# Myfxbook Analysis Discovery Extractor (`getdataforme/myfxbook-analysis-discovery-extractor`) Actor The Myfxbook Analysis Discovery Extractor scrapes and analyzes forex-related financial news from Myfxbook. It offers customizable searches, scalable data extraction, high-quality articles, and flexible export formats (JSON, CSV, Excel).... - **URL**: https://apify.com/getdataforme/myfxbook-analysis-discovery-extractor.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, 0 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 --- ## Myfxbook Analysis Discovery Extractor ### Introduction The Myfxbook Analysis Discovery Extractor is a powerful tool designed to scrape and analyze financial news articles from the Myfxbook platform. It allows users to search for specific topics related to forex trading and extract valuable insights, helping traders make informed decisions. ### Features - **Customizable Search**: Tailor your searches with specific keywords. - **Scalable Data Extraction**: Set item limits to control data volume. - **High-Quality Data**: Reliable and up-to-date financial news articles. - **Flexible Output Formats**: Export results in JSON, CSV, or Excel. - **User-Friendly Interface**: Easy setup and execution on the Apify platform. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|----------| | SearchKey | string | No | The search key for the spider. | "oil" | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | 10 | ### Example Usage #### Input JSON ```json { "SearchKey": "oil", "item_limit": 10 } ```` #### Output JSON ```json [ { "id": "51148", "title": "USD/JPY at 40-Year High: Multiple Factors Weigh on the Yen", "summary": "The USD/JPY soared to 162.78 in the middle of the week, reaching its highest level in nearly 40 years.", "url": "https://www.myfxbook.com/analysis/usdjpy-at-40-year-high-multiple-factors-weigh-on-the-yen/51148", "image_url": "https://static.mfbcdn.net/images/news/51148/desktop.webp", "image_alt": "USD/JPY at 40-Year High: Multiple Factors Weigh on the Yen", "publisher": "RoboForex", "published_relative": "2h 42min ago", "search_key": "oil", "actor_id": "9dJrbbfxZIAf62GdM", "run_id": "gTPoGl1OmnCZaIo0e" }, { "id": "51147", "title": "USD/JPY at 40-Year High: Multiple Factors Weigh on the Yen", "summary": "The USD/JPY soared to 162.78 in the middle of the week, reaching its highest level in nearly 40 years.", "url": "https://www.myfxbook.com/analysis/usdjpy-at-40-year-high-multiple-factors-weigh-on-the-yen/51147", "image_url": "https://static.mfbcdn.net/images/news/51147/desktop.webp", "image_alt": "USD/JPY at 40-Year High: Multiple Factors Weigh on the Yen", "publisher": "RoboForex", "published_relative": "2h 42min ago", "search_key": "oil", "actor_id": "9dJrbbfxZIAf62GdM", "run_id": "gTPoGl1OmnCZaIo0e" }, { "id": "51146", "title": "Fed hike bets support dollar, yen falls to multi-decade low", "summary": "Dollar supported by Fed hike expectations after JOLTS data - Yen falls to 40-year low as Fed-BoJ divergence intensifies - US equities rise on strong data, gold weakens on higher yields - Oil remains indifferent to geopolitical risks", "url": "https://www.myfxbook.com/analysis/fed-hike-bets-support-dollar-yen-falls-to-multi-decade-low/51146", "image_url": "https://static.mfbcdn.net/images/news/51146/desktop.webp", "image_alt": "Fed hike bets support dollar, yen falls to multi-decade low", "publisher": "XM Group", "published_relative": "2h 43min ago", "search_key": "oil", "actor_id": "9dJrbbfxZIAf62GdM", "run_id": "gTPoGl1OmnCZaIo0e" } ] ``` ### Use Cases - **Market Research and Analysis**: Gain insights into market trends. - **Competitive Intelligence**: Monitor competitors' strategies. - **Price Monitoring**: Track price movements in forex markets. - **Content Aggregation**: Collect relevant financial news articles. - **Academic Research**: Support studies on economic indicators. - **Business Automation**: Automate data collection for reports. ### Installation and Usage 1. Search for "Myfxbook Analysis Discovery Extractor" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters. 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 containing: - `id`: Unique identifier for the article. - `title`: Title of the news article. - `summary`: Brief summary of the content. - `url`: Link to the full article. - `image_url`: URL of the associated image. - `image_alt`: Alt text for the image. - `publisher`: Name of the publisher. - `published_relative`: Time since publication in a relative format. - `search_key`: The search key used during extraction. - `actor_id`: Identifier for the actor instance. - `run_id`: Unique identifier for the run. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Contact Us](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `SearchKey` (type: `string`): The search key for the spider. ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "SearchKey": "oil", "item_limit": 10 } ``` # 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/myfxbook-analysis-discovery-extractor").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/myfxbook-analysis-discovery-extractor").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/myfxbook-analysis-discovery-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/myfxbook-analysis-discovery-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Myfxbook Analysis Discovery Extractor", "description": "The Myfxbook Analysis Discovery Extractor scrapes and analyzes forex-related financial news from Myfxbook. It offers customizable searches, scalable data extraction, high-quality articles, and flexible export formats (JSON, CSV, Excel)....", "version": "0.0", "x-build-id": "FPAEynuXowkaGT5Cn" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~myfxbook-analysis-discovery-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-myfxbook-analysis-discovery-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~myfxbook-analysis-discovery-extractor/runs": { "post": { "operationId": "runs-sync-getdataforme-myfxbook-analysis-discovery-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~myfxbook-analysis-discovery-extractor/run-sync": { "post": { "operationId": "run-sync-getdataforme-myfxbook-analysis-discovery-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "SearchKey": { "title": "Search Key", "type": "string", "description": "The search key for the spider.", "default": "oil" }, "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```