# Tmx News Spider (`getdataforme/tmx-news-spider`) Actor Tmx News Spider is an Apify Actor that scrapes and extracts structured financial news from TMX Group's website, including titles, dates, descriptions, and full content.... - **URL**: https://apify.com/getdataforme/tmx-news-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 Tmx News Spider is an Apify Actor that scrapes and extracts structured financial news from TMX Group's website, including titles, dates, descriptions, and full content.... --- ## Tmx News Spider Tmx News Spider is an Apify Actor designed to scrape and extract news articles from TMX Group's official website, providing structured data on financial news, market updates, and corporate announcements. It enables users to gather timely and accurate information from Toronto Stock Exchange and related platforms, making it ideal for financial analysts, researchers, and businesses needing automated data collection. This tool ensures reliable extraction of news content, including titles, descriptions, full text, and metadata, without requiring manual browsing. ### Features - **Comprehensive News Extraction**: Scrapes detailed news articles, including titles, publish dates, descriptions, and full content paragraphs from TMX Group sources. - **Flexible Date Filtering**: Supports absolute dates (e.g., YYYY-MM-DD) or relative periods (e.g., "1 week", "2 months") for targeted scraping. - **Structured Output**: Delivers data in clean JSON format with fields like URLs, headings, and contact information for easy integration. - **High Reliability**: Handles dynamic web content and ensures data accuracy by parsing official TMX news pages. - **Scalable and Fast**: Optimized for performance, allowing quick runs even for large date ranges. - **No Coding Required**: User-friendly interface on Apify Store for non-technical users. - **Error-Resilient**: Includes built-in handling for missing data or site changes. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|--------|----------|-------------|---------| | Date | string | No | The date or period for news extraction. Accepts absolute dates in YYYY-MM-DD format or relative terms like "1 day", "2 weeks", "3 months", or "1 year". Defaults to a future date if not specified. | "2026-04-12" or "1 month" | ### Example Usage To run the Actor, configure the input as follows: ```json { "Date": "2026-04-12" } ```` This will scrape news from the specified date. For relative dates: ```json { "Date": "1 week" } ``` Example output (truncated for brevity): ```json [ { "publish_date": "February 24, 2026", "news_title": "TMX Group Limited Normal Course Issuer Bid Approved", "description": "TMX Group Limited (\"TMX Group\") announced today that its normal course issuer bid (\"NCIB\") has been accepted by Toronto Stock Exchange (\"TSX\"). Under the NCIB, TMX Group may purchase up to 2,800,000 of its common shares...", "detail_url": "https://www.tsx.com/en/news?id=1147&year=2026", "detail_page_title": "TMX Group Limited Normal Course Issuer Bid Approved", "detail_page_publish_date": "February 24, 2026", "content_paragraph_1": "February 24, 2026 (TORONTO) โ€“ TMX Group Limited (\"TMX Group\") announced today that its normal course issuer bid (\"NCIB\") has been accepted by Toronto Stock Exchange (\"TSX\").", "content_paragraph_2": "Under the NCIB, TMX Group may purchase up to 2,800,000 of its common shares by way of normal course purchases on Toronto Stock Exchange...", // Additional paragraphs up to content_paragraph_16 "full_content": "February 24, 2026 (TORONTO) โ€“ TMX Group Limited (\"TMX Group\") announced today that its normal course issuer bid (\"NCIB\") has been accepted...", "content_headings": "About TMX Group (TSX:X)", "actor_id": "npFfjsc6AO31XuJf0", "run_id": "n4psTB9Dslj8COSDH" }, // More news items... ] ``` ### Use Cases - **Market Research and Analysis**: Gather historical and current news to analyze trends in Canadian stock markets and TMX operations. - **Competitive Intelligence**: Monitor announcements from TMX Group and related entities for insights into financial services and trading. - **Price Monitoring**: Extract news on financing statistics and trading data to track market performance. - **Content Aggregation**: Build databases of financial news for blogs, reports, or newsletters. - **Academic Research**: Collect data on equity financings and trading statistics for studies in finance and economics. - **Business Automation**: Automate data feeds for dashboards or alerts on new TMX news releases. ### Installation and Usage 1. Search for "Tmx News Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters (e.g., set the Date). 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 Actor outputs a JSON array of objects, each representing a news article. Key fields include: - `publish_date`: Publication date of the news. - `news_title`: Headline of the article. - `description`: Brief summary. - `detail_url`: Link to the full article. - `detail_page_title` and `detail_page_publish_date`: Page-specific metadata. - `content_paragraph_1` to `content_paragraph_16`: Individual paragraphs of the article. - `full_content`: Complete text of the article. - `content_headings`: Section headings. - `actor_id` and `run_id`: Apify-specific identifiers. Data is structured for easy parsing, with links and contact info included where available. ### Error Handling The Actor includes robust error handling for common issues like network timeouts or invalid dates. If a date is malformed, it defaults to the specified default. For site changes, logs will indicate parsing failuresโ€”contact support for updates. Rate limiting is managed internally to avoid bans. ### Rate Limiting and Best Practices Apify enforces rate limits based on your plan; free users have lower limits. Best practices: Use specific dates to reduce load, avoid excessive runs, and monitor logs for errors. For large extractions, run during off-peak hours. ### Limitations and Considerations - Data is sourced from TMX's public news pages; private or archived content may not be accessible. - Output size depends on news volume for the date; very broad ranges may hit Apify's dataset limits. - Ensure compliance with TMX's terms of use for commercial applications. - The Actor may require updates if TMX changes its website structure. ### 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 ## `Date` (type: `string`): The date for the spider. ## Actor input object example ```json { "Date": "2026-04-12" } ``` # 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/tmx-news-spider").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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/tmx-news-spider").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call getdataforme/tmx-news-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/tmx-news-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Tmx News Spider", "description": "Tmx News Spider is an Apify Actor that scrapes and extracts structured financial news from TMX Group's website, including titles, dates, descriptions, and full content....", "version": "0.0", "x-build-id": "3qdlSiXL1rJbUHfSR" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~tmx-news-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-tmx-news-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~tmx-news-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-tmx-news-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~tmx-news-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-tmx-news-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Date": { "title": "Date", "pattern": "^(\\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[12]\\d|3[01])$|^(\\d+)\\s*(day|week|month|year)s?$", "type": "string", "description": "The date for the spider.", "default": "2026-04-12" } } }, "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 } } } } } } } } } } ```