# ๐ŸŽฒ Kalshi Prediction Markets โ€” Odds, Probabilities, Volume (`nexgendata/kalshi-prediction-markets-tracker`) Actor Track live Kalshi prediction markets: yes/no bid/ask, last price, implied probability, volume, liquidity, and close time. For traders, researchers, and media tracking event odds. - **URL**: https://apify.com/nexgendata/kalshi-prediction-markets-tracker.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 3 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 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 ## ๐ŸŽฒ Kalshi Prediction Markets โ€” Odds, Probabilities, Volume **Pay-per-result prediction-market data โ€” $0.10 per market record. Live Kalshi odds, implied probabilities, and volume as clean JSON. No account, no auth handshake, no cents-to-probability math, no Bloomberg seat.** Track live markets on [Kalshi](https://kalshi.com), the CFTC-regulated US event-contract exchange, in one structured feed. Every market comes back as a discrete record with its ticker, the full market question, the last traded price, the implied "yes" probability already normalized to 0โ€“1, total trading volume, and the scheduled close time. Filter by status, market title, or event ticker, cap the run size, and drop the result straight into a trading model, an odds dashboard, or a research notebook. Re-run on a schedule and you have a time series of how the crowd priced an event as it unfolded. ### Why use this The raw Kalshi trade API exists, but it is built for order entry, not analytics. It requires an authenticated account, hands you contracts priced in cents (a "62ยข YES" you divide by 100 yourself to get a 62% probability), rate-limits aggressive polling, and offers no convenience layer for filtering or snapshotting the board. This actor collapses all of that into a single call: - **No auth, no account state.** Run it with an Apify token like any other actor โ€” no Kalshi API key, no session management. - **Probabilities, not cents.** `impliedYesProbability` arrives pre-normalized to 0โ€“1, so a 62ยข contract is `0.62`, ready to compare against your model output or a Polymarket quote without a units conversion step. - **Filter at the source.** `status`, `titleContains`, and `eventContains` scope a run precisely, so you pull only the markets you care about and pay only for those rows. - **Pay only for results.** $0.10 per market record under Apify's pay-per-event model. A run that matches nothing costs ~one cent. No subscription, no seat license, no idle compute charges. - **Pipeline-ready JSON.** Flat records, stable field names, exportable to JSON / JSONL / CSV / Excel โ€” drop into pandas, a warehouse, or a webhook. ### What you get Each emitted record represents one Kalshi market. The six fields: - `ticker` โ€” the market's Kalshi ticker (e.g. `KXPRES-24-DJT`), the stable primary key you dedupe and join on across runs. - `title` โ€” the full human-readable market question ("Will the Fed cut rates at the September 2026 meeting?"), exactly as it appears on the exchange. - `lastPrice` โ€” the most recent traded price for the YES side, expressed as a probability-equivalent price. A market last trading at 62ยข comes back as `0.62`. - `impliedYesProbability` โ€” the market-implied probability that the YES side resolves true, normalized to the 0โ€“1 range. This is the number you benchmark a forecast against or chart over time. - `volume` โ€” cumulative contracts traded, as an integer. Your liquidity signal โ€” thin volume means a quoted probability is fragile. - `closeTime` โ€” the scheduled market close, in ISO 8601 (UTC). Tells you how much time is left for the event to resolve and lets you sort markets by horizon. ### Use cases - **Event-driven trading research** โ€” pull implied probabilities across dozens of contracts in one run and surface mispricings against your own event model or news flow. - **Probability / odds dashboards for media** โ€” power a live "the market currently implies X%" widget for election night, economic releases, or sports outcomes without licensing a data terminal. - **Cross-market arbitrage vs Polymarket** โ€” pair this with the Polymarket tracker, align on the same event, and flag spreads where Kalshi and Polymarket disagree on the same question. - **Election, sports, and macro event tracking** โ€” snapshot every market under an event ticker (presidential race, rate decision, CPI print) and watch the board move toward resolution. - **Model calibration and backtesting** โ€” schedule snapshots, store the time series, and score your forecasts against crowd-implied probabilities from a regulated exchange. - **Alerting on probability shifts** โ€” diff `impliedYesProbability` between scheduled runs and fire a Slack / webhook alert when a market jumps past a threshold. - **Liquidity screening** โ€” sort by `volume` to separate deep, tradeable markets from thin ones whose odds carry little information. ### Sample output One record, using only real fields: ```json { "ticker": "KXPRES-24-DJT", "title": "Will Donald Trump win the 2024 US presidential election?", "lastPrice": 0.62, "impliedYesProbability": 0.62, "volume": 4821567, "closeTime": "2024-11-05T23:59:00Z" } ```` A run returns an array of these โ€” one per market