# Kalshi Weather Markets – Temperature Odds & NWS Data (`rowfeed/kalshi-weather-odds`) Actor

Every open Kalshi daily high and low temperature market in 24 US cities, one row per bracket, with fair odds, the NWS forecast for the settlement station, the temperature measured there so far today, brackets already ruled out and the NWS daily report.

- **URL**: https://apify.com/rowfeed/kalshi-weather-odds.md
- **Developed by:** [Rowfeed](https://apify.com/rowfeed) (community)
- **Categories:** AI, Developer tools, Automation
- **Features:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.80 / 1,000 brackets

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?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Kalshi Weather Markets – Temperature Odds & NWS Data

Every open Kalshi daily high and low temperature market, one row per bracket, side by side with the National Weather Service data the market settles on. Built for weather traders, bot builders and researchers who want the odds and the weather in one table.

### What you get

- **Every bracket** of every open daily high and low temperature market in 24 US cities: bid, ask, last price, volume, open interest.
- **Fair odds:** the implied probability with Kalshi's overround removed across the brackets, so they add up to 100 %.
- **The settlement station** each market resolves on (e.g. KMDW, Chicago Midway), and for that station:
  - the **NWS forecast** high or low for the day,
  - the **temperature measured so far** that day,
  - **`ruled_out_by_observations`**: brackets that are already impossible, because a day's high can only rise and its low only fall,
  - the **NWS daily climate report** value and the yes/no it gives this bracket, once published, often before Kalshi settles.

Cities: Atlanta, Austin, Boston, Chicago, Dallas, Denver, Houston, Las Vegas, Los Angeles, Louisville, Miami, Minneapolis, New Orleans, New York City, Newark, Oklahoma City, Philadelphia, Phoenix, San Antonio, San Diego, San Francisco, Seattle, Trenton and Washington DC, as long as Kalshi lists them.

### Example output row

A real row:

```json
{
  "city": "Miami",
  "temperature": "high",
  "date": "2026-11-11",
  "bracket": "85° to 86°",
  "yes_bid": 0.69,
  "yes_ask": 0.71,
  "fair_probability": 0.6829,
  "volume": 2772.2,
  "station": "KMIA",
  "nws_forecast_f": 85,
  "observed_so_far_f": 79,
  "observed_at": "2026-11-11T06:20:00+00:00",
  "ruled_out_by_observations": false,
  "nws_report_f": null,
  "nws_report_result": null,
  "market_ticker": "KXHIGHMIA-26NOV11-B85.5"
}
```

### Pricing

Pay per event, no subscription:

- **$0.80 per 1,000 brackets** ($0.0008 per row), weather data included.
- $0.001 per run start.

Examples: the pre-filled run (daily highs in 5 cities, about 48 brackets) costs about $0.04. Every high and low market in all 24 cities, about 470 brackets, costs about $0.38. Error rows are never charged.

### Input

| Field | What it does |
|---|---|
| `cities` | City names as Kalshi writes them. Empty = all cities. |
| `temperature` | `high` (default), `low` or `both`. |
| `includeWeather` | Forecast, measurements and the NWS daily report. On by default. |

Schedule it every 10 to 30 minutes during the trading day to follow the odds against the measurements.

### Good to know

- Kalshi settles these markets on The Weather Company's value for the station. The NWS daily climate report for the same station (`nws_report_f`) usually matches it but is not the settlement source. The climate day runs from midnight to midnight local standard time, also during daylight saving time, and so does `observed_so_far_f`.
- `observed_so_far_f` comes from the station's regular readings. The daily report can be a degree off them, so `ruled_out_by_observations` only says true with 1 °F of margin. It is a strong hint, not a settlement; trade at your own risk.
- `nws_report_result` is filled once the NWS publishes the final report for that day, usually the next morning.
- `fair_probability` is left empty when a bracket has no quote, and is rough when the spread is wide.
- City names match from the start of a word, and `LA`, `NYC`, `DC`, `SF` and `Vegas` work too.
- Not affiliated with Kalshi or the National Weather Service. Data is from their public APIs.

### More tools from Rowfeed

- [Kalshi API](https://apify.com/rowfeed/kalshi-markets-scraper) – every Kalshi market, with prices, volume and optional order books.
- [METAR Decoder API](https://apify.com/rowfeed/nws-metar-weather-observations) – NWS weather observations as typed fields.
- [Prediction Market API](https://apify.com/rowfeed/prediction-market-odds-scraper) – Kalshi and Polymarket in one call and one format.
- [Polymarket API](https://apify.com/rowfeed/polymarket-markets-scraper) – live odds, bid/ask, volume and liquidity.
- [Google Sheets Export](https://apify.com/rowfeed/google-sheets-export) – send the results of any run to a Google Sheet.

# Actor input Schema

## `cities` (type: `array`):

City names as Kalshi writes them, e.g. "New York City", "Los Angeles". Leave empty for all 24 cities: Atlanta, Austin, Boston, Chicago, Dallas, Denver, Houston, Las Vegas, Los Angeles, Louisville, Miami, Minneapolis, New Orleans, New York City, Newark, Oklahoma City, Philadelphia, Phoenix, San Antonio, San Diego, San Francisco, Seattle, Trenton, Washington DC.

## `temperature` (type: `string`):

Which daily markets to list.

## `includeWeather` (type: `boolean`):

Add the NWS forecast, the temperature measured so far and the NWS daily report. Turn off for odds only, a little faster.

## Actor input object example

```json
{
  "cities": [
    "Denver",
    "Seattle"
  ],
  "temperature": "high",
  "includeWeather": true
}
```

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

// Prepare Actor input
const input = {
    "cities": [
        "New York City",
        "Chicago",
        "Los Angeles",
        "Miami",
        "Austin"
    ],
    "temperature": "high"
};

// Run the Actor and wait for it to finish
const run = await client.actor("rowfeed/kalshi-weather-odds").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 = {
    "cities": [
        "New York City",
        "Chicago",
        "Los Angeles",
        "Miami",
        "Austin",
    ],
    "temperature": "high",
}

# Run the Actor and wait for it to finish
run = client.actor("rowfeed/kalshi-weather-odds").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 '{
  "cities": [
    "New York City",
    "Chicago",
    "Los Angeles",
    "Miami",
    "Austin"
  ],
  "temperature": "high"
}' |
apify call rowfeed/kalshi-weather-odds --silent --output-dataset

```

## MCP server setup

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

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/V6jV9NFYjzSjU8hu3/builds/wD1kBWaVgD9Uh3WiK/openapi.json