full R3000 expansion delivered against client universe on engagement.",
      "ticker_count": 503,
      "with_data":    498,
      "source":       "google_trends",
      "tickers": [
        {
          "ticker":       "AAPL",
          "company_name": "Apple Inc.",
          "sector":       "Information Technology",
          "industry":     "Technology Hardware, Storage & Peripherals",
          "search_term":  "apple",
          "region":       "US",
          "has_data":     true
        },
        // ... 502 more
      ]
    }

Example: single ticker

curl -H "Authorization: Bearer $FLATNINE_KEY" \
         https://treendly.com/api/r3000/AAPL

{
      "ticker":       "AAPL",
      "company_name": "Apple Inc.",
      "sector":       "Information Technology",
      "industry":     "Technology Hardware, Storage & Peripherals",
      "trend": {
        "id":                412903,
        "term":              "apple",
        "region":            "US",
        "last_refreshed_at": "2026-05-19T03:14:00+00:00"
      },
      "series": {
        "source":          "google_trends",
        "interval":        "weekly",
        "history":         "5_years",
        "normalization":   "google_relative_index_0_to_100",
        "point_in_time":   "snapshot delivered from engagement start; no look-ahead",
        "pending_refresh": false,
        "point_count":     261,
        "data": [
          { "date": "2021-05-23", "unix_ts": 1621728000, "interest": 42 },
          { "date": "2021-05-30", "unix_ts": 1622332800, "interest": 39 },
          // ... 259 more weekly observations
        ]
      }
    }

Response codes

Status	Meaning
`200`	OK
`400`	Malformed ticker (must match `^[A-Z]{1,5}(\.[A-Z]{1,2})?$`)
`401`	Missing or invalid key
`404`	Ticker not in current preview mapping

MethodologyHow the data is built

Entity mapping: each ticker is bound to a Google Trends search term mapped to the issuer, not the raw symbol. Dual-class shares (GOOGL/GOOG, FOXA/FOX, NWSA/NWS) are merged onto the canonical issuer trend.
Native Google index: values are the raw 0–100 relative interest index Google returns; no proprietary smoothing, scaling or signal engineering.
Weekly cadence, 5-year rolling window per series, refreshed continuously.
Point-in-time: snapshots are versioned from the engagement start so backtests do not see data that did not exist at the bar.
Provenance documented: source (Google Trends), term, region and refresh timestamp travel with every series.
No consumer PII: aggregate search index only.

Delivery beyond the preview

Full Russell 3000 expanded against the client's investable universe on engagement.
Bulk extract: CSV / JSON / Parquet via S3 or SFTP, point-in-time history at full length.
Incremental delivery: weekly updates on the same channel; backfill on request.
Per-client API keys with rate limits for production read access.

Use casesWhat a research desk does with this

Signal Search-demand factor at the single-name level: weekly entity-mapped interest as an input to demand and revenue nowcasts.
Signal Cross-validation of card, web-traffic and app panels with a fully independent demand series.
Coverage Universe widening: small- and mid-cap R3000 names where card and web-traffic panels are too thin to model.
Coverage Consumer-discretionary and brand-led issuers where named search demand leads reported revenue.
Backtest Point-in-time history from engagement start, no look-ahead, for clean factor research and event studies.
Mapping Entity work already done: ticker keyed, dual-class shares merged, sector and industry tagged; ready to join to your security master.

AccessGetting the data

Delivered by API or bulk export (CSV, JSON, Parquet, S3), with history and refresh cadence agreed per engagement.

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