Aggregate Transactions

Introduction

This endpoint computes a per-bucket median multiple for a single cohort of deals.

You choose one metric (such as ev_revenue or valuation), slice the cohort by time_bucket (month, quarter, year, or a single overall bucket), and scope it with the same filters vocabulary as the transactions list.

The response returns {bucket, n, median, q1, q3} arrays plus the methodology constants used, so you can chart valuation trends or benchmark a comparable set without pulling and reducing the raw deals yourself.

Set up

Endpoint

POST https://multiples.vc/api/private/v1/transactions/aggregate

Request

Send a JSON body with the metric to aggregate, an optional time bucket, and a filters object that defines the cohort. metric and filters are required.

Median EV/Revenue per quarter across M&A deals:

{
  "metric": "ev_revenue",
  "time_bucket": "quarter",
  "filters": {
    "deal_type": ["M&A"],
    "date_from": "2021-01-01",
    "date_to": "2025-12-31"
  }
}

Median EV/Revenue per year for European Growth/Venture rounds:

{
  "metric": "ev_revenue",
  "time_bucket": "year",
  "filters": {
    "deal_type": [
      "Growth",
      "Venture"
    ],
    "target_region": [
      "Western Europe",
      "Northern Europe"
    ],
    "date_from": "2020-01-01",
    "date_to": "2024-12-31"
  }
}

Top-level parameters:

field	type	description
`metric`	string (required)	The value aggregated per bucket. One of `ev_revenue`, `ev_ebitda`, `valuation`, `raised`, `revenue`, `ebitda`.
`time_bucket`	string	How deals are grouped over time: `month`, `quarter`, `year`, or `none`. Defaults to `none` (one overall bucket).
`filters`	object (required)	Cohort definition. Accepts the full `/transactions` filter vocabulary (below).

filters fields (all optional):

field	type	description
`id`	array of uuid	Match specific deals by ID.
`target_id`	array of uuid	Restrict to deals whose target is one of these companies.
`investor_id`	array of uuid	Match deals where any of these companies participated as a lead investor or buyer.
`deal_type`	array of string	One or more of `Early`, `Growth`, `M&A`, `Public listing`, `Secondary`, `Venture`.
`deal_subtype`	array of string	One or more of `Angel`, `ICO`, `PE buyout`, `Pre-seed`, `Secondary - private`, `Secondary - public`, `Seed`, `Series A`-`Series J`, `Strategic M&A`, `Strategic investment`, `Undisclosed stage`.
`date_from`	date (`YYYY-MM-DD`)	Earliest deal date to include. Defaults to 5 years before today when omitted.
`date_to`	date (`YYYY-MM-DD`)	Latest deal date to include. Defaults to today when omitted.
`valuation_min` / `valuation_max`	number	Bound deal valuation (millions USD).
`raised_min` / `raised_max`	number	Bound amount raised.
`revenue_min` / `revenue_max`	number	Bound deal revenue.
`ebitda_min` / `ebitda_max`	number	Bound deal EBITDA.
`ev_revenue_min` / `ev_revenue_max`	number	Bound the EV/Revenue multiple.
`ev_ebitda_min` / `ev_ebitda_max`	number	Bound the EV/EBITDA multiple.
`target_country`	array of string	Target country (ISO 3166-1 alpha-3 codes).
`target_region`	array of string	Target region (e.g. `North America`, `Western Europe`).
`target_verticals`	array of string	Match deals whose target operates in any of these verticals.
`target_themes`	array of string	Match deals whose target maps to any of these themes.
`target_client_focus`	array of string	Match by target client focus (e.g. `B2C`).
`target_revenue_model`	array of string	Match by target revenue model.
`target_company_type`	array of string	Match by target company type.
`updated_at`	date-time	Change-feed checkpoint: only deals updated at or after this timestamp.
`include_deleted`	boolean	Include soft-deleted deals. Defaults to `false`.

The accepted filter fields, metric enums, time buckets, and methodology constants are also discoverable at runtime via GET /lookup/transactions.

