# Kalshi Leaderboard Scraper (`saswave/kalshi-leaderboard-scraper`) Actor $0.5 / 1000 results. Extraction tool designed for the Kalshi prediction market exchange. Monitor top-performing traders across various categories and timeframes. Providing a quantitative edge for social sentiment analysis and performance benchmarking, a CFTC-regulated prediction market in the US. - **URL**: https://apify.com/saswave/kalshi-leaderboard-scraper.md - **Developed by:** [SASWAVE](https://apify.com/saswave) (community) - **Categories:** Automation, Integrations, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## 📊 Kalshi Leaderboard Scraper Extraction tool designed for the Kalshi prediction market exchange. It allows users to monitor top-performing traders across various categories and timeframes Providing a quantitative edge for social sentiment analysis and performance benchmarking on the only CFTC-regulated prediction market in the US. ### ✨ Features Multi-Metric Extraction: Scrape leaderboards for Volume, Profit, and Predictions accuracy. Granular Filtering: Target specific sub-markets including Politics, Sports, Crypto, Economics, and "Mentions" (exotic markets). Time-Series Analysis: Switch between Daily, Weekly, Monthly, Yearly, and All Time frequencies to identify short-term scalpers vs. long-term institutional traders. Anonymous vs. Public Insights: Automatically identifies anonymous traders vs. those with verified social profiles and avatars. Real-time Ranking: Captures dynamic rank changes and value fluctuations in high-liquidity markets like the 2026 World Cup or Fed interest rate decisions. ### 🚀 Use Cases Copy Trading Research: Identify the most consistent "sharps" in specific categories like Economics or Elections to study their positioning. Market Sentiment Monitoring: Track how top traders are shifting volume in high-stakes markets (e.g., Politics or Climate) as a proxy for real-world event probability. Platform Gamification Analysis: Track community engagement and user growth by monitoring leaderboard activity and rank competition. Social Proofing: Verify the performance of high-profile "fin-fluencers" or traders who share their Kalshi predictions on social media. Arbitrage Strategy: Correlate top Kalshi performers with other platforms (like Polymarket) to detect arbing activity between regulated and crypto markets. ### INPUT Multiple filters Leaderboard: Volume, Profit, Predictions Categories: All, Politics, Sports, Entertainment, Crypto, Climate, Economics, Mentions, Companies, Financials, Science and Technology, Elections Frequence: daily, weekly, monthly, yearly, all_time ### OUTPUT ```json { "nickname": "drag", "social_id": "", "profile_image_path": "https://kalshi-social-public.s3.amazonaws.com/profile-pictures/drag-2026-01-13 13:43:25.webp", "value": 33079.953, "rank": 14, "is_anonymous": false } ```` ### 🛟 SUPPORT Share your runs with the developer team and create issues on error to help us improve actor quality. You might discover edge case we didn't test yet We stay available anytime # Actor input Schema ## `name` (type: `string`): Select leaderboard ## `time` (type: `string`): Select date filter ## `category` (type: `string`): Select category section to extract data from ## Actor input object example ```json { "name": "projected_pnl", "time": "daily" } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "name": "projected_pnl", "time": "daily", "category": "" }; // Run the Actor and wait for it to finish const run = await client.actor("saswave/kalshi-leaderboard-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 = { "name": "projected_pnl", "time": "daily", "category": "", } # Run the Actor and wait for it to finish run = client.actor("saswave/kalshi-leaderboard-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 '{ "name": "projected_pnl", "time": "daily", "category": "" }' | apify call saswave/kalshi-leaderboard-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=saswave/kalshi-leaderboard-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Kalshi Leaderboard Scraper", "description": "$0.5 / 1000 results. Extraction tool designed for the Kalshi prediction market exchange. Monitor top-performing traders across various categories and timeframes. Providing a quantitative edge for social sentiment analysis and performance benchmarking, a CFTC-regulated prediction market in the US.", "version": "0.0", "x-build-id": "MWsbjJ7ndm9e7rIt6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/saswave~kalshi-leaderboard-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-saswave-kalshi-leaderboard-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/saswave~kalshi-leaderboard-scraper/runs": { "post": { "operationId": "runs-sync-saswave-kalshi-leaderboard-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/saswave~kalshi-leaderboard-scraper/run-sync": { "post": { "operationId": "run-sync-saswave-kalshi-leaderboard-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": [ "name", "time" ], "properties": { "name": { "title": "Leaderboard", "enum": [ "volume", "projected_pnl", "num_markets_traded" ], "type": "string", "description": "Select leaderboard" }, "time": { "title": "Date Range", "enum": [ "daily", "weekly", "monthly", "yearly", "all_time" ], "type": "string", "description": "Select date filter" }, "category": { "title": "Leaderboard Category section", "enum": [ "", "Politics", "Sports", "Entertainment", "Crypto", "Climate+and+Weather", "Economics", "Mentions", "Companies", "Financials", "Science+and+Technology", "Elections" ], "type": "string", "description": "Select category section to extract data from" } } }, "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 } } } } } } } } } } ```