# E-commerce Trend Intelligence Scraper (`datapilot/e-commerce-trend-intelligence-scraper`) Actor Track e-commerce trends from Hacker News and Yahoo Finance by keyword. Discover AI, social commerce, marketplaces, payments, logistics, subscriptions, and retail innovations. Extract titles, dates, platforms, publishers, engagement metrics, article links, and trend categories with proxy support. - **URL**: https://apify.com/datapilot/e-commerce-trend-intelligence-scraper.md - **Developed by:** [Data Pilot](https://apify.com/datapilot) (community) - **Categories:** AI, Agents, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 scraped results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## E-commerce Trend Intelligence Scraper 🛒 **E-commerce Trend Intelligence Scraper** is a powerful Apify Actor designed to discover, analyze, and monitor **E-commerce Trend** intelligence from Hacker News and Yahoo Finance. This tool provides real-time **E-commerce Trend** insights including emerging trends, platform developments, business models, and market opportunities. Whether you're tracking e-commerce innovations, monitoring market trends, or identifying business opportunities, the E-commerce Trend Intelligence Scraper delivers actionable **E-commerce Trend** intelligence efficiently. With multi-source integration from Hacker News and Yahoo Finance, intelligent trend classification, platform detection, financial value extraction, engagement metrics, and deduplication, the E-commerce Trend Intelligence Scraper ensures comprehensive **E-commerce Trend** discovery and analysis. It focuses on key **E-commerce Trend** signals including trend type, e-commerce platform, market developments, and community engagement, making it an essential tool for **E-commerce Trend** research and e-commerce business intelligence. --- ### 📋 Table of Contents - [Features](#-features) - [Data Sources](#-data-sources) - [How It Works](#-how-it-works) - [Input](#-input) - [Output](#-output) - [Technical Stack](#-technical-stack) - [Data Fields](#-data-fields) - [Trend Types](#-trend-types) - [Platforms Tracked](#-platforms-tracked) - [Use Cases](#-use-cases) - [Quick Start](#-quick-start) - [Configuration](#-configuration) - [Performance](#-performance) - [Billing](#-billing) - [Important Notes](#-important-notes) - [Keywords](#-keywords) - [Changelog](#-changelog) - [Support](#-support) --- ### 🔥 Features - **Hacker News Integration** – Real-time **E-commerce Trend** monitoring from Hacker News via Algolia API. - **Yahoo Finance Integration** – E-commerce trend tracking from Yahoo Finance news feed. - **Dual-Source Intelligence** – Aggregates **E-commerce Trend** signals from 2 major sources. - **Keyword Search** – Search **E-commerce Trend** by keyword, platform, or trend type. - **Trend Discovery** – Discovers emerging **E-commerce Trend** stories and announcements. - **Trend Classification** – Intelligent detection of 10 different e-commerce trend types. - **Platform Detection** – Identifies e-commerce platforms mentioned (Shopify, Amazon, TikTok, etc.). - **AI/Automation Detection** – Identifies AI and automation trends in e-commerce. - **Social Commerce Trends** – Tracks social commerce and live shopping trends. - **Dropshipping Trends** – Monitors dropshipping and POD model trends. - **Subscription Trends** – Identifies subscription and membership model developments. - **Payment/Fintech Trends** – Tracks payment solutions and fintech innovations. - **Sustainable Commerce** – Monitors sustainable and circular commerce trends. - **Logistics Trends** – Tracks fulfillment and logistics innovations. - **Marketplace Trends** – Monitors marketplace platform developments. - **Mobile Commerce** – Tracks m-commerce and app-based shopping trends. - **Value Extraction** – Automatically extracts financial amounts mentioned. - **Engagement Metrics** – Captures community engagement (HN points, comments). - **URL Capture** – Provides direct links to articles and HN discussions. - **Source Tracking** – Records publication source for each trend. - **Date Tracking** – Captures announcement dates for trend analysis. - **Deduplication** – Removes duplicate trend announcements. - **Performance Optimization** – Rapid trend detection with minimal overhead. - **Proxy Support** – Apify residential proxy support for reliability. - **Real-Time Dataset Push** – Pushes results to Apify Dataset with