# Polymarket + Kalshi - Prediction Market Intelligence (`straightforward_hydra/prediction-market-intelligence`) Actor Unified live odds & implied probabilities from Polymarket and Kalshi in one feed. No key. - **URL**: https://apify.com/straightforward\_hydra/prediction-market-intelligence.md - **Developed by:** [Dev D](https://apify.com/straightforward_hydra) (community) - **Categories:** Automation, Other - **Stats:** 1 total users, 1 monthly users, 80.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 markets 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Intelligence — Polymarket + Kalshi 🎲 **Unified live odds and implied probabilities from Polymarket and Kalshi in one feed. No API key, no proxy.** Prediction markets are where people put real money on the questions everyone's arguing about — elections, the Fed, crypto, sports, pop culture. This actor pulls **both Polymarket and Kalshi** into a **single normalized schema**, so you get every market's **implied probability (0–100%)**, volume and liquidity in one place — and can compare the same topic across both platforms. Perfect for **traders, journalists & newsletters, quant/research, dashboards and content** ("Polymarket says 68%…"). > Uses the public **Polymarket Gamma** and **Kalshi** APIs — no key, no login. The unifying metric is **implied probability**. --- ### The angle - 🔥 **Unified odds feed** — top markets across both platforms, ranked by volume, with implied probabilities. - 📈 **Trending** — sort by volume to see the markets everyone's betting on right now. - ⚖️ **Cross-platform compare** — search a keyword (`election`, `Fed`, `Bitcoin`) with `source: both` to see Polymarket *and* Kalshi odds on the same topic side by side. ### Features - ✅ **No API key, no proxy** — public APIs, clean JSON. - ✅ **Both platforms, one schema** — `implied probability` normalizes Polymarket prices and Kalshi cents. - ✅ **Volume/liquidity ranking** — surface the markets that matter. - ✅ **Keyword + category filters** — zero in on a topic. --- ### Input | Field | Description | |---|---| | **Source** | both / polymarket / kalshi. | | **Keyword** | Filter market questions (e.g. `election`, `Bitcoin`). | | **Category** | e.g. `Politics`, `Economics`, `Crypto`, `Sports`, `World`. | | **Status** | open / closed / all. | | **Sort** | volume / liquidity / probability. | | **Min volume** | Filter out inactive/combo markets. | | **Max results** | Markets to return after ranking. | | **Max scan per source** | How deep to scan each platform before ranking. | #### Example — the hottest markets right now ```json { "source": "both", "status": "open", "sort": "volume", "minVolume": 10000, "maxResults": 100 } ```` #### Example — compare a topic across both platforms ```json { "source": "both", "keyword": "election", "maxScanPerSource": 3000 } ``` ### Output ```json { "source": "polymarket", "market_id": "512...", "question": "Will Bitcoin reach $150k by December 31, 2026?", "outcomes": [ { "name": "Yes", "probability": 3.5 }, { "name": "No", "probability": 96.5 } ], "top_outcome": "No", "top_probability": 96.5, "volume": 2657739, "liquidity": 84210, "category": "Crypto", "close_date": "2026-12-31T12:00:00Z", "status": "open", "url": "https://polymarket.com/event/..." } ``` Kalshi markets are binary (Yes/No) with the same fields; `category` comes from the Kalshi event. ### Run it on a schedule Snapshot the top markets (or one topic) every hour to build a probability time-series — pipe to a sheet, database, dashboard or webhook. ### Notes & limitations - **Volume differs by platform** — Polymarket often has higher volume, so a volume-sorted "both" feed can be Polymarket-heavy. Use `source: kalshi` or a keyword/category to isolate Kalshi. - Cross-platform "compare" groups by keyword (you eyeball the spread); it does not claim perfect auto event-matching, since the two platforms word questions differently. - Implied probability is derived from current prices and moves in real time. - Informational market data, not financial or betting advice. Sources: Polymarket, Kalshi. *** #### Keywords prediction markets, Polymarket, Kalshi, implied probability, odds, betting odds, event markets, election odds, forecasting, market data, crypto odds, Fed odds, trading, quant, sentiment, probability, wisdom of crowds, decision markets, real-time odds, market intelligence. # Actor input Schema ## `source` (type: `string`): Which platform(s) to pull from. ## `keyword` (type: `string`): Filter markets whose question contains this text, e.g. "election", "Fed", "Bitcoin", "World Cup". Use with source=both to compare the same topic across platforms. ## `category` (type: `string`): Filter by category, e.g. "Politics", "Economics", "Crypto", "Sports", "World". ## `status` (type: `string`): Open (tradeable) or closed/settled markets. ## `sort` (type: `string`): Rank results by volume, liquidity, or the top outcome's probability. ## `minVolume` (type: `integer`): Only markets with at least this much traded volume (filters out inactive/combo markets). ## `maxResults` (type: `integer`): Number of markets to return (after ranking). ## `maxScanPerSource` (type: `integer`): How many markets to scan per platform before ranking/filtering (higher = more thorough, slower). ## Actor input object example ```json { "source": "both", "status": "open", "sort": "volume", "maxResults": 200, "maxScanPerSource": 1500 } ``` # 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("straightforward_hydra/prediction-market-intelligence").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("straightforward_hydra/prediction-market-intelligence").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 straightforward_hydra/prediction-market-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=straightforward_hydra/prediction-market-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Polymarket + Kalshi - Prediction Market Intelligence", "description": "Unified live odds & implied probabilities from Polymarket and Kalshi in one feed. No key.", "version": "0.1", "x-build-id": "tGdIsBXc2sJ2m9hhM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/straightforward_hydra~prediction-market-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-straightforward_hydra-prediction-market-intelligence", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/straightforward_hydra~prediction-market-intelligence/runs": { "post": { "operationId": "runs-sync-straightforward_hydra-prediction-market-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/straightforward_hydra~prediction-market-intelligence/run-sync": { "post": { "operationId": "run-sync-straightforward_hydra-prediction-market-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "source": { "title": "Source", "enum": [ "both", "polymarket", "kalshi" ], "type": "string", "description": "Which platform(s) to pull from.", "default": "both" }, "keyword": { "title": "Keyword", "type": "string", "description": "Filter markets whose question contains this text, e.g. \"election\", \"Fed\", \"Bitcoin\", \"World Cup\". Use with source=both to compare the same topic across platforms." }, "category": { "title": "Category", "type": "string", "description": "Filter by category, e.g. \"Politics\", \"Economics\", \"Crypto\", \"Sports\", \"World\"." }, "status": { "title": "Status", "enum": [ "open", "closed", "all" ], "type": "string", "description": "Open (tradeable) or closed/settled markets.", "default": "open" }, "sort": { "title": "Sort by", "enum": [ "volume", "liquidity", "probability" ], "type": "string", "description": "Rank results by volume, liquidity, or the top outcome's probability.", "default": "volume" }, "minVolume": { "title": "Minimum volume", "type": "integer", "description": "Only markets with at least this much traded volume (filters out inactive/combo markets)." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Number of markets to return (after ranking).", "default": 200 }, "maxScanPerSource": { "title": "Max scan per source", "minimum": 100, "maximum": 20000, "type": "integer", "description": "How many markets to scan per platform before ranking/filtering (higher = more thorough, slower).", "default": 1500 } } }, "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 } } } } } } } } } } ```