24h Weather Distribution Trader

This is a template. The default signal is distribution-sum and monotonicity violation detection across weather temperature-bin markets — remix it with weather API feeds, ensemble forecasts, or cross-city correlation models. The skill handles all the plumbing (market discovery, distribution construction, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Polymarket lists multiple temperature bins for the same city and date:

• "Will the highest temperature in Munich be 8C on March 28?" = 40%
• "Will the highest temperature in Munich be 9C on March 28?" = 45%
• "Will the highest temperature in Munich be 10C on March 28?" = 16%

These bins form a probability distribution that must sum to ~100%. When they don't, individual bins are mispriced. Additionally, cumulative markets ("X C or below", "X C or higher") impose monotonicity constraints.

The Edge: Distribution Arbitrage for Temperature Markets

In traditional markets, discrete outcome probabilities must sum to 1.0 — this is a fundamental axiom. On Polymarket, each temperature bin trades independently with its own order book and liquidity. Retail treats each bin as an isolated bet without checking the full distribution.

Violation Type 1: Sum Deviation

All exact bins for a (city, date) must sum to ~100%:

P(8C) + P(9C) + P(10C) + P(11C) + ... = 100%

If the sum is 108%, at least one bin is overpriced. If the sum is 92%, at least one bin is underpriced.

Violation Type 2: Cumulative Monotonicity Break

Cumulative markets must be monotonic:

P(<=8C) <= P(<=9C) <= P(<=10C)    [or_below: increasing]
P(>=10C) >= P(>=9C) >= P(>=8C)    [or_higher: decreasing with temp]

If a lower threshold has a higher "or below" probability than a higher threshold, the curve is broken.

Why This Works

1. Retail trades in silos — most users view each temperature bin independently and don't cross-reference the full distribution
2. No market maker enforcing consistency — unlike bookmakers who balance their book, Polymarket has no mechanism to keep bins summing to 100%
3. Mathematical, not opinion — the violations are provable inconsistencies in the probability axioms
4. Many cities, daily resolution — 14 cities with daily temperature markets create a large opportunity surface

Signal Logic

1. Discover all weather temperature markets via keyword search
2. Parse each question: extract city, temperature value, date, and type (exact/or_below/or_higher)
3. Group into distributions by (city, date)
4. For each distribution with 2+ bins:
   • Check if exact bins sum to ~100% (tolerance configurable via SIMMER_SUM_TOLERANCE)
   • If sum > 105%: identify and sell the most overpriced bin (highest relative to neighbors)
   • If sum < 95%: identify and buy the most underpriced bin (lowest relative to neighbors)
   • Check monotonicity on cumulative bins
5. Rank violations by magnitude
6. Trade only violations that also pass threshold gates (YES_THRESHOLD / NO_THRESHOLD)
7. Size by conviction (violation magnitude + threshold distance), not flat amount

Supported Cities

Chengdu, Shenzhen, Munich, Dallas, Austin, San Francisco, Seoul, Chicago, Wuhan, Miami, Seattle, Los Angeles, Denver, New York City.

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.

Edge Thesis

Weather temperature markets on Polymarket are structured as discrete probability distributions. Each bin trades independently, but they are mathematically constrained to sum to 100%. When retail order flow pushes individual bins without propagating to the full distribution, the sum deviates — creating pure mathematical arbitrage. This skill reconstructs the distribution, finds where the axioms break, and trades the repair.

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

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