metadata. - **Detailed Logging** – Comprehensive logging of trend detection and processing. - **Asyncio-Friendly** – Non-blocking async/await architecture. --- ### 📊 Data Sources #### **1. Hacker News (via Algolia API)** - **Authority**: Hacker News community and tech news aggregation - **Coverage**: E-commerce and startup-related stories - **Data**: Story titles, points (upvotes), comments, URLs - **Update Frequency**: Real-time as stories are published - **Engagement**: Community votes and discussion count - **Focus**: Tech-savvy audience, startup trends, innovation #### **2. Yahoo Finance** - **Authority**: Yahoo Finance news aggregation - **Coverage**: E-commerce company news and market trends - **Data**: News headlines, publishers, dates, article links - **Update Frequency**: Real-time financial news updates - **Sources**: Thousands of financial news outlets - **Focus**: Market analysis, company announcements, financial trends --- ### ⚙️ How It Works The E-commerce Trend Intelligence Scraper accepts a keyword to search for e-commerce trends. It simultaneously queries Hacker News Algolia API and Yahoo Finance API with the keyword combined with "ecommerce". For each result, it extracts title, date, engagement metrics, and URL information. Each trend is analyzed to detect trend type (AI, Social Commerce, Dropshipping, etc.), identify platforms mentioned (Shopify, Amazon, TikTok, etc.), and extract financial values. Results are deduplicated by title and pushed to the dataset in batch. A completion summary is logged with statistics. **Key Processing Steps:** 1. **Input Parsing** – Accept keyword 2. **Proxy Setup** – Configure Apify residential proxy 3. **Hacker News Query** – Search Algolia API for e-commerce stories 4. **Yahoo Finance Query** – Search finance news by keyword 5. **Result Parsing** – Extract title, date, engagement metrics 6. **Trend Analysis** – Detect trend type from title 7. **Platform Detection** – Identify e-commerce platforms 8. **Value Extraction** – Extract financial amounts 9. **URL Capture** – Collect article and discussion links 10. **Deduplication** – Check against seen titles 11. **Lead Object Creation** – Build structured trend object 12. **Dataset Push** – Push trends in batch 13. **Summary Logging** – Report statistics **Key Benefits:** - Discover emerging **E-commerce Trend** opportunities quickly - Monitor competitor and market developments - Identify innovation opportunities - Track community sentiment via engagement - Stay informed on e-commerce innovations - Build trend intelligence database --- ### 📥 Input The Actor accepts the following input parameters: | Field | Type | Default | Description | |-------|------|---------|-------------| | `keyword` | string | required | Keyword to search for **E-commerce Trend** (e.g., "AI", "social commerce") | | `useApifyProxy` | boolean | `true` | Enable Apify residential proxies | | `apifyProxyGroups` | array | `["RESIDENTIAL"]` | Proxy group configuration | **Example Input:** ```json { "keyword": "AI e-commerce", "useApifyProxy": true } ```` **Search for Social Commerce:** ```json { "keyword": "social commerce" } ``` **Track Specific Platform:** ```json { "keyword": "Shopify" } ``` *** ### 📤 Output The Actor pushes **E-commerce Trend** records with the following structure: **E-commerce Trend Record:** | Field | Type | Description | |-------|------|-------------| | `title` | string | **E-commerce Trend** headline/story title | | `date` | string | Publication/announcement date (YYYY-MM-DD) | | `trend_type` | string | Type of trend detected (AI/Automation, Social Commerce, etc.) | | `platform` | string | E-commerce platform mentioned (Shopify, Amazon, TikTok, etc.) | | `value` | string | Financial value mentioned (if any) | | `summary` | string | Brief summary (title or snippet) | | `points` | string | Hacker News points/upvotes (if from HN) | | `comments` | string | Comment count on discussion (if from HN) | | `publisher` | string | News publisher (if from Yahoo Finance) | | `article_url` | string | Direct link to article | | `hn_url` | string | Link to Hacker News discussion (if from HN) | | `source` | string | Data source (Hacker News or Yahoo Finance) | | `keyword` | string | Search keyword used | | `scraped_at` | string | ISO 8601 scrape timestamp | **Example E-commerce Trend Record:** ```json { "title": "AI-Powered Product Recommendations Boost Conversion Rates by 