Happy Flows

Quick Start: Choose Your Workflow

Workflow	When to Use	Inputs	Outputs
Company-First	You know the company ticker/name and want to retrieve specific financial data	Company Ticker or Name	Fundamental Data
Taxonomy-First	You want to compare a specific metric across multiple companies	Standardized Metric	Fundamental Data
Poll for Updates	You need to monitor companies for earnings updates and retrieve new data	Webhook(s) or Company ID(s)	Fundamental Data
Export to CSV	You want to bulk export all datapoints for a company	Company Ticker	CSV file with Fundamental Data
Download Models	You prefer working with Excel models for analysis	Company ID	CSV file with Fundamental Data
Industry Analysis	You want to compare companies within an industry	None or sub-industry	Fundamental Data
Document Search	You need to find specific information in SEC filings	Keyword	Snippets from Documents
Stock Prices	You need daily OHCLV stock prices data	Company ID and dates	Daily OHLCV data

Company-First Workflow

Use this when: You know the company ticker or name and want to retrieve specific financial metrics.

Step 1: Discover the Company

Search for companies by ticker symbol or name:

curl --location 'https://app.daloopa.com/api/v2/companies?keyword=AAPL' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

[
  {
    "id": 2,
    "name": "Apple Inc.",
    "ticker": "AAPL",
    "industry_": "Technology Hardware, Storage and Peripherals",
    "sector_": "Information Technology",
    "companyidentifier_set": [
      {
        "identifier_type": "CIK",
        "identifier_value": "320193"
      },
      {
        "identifier_type": "CapIQCompanyId",
        "identifier_value": "24937"
      },
      {
        "identifier_type": "CapIQCompanyTicker",
        "identifier_value": "NasdaqGS:AAPL"
      },
      {
        "identifier_type": "CapIQCompanyId",
        "identifier_value": "IQ24937"
      },
      {
        "identifier_type": "ISIN",
        "identifier_value": "I_US0378331005"
      }
    ],
    "model_updated_at": "2026-02-02T12:12:38.482128Z",
    "earliest_quarter": "2012Q4",
    "latest_quarter": "2025Q4"
  }
]

What to capture:

id: Company ID (needed for all subsequent requests)
latest_quarter: Most recent quarter available
latest_datapoint_created_at: Last update timestamp (for polling)

Step 2: Discover Available Series

Find what financial metrics are available for this company:

curl --location 'https://app.daloopa.com/api/v2/companies/series?company_id=2&keywords=revenue&keywords=sales' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Tips:

Use multiple keywords parameters to search for related metrics
Common keywords: revenue, net income, EPS, cash flow, gross profit
Results show the exact series names and IDs available

Response:

[
    {
      "id": 2467997,
      "full_series_name": "Income Statement | Net sales | Products"
    },
    {
      "id": 2467998,
      "full_series_name": "Income Statement | Net sales | Services"
    },
    {
      "id": 2467999,
      "full_series_name": "Income Statement | Total net sales"
    },
  ]

Step 3: Retrieve Fundamentals

Now fetch the actual financial data:

curl --location 'https://app.daloopa.com/api/v2/companies/fundamentals?company_id=2&periods=2024Q1&periods=2024Q2&series_ids=12345&series_ids=12346' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

{
  "count": 2,
  "next": null,
  "previous": null,
  "results": [
    {
      "id": 95541471,
      "label": "Total net sales",
      "category": "Income Statement",
      "restated": false,
      "filing_type": "8-K",
      "series_id": 2467999,
      "title": "Income Statement | Total net sales",
      "value_raw": 90753,
      "value_normalized": 90753,
      "unit": "Million",
      "calendar_period": "2024Q1",
      "fiscal_period": "2024Q2",
      "span": "Quarterly",
      "fiscal_date": "2023-03-30",
      "document_id": 25165413,
      "filing_date": "2024-05-02",
      "document_released_at": "2024-05-02T20:30:34Z",
      "created_at": "2024-05-02T20:33:56.013697Z",
      "updated_at": "2026-02-02T12:12:56.284556Z"
    },
    {
      "id": 102519304,
      "label": "Total net sales",
      "category": "Income Statement",
      "restated": false,
      "filing_type": "8-K",
      "series_id": 2467999,
      "title": "Income Statement | Total net sales",
      "value_raw": 85777,
      "value_normalized": 85777,
      "unit": "Million",
      "calendar_period": "2024Q2",
      "fiscal_period": "2024Q3",
      "span": "Quarterly",
      "fiscal_date": "2024-06-29",
      "document_id": 26052485,
      "filing_date": "2024-08-01",
      "document_released_at": "2024-08-01T20:30:26Z",
      "created_at": "2024-08-01T20:40:09.417865Z",
      "updated_at": "2026-02-02T12:12:56.284616Z"
    }
  ]
}