matching your filters. ### Input parameters | Parameter | Description | Example | | --- | --- | --- | | `status` | Market status filter โ€” scope to live markets only. | `"open"` | | `titleContains` | Keyword match against the market title / question. | `"inflation"` | | `eventContains` | Filter by event ticker to group all markets under one event. | `"KXPRES"` | | `maxResults` | Maximum number of markets to return โ€” your hard cost cap. | `200` | All parameters are optional. Omit everything for the broad live board (bounded by `maxResults`), or combine `eventContains` with `status: "open"` to pull every open contract under one event. ### How to use **Python (apify-client):** ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run_input = { "status": "open", "titleContains": "inflation", "maxResults": 200, } run = client.actor("nexgendata/kalshi-prediction-markets-tracker").call(run_input=run_input) for market in client.dataset(run["defaultDatasetId"]).iterate_items(): print(market["ticker"], market["impliedYesProbability"], market["volume"]) ``` **cURL (run synchronously, get the dataset items back in one call):** ```bash curl -X POST \ "https://api.apify.com/v2/acts/nexgendata~kalshi-prediction-markets-tracker/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "status": "open", "eventContains": "KXPRES", "maxResults": 200 }' ``` Results stream into an Apify dataset you can export as JSON, JSONL, CSV, or Excel, or wire to a webhook that fires the moment new rows land. ### Pricing This actor runs on Apify's **pay-per-event (PPE)** model โ€” you pay for results, not run time: - **Actor start** โ€” $0.01, charged once per run. - **Market record** โ€” **$0.10 per market** pushed to the dataset. **Worked example:** a run that returns 200 markets costs `$0.01 + 200 ร— $0.10 = $20.01`. A tightly filtered run that returns 12 markets under one event costs `$0.01 + 12 ร— $0.10 = $1.21`. A run whose filters match nothing costs just the `$0.01` start โ€” you never pay for empty results. New to Apify? Your account includes **$5 in free platform credit** to try this actor and 30,000+ others โ€” no credit card required. Browse the full NexGenData catalog at ****. ### How this compares to raw exchange APIs | | Raw Kalshi / exchange API | Kalshi Prediction Markets Tracker (this actor) | | --- | --- | --- | | Authentication | Account + API key + session handling required | Apify token only โ€” no Kalshi account | | Price format | Cents per contract โ€” you normalize yourself | `impliedYesProbability` pre-normalized to 0โ€“1 | | Filtering | Roll your own client-side filtering | `status`, `titleContains`, `eventContains` at source | | Rate limits | Aggressive polling throttled / blocked | Handled on Apify infrastructure | | Dedupe / snapshotting | DIY | Stable `ticker` key, schedule-friendly rows | | Output | JSON you must reshape for analytics | Flat analytics-ready JSON, CSV/Excel export | | Cost model | Free API but engineering + maintenance cost | $0.10 per result, zero maintenance | The raw exchange API is the right tool if you are placing orders โ€” it is an order-entry interface first. But if your job is to *read* the board โ€” pull a slice of markets, normalize the odds, and feed them into a model, a dashboard, or a story โ€” you do not want to babysit an authenticated client, divide cents by 100, and write your own filtering and dedupe layer. This actor gives you normalized implied probabilities as pay-per-result JSON: describe the slice you want, get exactly those rows, priced as probabilities, at $0.10 each. ### FAQ **Q: How fresh is the data?** A: Each run pulls live from Kalshi at execution time โ€” the prices, probabilities, and volumes reflect the market at the moment the run fires. For trading-style monitoring, schedule the actor on a tight interval (every few minutes) and dedupe on `ticker` to build a time series. **Q: What exactly is `impliedYesProbability`?** A: It is the market-implied probability that the YES side of a contract resolves true, normalized to 0โ€“1. Kalshi prices contracts in cents (a YES at 62ยข pays $1 if it resolves true), so the cent price is itself a probability estimate โ€” we hand it to you as `0.62` so you can compare it directly against a model output or another venue's quote. **Q: Can I filter by event?** A: Yes โ€” `eventContains` matches against the event ticker, so you can pull every market under a single event (an election, a rate decision, a tournament) in one run. Combine it with `status: "open"` to restrict to live contracts. **Q: Do you provide historical data?** A: The actor returns the current state of the board on each run. To build history, schedule it and persist the snapshots โ€” `ticker` + `closeTime` give you stable keys to assemble a time series in your own warehouse. **Q: What output formats are supported?** A: JSON, JSONL, CSV, and Excel via Apify dataset export, plus webhooks for streaming new rows into a downstream pipeline. **Q: Is this legal?