40%", "date": "2025-02-14", "trend_type": "AI/Automation", "platform": "Shopify", "value": "", "summary": "AI-Powered Product Recommendations Boost Conversion Rates by 40%", "points": "342", "comments": "87", "publisher": "TechCrunch", "article_url": "https://techcrunch.com/...", "hn_url": "https://news.ycombinator.com/item?id=...", "source": "Hacker News", "keyword": "AI e-commerce", "scraped_at": "2025-02-14T12:00:00" } ``` **Example Multiple Trends (Batch):** ```json [ { "title": "TikTok Shop Reaches 10 Million Sellers Milestone", "date": "2025-02-12", "trend_type": "Social Commerce", "platform": "TikTok", "value": "$500 million", "summary": "TikTok Shop Reaches 10 Million Sellers Milestone", "publisher": "Reuters", "article_url": "https://reuters.com/...", "source": "Yahoo Finance", "keyword": "social commerce", "scraped_at": "2025-02-14T12:00:00" }, { "title": "Sustainable Fashion E-commerce Growing 3x Faster Than Traditional Retail", "date": "2025-02-10", "trend_type": "Sustainable Commerce", "platform": "Etsy", "value": "", "summary": "Sustainable Fashion E-commerce Growing 3x Faster", "points": "201", "comments": "45", "hn_url": "https://news.ycombinator.com/item?id=...", "source": "Hacker News", "keyword": "sustainable e-commerce", "scraped_at": "2025-02-14T12:00:00" } ] ``` *** ### 🧰 Technical Stack - **APIs:** Hacker News Algolia API, Yahoo Finance API - **HTTP Client:** httpx for async HTTP requests - **Pattern Matching:** Regex for trend type, platform, and value detection - **Date Parsing:** datetime for standardized formatting - **Query Building:** urllib.parse for URL encoding - **Async:** asyncio for non-blocking operations - **Deduplication:** Set-based tracking by title - **Proxy:** Apify Proxy with RESIDENTIAL configuration - **Logging:** Apify Actor logging system - **Platform:** Apify Actor serverless environment *** ### 📊 Data Fields Explained #### **Trend Identification** - **title**: Complete trend story/headline - **trend\_type**: Classification of trend - **platform**: E-commerce platform involved #### **Engagement & Signals** - **points**: Hacker News upvotes (popularity indicator) - **comments**: Discussion count (engagement indicator) - **publisher**: News source credibility #### **Financial & Timeline** - **value**: Investment or financial amount - **date**: Announcement/publication date #### **Reference** - **article\_url**: Full article link - **hn\_url**: Hacker News discussion link - **source**: Data origin - **keyword**: Search term used *** ### 🎯 Trend Types The E-commerce Trend Intelligence Scraper detects 10 **E-commerce Trend** types: #### **1. AI/Automation** Artificial intelligence, chatbots, automation, GPT, LLM in e-commerce #### **2. Social Commerce** TikTok Shop, Instagram Shopping, Facebook Commerce, Live shopping, Social selling #### **3. Dropshipping** Dropshipping models, print-on-demand, wholesale, POD business #### **4. Subscription** Subscription boxes, recurring revenue, membership models, subscription commerce #### **5. Payment/Fintech** Payment solutions, BNPL, cryptocurrency, digital wallets, payment innovation #### **6. Sustainable Commerce** Eco-friendly products, resale, thrift, circular commerce, sustainable practices #### **7. Logistics** Fulfillment, logistics innovation, shipping, delivery, warehouse, 3PL services #### **8. Marketplace** Marketplace platforms, seller networks, vendor management, multi-vendor systems #### **9. Mobile Commerce** Mobile apps, m-commerce, app-based shopping, mobile-first platforms #### **10. General E-commerce** Other e-commerce trends and developments *** ### 🏪 Platforms Tracked The Actor identifies these e-commerce platforms: - Shopify - Amazon - eBay - Etsy - WooCommerce - BigCommerce - TikTok - Instagram - Facebook - Alibaba - AliExpress - Walmart - Temu - Shein *** ### 🎯 Use Cases - **Trend Intelligence** – Monitor **E-commerce Trend** for market insights - **Competitive Analysis** – Track competitor **E-commerce Trend** activities - **Innovation Tracking** – Identify emerging **E-commerce Trend** innovations - **Business Planning** – Inform business strategy with **E-commerce Trend** data - **Market Research** – Analyze e-commerce market via trends - **Investment Research** – Identify investment opportunities via trends - **Platform Monitoring** – Track specific platform developments - **Community Sentiment** – Understand tech community interest via