# Databento Details Spider (`getdataforme/databento-details-spider`) Actor The Databento Details Spider is an Apify Actor that scrapes comprehensive financial instrument data from Databento catalog pages, including expirations, volumes, open interest, and detailed sample schemas for MBO, MBP, and OHLCV.... - **URL**: https://apify.com/getdataforme/databento-details-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ### Description The Databento Details Spider is an Apify Actor that scrapes comprehensive financial instrument data from Databento catalog pages, including expirations, volumes, open interest, and detailed sample schemas for MBO, MBP, and OHLCV.... --- ## Databento Details Spider ### Introduction The Databento Details Spider is an Apify Actor designed to scrape comprehensive details from Databento catalog pages, extracting structured data on financial instruments such as futures. It provides detailed instrument information, including expiration dates, volumes, and open interest, alongside sample data schemas with full field descriptions for various data types like market by order (MBO), market by price (MBP), and OHLCV bars. This tool is invaluable for financial analysts, traders, and researchers needing reliable, automated data extraction from Databento's platform. ### Features - **Comprehensive Data Extraction**: Scrapes instrument details, including symbols, expirations, prices, volumes, and open interest from Databento URLs. - **Schema-Rich Sample Data**: Provides detailed field descriptions for multiple schemas (e.g., MBO, MBP-1, OHLCV-1s), enabling deep understanding of data structures. - **Structured JSON Output**: Delivers clean, machine-readable results with metadata like dataset, symbol, and asset class. - **Flexible Input**: Accepts multiple URLs for batch processing, supporting various Databento catalog pages. - **High Reliability**: Handles web scraping with robust error handling to ensure consistent data retrieval. - **Performance Optimized**: Efficiently processes pages to minimize runtime and resource usage. - **No Coding Required**: User-friendly interface for quick setup and execution on the Apify platform. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|--------|----------|-------------|---------| | Urls | array | No | An array of URLs to Databento catalog pages for scraping. Each URL must be a valid HTTP/HTTPS link. | ["https://databento.com/catalog/cme/GLBX.MDP3/futures/ZYSE"] | ### Example Usage #### Input ```json { "Urls": [ "https://databento.com/catalog/cme/GLBX.MDP3/futures/ZYSE" ] } ```` #### Output ```json [ { "input_url": "https://databento.com/catalog/cme/GLBX.MDP3/futures/ZYSE", "dataset": "GLBX.MDP3", "symbol": "ZYSE", "asset_class": "Futures", "instrument_data": [ { "symbol": "ZYSEZ6", "expiration": "2026-12-14", "last_price": null, "volume": 0, "open_interest": 0 } ], "sample_data": [ { "schema_id": "mbo", "name": "MBO", "description": "Full order book data, including all buy and sell orders at every price level.", "fields": [ { "name": "ts_recv", "description": "The capture-server-received timestamp expressed as the number of nanoseconds since the UNIX epoch." } ] } ], "actor_id": "CvRq3ozTK0nG5BoA6", "run_id": "tmbmDZdh520D7zfx3" } ] ``` ### Use Cases - **Market Research**: Gather detailed instrument data for analyzing futures markets and trends. - **Competitive Intelligence**: Monitor competitor positions and trading volumes from Databento catalogs. - **Price Monitoring**: Track expiration dates, last prices, and open interest for informed trading decisions. - **Content Aggregation**: Collect structured data for financial databases or reporting tools. - **Academic Research**: Extract sample schemas and field details for studies on market data structures. - **Business Automation**: Automate data pipelines for real-time financial insights and alerts. ### Installation and Usage 1. Search for "Databento Details Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters by adding URLs to scrape. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is a JSON array of objects, each corresponding to a scraped URL. Key fields include: - `input_url`: The original URL scraped. - `dataset`, `symbol`, `asset_class`: Metadata about the instrument group. - `instrument_data`: An array of objects with details like symbol, expiration, last\_price, volume, and open\_interest. - `sample_data`: An array of schema objects, each with `schema_id`, `name`, `description`, and a `fields` array containing field names and descriptions. - `actor_id` and `run_id`: Apify-specific identifiers for tracking. This structured format ensures easy parsing and integration into downstream applications. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! # Actor input Schema ## `Urls` (type: `array`): The urls for the spider. ## Actor input object example ```json { "Urls": [ "https://databento.com/catalog/cme/GLBX.MDP3/futures/ZYSE" ] } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/databento-details-spider").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/databento-details-spider").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call getdataforme/databento-details-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/databento-details-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Databento Details Spider", "description": "The Databento Details Spider is an Apify Actor that scrapes comprehensive financial instrument data from Databento catalog pages, including expirations, volumes, open interest, and detailed sample schemas for MBO, MBP, and OHLCV....", "version": "0.0", "x-build-id": "Z1IJ1DYdsYjT9wxMf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~databento-details-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-databento-details-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~databento-details-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-databento-details-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~databento-details-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-databento-details-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "Urls": { "title": "Urls", "minItems": 1, "type": "array", "description": "The urls for the spider.", "default": [ "https://databento.com/catalog/cme/GLBX.MDP3/futures/ZYSE" ], "items": { "type": "string" } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```