Key fields:

fundamental_id: Unique ID for this datapoint (use for source linking: https://daloopa.com/src/{fundamental_id})
value: The actual financial value
quarterized_value: Normalized to quarterly basis (useful for comparing Q vs annual data)
period: Time period in YYYYQQ format (e.g., 2024Q1) or YYYYFY for annual (e.g., 2023FY)
unit: Scale of the number (billion, million, percent, etc.)

Extra Credit: Check for Deprecated Series IDs

Some series IDs may have been replaced over time. Check for continuations:

curl --location 'https://app.daloopa.com/api/v2/series-continuation?company_id=2' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

[
  {
    "old_series": [
      {
        "id": 303689,
        "full_series_name": "Other Breakdown | Purchases of equity securities by the issuer and affiliated purchasers | Total number of shares purchased | Period 2 | ASR 2",
        "created_at": "2020-10-15T11:42:27.469449Z"
      },
      {
        "id": 303685,
        "full_series_name": "Other Breakdown | Purchases of equity securities by the issuer and affiliated purchasers | Total number of shares purchased | Period 1 | ASR 2",
        "created_at": "2020-10-15T11:42:26.131450Z"
      }
    ],
    "new_series": [
      {
        "id": 3544161,
        "full_series_name": "Other Breakdown | Purchases of equity securities by the issuer and affiliated purchasers | Total number of shares purchased | First month | ASR 2",
        "created_at": "2022-11-07T08:55:26.660085Z"
      },
      {
        "id": 3544162,
        "full_series_name": "Other Breakdown | Purchases of equity securities by the issuer and affiliated purchasers | Total number of shares purchased | Second month | ASR 2",
        "created_at": "2022-11-07T08:55:26.670273Z"
      }
    ],
    "type": "COMPOSITE",
    "created_at": "2022-11-07T08:55:28.392781Z"
  }
]

When to use this:

You're using cached series IDs that may be outdated
You get unexpected missing data in your fundamentals response
You want to ensure you're always using the latest series structure

Taxonomy-First Workflow (Beta)

Use this when: You want to compare a specific metric (like "Total Revenue") across multiple companies.

Step 1: Search Taxonomy Metrics

Find the standardized metric you want:

curl --location 'https://app.daloopa.com/api/v2/taxonomy/metrics?keyword=total%20revenue' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

{
  "count": 8,
  "next": null,
  "previous": null,
  "results": [
    {
      "metric_id": 1617,
      "metric_name": "Total Revenue",
      "metric_description": "This metric measures the total amount of money a company earns from its business activities before any expenses are deducted, typically over a specific period such as a quarter or year."
    },
  ]
}

Step 2: Find Companies in Taxonomy

Discover which companies have this metric:

curl --location 'https://app.daloopa.com/api/v2/taxonomy/metrics/501' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

{
  "metric_id": 1617,
  "metric_name": "Total Revenue",
  "metric_description": "This metric measures the total amount of money a company earns from its business activities before any expenses are deducted, typically over a specific period such as a quarter or year.",
  "metric_series": [
    {
       "company_id": 2,
       "ticker": "AAPL",
       "series_id": 2467999,
       "full_series_name": "Income Statement | Total net sales"
     },
    {
      "company_id": 135,
      "ticker": "MSFT",
      "series_id": 2542359,
      "full_series_name": "Income Statement | Total revenue"
    }
  ]
}

Step 3: Retrieve Fundamentals for All Companies

Use the company IDs to fetch the metric across all companies:

# For Apple
curl --location 'https://app.daloopa.com/api/v2/companies/fundamentals?company_id=2&periods=2024Q1&periods=2024Q2&series_ids=2467999' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

# For Microsoft
curl --location 'https://app.daloopa.com/api/v2/companies/fundamentals?company_id=135&periods=2024Q1&periods=2024Q2&series_ids=2542359' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Pro tip: You can batch requests by making multiple API calls in parallel to speed up data retrieval.

Poll for Updates Workflow

Use this when: You want to automatically detect when companies release new earnings data and retrieve it.

Approach 1: Webhooks (Recommended)

Webhooks push updates to your server automatically when new data is available.

