# Kalshi Prediction Markets Scraper (`parseforge/kalshi-prediction-markets-scraper`) Actor
Tap the Kalshi public markets API and pull ticker, title, status, closeTime, yesAsk, noBid, volume, openInterest, category, and expirationTime. Filter by status, event, or series ticker. Useful for event forecasting and tracking sentiment on US prediction markets.
- **URL**: https://apify.com/parseforge/kalshi-prediction-markets-scraper.md
- **Developed by:** [ParseForge](https://apify.com/parseforge) (community)
- **Categories:** Automation, Integrations
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $7.50 / 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
## ๐ Kalshi Prediction Markets Scraper
> ๐ **Export Kalshi prediction markets data in seconds. Structured records direct from the public api.elections.kalshi.com endpoint to CSV, Excel, JSON, or XML.**
> ๐ **Last updated:** 2026-05-29 ยท **๐ 12 fields** per record ยท Public REST API ยท Kalshi prediction markets
The Kalshi Prediction Markets Scraper turns the [api.elections.kalshi.com](https://api.elections.kalshi.com) public REST endpoint into a clean, structured dataset. It calls the endpoint with the parameters you supply, parses the JSON, and flattens each record into one row.
Dataset coverage depends on the upstream source. All field names are clean snake_case / camelCase, numeric strings are auto-cast to numbers, and upstream rate-limit notes are surfaced as a clean `error` record instead of crashing your pipeline.
| ๐ฏ Target Audience | ๐ก Primary Use Cases |
|---|---|
| ๐ Analysts | Pull structured records for analysis |
| ๐ข Finance teams | Snapshot data for dashboards and reports |
| ๐ค ML engineers | Build training sets from public sources |
| ๐ฐ Journalists | Verify numbers for stories with one click |
| ๐ฉโ๐ป Developers | Mirror upstream data into your own DB without writing client code |
| ๐ Researchers | Run reproducible studies on public data |
### ๐ What the Kalshi Prediction Markets Scraper does
- Calls the public `api.elections.kalshi.com` endpoint.
- Parses the JSON response and flattens each record into a clean row.
- Casts numeric fields to numbers so they import cleanly into Excel and BI tools.
- Surfaces upstream rate-limit or error notes as a single-row `error` record instead of crashing.
- Exports as CSV, Excel, JSON, JSONL, XML, RSS, or HTML.
> ๐ก **Why it matters:** The raw Kalshi prediction markets JSON is verbose and hard to consume in spreadsheets. This actor normalizes the payload into one row per record so the data drops straight into pandas, BigQuery, or a Google Sheet.
### ๐ฌ Full Demo
_๐ง Coming soon._
### โ๏ธ Input
Field
Type
Required
Description
status
enum
No
open / closed / settled.
eventTicker
string
No
Filter to one event.
seriesTicker
string
No
Filter to one series.
maxItems
integer
No
Free users: 10. Paid users: up to 1,000,000. Prefill: 10.
**Example 1:**
```json
{
"status": "open",
"maxItems": 50
}
````
**Example 2:**
```json
{
"eventTicker": "KXPRES",
"maxItems": 100
}
```
> โ ๏ธ **Good to Know:** The Kalshi prediction markets API is rate-limited. This actor surfaces rate-limit notes as a clean `error` record so your downstream pipeline can decide how to retry.
### ๐ Output
Each record is a flat object. `error` is always last.
| Field | Type | Description |
|---|---|---|
| ๐๏ธ `ticker` | string | Market ticker. |
| ๐ฐ `title` | string | Market title. |
| โก `status` | string | open / closed / settled. |
| ๐ `closeTime` | string | When the market closes. |
| โ `yesAsk` | number | Yes side ask price (cents). |
| โ `noBid` | number | No side bid price (cents). |
| ๐ `volume` | number | Total contracts traded. |
| ๐ `openInterest` | number | Open contracts. |
| ๐ท๏ธ `category` | string | Market category. |
| โณ `expirationTime` | string | Expiration time. |
| ๐ `scrapedAt` | string | Fetch timestamp. |
| โ `error` | string | Set on upstream error. |
**Sample record:**
```json
{
"ticker": "KXPRES-26-DJT",
"title": "Will Trump win the 2026 election?",
"status": "open",
"closeTime": "2026-11-03T23:59:00Z",
"yesAsk": 52,
"noBid": 47,
"volume": 1234567,
"openInterest": 89012,
"category": "Politics",
"expirationTime": "2026-11-04T00:00:00Z",
"scrapedAt": "2026-05-29T12:00:00.000Z",
"error": null
}
```
### โจ Why choose this Actor
| ๐ | Works with the free, public Kalshi prediction markets endpoint. |
| ๐งน | Clean snake\_case keys ready for BI. |
| ๐ข | Auto-casts numeric strings to real numbers. |
| ๐ | Surfaces rate-limit notes as a clean `error` record. |
| ๐ | Drop-in dataset replacement for the raw API. |
| ๐พ | Push to dataset โ instant CSV / Excel / JSON / XML / RSS / HTML export. |
### ๐ How it compares to alternatives
| Approach | Setup time | Clean keys? | Numeric casting? | Rate-limit handling? |
|---|---|---|---|---|
| Roll your own `fetch` | 30 min + | โ | โ | โ |
| Generic API client | 1 hr install + script | partial | partial | partial |
| **This Actor** | 5 sec, no install | โ | โ | โ |
### ๐ How to use
1. Click **Try for free**.
2. Adjust input fields (or leave defaults).
3. Click **Start**. Within seconds, your dataset is ready โ download as CSV, Excel, JSON, or XML, or pipe to your warehouse.
