Financial Datasets

Financial Datasets provides machine-ready company and market data for AI agents. Through Exa Connect, agents can retrieve real-time and historical prices, company facts, financial statements and valuation metrics, earnings, insider and institutional ownership, SEC filings and filing sections, company news, and screen the U.S. market by fundamental criteria. Attach financial_datasets to an Exa Agent run, and the agent queries Financial Datasets alongside Exa web search.

Use it for

Building structured company research snapshots.
Analyzing financial performance, valuation, and historical trends.
Reading SEC filings and extracting sections such as risk factors and MD&A.
Examining insider transactions and institutional ownership.
Screening the U.S. market by fundamental criteria.
Monitoring company news and relevant developments.

Data available

Each of the following datasets is available under the financial_datasets provider; the agent selects whichever fits the task:

Dataset	What it returns
Beneficial ownership	5%+ beneficial owners from Schedules 13D/13G, including activist and passive stakes.
Company facts	Name, sector, industry, exchange, location, SEC CIK, SIC classification.
Company news	Recent news articles for a ticker.
Earnings	Quarterly revenue and EPS with YoY change and beat/miss surprises.
Financial metrics	Market cap, EV, P/E, P/B, P/S, EV/EBITDA, PEG, margins, ROE/ROA/ROIC, growth, EPS.
Financial statements	Income statement, balance sheet, and cash flow from SEC filings.
Historical stock prices	OHLCV bars over a date range at day/week/month/year granularity.
Index-fund holdings	ETF/index-fund constituents by weight, or the funds that hold a given security.
Insider ownership	Insider holdings from SEC Forms 3 and 5 (shares owned by officers, directors, 10% owners).
Insider trades	SEC Form 4 insider transactions (name, role, type, shares, value).
Institutional ownership	13F institutional holders, shares, and reported value.
Interest rates	Current and historical central-bank policy rates (Fed, ECB, BOJ, and more).
SEC filing items	Extracted text of specific 10-K/10-Q/8-K items (e.g. risk factors, MD&A).
SEC filings	Filing metadata and direct EDGAR links, optionally filtered by form type.
Segmented financials	Revenue, operating income, and other line items broken down by product, business segment, and geography.
Stock price snapshot	Current real-time price, day change, and quote time.
Stock screener	Companies matching fundamental filter criteria.

Provider ID

Use this value in dataSources:

financial_datasets

Example

Build a structured company-research snapshot for NVIDIA.

