48h Precipitation Range Trader

> This is a template.

> The default signal is distribution-sum and monotonicity violation detection across precipitation-range bin markets — remix it with weather API feeds, historical precipitation data, or climate model ensembles.

> The skill handles all the plumbing (market discovery, distribution construction, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Polymarket lists multiple precipitation range bins for the same city and period:

• "Will Seattle have more than 5 inches of precipitation in April?" = 18.8%
• "Will Seattle have between 4.5 and 5 inches in April?" = 17.5%
• "Will Seattle have between 4 and 4.5 inches in April?" = 29%
• "Will Seattle have between 3.5 and 4 inches in April?" = 19%
• "Will Seattle have between 3 and 3.5 inches in April?" = 19%
• "Will Seattle have between 2.5 and 3 inches in April?" = 20%

These range bins form a probability distribution that must sum to ~100%. When they don't, individual bins are mispriced. Additionally, cumulative markets ("more than X inches") must be monotonically decreasing as X increases.

The Edge: Distribution Arbitrage for Precipitation Markets

In traditional markets, discrete outcome probabilities must sum to 1.0 — this is a fundamental axiom. On Polymarket, each precipitation range 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 range bins for a (city, period) must sum to ~100%:

P(2.5-3in) + P(3-3.5in) + P(3.5-4in) + P(4-4.5in) + P(4.5-5in) + P(>5in) = 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(>3in) >= P(>3.5in) >= P(>4in) >= P(>4.5in) >= P(>5in)    [more_than: decreasing]
P(<3in) <= P(<3.5in) <= P(<4in) <= P(<4.5in) <= P(<5in)    [less_than: increasing]

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

Why This Works

1. Retail trades in silos — most users view each precipitation 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. Multiple cities, monthly resolution — many cities with monthly precipitation markets create a large opportunity surface

Signal Logic

1. Discover all precipitation range markets via keyword search ("precipitation", "inches", "rainfall", "Seattle")
2. Parse each question: extract city, precipitation range (between X and Y / more than X / less than X), and period (month)
3. Group into distributions by (city, period)
4. For each distribution with 2+ bins:
• Check if all range 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 ("more than X" decreasing, "less than X" increasing)
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

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

Precipitation range 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