# Polymarket & Kalshi Odds Scraper — Prices & Arbitrage (`galterapp/polymarket-kalshi-odds-scraper`) Actor

Scrape live prediction-market odds from Polymarket and Kalshi in one run: yes/no prices, implied probabilities, spread, 24h volume, liquidity and close time for every open market, plus cross-venue matches with the net arbitrage return. Public APIs, no login, JSON output.

- **URL**: https://apify.com/galterapp/polymarket-kalshi-odds-scraper.md
- **Developed by:** [Galter Time](https://apify.com/galterapp) (community)
- **Categories:** Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.00 / 1,000 result rows

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/actors/running/actors-in-store.md#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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Polymarket & Kalshi Odds Scraper — prices, implied probabilities and cross-venue arbitrage

Scrape **live prediction-market odds from Polymarket and Kalshi** in a single run and get clean JSON: every open
market's yes/no prices, **implied probability**, bid–ask spread, 24-hour volume, liquidity, close time and link —
plus **cross-venue rows** that pair the same question on both venues and compute the probability gap and the
**net arbitrage return after Kalshi fees**. No login, no API keys, public endpoints only, ~10 seconds per run.

![Polymarket & Kalshi odds table](https://files.catbox.moe/zrx94h.png)

### What can you do with Polymarket and Kalshi odds data?

- **Track election, Fed, crypto and sports probabilities** over time — schedule the Actor hourly and chart `implied_prob_yes`.
- **Find prediction-market arbitrage** — `venue: cross_venue` rows show where buying YES on one venue and NO on the other costs less than $1 (`net_return_pct > 0`, `arb: true`).
- **Compare venues** — `prob_gap_pts` shows where Kalshi and Polymarket disagree by 5, 10, 20 points.
- **Feed dashboards, alerts, spreadsheets and AI agents** — stable schema, one row per market, MCP-ready.

### How to get Polymarket odds as JSON (quick start)

Run with the defaults: you get the ~300 most-traded open markets on each venue plus cross-venue pairs.
Narrow with `keyword` (e.g. `"Fed"`, `"Bitcoin"`, `"Super Bowl"`) or `categories` (`["sports"]`, `["economics"]`, `["nfl"]`).

```python
from apify_client import ApifyClient
client = ApifyClient("<YOUR_API_TOKEN>")
run = client.actor("galterapp/polymarket-kalshi-odds-scraper").call(run_input={"keyword": "Fed", "maxMarketsPerVenue": 100})
for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(row["venue"], row["question"], row["implied_prob_yes"])
```

```javascript
const { ApifyClient } = require('apify-client');
const client = new ApifyClient({ token: '<YOUR_API_TOKEN>' });
const run = await client.actor('galterapp/polymarket-kalshi-odds-scraper').call({ categories: ['sports'] });
const { items } = await client.dataset(run.defaultDatasetId).listItems();
```

```bash
curl -X POST "https://api.apify.com/v2/acts/galterapp~polymarket-kalshi-odds-scraper/run-sync-get-dataset-items?token=<YOUR_API_TOKEN>" \
  -H "content-type: application/json" -d '{"keyword":"Bitcoin"}'
```

### Sample market row

```json
{"venue":"polymarket","market_id":"will-the-fed-cut-rates-in-september","event":"Fed decision in September?",
 "question":"Will the Fed cut rates in September?","category":"economy","yes_bid":0.62,"yes_ask":0.63,
 "implied_prob_yes":0.625,"spread":0.01,"volume_24h_usd":1834211.5,"liquidity_usd":412560.2,
 "close_time":"2026-09-17T18:00:00Z","url":"https://polymarket.com/event/fed-decision-in-september","ts":1788400000}
```

### Sample cross-venue row

```json
{"venue":"cross_venue","match_score":0.71,"kalshi_question":"Will the Fed cut rates at the September meeting?",
 "polymarket_question":"Will the Fed cut rates in September?","kalshi_prob_yes":0.66,"polymarket_prob_yes":0.625,
 "prob_gap_pts":3.5,"best_direction":"polymarket_yes+kalshi_no","net_return_pct":0.84,"arb":true}
```