How it works:

Contact Daloopa support to configure webhooks for your companies
Daloopa team adds webhook subscriptions for the companies you care about
When new data is available, Daloopa sends a POST request to your endpoint

Webhook payload example:

{
  "event_type": "clientview_updated",
  "company_id": 2,
  "series": {
    "SERIES_ID_1": {
      "periods": [
        "2025Q1"
      ]
    },
    "SERIES_ID_2": {
      "periods": [
        "2025Q1"
      ]
    }
  }
}

Your endpoint should:

Receive the POST request
Validate the payload
Call /api/v2/companies/fundamentals to fetch the new data
Return a 200 OK response

Testing your webhook endpoint:

Use ngrok to expose localhost for testing
Use webhook.site to inspect incoming payloads
Test with a company you don't actively monitor

Approach 2: Polling (Alternative)

If webhooks aren't set up yet, you can poll for updates:

curl --location --request POST 'https://app.daloopa.com/api/v2/companies/status' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)' \
--header 'Content-Type: application/json' \
--data '{
  "company_ids": [2]
}'

Response:

[
  {
    "company_id": 2,
    "latest_datapoint_created_at": "2026-02-02T11:57:43.113262Z",
    "latest_period": "2025Q4",
    "model_updated_at": "2026-02-02T12:13:55.029076Z"
  }
]

Polling logic:

Store the last latest_datapoint_created_at timestamp for each company
Poll /api/v2/companies/status every 15-30 minutes
Compare new latest_datapoint_created_at with stored timestamp
If changed, fetch new data with /api/v2/companies/fundamentals

Best practices:

Poll during market hours when earnings are typically released
Use exponential backoff if you're polling frequently
Consider time zones (most earnings are released 4-5 PM ET)

Comparison: Webhooks vs Polling

Feature	Webhooks	Polling
Latency	Instant (< 1 minute)	5 minutes (depends on poll frequency)
Server load	Low (only when updates occur)	Higher (constant requests)
Setup complexity	Requires Daloopa team setup	Self-service via API
Best for	Production systems, real-time needs	Testing, low-frequency monitoring

Export to CSV Workflow

Use this when: You want to export all datapoints for a company to analyze in Excel or other tools.

Base Export (Historical Data)

Export all historical datapoints:

curl --location 'https://app.daloopa.com/api/v2/export/AAPL' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)' \
--output aapl_data.csv

What you get:

CSV format with all datapoints
Includes: id, label, category, span, calendar_period, fiscal_period, value_raw, value_normalized, unit, source_link, series_id
Historical data available in the published datasheet

Real-Time Export (Latest Updates)

Export incrementally updated data that hasn't been published to the datasheet yet:

curl --location 'https://app.daloopa.com/api/v2/export/MMM?real_time=true&show_historical_data=false' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)' \
--output mmm_realtime.csv

Parameters:

real_time=true: Include incrementally updated data (not yet in published datasheet)
show_historical_data=false: Only return new incremental data (no historical)
show_historical_data=true: Return both historical + incremental data

Use case:

real_time=true&show_historical_data=false: Get only the latest earnings data before it's published
real_time=true&show_historical_data=true: Get complete dataset including latest updates

CSV Schema

Column	Description	Example
`id`	Unique datapoint ID	123456789
`label`	Short description	"Total Revenue"
`category`	Section in model	"Income Statement"
`span`	Periodicity	"Quarterly"
`calendar_period`	Calendar quarter/year	"2024Q2"
`fiscal_period`	Fiscal quarter/year	"2024Q2"
`fiscal_date`	End of fiscal period	"2024-06-30"
`unit`	Scale	"billion"
`filing_type`	SEC filing type	"10-Q"
`value_raw`	Raw value	90.753
`value_normalized`	Normalized value	90.753
`source_link`	Link to source	"https://daloopa.com/src/123456789"
`series_id`	Series identifier	12345
`filing_date`	Filing date	"2024-05-03"
`restated`	Was restated?	false
`title`	Full hierarchy	"Revenue \| Total Revenue"
`created_at`	Creation timestamp	"2024-05-03T14:30:00Z"
`updated_at`	Last update	"2024-05-03T14:30:00Z"
`document_released_at`	Document ingestion time	"2024-05-03T15:00:00Z"

Download Excel Model Workflow

Use this when: You want to download the full Excel model for offline analysis.

