Micro Weather Sniper Trader

This is a template. The default signal uses NOAA (US) and Open-Meteo (global) weather forecasts to find mispriced temperature bins on Polymarket -- remix it with additional weather sources, ensemble model averaging, or multi-day position building. The skill handles all the plumbing (market discovery, forecast fetching, bin matching, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Polymarket lists "highest temperature" bin markets for cities worldwide (e.g. "Will the highest temperature in Warsaw be 10°C on April 10?"). These bins are priced by market participants who often lack access to professional weather forecast data.

This skill fetches actual weather forecasts from NOAA (US cities, ~85% accuracy for 1-2 day predictions) and Open-Meteo (global cities, free API, similar accuracy). It then compares the forecast temperature against each market bin:

• If the forecast matches the bin and the market price is low (p <= 20%): BUY YES -- the market is underpricing a likely outcome.
• If the forecast disagrees with the bin and the market price is high (p >= 80%): SELL NO -- the market is overpricing an unlikely outcome.

Example: Open-Meteo forecasts Warsaw high = 10°C on April 10. The bin "Warsaw 10°C" is priced at p=20%. Edge = 0.85 - 0.20 = 0.65. Conviction = 0.65 / 0.85 = 0.76. Size = max($2, 0.76 * $5) = $3.82. The skill buys YES at $3.82.

Edge

Weather forecasts from NOAA and Open-Meteo are publicly available but not widely used by Polymarket participants. NOAA 1-2 day high temperature forecasts have documented accuracy of approximately 85%. This creates a structural information edge:

• The forecast provides a probability estimate (~85%) that the temperature will fall in a specific range
• Polymarket bins are priced by retail participants without systematic forecast data
• When the forecast-implied probability (85%) diverges from market price (e.g. 20%), the difference is pure edge
• Weather markets resolve daily, providing rapid feedback and capital recycling
• Micro-sizing ($2-$5) means each trade has minimal risk while maintaining consistent exposure

Signal Logic

1. Discover active weather markets via keyword search across all supported cities plus get_fast_markets() and get_markets(limit=200) bulk scan
2. Parse each market question: extract city name, temperature range (bin), and resolution date
3. Fetch NOAA forecast (US cities) or Open-Meteo forecast (global cities) for each relevant city
4. For each market: check if the forecast temperature falls within the bin
5. If forecast matches bin AND p <= YES_THRESHOLD (0.40): buy YES -- conviction = (0.85 - p) / 0.85, size = max($2, conviction * $5)
6. If forecast disagrees with bin AND p >= NO_THRESHOLD (0.80): sell NO -- conviction = (p - 0.15) / 0.85, size = max($2, conviction * $5)
7. Spread gate: skip if spread > MAX_SPREAD (15%)
8. Place up to MAX_POSITIONS (10) micro trades per run
9. All trades include signal_data with forecast temperature, city, and edge for backtest support

Remix Signal Ideas

• Ensemble averaging: Combine NOAA + Open-Meteo + AccuWeather forecasts and weight by historical accuracy per city
• Confidence scaling: NOAA is more accurate for D+1 than D+3 -- scale conviction by forecast horizon
• Multi-bin strategy: When forecast is between two bins (e.g. 9.9°C between 9°C and 10°C bins), buy YES on both at half size
• Exit management: Monitor positions and sell when price reaches EXIT_THRESHOLD (take profit) or forecast changes
• Historical calibration: Track forecast accuracy per city per season to dynamically adjust NOAA_ACCURACY

Safety & Execution Mode

The skill defaults to paper trading (venue="sim"). Real trades only with --live flag.

autostart: false and cron: null mean nothing runs automatically until configured in Simmer UI.

Required Credentials

Tunables (Risk Parameters)

All declared as tunables in clawhub.json and adjustable from the Simmer UI.

Dependency

simmer-sdk is published on PyPI by Simmer Markets.

• PyPI: https://pypi.org/project/simmer-sdk/
• GitHub: https://github.com/SpartanLabsXyz/simmer-sdk