### Input

| Field | Default | Meaning |
|---|---|---|
| `venues` | both | `polymarket`, `kalshi` |
| `keyword` | — | substring filter on event + question |
| `categories` | all | Kalshi categories or Polymarket tag slugs |
| `minVolume24hUsd` | 0 | drop thin markets |
| `maxMarketsPerVenue` | 300 | rows per venue, most-traded first |
| `includeCrossVenue` | true | add paired rows with `net_return_pct` |
| `matchThreshold` | 0.5 | question-similarity needed to pair venues |

### How the numbers are computed

- `implied_prob_yes` = YES mid price (Kalshi: (bid+ask)/2, fallback last trade; Polymarket: outcome price).
- Cross-venue `net_return_pct` = `1 − (YES ask on A + NO ask on B) − Kalshi fee (0.07·p·(1−p)) − 0.5% slippage buffer`, best of both directions.
- Pairing is by word overlap of the questions (`match_score`). **Always verify resolution rules and dates on both sites before trading** — a pair with a high gap is often two *different* questions.

### Notes

- Public market data only (Polymarket Gamma API, Kalshi trade API v2). Nothing stored, no wallet, no account.
- Not financial advice. Kalshi is US-regulated; Polymarket availability depends on your jurisdiction.
- Pricing: pay per row. A default run is ~600 market rows + a few dozen cross-venue rows.

# Actor input Schema

## `venues` (type: `array`):

Which prediction markets to scrape.

## `keyword` (type: `string`):

Only markets whose event or question contains this text, e.g. "Fed", "Super Bowl", "Bitcoin". Leave empty for all.

## `categories` (type: `array`):

Kalshi categories (sports, economics, politics, elections, entertainment, science and technology, climate and weather, financials, companies, world) or Polymarket tag slugs (sports, nfl, mlb, politics, crypto, …). Empty = all.

## `minVolume24hUsd` (type: `integer`):

Skip markets with less 24h volume than this.

## `maxMarketsPerVenue` (type: `integer`):

Cap on rows per venue (most-traded first). Rows are what you pay for.

## `includeCrossVenue` (type: `boolean`):

Pair markets that exist on both venues (by question similarity) and compute the probability gap and net arbitrage return after Kalshi fees.

## `matchThreshold` (type: `string`):

Share of question words that must overlap to pair a Kalshi market with a Polymarket market (overlap coefficient). Lower = more (looser) pairs.

## Actor input object example

```json
{
  "venues": [
    "polymarket",
    "kalshi"
  ],
  "categories": [],
  "minVolume24hUsd": 0,
  "maxMarketsPerVenue": 300,
  "includeCrossVenue": true,
  "matchThreshold": "0.75"
}
```

# Actor output Schema

## `markets` (type: `string`):

Dataset items. venue = polymarket | kalshi for market rows; venue = cross\_venue for paired rows with net\_return\_pct and arb flag.

## `summary` (type: `string`):

Counts by venue, top\_volume\_24h, arbitrage\[] (positive net return) and largest\_prob\_gaps\[].

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "venues": [
        "polymarket",
        "kalshi"
    ],
    "keyword": "",
    "categories": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("galterapp/polymarket-kalshi-odds-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "venues": [
        "polymarket",
        "kalshi",
    ],
    "keyword": "",
    "categories": [],
}

# Run the Actor and wait for it to finish
run = client.actor("galterapp/polymarket-kalshi-odds-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "venues": [
    "polymarket",
    "kalshi"
  ],
  "keyword": "",
  "categories": []
}' |
apify call galterapp/polymarket-kalshi-odds-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,galterapp/polymarket-kalshi-odds-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/EU4wbZozYHd9IaVsg/builds/DgTjWhWj1AVQasKhv/openapi.json