engagement - **Trend Forecasting** – Predict future trends from current signals - **Opportunity Identification** – Find business opportunities in trends - **Risk Assessment** – Identify risks and challenges in trends - **Startup Analysis** – Track startup activity in e-commerce space - **Product Development** – Inform product strategy with trend data - **Marketing Strategy** – Develop marketing around trending topics - **Networking** – Connect with others interested in trends *** ### 🚀 Quick Start #### **1. Prepare Input** Go to Apify Console and enter: ```json { "keyword": "e-commerce AI", "useApifyProxy": true } ``` #### **2. Run the Actor** Click **Start** button. The Actor will: - Query Hacker News for e-commerce stories - Query Yahoo Finance for e-commerce news - Detect trend types - Extract platforms mentioned - Deduplicate results - Push to Dataset #### **3. Monitor Progress** Console shows: ``` Initializing rapid trend tracker for: 'e-commerce AI' [1] Scraping Hacker News Stories Found 28 raw posts from Hacker News Index. [2] Scraping Yahoo Finance Trends Found 30 articles from Yahoo API. Successfully processed and stored 45 records instantly! ``` #### **4. View & Download Results** - **Results Tab**: All **E-commerce Trend** records - **Export**: JSON, CSV, Excel - **Filter**: By trend type or platform - **Sort**: By date or engagement *** ### ⚙️ Configuration #### **Search Scope** Specific platform: ```json { "keyword": "TikTok Shop" } ``` Technology focus: ```json { "keyword": "AI automation" } ``` *** ### 📈 Performance #### **Processing Speed** - \~10-15 seconds for complete search - Hacker News typically 30 results - Yahoo Finance typically 30 results - Total: ~60 unique trends per search - Minimal overhead with optimized queries #### **Resource Usage** - Memory: ~80-120MB - CPU: ~30-40% during processing - Network: ~3-4MB per search - API calls: 2 searches *** ### 💰 Billing #### **Batch Billing** - **Billing Model:** Batch push (not per-trend PPE) - **Typical Cost:** Single operation per search - **Efficiency:** Very high, optimized queries *** ### ⚠️ Important Notes #### **Legal & Compliance** - **Fair Use**: Respects Hacker News and Yahoo Finance ToS - **Attribution**: Respects content creators - **Accuracy**: Data as reported by sources - **Verification**: Always verify trends independently - **Rate Limiting**: Minimal API usage #### **Data Quality** - **Freshness**: Real-time from HN and Yahoo Finance - **Completeness**: Depends on search query - **Accuracy**: As accurate as original sources - **Reliability**: Both sources highly reliable - **Verification**: Verify with official sources #### **Best Practices** - Verify trends with original sources - Check community discussion quality - Review engagement metrics - Cross-reference with official sources - Monitor multiple keywords - Track trends over time - Combine with financial analysis - Understand market context - Follow industry experts - Build trend portfolio *** *** ### 📦 Changelog #### v1.0.0 (February 2025) **Initial Release:** - Hacker News Algolia API integration - Yahoo Finance news API integration - Multi-source simultaneous querying - Keyword-based trend search - Hacker News story extraction (title, points, comments, URL) - Yahoo Finance extraction (title, publisher, date, link) - 10-type trend detection algorithm - AI/Automation detection - Social Commerce detection - Dropshipping detection - Subscription model detection - Payment/Fintech detection - Sustainable Commerce detection - Logistics detection - Marketplace detection - Mobile Commerce detection - General E-commerce detection - E-commerce platform detection (13 major platforms) - Financial value extraction from text - Community engagement metrics (HN points, comments) - Deduplication by title normalization - Date parsing and standardization - Performance optimization (rapid detection) - Real-time Dataset push - Detailed progress logging - Comprehensive error handling - Asyncio executor support *** ### 🧑‍💻 Support & Feedback - **Issues:** Submit via Apify console with keyword - **Documentation:** Check Actor details page - **Community:** Apify forum discussions - **Feature Requests:** Suggest improvements - **Bug Reports:** Include keyword and error details *** #### **Output Access** - **Results Tab**: All **E-commerce Trend** records - **Export**: JSON, CSV, Excel - **Filter**: By trend type or platform - **API**: Query via Apify API *** ### 📄 License & Legal **Terms of Use:** - Use for legitimate business and research - Respect Hacker News and Yahoo Finance ToS - Respect content creators and sources - Don't republish without permission - Comply with applicable laws - Use data responsibly **Disclaimer:** E-commerce Trend Intelligence Scraper is provided as-is for research purposes. Users are responsible for compliance with HN and YF ToS. Always verify trend information with original sources. *** ### 🎉 Get Started Today **Deploy now for **E-commerce Trend** research!