from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query=(
        "Analyze NVIDIA using its latest price, valuation metrics, most recent "
        "quarterly financial statements and earnings, institutional and insider "
        "activity, and material SEC filing sections. Return a structured "
        "company-research snapshot with reporting dates."
    ),
    data_sources=[{"provider": "financial_datasets"}],
    output_schema={
        "type": "object",
        "required": ["ticker", "price", "valuation", "financials", "earnings", "ownership", "filings"],
        "properties": {
            "ticker": {"type": "string"},
            "price": {
                "type": "object",
                "required": ["latest", "asOf"],
                "properties": {
                    "latest": {"type": "number"},
                    "asOf": {"type": "string"},
                },
            },
            "valuation": {
                "type": "object",
                "properties": {
                    "marketCap": {"type": "number"},
                    "peRatio": {"type": "number"},
                    "evToEbitda": {"type": "number"},
                },
            },
            "financials": {
                "type": "object",
                "required": ["reportPeriod", "summary"],
                "properties": {
                    "reportPeriod": {"type": "string"},
                    "summary": {"type": "string"},
                },
            },
            "earnings": {
                "type": "object",
                "required": ["reportPeriod", "summary"],
                "properties": {
                    "reportPeriod": {"type": "string"},
                    "summary": {"type": "string"},
                },
            },
            "ownership": {
                "type": "object",
                "properties": {
                    "institutionalHighlights": {"type": "string"},
                    "insiderActivity": {"type": "string"},
                },
            },
            "filings": {
                "type": "array",
                "maxItems": 5,
                "items": {
                    "type": "object",
                    "required": ["formType", "filedAt", "keySection"],
                    "properties": {
                        "formType": {"type": "string"},
                        "filedAt": {"type": "string"},
                        "keySection": {"type": "string"},
                    },
                },
            },
        },
    },
)
run = exa.agent.runs.poll_until_finished(run.id)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.agent.runs.create({
  query:
    "Analyze NVIDIA using its latest price, valuation metrics, most recent quarterly financial statements and earnings, institutional and insider activity, and material SEC filing sections. Return a structured company-research snapshot with reporting dates.",
  dataSources: [{ provider: "financial_datasets" }],
  outputSchema: {
    type: "object",
    required: ["ticker", "price", "valuation", "financials", "earnings", "ownership", "filings"],
    properties: {
      ticker: { type: "string" },
      price: {
        type: "object",
        required: ["latest", "asOf"],
        properties: {
          latest: { type: "number" },
          asOf: { type: "string" },
        },
      },
      valuation: {
        type: "object",
        properties: {
          marketCap: { type: "number" },
          peRatio: { type: "number" },
          evToEbitda: { type: "number" },
        },
      },
      financials: {
        type: "object",
        required: ["reportPeriod", "summary"],
        properties: {
          reportPeriod: { type: "string" },
          summary: { type: "string" },
        },
      },
      earnings: {
        type: "object",
        required: ["reportPeriod", "summary"],
        properties: {
          reportPeriod: { type: "string" },
          summary: { type: "string" },
        },
      },
      ownership: {
        type: "object",
        properties: {
          institutionalHighlights: { type: "string" },
          insiderActivity: { type: "string" },
        },
      },
      filings: {
        type: "array",
        maxItems: 5,
        items: {
          type: "object",
          required: ["formType", "filedAt", "keySection"],
          properties: {
            formType: { type: "string" },
            filedAt: { type: "string" },
            keySection: { type: "string" },
          },
        },
      },
    },
  },
});

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $EXA_API_KEY" \
  -d '{
    "query": "Analyze NVIDIA using its latest price, valuation metrics, most recent quarterly financial statements and earnings, institutional and insider activity, and material SEC filing sections. Return a structured company-research snapshot with reporting dates.",
    "dataSources": [{ "provider": "financial_datasets" }],
    "outputSchema": {
      "type": "object",
      "required": ["ticker", "price", "valuation", "financials", "earnings", "ownership", "filings"],
      "properties": {
        "ticker": { "type": "string" },
        "price": {
          "type": "object",
          "required": ["latest", "asOf"],
          "properties": {
            "latest": { "type": "number" },
            "asOf": { "type": "string" }
          }
        },
        "valuation": {
          "type": "object",
          "properties": {
            "marketCap": { "type": "number" },
            "peRatio": { "type": "number" },
            "evToEbitda": { "type": "number" }
          }
        },
        "financials": {
          "type": "object",
          "required": ["reportPeriod", "summary"],
          "properties": {
            "reportPeriod": { "type": "string" },
            "summary": { "type": "string" }
          }
        },
        "earnings": {
          "type": "object",
          "required": ["reportPeriod", "summary"],
          "properties": {
            "reportPeriod": { "type": "string" },
            "summary": { "type": "string" }
          }
        },
        "ownership": {
          "type": "object",
          "properties": {
            "institutionalHighlights": { "type": "string" },
            "insiderActivity": { "type": "string" }
          }
        },
        "filings": {
          "type": "array",
          "maxItems": 5,
          "items": {
            "type": "object",
            "required": ["formType", "filedAt", "keySection"],
            "properties": {
              "formType": { "type": "string" },
              "filedAt": { "type": "string" },
              "keySection": { "type": "string" }
            }
          }
        }
      }
    }
  }'

Pairs well with

Particle: compare published coverage with podcast commentary.
Baselayer: verify the underlying entity behind a ticker.
Fiber.ai: enrich a public company with private-market peers and leadership contacts.

Next steps

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Financial Datasets

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​Data available

​Provider ID

​Example

​Pairs well with

​Next steps

Attach it to a run

Combine providers

Learn Exa Agent

Get an API key

Use it for

Data available

Provider ID

Example

Pairs well with

Next steps