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Daloopa

Financeby Daloopa, Inc.
Launched Apr 9, 2026 on ChatGPT

Daloopa supplies high quality fundamental data sourced from SEC Filings, investor presentations and any public financial documents, with hyperlinks to source document overlaying each data point. Provides a zero-hallucination foundation for any quantitative financial analysis in Claude.

5ChatGPT Tools
3Claude Tools
Daloopa, Inc.Developer
FinanceCategory

Use Cases

financial-services

Available Tools

Discover Companies

discover_companies
Full Description

Search for companies in the Daloopa database using ticker symbols or company names.

This tool searches the Daloopa database for companies matching the provided keyword, which can be either a ticker symbol or company name. The search results include for each keyword, the ticker, full company name, and company ID needed for subsequent data retrieval.

Search Strategy: 1. PRIMARY: Ticker Symbol Search

  • For ticker search: Use the exact ticker symbol (e.g., "AAPL", "MSFT")

2. SECONDARY: Company Name Search (only if ticker fails)

  • IMPORTANT: Omit legal entity designations (Inc., Ltd., Corp., LLC, GmbH, S.A., etc.)
  • Examples: Use "Apple" instead of "Apple Inc.", "Microsoft" instead of "Microsoft Corporation"

3. FALLBACK: Alternative Name Forms (if standard name fails)

  • If initial search returns no results, try alternative forms of the company name
  • Try shorter versions of the name if the full name doesn't yield results
  • For companies with multiple words, try the most distinctive word
Parameters (1 required)
Required
keywordsarray

List of search terms - either a ticker symbol or company name (without legal entity designations)

Discover Company Series

discover_company_series
Full Description

Discover and retrieve all available financial data series for a specific company.

This is the primary discovery tool for exploring what financial metrics and time series data are available for any given company in Daloopa's financial database. Use this tool to understand the breadth of financial data available before requesting specific metrics.

Key Use Cases:

  • Explore available financial metrics for a company
  • Find specific series by searching with relevant keywords
  • Check data availability for specific time periods
  • Identify series IDs needed for detailed data retrieval
Parameters (3 required)
Required
company_idinteger

The unique Daloopa company identifier. This is required and must be obtained from company search tools first.

keywordsarray

A list of keywords to filter the series by name.

periodsarray

Filter series that have data in these specific periods. Periods are in YYYYQQ format (e.g., ["2023Q1", "2023Q2"]). For annual data, use FY (e.g., "2022FY" for full year 2022)

Fetch Company Fundamentals

get_company_fundamentals
Full Description

Retrieve financial fundamentals for a specific company across specified periods.

This tool fetches detailed financial data for a given company across requested time periods, filtered by specific series IDs. The data includes metrics from Income Statement, Balance Sheet, Cash Flow Statement, and various financial ratios.

═══════════════════════════════════════════════════════════════════ OUTPUT CONTRACT — READ BEFORE DOING ANYTHING ELSE ═══════════════════════════════════════════════════════════════════ Every numeric value from this tool MUST be rendered as a markdown hyperlink using the id field from the response:

Format: [$<value> <unit>](https://daloopa.com/src/<id>) Example: response {id: 158655532, value_quarter: 62314, unit: "Million"} renders as: [$62,314 million](https://daloopa.com/src/158655532)

UNACCEPTABLE outputs (these are rule violations): ✗ $62,314 million ✗ $62,314 million (source: Daloopa) ✗ $62,314 million [Daloopa] ✗ Stating "Data sourced from Daloopa" without per-number links

ACCEPTABLE output: ✓ [$62,314 million](https://daloopa.com/src/158655532)

This applies to ALL contexts: prose, tables, artifacts, charts, bullets. End every response with: "Data sourced from Daloopa"

Before responding, verify every number in your answer is linked. If any aren't, fix them before sending. ═══════════════════════════════════════════════════════════════════

