Weather-Commodity Trader

This is a template. The default signal maps extreme weather readings to commodity market mispricings -- remix it with NOAA API data, satellite crop imagery, or energy grid load forecasts. The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Extreme weather is a leading indicator for commodity disruption, but the transmission from weather prediction markets to commodity prediction markets is slow. Weather specialists trade temperature markets. Commodity traders watch OPEC and inventory reports. Few participants systematically connect the two.

This skill bridges that gap:

1. Scan Polymarket temperature/weather markets for stress signals
2. Compute weather stress: are temperatures showing extremes that imply energy or crop disruption?
3. Check commodity markets: have they already priced in the weather stress?
4. Trade commodity markets that are lagging behind the weather signal

Edge Thesis

Weather-to-commodity transmission has a structural delay on prediction markets because:

• Different participant pools: Weather market traders are weather nerds; commodity traders watch financial news
• Indirect causation: "Dallas 84F+" does not directly say "oil up" -- it requires a mental model of AC demand, grid load, refinery stress
• Temporal mismatch: Weather markets resolve daily; commodity impacts play out over weeks

The edge is in connecting the dots faster than the market.

Signal Logic

1. Weather Stress Computation

For each weather/temperature market, extract:

• City (Dallas, Chicago, Miami, New York, Houston, Phoenix)
• Temperature threshold (e.g., 84F)
• Direction (above/below)
• Probability of the extreme outcome

City-specific stress models:

Stress score = probability that the extreme outcome occurs. Only signals above WEATHER_STRESS_THRESHOLD (default 50%) trigger cascade analysis.

2. Commodity Impact Mapping

3. Lag Detection & Trading

For each commodity market matching the stress type, check if it has already repriced. If the commodity market probability is still low (below YES_THRESHOLD) despite high weather stress, it is underpriced -- buy YES.

4. Conviction-Based Sizing

All trades use the standard conviction formula:

• YES: conviction = (YES_THRESHOLD - p) / YES_THRESHOLD, boosted by weather stress
• NO: conviction = (p - NO_THRESHOLD) / (1 - NO_THRESHOLD)
• Size: max(MIN_TRADE, conviction * MAX_POSITION)

Remix Ideas

• Add NOAA API for real-time temperature confirmation beyond prediction markets
• Incorporate energy grid load data (ERCOT for Texas, PJM for East Coast)
• Weight stress by city population and industrial activity
• Add crop calendar: spring cold in April is far worse than in February
• Cross-reference with commodity futures (CME) for additional price signals
• Add satellite-based crop health indices (NDVI) for agriculture stress

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 by Simmer Markets (SpartanLabsXyz)

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