Finance & Investment Research API

Public search data is often a leading indicator for financial questions. What analysts write, what retail investors ask, and which themes dominate current coverage all show up in search before they become standardized datasets. OrbitScraper is useful here because it can provide both structured search modules and, through Research API, sourced summaries for analyst workflows.

Who this is for

Built for fintech product teams, investment research analysts, hedge fund data teams, quantitative researchers, and financial intelligence SaaS platforms.

What teams use it for

Use SERP API for fast snapshots of company, sector, and earnings-related query results.
Use Research API when you want OrbitScraper to fetch sources and return a sourced summary.
Track people_also_ask, knowledge_graph, and news-focused queries as investor-signal inputs.

Visual reference

Existing OrbitScraper-style dashboards and workflow surfaces related to this use case.

Company research snapshot

Store structured search context for companies, sectors, or market themes.

Analyst reporting panel

Blend search context and structured summaries into finance workflows.

Theme map

Compare which companies and sources dominate a tracked market theme.

Signal view

Keep current-event and investor-question signals visible through recurring queries.

Why search data is useful in finance

Search result composition for a company name can reveal which narratives and sources currently dominate attention.
people_also_ask for a ticker or company name shows what retail investors are trying to understand.
knowledge_graph can return structured company facts such as entity type, description, and website.
time_period filtering separates current analyst coverage from evergreen explainer content.
SERP API can return markdown-ready search context when you set markdown: true, which is helpful for LLM-driven finance tooling.

Code example — company research workflow

In the current product contract, markdown is a SERP API option. Research API is the separate sourced-summary workflow.

SERP snapshot request

curl -X POST "https://api.orbitscraper.com/v1/search" \
  -H "x-api-key: ORS_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "NVIDIA earnings Q2 2026 analyst forecast",
    "markdown": true,
    "time_period": "past_week",
    "num": 20,
    "engine": "google"
  }'

Response fields for financial research

{
  "result": {
    "markdown": "# Search Results\n\n## NVIDIA Q2 2026 Earnings\n...",
    "organic_results": [
      {
        "position": 1,
        "title": "NVIDIA Q2 2026 Earnings Preview",
        "link": "...",
        "snippet": "Consensus estimate of revenue..."
      }
    ],
    "people_also_ask": [
      { "question": "What is NVIDIA's Q2 2026 revenue estimate?" }
    ],
    "knowledge_graph": {
      "title": "NVIDIA Corporation",
      "type": "Public company",
      "website": "https://nvidia.com"
    }
  }
}

Research API for sourced finance summaries

For fintech products or analyst tooling that need a sourced narrative rather than a raw SERP snapshot, Research API can fetch sources and return a synthesized summary plus source objects.

Research API request

curl -X POST "https://api.orbitscraper.com/v1/research" \
  -H "x-api-key: ORS_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Summarize the latest analyst coverage around NVIDIA earnings guidance",
    "depth": 5,
    "output_format": "summary",
    "include_sources": true
  }'

Research API completion shape

{
  "status": "completed",
  "summary": "Analyst coverage is focused on...",
  "sources": [
    {
      "title": "NVIDIA earnings preview",
      "url": "https://example.com/analysis"
    }
  ]
}

Financial research workflows

Due diligence pre-processing: run recurring company queries and store knowledge_graph, top domains, and question modules before a deal review.
Earnings monitoring: use news-focused queries with tbm set to nws and a past_day filter to capture fresh coverage.
Competitor landscape mapping: monitor sector-level queries and track which companies keep appearing in the top results.
LLM-powered finance tools: pass the markdown SERP block or Research API summary into downstream analyst tooling.

Earnings monitoring request

{
  "q": "TSLA earnings Q3 2026 beat miss",
  "tbm": "nws",
  "time_period": "past_day",
  "engine": "google"
}

Key parameters for financial research

markdown: true on SERP API returns a formatted context block for LLM-powered workflows.
time_period: past_week or past_day keeps analyst and news coverage current.
tbm: nws focuses the request on news coverage for earnings and breaking-company updates.
knowledge_graph helps verify entity identity and high-level company facts.
people_also_ask surfaces the investor questions showing up around a company or theme.

Build this workflow with OrbitScraper

Start with the product tags above, wire the request pattern into your app, and use the structured response fields that match this workflow. OrbitScraper is most valuable when your team stops manually checking search results and starts treating search data like a dependable input to software.

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