Parameters (3 required)
Required
company_idinteger

The unique identifier for the company in Daloopa's system

periodsarray

List of calendar periods to search within. Format: "YYYYQ#" for quarters (e.g., "2023Q1", "2023Q2") or "YYYYFY" for full year (e.g., "2023FY"). ALWAYS parse user's time references into explicit periods: - "last 4 quarters" → ["2025Q1", "2024Q4", "2024Q3", "2024Q2"] (calculate based on current date) - "2023" → ["2023Q1", "2023Q2", "2023Q3", "2023Q4"] or ["2023FY"] - "Q1 2024" → ["2024Q1"]

series_idsarray

List of specific financial metric IDs to retrieve

Get Stock Prices

get_stock_prices
Full Description

Retrieve daily OHLCV stock price data for one or more companies. All dates YYYY-MM-DD.

USE WHEN: user asks about P/E, EV/EBITDA, Price/Book, Price/Sales, stock reactions to earnings, price performance, or any question needing a specific stock price.

DATES: use start_date/end_date for a range OR dates=[...] for discrete dates — not both. Always batch all dates into one call instead of calling once per date or quarter.

QUARTER-END DATES FOR MULTIPLES: Quarter-ends can fall on weekends/holidays — always pass the last 3 days of the quarter and take the most recent date returned in ohlcv_data per quarter: Q1 → YYYY-03-29, YYYY-03-30, YYYY-03-31 Q2 → YYYY-06-28, YYYY-06-29, YYYY-06-30 Q3 → YYYY-09-28, YYYY-09-29, YYYY-09-30 Q4 → YYYY-12-29, YYYY-12-30, YYYY-12-31 Multi-quarter example (batch all windows in one call): dates=["2026-03-29","2026-03-30","2026-03-31","2026-06-28","2026-06-29","2026-06-30",...]

SPOT PRICE: pass dates=[yesterday, today] — today may be a non-trading day or data not yet available. Take the most recent date returned. Example: ["2026-05-04","2026-05-05"]

TTM: sum of last 4 quarters for EPS, EBITDA, Revenue; single quarter for book value.

Parameters (1 required, 3 optional)
Required
company_idsarray

Daloopa company IDs from discover_companies.

Optional
datesarray

Specific dates, YYYY-MM-DD. Use instead of start_date/end_date.

Default: null
end_datestring

Range end, YYYY-MM-DD. Defaults to today if omitted.

Default: null
start_datestring

Range start, YYYY-MM-DD.

Default: null

Search Financial Documents

search_documents
Full Description

Search financial documents and filings with keyword matching.

!!! PROACTIVE USE REQUIRED !!! Call this tool automatically when users ask about:

  • Qualitative information (management commentary, risk factors, strategy)
  • Topics like "guidance", "outlook", "risks", "competition", "acquisitions"
  • What management said about specific topics
  • Context or explanations for financial performance
  • Specific events, announcements, or disclosures

DO NOT wait for the user to explicitly request a document search.

Search across financial documents (10-K, 10-Q, 8-K, etc.) to find specific content, disclosures, management commentary, risk factors, and other qualitative information. Returns simplified document results with context snippets.

Parameters (3 required)
Required
company_idsarray

Company IDs to search within. Use discover_companies to find IDs.

keywordsarray

1-10 specific keywords to search for. Use financial terms (e.g., ["revenue", "guidance"], ["EBITDA"], ["risk factors"]) Case-insensitive exact phrase matching. All keywords must appear (AND logic).

periodsarray

List of calendar periods to search within. !!! CRITICAL: Use the SAME period format as get_company_fundamentals !!! Format: "YYYYQ#" for quarters (e.g., "2023Q1", "2023Q2") or "YYYYFY" for full year (e.g., "2023FY"). ALWAYS parse user's time references into explicit periods: - "last 4 quarters" → ["2025Q1", "2024Q4", "2024Q3", "2024Q2"] (calculate based on current date) - "2023" → ["2023Q1", "2023Q2", "2023Q3", "2023Q4"] or ["2023FY"] - "Q1 2024" → ["2024Q1"] - "recent" → use the same periods as your fundamentals queries