Bloomberg Data API: Extract Structured JSON in 2026
This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.
TL;DR
Use AlterLab's Extract API to send a URL and a JSON schema describing the fields you want (e.g., ticker, price, change_percent). The API returns validated, typed JSON—no HTML parsing, no fragile selectors. See the Python and cURL examples below for a quick start.
Why use Bloomberg data?
- AI training: Feed structured price and volume time‑series into models for forecasting or anomaly detection.
- Analytics: Build dashboards that track market movements across sectors without manual CSV downloads.
- Competitive intelligence: Monitor peer companies' fundamentals and real‑time quotes to inform strategy.
What data can you extract?
Bloomberg's public pages expose finance‑focused fields that are useful for pipelines:
-
ticker: Symbol like AAPL or USDCAD. -
price: Last traded price as reported on the page. -
change_percent: Percentage change from previous close. -
volume: Shares or contracts traded in the session. -
market_cap: Total market capitalization derived from price × shares outstanding. All of these can be captured via a JSON schema and returned as strongly typed values.
The extraction approach
Raw HTTP requests followed by HTML parsing break whenever Bloomberg updates its front‑end or serves different markup to bot‑like traffic. Maintaining selectors, handling pagination, and dealing with JavaScript‑rendered content adds overhead. A data API abstracts that complexity: you declare the shape of the data you need, and the service handles retrieval, rendering, anti‑bot measures, and validation.
Quick start with AlterLab Extract API
First, install the client and follow the Getting started guide. Then call the extract endpoint with your schema.
```python title="extract_bloomberg-com.py" {5-12}
client = alterlab.Client("YOUR_API_KEY")
schema = {
"type": "object",
"properties": {
"ticker": {
"type": "string",
"description": "The ticker field"
},
"price": {
"type": "string",
"description": "The price field"
},
"change_percent": {
"type": "string",
"description": "The change percent field"
},
"volume": {
"type": "string",
"description": "The volume field"
},
"market_cap": {
"type": "string",
"description": "The market cap field"
}
}
}
result = client.extract(
url="https://bloomberg.com/quote/SPX:IND",
schema=schema,
)
print(result.data)
```bash title="Terminal"
curl -X POST https://api.alterlab.io/v1/extract \
-H "X-API-Key: YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"url": "https://bloomberg.com/quote/SPX:IND",
"schema": {"properties": {"ticker": {"type": "string"}, "price": {"type": "string"}, "change_percent": {"type": "string"}}}
}'
The response is a JSON object matching your schema, with proper types and no extra markup.
Define your schema
The schema parameter drives the entire extraction. AlterLab uses an LLM‑guided extractor to locate the requested fields on the page, then coerces the output to the declared JSON types. If a field cannot be found or converted, the API returns an error rather than garbage data. This guarantees that downstream pipelines receive clean, predictable JSON.
Example schema for a Bloomberg commodity page:
{
"type": "object",
"properties": {
"ticker": {"type": "string"},
"price": {"type": "number"},
"change_percent": {"type": "number"},
"volume": {"type": "integer"},
"market_cap": {"type": "integer"}
},
"required": ["ticker", "price"]
}
Notice that numeric fields are declared as number or integer; AlterLab will parse strings like "$123.45" or "1.2M" into actual numbers.
Handle pagination and scale
For bulk extraction—say, pulling quotes for hundreds of tickers—batch your requests and respect rate limits. AlterLab supports asynchronous job submission via the same endpoint; you can poll for results or use webhooks.
```python title="async_batch.py" {7-15}
client = alterlab.Client("YOUR_API_KEY")
urls = [f"https://bloomberg.com/quote/{sym}:IND" for sym in ["SPX", "DJI", "IXIC"]]
schema = {
"type": "object",
"properties": {
"ticker": {"type": "string"},
"price": {"type": "number"},
"change_percent": {"type": "number"}
}
}
async def fetch(url):
return await client.extract_async(url=url, schema=schema)
async def main():
results = await asyncio.gather(*[fetch(u) for u in urls])
for r in results:
print(r.data)
if name == "main":
asyncio.run(main())
See the [Extract API docs](/docs/api/extract) for details on async job IDs, webhook configuration, and concurrency limits. For cost estimates, visit the [pricing](/pricing) page—charges are per successful extraction, with volume discounts available at higher tiers.
<div data-infographic="steps">
<div data-step data-number="1" data-title="Define Schema" data-description="Specify the fields you want as a JSON schema"></div>
<div data-step data-number="2" data-title="Call Extract API" data-description="POST the URL + schema to AlterLab"></div>
<div data-step data-number="3" data-title="Receive Typed JSON" data-description="Get back validated, structured data — no parsing needed"></div>
</div>
## Key takeaways
- Declarative schema‑based extraction eliminates fragile HTML parsing.
- AlterLab handles rendering, anti‑bot, and validation so you receive typed JSON instantly.
- Start with a simple schema, scale with async batches, and monitor usage via the dashboard.
- Always verify that the data you target is publicly accessible and compliant with Bloomberg's robots.txt and Terms of Service.
<div data-infographic="try-it" data-url="https://bloomberg.com" data-description="Extract structured finance data from Bloomberg"></div>
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