Step 1: Request Download URL

Get a pre-signed URL to download the model:

curl --location 'https://app.daloopa.com/api/v2/download-company-model?company_id=2&model_type=company' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

{
  "download_url": "https://daloopa-excel-model.s3.amazonaws.com/1031277.xlsx?AWSAccessKeyId=AKIAYH2MSS6UCWRAXJ23&Signature=XtKT7JwJYKSXOp3NPBl5tJWSRAw%3D&Expires=1770152604"
}

Step 2: Download the Model

Use the pre-signed URL to download:

curl --location 'https://daloopa-models.s3.amazonaws.com/models/AAPL_model.xlsx?AWSAccessKeyId=...' \
--output AAPL_model.xlsx

Important:

URLs expire after 1 hour
Download must complete before expiration
Models are full Excel files with all sheets and formulas

What's in the Model?

Historical data: All quarters/years in the datasheet
Line items: Full detail for Income Statement, Balance Sheet, Cash Flow
KPIs: Company-specific metrics
Guidance: Management guidance when available
Charts: Pre-built visualizations

Industry Model Workflow

Use this when: You want to compare companies within the same industry using standardized metrics.

Step 1: List Sub-Industries

Find available sub-industries:

curl --location 'https://app.daloopa.com/api/v2/taxonomy/sub-industries' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

{
  "count": 220,
  "next": "http://app.daloopa.com/api/v2/taxonomy/sub-industries?limit=100&offset=100",
  "previous": null,
  "results": [
    {
      "sub_industry_id": 281,
      "sub_industry_name": "Cruise Lines",
      "industry_name": "Restaurants, Hotels & Leisure",
      "sub_sector_name": "Consumer Services",
      "sector_name": "Consumer Discretionary",
      "companies": [
        {
          "id": 312,
          "ticker": "CCL",
          "name": "Carnival Corp"
        },
        {
          "id": 508,
          "ticker": "NCLH",
          "name": "Norwegian Cruise Line Holdings Ltd."
        },
        {
          "id": 555,
          "ticker": "RCL",
          "name": "Royal Caribbean Cruises Ltd"
        }
      ]
	   }
  ]
}

Step 2: Search Taxonomy Metrics

Find the standardized metric(s) you want using the sub_industry_id:

curl --location 'https://app.daloopa.com/api/v2/taxonomy/metrics?sub_industry_id=281' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

{
  "count": 69,
  "next": null,
  "previous": null,
  "results": [
    {
      "metric_id": 1558,
      "metric_name": "Passenger Revenue",
      "metric_description": "Total revenue generated from transporting passengers, excluding ancillary (e.g., fees, onboard add-ons) and cargo/freight revenues. Includes passenger ticket sales and related passenger-service revenues across passenger transportation operators (e.g., airlines, cruise lines)."
    },
    {
      "metric_id": 1560,
      "metric_name": "Operating Income",
      "metric_description": "Operating Income (Loss) represents the profit or loss a company generates from its core operating activities before non-operating items (e.g., interest and other financing costs/income) and income taxes. It is generally calculated as revenue (or net sales) minus operating costs and expenses (e.g., cost of goods/services, selling/general/administrative, R&D, depreciation and amortization, and other operating items), and may be presented as EBIT when interest is excluded by definition."
    },
 ]
}

Step 3: Retrieve Company Series IDs for All Companies

Use the company IDs to fetch the metric across all companies:

# For Passenger Revenue
curl --request GET \
     --url 'https://app.daloopa.com/api/v2/taxonomy/metrics/1558?sub_industry_id=281' \
     --header 'Authorization: Basic YXBpLW1hcml6dUBwb2ludDcyLmNvbTp4cU84RFdNaFM5dkdzeWZ3SXI1cGVHMGFudUNXcGVMbThRelFzV1FYQW5WRnI3cjFHWHlEYUE=' \
     --header 'accept: application/json'

# For Operating Income
curl --request GET \
     --url 'https://app.daloopa.com/api/v2/taxonomy/metrics/1560?sub_industry_id=281' \
     --header 'Authorization: Basic YXBpLW1hcml6dUBwb2ludDcyLmNvbTp4cU84RFdNaFM5dkdzeWZ3SXI1cGVHMGFudUNXcGVMbThRelFzV1FYQW5WRnI3cjFHWHlEYUE=' \
     --header 'accept: application/json'

Response:

{
  "count": 69,
  "next": null,
  "previous": null,
  "results": [
    {
      "metric_id": 1558,
      "metric_name": "Passenger Revenue",
      "metric_description": "Total revenue generated from transporting passengers, excluding ancillary (e.g., fees, onboard add-ons) and cargo/freight revenues. Includes passenger ticket sales and related passenger-service revenues across passenger transportation operators (e.g., airlines, cruise lines)."
    },
    {
      "metric_id": 1560,
      "metric_name": "Operating Income",
      "metric_description": "Operating Income (Loss) represents the profit or loss a company generates from its core operating activities before non-operating items (e.g., interest and other financing costs/income) and income taxes. It is generally calculated as revenue (or net sales) minus operating costs and expenses (e.g., cost of goods/services, selling/general/administrative, R&D, depreciation and amortization, and other operating items), and may be presented as EBIT when interest is excluded by definition."
    },
 ]
}

Step 4: Retrieve Fundamentals for All Companies

Use the company IDs to fetch the metric across all companies:

# For CCL
curl --location 'https://app.daloopa.com/api/v2/companies/fundamentals?company_id=312&periods=2024Q1&periods=2024Q2&series_ids=2729066&series_ids=2729081' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

# For NCLH
curl --location 'https://app.daloopa.com/api/v2/companies/fundamentals?company_id=508&periods=2024Q1&periods=2024Q2&series_ids=1866377&series_ids=1866391' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

# For RCL
curl --location 'https://app.daloopa.com/api/v2/companies/fundamentals?company_id=555&periods=2024Q1&periods=2024Q2&series_ids=2865083&series_ids=2865097' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Pro tip: You can batch requests by making multiple API calls in parallel to speed up data retrieval.

Document Search Workflow

Use this when: You need to find specific information in SEC filings (10-K, 10-Q, 8-K, earnings calls).

Pattern 1: Keyword Search

Search for keywords across all document types:

curl --location 'https://app.daloopa.com/api/v2/documents/search?keyword=cybersecurity' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

{
  "success": true,
  "total_hits": 11,
  "documents": [
    {
      "document_id": "23465209",
      "company_id": 2,
      "filing_type": "PRIVATE",
      "affinitized_date": "2022-12-31",
      "document_title": "c3f1ec093cb388c4282baebcce982bf2",
      "score": 0.9860914,
      "matches": [
        {
          "keyword": "guidance",
          "indexed_position": "24187-24195",
          "context": "[...] ncertainty around the world in the near term, we are not providing revenue guidance, but we are sharing some directional insights based on the assumption that [...]",
          "match_id": "eyJkb2N1bWVudF9pZCI6ICIyMzQ2NTIwOSIsICJlbmQiOiAyNDE5NSwgImtleXdvcmQiOiAiZ3VpZGFuY2UiLCAic3RhcnQiOiAyNDE4N30="
        },
      ]
    }
  ],
  "offset": 0,
  "limit": 10
}

Pattern 2: Company-Specific Document Lookup

Find documents for a specific company:

curl --location 'https://app.daloopa.com/api/v2/companies/2/documents?filing_type=10-Q&fiscal_period=2024Q2' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

{
  "company_id": 2,
  "ticker": "AAPL",
  "documents": [
    {
      "document_id": 789012,
      "filing_type": "10-Q",
      "fiscal_period": "2024Q2",
      "filing_date": "2024-05-03",
      "url": "https://daloopa.com/documents/789012"
    }
  ]
}

Pattern 3: Retrieve Full Document

Get the complete document content:

curl --location 'https://app.daloopa.com/api/v2/documents/789012' \
--header 'Authorization: Basic $(echo -n '[email protected]:abc123' | base64)'

Response:

{
  "document_id": 789012,
  "company_id": 2,
  "ticker": "AAPL",
  "filing_type": "10-Q",
  "fiscal_period": "2024Q2",
  "filing_date": "2024-05-03",
  "content": "Full document text...",
  "source_url": "https://www.sec.gov/..."
}

Search tips:

Use specific keywords for better results
Filter by filing_type to narrow search (10-K, 10-Q, 8-K)
Combine with fiscal_period to find specific quarterly/annual disclosures
Use match_count to prioritize documents with more mentions

Stock Prices Workflow

Use this when: you need daily OHLCV (open, high, low, close, volume) price data for a company, whether for spot pricing, quarter-end multiple calculations, or historical price performance analysis.