** Use for: - 📊 Trend Discovery - 🔍 Market Analysis - 💡 Innovation Tracking - 📈 Opportunity Identification - 🎯 Competitive Intelligence **Perfect for:** - E-commerce Entrepreneurs - Market Analysts - Business Strategists - Investors - Researchers *** **Last Updated:** February 2025\ **Version:** 1.0.0\ **Status:** Production Ready\ **Platform:** Apify Actor\ **Architecture:** Async/Await\ **Data Sources:** Hacker News, Yahoo Finance *** ### 📚 Related Tools - Public Company Expansion Signals Scraper - Franchise Expansion Tracker - News Mention Alert Engine - SaaS Launch Monitor ### 🛒 E-commerce Excellence This Actor is optimized for **E-commerce Trend** research with: - ✅ Real-time HN + Yahoo Finance integration - ✅ 10-type trend detection - ✅ Platform identification - ✅ Financial value extraction - ✅ Community engagement tracking - ✅ Performance optimization - ✅ Real-time Dataset push - ✅ Error recovery - ✅ Production-ready code # Actor input Schema ## `keyword` (type: `string`): Enter the e-commerce keyword to track trends (e.g., Shopify, TikTok, dropshipping, AI). ## `useApifyProxy` (type: `boolean`): Highly recommended to prevent Hacker News and Yahoo Finance from flagging the client. ## `apifyProxyGroups` (type: `array`): Residential proxies are ideal for bypassing cloud firewall filters. ## Actor input object example ```json { "keyword": "Shopify", "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } ``` # 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 = { "keyword": "Shopify" }; // Run the Actor and wait for it to finish const run = await client.actor("datapilot/e-commerce-trend-intelligence-scraper").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 = { "keyword": "Shopify" } # Run the Actor and wait for it to finish run = client.actor("datapilot/e-commerce-trend-intelligence-scraper").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 '{ "keyword": "Shopify" }' | apify call datapilot/e-commerce-trend-intelligence-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datapilot/e-commerce-trend-intelligence-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "E-commerce Trend Intelligence Scraper", "description": "Track e-commerce trends from Hacker News and Yahoo Finance by keyword. Discover AI, social commerce, marketplaces, payments, logistics, subscriptions, and retail innovations. Extract titles, dates, platforms, publishers, engagement metrics, article links, and trend categories with proxy support.", "version": "0.0", "x-build-id": "iVgAIeYHqnJgGeOQg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datapilot~e-commerce-trend-intelligence-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datapilot-e-commerce-trend-intelligence-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datapilot~e-commerce-trend-intelligence-scraper/runs": { "post": { "operationId": "runs-sync-datapilot-e-commerce-trend-intelligence-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datapilot~e-commerce-trend-intelligence-scraper/run-sync": { "post": { "operationId": "run-sync-datapilot-e-commerce-trend-intelligence-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "keyword" ], "properties": { "keyword": { "title": "Search Keyword", "type": "string", "description": "Enter the e-commerce keyword to track trends (e.g., Shopify, TikTok, dropshipping, AI)." }, "useApifyProxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Highly recommended to prevent Hacker News and Yahoo Finance from flagging the client.", "default": true }, "apifyProxyGroups": { "title": "Proxy Groups", "uniqueItems": true, "type": "array", "description": "Residential proxies are ideal for bypassing cloud firewall filters.", "items": { "type": "string", "enum": [ "RESIDENTIAL", "DATACENTER" ] }, "default": [ "RESIDENTIAL" ] } } }, "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 } } } } } } } } } } ```