Multi-source weather intelligence

Weather & markets,perfectly aligned

Turn any Polymarket temperature event into a complete quantitative weather thesis with ensemble forecasts, market probabilities, confidence scoring, reports, and publication-ready visualizations.

Free data sourcesNine visualizationsJSON + Markdown reports
poly-weather.local
◈ Fetching Polymarket event 986717
✔ Location resolved: Shanghai, China
✔ Target date: September 10, 2026
◆ Combining 6 forecast sources
◉ Ensemble prediction: 28.75°C
ℹ Model favorite: 29°C · 39.3%
↗ Largest positive edge: 30°C · +13.9%
✔ Confidence score: 71.8 / 100 · High
✦ Generated 9 quantitative visualizations
Python · Polymarket · Weather APIs
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Forecast sources

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Visualizations

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Outcome markets

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Automated reports

Purpose-built intelligence

Forecast data becomes market intelligence

One event slug becomes a reproducible research package that explains what the models expect, what the market believes, and where those systems disagree.

Poly Weather Analyzer

EVENT 986717

Shanghai maximum temperature

September 10, 2026 · Asia/Shanghai

Analysis complete

Ensemble mean

28.75°C

6 sources

Model favorite

29°C

39.3%

Confidence

71.8

High
Model vs market probability
Probability distribution by temperature outcome
Live market

26°

27°

28°

29°

30°

31°

PolymarketWeather model

Visual research suite

Every forecast has a story.
See the complete picture.

From model consensus to pricing sensitivity, every illustration is designed to make complex probability relationships immediately understandable.

Analysis Dashboard
A complete visual thesis combining probabilities, forecast distributions, market edges, source intervals, and price history.
Explore insight
Probability Comparison
Compare normalized Polymarket prices directly with multi-source weather-model probabilities for every outcome.
Explore insight
Ensemble Fan Chart
Track hourly forecast percentiles and see how uncertainty expands around the expected daily temperature peak.
Explore insight
Opportunity Matrix
Discover outcomes where modeled probability differs from market pricing, scaled by available liquidity.
Explore insight
Sensitivity Heatmap
Stress-test each apparent edge against warmer, cooler, wider, and narrower forecast assumptions.
Explore insight
Source Decomposition
Inspect how each provider allocates probability across outcomes and identify disagreement or outlier forecasts.
Explore insight
Analysis pipeline

From event slug to prediction thesis

A modular data, logic, reporting, and visualization pipeline turns fragmented information into one coherent analysis.

Forecast. Compare. Understand.

STEP 01

Fetch the market

Download the event, outcome brackets, liquidity, volume, prices, and historical CLOB data.

STEP 02

Resolve the forecast

Extract the location and date, geocode the city, and query independent weather providers.

STEP 03

Model probabilities

Transform temperature forecasts and uncertainty intervals into outcome-level probabilities.

STEP 04

Generate intelligence

Calculate edges, rank opportunities, score confidence, and create reports and illustrations.

Connected intelligence

Multiple APIs.
One probability engine.

Public market data and independent meteorological sources are normalized into a common forecast structure.

Polymarket

Market

Gamma event metadata and CLOB price history

Open-Meteo

No key

Deterministic global forecast models

Open-Meteo Ensemble

No key

Hourly ensemble members and forecast spread

MET Norway

No key

Global compact location forecasts

NOAA / NWS

No key

Official United States weather forecasts

Optional Providers

API key

OpenWeather, Visual Crossing, and WeatherAPI

Analyze the forecast

Turn weather uncertainty into clear market insight.

Follow development, discuss new market analyzers, and get updates as Poly Weather expands beyond temperature.

Questions answered

Understand the analyzer.

Learn what the project measures, which sources it uses, and how to interpret model-market disagreement responsibly.