** A: The actor reads public market data the same way a logged-out browser would. Kalshi is a CFTC-regulated exchange and the market data is public. You are responsible for your downstream use and for complying with prediction-market and event-contract laws in your own jurisdiction โ€” see Compliance & legal below. ### Schema stability & versioning This actor follows NexGenData's **additive-only schema** contract: - New fields may be **added** at any time โ€” they appear as new keys in the JSON output and default to `null` for older runs, so they never break an existing parser. - Existing fields are **never renamed or removed** without a major-version bump and an advance changelog notice. - Field semantics โ€” units, timezones, value ranges (e.g. `impliedYesProbability` staying in 0โ€“1, `closeTime` staying ISO 8601 UTC) โ€” are **never silently changed**. If we must change a meaning, we add a new field and deprecate the old one for at least 90 days. You can build a production pipeline on these six fields and trust that a Tuesday change won't break a Friday ETL job. Spot something unexpected? Open an issue on the actor's Apify Issues tab and we'll look at it the same day. ### Compliance & legal - The actor reads **public market data** from a CFTC-regulated exchange, the same way a logged-out browser does. It does not place orders, move funds, or hold positions. - All requests route through Apify's compliant infrastructure with polite pacing. - You are responsible for ensuring your downstream use complies with Kalshi's Terms of Service, applicable financial-data rules, and your jurisdiction's prediction-market and event-contract laws โ€” these vary by region and by US state. - We do not collect, store, or transmit any credentials for the exchange. - Reading public market data for research, journalism, and analytics is widely accepted; consult counsel before redistribution or before using this data to operate a regulated product. ### Related NexGenData actors Part of the NexGenData **Finance & Markets** suite โ€” compose these to build a full markets-data backend without a terminal seat: - [**Polymarket Prediction Markets Tracker**](https://apify.com/nexgendata/polymarket-prediction-markets-tracker?fpr=2ayu9b) โ€” the same odds + volume feed for Polymarket; pair with this actor for cross-venue arbitrage on identical events. - [**US Treasury Auction Results**](https://apify.com/nexgendata/us-treasury-auction-results?fpr=2ayu9b) โ€” bid-to-cover, high yield, and award data behind the macro events Kalshi prices. - [**Global Central Bank Policy Rates**](https://apify.com/nexgendata/global-central-bank-policy-rates?fpr=2ayu9b) โ€” the live policy-rate context for every "will the Fed cut?" market. - [**Crypto Top-100 Screener**](https://apify.com/nexgendata/crypto-top100-screener?fpr=2ayu9b) โ€” market cap, price, and volume across the top 100 coins. - [**DeFi TVL Protocol Tracker**](https://apify.com/nexgendata/defi-tvl-protocol-tracker?fpr=2ayu9b) โ€” total-value-locked across DeFi protocols. - [**Finviz Stock Screener**](https://apify.com/nexgendata/finviz-stock-screener?fpr=2ayu9b) โ€” US equities filtered by sector, fundamentals, and technicals. Explore the full catalog of 200+ pay-per-result finance and markets actors at ****. # Actor input Schema ## `status` (type: `string`): Market status filter (e.g. 'open'). ## `titleContains` (type: `string`): Filter by market title. ## `eventContains` (type: `string`): Filter by event ticker. ## `maxResults` (type: `integer`): Maximum markets to return. ## Actor input object example ```json { "status": "open", "maxResults": 500 } ``` # 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("nexgendata/kalshi-prediction-markets-tracker").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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("nexgendata/kalshi-prediction-markets-tracker").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call nexgendata/kalshi-prediction-markets-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/kalshi-prediction-markets-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "๐ŸŽฒ Kalshi Prediction Markets โ€” Odds, Probabilities, Volume", "description": "Track live Kalshi prediction markets: yes/no bid/ask, last price, implied probability, volume, liquidity, and close time. For traders, researchers, and media tracking event odds.", "version": "0.0", "x-build-id": "69RigJacfdRSrxU6T" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~kalshi-prediction-markets-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-kalshi-prediction-markets-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~kalshi-prediction-markets-tracker/runs": { "post": { "operationId": "runs-sync-nexgendata-kalshi-prediction-markets-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~kalshi-prediction-markets-tracker/run-sync": { "post": { "operationId": "run-sync-nexgendata-kalshi-prediction-markets-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "status": { "title": "Status", "type": "string", "description": "Market status filter (e.g. 'open').", "default": "open" }, "titleContains": { "title": "Title contains", "type": "string", "description": "Filter by market title." }, "eventContains": { "title": "Event contains", "type": "string", "description": "Filter by event ticker." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum markets to return.", "default": 500 } } }, "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 } } } } } } } } } } ```