# Prediction Market Data Scraper (`constructive_wainscot/prediction-market-data`) Actor Scrape structured prediction market data from Kalshi. Browse events by category (weather, politics, economics, elections, sports), search markets by keyword, or fetch specific events. Returns current odds, volume, open interest, liquidity, and settlement results. No API key needed. - **URL**: https://apify.com/constructive\_wainscot/prediction-market-data.md - **Developed by:** [Stew Williamson](https://apify.com/constructive_wainscot) (community) - **Categories:** AI, Integrations - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Prediction Market Data Scraper Scrape structured prediction market data from [Kalshi](https://kalshi.com), the largest CFTC-regulated prediction market exchange. Browse events by category, search by keyword, or fetch specific event details with all associated markets. ### What it does - **Browse events** by category: Climate & Weather, Elections, Politics, Economics, Entertainment, Sports, Science & Technology, and more - **Search markets** by keyword across all active tickers and titles - **Fetch event details** with all nested markets, odds, and metadata - **Include settled events** alongside open ones for historical analysis ### Output data Each market record includes: | Field | Description | |-------|-------------| | `ticker` | Kalshi market ticker (e.g., KXWARMING-50) | | `title` | Human-readable market question | | `yes_bid` / `yes_ask` | Current YES side prices | | `no_bid` / `no_ask` | Current NO side prices | | `last_price` | Most recent trade price | | `volume` | Total contracts traded | | `volume_24h` | 24-hour trading volume | | `open_interest` | Open contracts | | `liquidity` | Available liquidity | | `close_time` | Market close timestamp | | `result` | Settlement result (for settled markets) | ### Use cases - **Academic research** on prediction accuracy and market efficiency - **Journalism** covering elections, climate, and event markets - **Trading analysis** — export bulk data for backtesting and model building - **AI/ML training** — build prediction models on historical odds data - **Political analysis** — track election odds over time - **Event monitoring** — track real-money forecasts on any topic Kalshi covers ### How to use #### Browse by category ```json { "mode": "events", "category": "Elections", "limit": 50 } ```` #### Search markets ```json { "mode": "search", "query": "pope", "limit": 20 } ``` #### Get specific event with all markets ```json { "mode": "event_detail", "event_ticker": "KXNEWPOPE-70" } ``` #### Include settled markets for historical analysis ```json { "mode": "events", "category": "Climate and Weather", "include_settled": true, "limit": 100 } ``` ### No API key required This actor uses Kalshi's public market data API. No authentication or API keys needed. ### Categories available Climate and Weather, Economics, Elections, Entertainment, Financials, Politics, Science and Technology, Sports, Transportation, World ### Pricing Pay-per-event on Apify Store. Typically costs less than $0.01 per run for standard queries. # Actor input Schema ## `mode` (type: `string`): Browse events, search markets by keyword, or fetch a specific event with all its markets. ## `category` (type: `string`): Filter events by category. Leave empty for all categories. ## `query` (type: `string`): Search keyword for 'search' mode. Matches against market tickers and titles. ## `event_ticker` (type: `string`): Specific event ticker for 'event\_detail' mode (e.g., KXHIGHAUS-26APR09). ## `status` (type: `string`): Filter by market status. ## `limit` (type: `integer`): Maximum number of events/markets to return. ## `include_settled` (type: `boolean`): Also include recently settled events alongside open ones. ## Actor input object example ```json { "mode": "events", "category": "", "query": "", "event_ticker": "", "status": "open", "limit": 50, "include_settled": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("constructive_wainscot/prediction-market-data").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("constructive_wainscot/prediction-market-data").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 constructive_wainscot/prediction-market-data --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=constructive_wainscot/prediction-market-data", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Prediction Market Data Scraper", "description": "Scrape structured prediction market data from Kalshi. Browse events by category (weather, politics, economics, elections, sports), search markets by keyword, or fetch specific events. Returns current odds, volume, open interest, liquidity, and settlement results. No API key needed.", "version": "0.1", "x-build-id": "w0LPWrD8b3nT0EBLC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/constructive_wainscot~prediction-market-data/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-constructive_wainscot-prediction-market-data", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/constructive_wainscot~prediction-market-data/runs": { "post": { "operationId": "runs-sync-constructive_wainscot-prediction-market-data", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/constructive_wainscot~prediction-market-data/run-sync": { "post": { "operationId": "run-sync-constructive_wainscot-prediction-market-data", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "mode": { "title": "Mode", "enum": [ "events", "search", "event_detail" ], "type": "string", "description": "Browse events, search markets by keyword, or fetch a specific event with all its markets.", "default": "events" }, "category": { "title": "Category", "enum": [ "", "Climate and Weather", "Economics", "Elections", "Entertainment", "Financials", "Politics", "Science and Technology", "Sports", "Transportation", "World" ], "type": "string", "description": "Filter events by category. Leave empty for all categories.", "default": "" }, "query": { "title": "Search Query", "type": "string", "description": "Search keyword for 'search' mode. Matches against market tickers and titles.", "default": "" }, "event_ticker": { "title": "Event Ticker", "type": "string", "description": "Specific event ticker for 'event_detail' mode (e.g., KXHIGHAUS-26APR09).", "default": "" }, "status": { "title": "Market Status", "enum": [ "open", "closed", "settled" ], "type": "string", "description": "Filter by market status.", "default": "open" }, "limit": { "title": "Max Results", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of events/markets to return.", "default": 50 }, "include_settled": { "title": "Include Settled", "type": "boolean", "description": "Also include recently settled events alongside open ones.", "default": false } } }, "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 } } } } } } } } } } ```