Endpoint: GET /api/v2/companies/{company_id}/stock-prices

Basic Workflow

If you do not already have the company_id, use the /companies endpoint to look it up by ticker or name first. See the Company Search Workflow for details.

curl -X GET "https://app.daloopa.com/api/v2/companies/2/stock-prices?start_date=2024-01-01&end_date=2024-12-31" \
  -H "Authorization: Basic <base64(email:apiKey)>"

Response:

{
  "count": 251,
  "next": "https://app.daloopa.com/api/v2/companies/2/stock-prices?offset=100&limit=100",
  "previous": null,
  "results": [
    {
      "date": "2024-01-02",
      "open": 187.15,
      "high": 188.44,
      "low": 183.89,
      "close": 185.20,
      "volume": 79763700
    }
  ]
}

Key fields in each result:

date — trading date for the data point
open / high / low / close — prices in currency units
volume — total shares traded on that date

Tip: Paginate through large date ranges

The response is paginated. Use limit and offset to page through results when retrieving multi-year windows.

curl -X GET "https://app.daloopa.com/api/v2/companies/2/stock-prices?start_date=2020-01-01&end_date=2024-12-31&limit=100&offset=100" \
  -H "Authorization: Basic <base64(email:apiKey)>"

Follow the next URL in each response until it returns null.

Best practices

Spot pricing: To get the most recent available price, set end_date to today and start_date to 3 calendar days prior. This ensures you capture the latest trading day regardless of weekends or market holidays.
Quarter-end multiples: Use the last calendar day of each quarter as your date to align stock prices with reported financials. Combine the close price with fundamentals data (shares outstanding, EPS, book value) to calculate P/E, EV/EBITDA, or Price/Book ratios.

Use Cases

P/E Ratio Calculation Workflow

Use this when you already know the series_id for a key metric and want to combine it with stock prices to calculate a trading multiple. This example walks through calculating the trailing P/E ratio for Apple across the last four quarters.

Endpoints used:

GET /api/v2/companies/fundamentals
GET /api/v2/companies/{company_id}/stock-prices

Step 1 — Pull EPS from the Fundamentals API

Using the known series_id for diluted EPS, request the last four quarterly periods.

curl -X GET "https://app.daloopa.com/api/v2/companies/fundamentals?company_id=2&series_ids=4521&periods=2024Q1&periods=2024Q2&periods=2024Q3&periods=2024Q4" \
  -H "Authorization: Basic <base64(email:apiKey)>"

Response:

{
  "count": 4,
  "next": null,
  "previous": null,
  "results": [
    { "calendar_period": "2024Q1", "fiscal_date": "2024-03-31", "title": "Diluted EPS", "value_normalized": 1.53, "unit": "USD" },
    { "calendar_period": "2024Q2", "fiscal_date": "2024-06-30", "title": "Diluted EPS", "value_normalized": 1.40, "unit": "USD" },
    { "calendar_period": "2024Q3", "fiscal_date": "2024-09-30", "title": "Diluted EPS", "value_normalized": 1.64, "unit": "USD" },
    { "calendar_period": "2024Q4", "fiscal_date": "2024-12-31", "title": "Diluted EPS", "value_normalized": 2.40, "unit": "USD" }
  ]
}

Note the fiscal_date field for each period — you will use these dates to fetch the matching quarter-end stock prices in the next step.

Step 2 — Pull quarter-end stock prices

Using the fiscal_date values from Step 1, request the closing price at each quarter end. Set start_date and end_date to a 3-day window around each date to account for weekends or market holidays, then use the close value from the nearest available trading day.

curl -X GET "https://app.daloopa.com/api/v2/companies/2/stock-prices?start_date=2024-03-29&end_date=2024-03-31" \
  -H "Authorization: Basic <base64(email:apiKey)>"

Repeat for each quarter-end date. Collected closing prices:

Period	Fiscal Date	Close Price
2024Q1	2024-03-31	$171.48
2024Q2	2024-06-30	$210.62
2024Q3	2024-09-30	$233.00
2024Q4	2024-12-31	$243.85

Step 3 — Calculate trailing P/E

Divide the quarter-end closing price by the diluted EPS for that period.

P/E = Close Price / Diluted EPS

Period	Close Price	Diluted EPS	P/E Ratio
2024Q1	$171.48	$1.53	112.1x
2024Q2	$210.62	$1.40	150.4x
2024Q3	$233.00	$1.64	142.1x
2024Q4	$243.85	$2.40	101.6x

Best practices

Use value_normalized from the fundamentals response rather than value_raw to ensure consistent units across periods.
Match on fiscal_date, not calendar_period, when aligning fundamentals to stock prices to handle companies with non-calendar fiscal years correctly.
Filter out restated values by passing restated=false if you want only originally reported figures, or omit the parameter to include both.