# Weather Market Probabilities (Polymarket brackets) (`datahamster/weather-market-probabilities`) Actor

Every active Polymarket daily-temperature market with a model probability per bracket from a regional forecast model plus ECMWF ensemble spread, the market best ask, and the difference. Calibrated, documented, no anti-bot, no login.

- **URL**: https://apify.com/datahamster/weather-market-probabilities.md
- **Developed by:** [Viktor Dubnytskiy](https://apify.com/datahamster) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.00 / 1,000 result items

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

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

## Weather Market Probabilities — Polymarket temperature brackets

For every active Polymarket daily high/low temperature market, this actor returns the market bracket, a model probability for that bracket, the market's best ask, and the difference, in one row per bracket. It is a research and monitoring dataset for people who trade, study or report on weather prediction markets.

### What you get

One row per bracket market: `slug`, `question`, `city`, `date`, `kind` (high/low), `bracket` (e.g. `34C`, `76-77F`, `≥84F`), `low`, `high`, `unit`, `bounds`, `forecastC`, `sigmaC`, `modelPrimary`, `modelCrossVal`, `divergenceC`, `ensembleStdC`, `ensembleMembers`, `probability`, `probabilityCalibrated`, `marketYesPrice`, `bestBid`, `bestAsk`, `diffVsAsk`, `liquidity`, `endDate`, `stationNote`, `url`, `scrapedAt`.

### How the probability is computed

1. Forecast: an hourly temperature series for the market date from a per-city regional model (HRRR for US cities, ICON-D2/EU for Germany and Europe, JMA MSM for Japan, ECMWF IFS elsewhere), reduced to the daily max (high markets) or min (low markets) in the city's local day.
2. Uncertainty: the ECMWF ensemble (50 members) spread for the same day, floored at 2.0 °C, inflated by 15 % for max/min markets and further when a cross-validation model disagrees by more than 1 °C.
3. Bracket probability: normal distribution with continuity correction at bracket edges; open-ended brackets ("X or higher") use the half-line form so mass is not truncated.
4. `probabilityCalibrated`: a Platt correction fitted on 3,301 resolved markets (a = 0.528, b = −0.297). Raw model probabilities were **over-confident by 17–20 percentage points** on our own historical bets; the calibrated column is the one to compare with prices.

### Calibration note (read before using `diffVsAsk`)

On 36,778 resolved Polymarket weather markets, Yes prices sit within ~1.7 points of realised frequency and a group's Yes prices sum to ~1.08. A positive `diffVsAsk` is therefore usually the market's margin, not an edge. This dataset gives you a documented, reproducible model probability next to the price; it is not betting advice and does not claim an edge.

### Input

- `cities` (optional): city slugs as in market URLs; empty = all.
- `dates` (optional): `YYYY-MM-DD`; empty = all active market dates.
- `maxItems`: stop after this many bracket rows.
- `includeKalshi`: off by default (Kalshi Data Terms restrict commercial use).

### Pricing

| Event | Price |
|---|---|
| result | $0.005 per bracket row ($5 per 1,000) |

Charged only for rows with a computed probability. If a city has no coordinates or the forecast source fails, its markets are skipped and counted in the run summary.

### Limits

- Forecast is a grid point at the city coordinates (LaGuardia for NYC), not the exact reporting station; `stationNote` states the local-day resolution rule.
- Typically 200–300 active bracket markets across ~20 cities; one forecast bundle (3 requests) per city/date, one order-book request per market.
- Open-Meteo commercial plan key is required for commercial use of the forecast source (set the `OPEN_METEO_API_KEY` secret); the free endpoints are for evaluation only.

### Changelog

- 0.1: initial release — Polymarket temperature markets, regional models + ECMWF ensemble sigma, Platt calibration, CLOB best ask.

# Actor input Schema

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

City slugs as in market URLs (tokyo, nyc, london...). Empty = all cities with active markets.

## `dates` (type: `array`):

YYYY-MM-DD. Empty = all active market dates.

## `includeKalshi` (type: `boolean`):

Off by default: Kalshi Data Terms restrict commercial use. Enable only if your own use is permitted.

## `maxItems` (type: `integer`):

Stop after this many results (you are charged only for pushed items)

## `mode` (type: `string`):

Mode

## `monitorKey` (type: `string`):

Optional state key when not running as a saved task

## `webhookUrl` (type: `string`):

POST a change summary here in monitor mode

## `telegramBotToken` (type: `string`):

Telegram bot token

## `telegramChatId` (type: `string`):

Telegram chat id

## Actor input object example

```json
{
  "includeKalshi": false,
  "maxItems": 100,
  "mode": "scrape"
}
```

# Actor output Schema

## `results` (type: `string`):

All pushed rows (dataset, JSON)

## `resultsTable` (type: `string`):

Dataset in the Console viewer

## `runSummary` (type: `string`):

RUN\_SUMMARY record (pushed, skipped, emptyReason)

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("datahamster/weather-market-probabilities").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("datahamster/weather-market-probabilities").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 '{}' |
apify call datahamster/weather-market-probabilities --silent --output-dataset

```

## MCP server setup

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

```

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/C97jwITA3lu0xBJzG/builds/j7s9HD43pjzllGc98/openapi.json