### ๐ผ Business use cases
**๐ Analytics & dashboards.** Pull Kalshi prediction markets data into a single CSV, feed BI tools, share with stakeholders.
**๐ค ML feature engineering.** Build clean training sets from Kalshi prediction markets for downstream models.
**๐ฐ Newsroom fact-checking.** Verify numbers for stories in 30 seconds.
**๐ข Treasury / ops.** Schedule daily runs and pipe to your dashboard.
### ๐ Automating Kalshi Prediction Markets Scraper
- **Make / Zapier**: trigger this actor on a schedule, push results to Airtable, Google Sheets, or Slack.
- **Cron schedule**: native Apify scheduler.
- **Webhooks**: POST to your endpoint the moment a run finishes.
- **Pipe to BigQuery / Snowflake / Postgres**: native Apify integrations.
### ๐ Beyond business use cases
**๐ Education.** Teach a class with real Kalshi prediction markets data instead of toy datasets.
**๐งช Personal research.** Track Kalshi prediction markets signals on your own schedule.
**๐ค Non-profit & open data.** Build public dashboards without writing client code.
**๐งฐ Tinkering & prototyping.** Spin up a feed in 5 seconds to test a new idea.
### ๐ค Ask an AI assistant about this scraper
Pop this README into ChatGPT, Claude, or any AI assistant and ask it to map your specific workflow to the actor's inputs.
### โ Frequently Asked Questions
**โ Do I need an API key?** No. The Kalshi public markets endpoint is open.
**โ Is there a rate limit?** Yes. We surface 429 responses as a clean error record.
**โ What markets are included?** All public markets returned by /trade-api/v2/markets.
**โ Can I filter by event?** Yes. Use the eventTicker input.
**โ Is this real-time?** Snapshot at the time of fetch.
**โ What format can I download?** CSV, Excel, JSON, JSONL, XML, RSS, or HTML.
### ๐ Integrate with any app
Apify ships native integrations with Make, Zapier, Slack, Discord, Google Drive, Google Sheets, Gmail, Airbyte, Keboola, Telegram, GitHub, and any REST API or webhook endpoint.
### ๐ Recommended Actors
| Actor | What it does |
|---|---|
| [ParseForge Alpha Vantage Scraper](https://apify.com/parseforge/alpha-vantage-public-scraper) | Stocks, FX, crypto, indicators. |
| [ParseForge Yahoo Finance Scraper](https://apify.com/parseforge) | Equities, funds, indices. |
| [ParseForge OurAirports Scraper](https://apify.com/parseforge/ourairports-scraper) | Global airport database. |
| [ParseForge NBA Stats Scraper](https://apify.com/parseforge/nba-stats-scraper) | Player and team stats from NBA.com. |
> ๐ก **Pro Tip:** browse the complete [ParseForge collection](https://apify.com/parseforge) for 900+ production-grade scrapers.
***
**Disclaimer:** This actor scrapes only publicly available data. ParseForge is not affiliated with, endorsed by, or sponsored by any of the third-party services referenced. Users are responsible for complying with the target site's terms of service and applicable law. [Create a free account w/ $5 credit](https://console.apify.com/sign-up?fpr=vmoqkp).
# Actor input Schema
## `status` (type: `string`):
Filter markets by status.
## `eventTicker` (type: `string`):
Optional event ticker filter (e.g. KXPRES).
## `seriesTicker` (type: `string`):
Optional series ticker filter.
## `maxItems` (type: `integer`):
Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000
## Actor input object example
```json
{
"status": "open",
"maxItems": 10
}
```
# Actor output Schema
## `results` (type: `string`):
No description
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"maxItems": 10
};
// Run the Actor and wait for it to finish
const run = await client.actor("parseforge/kalshi-prediction-markets-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`๐พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// ๐ Want to learn more ๐? 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 = { "maxItems": 10 }
# Run the Actor and wait for it to finish
run = client.actor("parseforge/kalshi-prediction-markets-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("๐พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# ๐ Want to learn more ๐? Go to โ https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"maxItems": 10
}' |
apify call parseforge/kalshi-prediction-markets-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=parseforge/kalshi-prediction-markets-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Kalshi Prediction Markets Scraper",
"description": "Tap the Kalshi public markets API and pull ticker, title, status, closeTime, yesAsk, noBid, volume, openInterest, category, and expirationTime. Filter by status, event, or series ticker. Useful for event forecasting and tracking sentiment on US prediction markets.",
"version": "0.1",
"x-build-id": "aZb9NPqKcYYmAOfb2"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/parseforge~kalshi-prediction-markets-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-parseforge-kalshi-prediction-markets-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/parseforge~kalshi-prediction-markets-scraper/runs": {
"post": {
"operationId": "runs-sync-parseforge-kalshi-prediction-markets-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/parseforge~kalshi-prediction-markets-scraper/run-sync": {
"post": {
"operationId": "run-sync-parseforge-kalshi-prediction-markets-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"properties": {
"status": {
"title": "Status",
"enum": [
"open",
"closed",
"settled"
],
"type": "string",
"description": "Filter markets by status.",
"default": "open"
},
"eventTicker": {
"title": "Event ticker",
"type": "string",
"description": "Optional event ticker filter (e.g. KXPRES)."
},
"seriesTicker": {
"title": "Series ticker",
"type": "string",
"description": "Optional series ticker filter."
},
"maxItems": {
"title": "Max Items",
"minimum": 1,
"maximum": 1000000,
"type": "integer",
"description": "Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000"
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```