Response

Each data entry is one bucket. Buckets with no deals (n: 0) are returned so charts have explicit gaps, and buckets below the small-N threshold report n but suppress the statistics with warning: "insufficient_sample". Example for median EV/Revenue per year for European Growth/Venture rounds (the second request above):

{
  "data": [
    {
      "bucket": "2020",
      "n": 42,
      "median": 10.7718120805369,
      "q1": 5.55991124260355,
      "q3": 17.9166666666667,
      "warning": null
    },
    {
      "bucket": "2021",
      "n": 90,
      "median": 9.88470557582074,
      "q1": 5,
      "q3": 18.7724830128347,
      "warning": null
    },
    {
      "bucket": "2022",
      "n": 68,
      "median": 12.5009557522124,
      "q1": 6.70950704225352,
      "q3": 22.0485210171251,
      "warning": null
    },
    {
      "bucket": "2023",
      "n": 24,
      "median": 6.76799805385352,
      "q1": 4.93762963252847,
      "q3": 11.3538205980066,
      "warning": null
    },
    {
      "bucket": "2024",
      "n": 37,
      "median": 9.5,
      "q1": 4.46632478632479,
      "q3": 17,
      "warning": null
    }
  ],
  "meta": {
    "metric": "ev_revenue",
    "time_bucket": "year",
    "total_deals": 284,
    "outliers_trimmed": 23,
    "methodology": {
      "small_n_threshold": 3,
      "outlier_rule": "iqr_1.5",
      "default_date_range_years": 5,
      "empty_buckets_returned": true,
      "date_range": {
        "from": "2020-01-01",
        "to": "2024-12-31"
      }
    }
  }
}

When time_bucket is none, data holds a single entry whose bucket is null, covering the whole cohort:

{
  "data": [
    { "bucket": null, "n": 211, "median": 390, "q1": 100.05, "q3": 1000, "warning": null }
  ],
  "meta": {
    "metric": "valuation",
    "time_bucket": "none",
    "total_deals": 242,
    "outliers_trimmed": 31,
    "methodology": {
      "small_n_threshold": 3,
      "outlier_rule": "iqr_1.5",
      "default_date_range_years": 5,
      "empty_buckets_returned": true,
      "date_range": { "from": "2021-06-15", "to": "2026-06-15" }
    }
  }
}

Fields

data[] - one object per bucket:

field	type	description
`bucket`	string \| null	The bucket label: `YYYY-MM` (month), `YYYY-Qn` (quarter), or `YYYY` (year). `null` when `time_bucket` is `none`.
`n`	integer	Number of deals in the bucket after outlier trimming.
`median`	number \| null	Median of the chosen metric for the bucket. `null` when `n` is below `small_n_threshold` or the bucket is empty.
`q1`	number \| null	First quartile (25th percentile) of the metric. `null` under the same conditions as `median`.
`q3`	number \| null	Third quartile (75th percentile) of the metric. `null` under the same conditions as `median`.
`warning`	string \| null	`insufficient_sample` when the bucket has too few deals to report statistics; otherwise `null`.

meta - context and methodology:

field	type	description
`metric`	string	The metric that was aggregated (echoes the request).
`time_bucket`	string	The time bucket that was applied (echoes the request).
`total_deals`	integer	Total matching deals across all buckets, before outlier trimming.
`outliers_trimmed`	integer	Number of deals removed by the outlier rule before computing statistics.
`methodology`	object	Constants used for this aggregation (see below).

meta.methodology:

field	type	description
`small_n_threshold`	integer	Buckets with fewer than this many deals report `n` but suppress `median`/`q1`/`q3` and flag `insufficient_sample`.
`outlier_rule`	string	The outlier-trimming rule applied, e.g. `iqr_1.5` (values outside 1.5×IQR of the quartiles are dropped).
`default_date_range_years`	integer	Lookback window (in years) used when `date_from`/`date_to` are omitted.
`empty_buckets_returned`	boolean	Whether zero-deal buckets are emitted in `data` (so time series have explicit gaps).
`date_range`	object	The effective date window applied, with `from` and `to` (`YYYY-MM-DD`).
`date_range.from`	string (date)	Start of the effective window (inclusive).
`date_range.to`	string (date)	End of the effective window (inclusive).

Introduction

Set up

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