Polymarket 48h Weather Distribution Trader

Trades mispricings in weather temperature-bin markets by reconstructing the implied probability distribution across bins for the same city and date, detectin...

Security Scan

The skill's code, declared dependency, and required credential align with a Polymarket trading bot that uses the Simmer SDK; nothing in the files requests unrelated secrets or system access.

Purpose & Capability

Name/description match the actual footprint: it discovers Polymarket weather markets, reconstructs distributions, and places trades via the Simmer SDK. The only required credential (SIMMER_API_KEY) and the pip dependency (simmer-sdk) are consistent with a trading agent.

Instruction Scope

SKILL.md and trader.py focus on market discovery, parsing, distribution construction, violation detection, and trade execution. Instructions and code reference only trading logic and Simmer interactions; there are no instructions to read unrelated local files, other env vars, or exfiltrate data to unexpected endpoints.

Install Mechanism

No install script in the skill bundle; clawhub.json declares a single pip dependency (simmer-sdk) pointing to PyPI/GitHub. Using PyPI for the SDK is expected for this purpose and is moderate-risk but traceable.

Credentials

Only SIMMER_API_KEY is required and used. That is proportionate because the skill needs trading authority. No other credentials or config paths are requested.

Persistence & Privilege

always is false and autostart is false, so the skill is not forced on or auto-started by default. disable-model-invocation is false (normal) which allows autonomous invocation by the agent; combined with autostart=false this is reasonable for a trading skill but worth noting.

Assessment

This appears coherent for a Polymarket trading bot, but take these precautions before enabling live use: (1) Verify the simmer-sdk PyPI/GitHub project is legitimate and review its network behavior; (2) Keep SIMMER_API_KEY limited to a least-privilege or test account and start with paper mode only (the skill defaults to sim); (3) Review the full trader.py (the bundle was truncated in the listing) to confirm there are no hidden network calls or unexpected behaviors; (4) Ensure autostart remains disabled and only give the live flag intentionally; (5) If running in a shared environment, isolate the API key and funds (use a small test balance) to limit financial exposure. Overall the skill's requests and code are consistent with its stated purpose.

48h 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.

Scenario	Mode	Financial risk
python trader.py	Paper (sim)	None
Cron / automaton	Paper (sim)	None
python trader.py --live	Live (polymarket)	Real USDC

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

Required Credentials

Variable	Required	Notes
SIMMER_API_KEY	Yes	Trading authority. Treat as a high-value credential.

Tunables (Risk Parameters)

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

Variable	Default	Purpose
SIMMER_MAX_POSITION	40	Max USDC per trade at full conviction
SIMMER_MIN_TRADE	5	Floor for any trade
SIMMER_MIN_VOLUME	5000	Min market volume filter (USD)
SIMMER_MAX_SPREAD	0.08	Max bid-ask spread
SIMMER_MIN_DAYS	0	Min days until resolution (0 = allow same-day)
SIMMER_MAX_POSITIONS	8	Max concurrent open positions
SIMMER_YES_THRESHOLD	0.38	Buy YES only if market probability <= this
SIMMER_NO_THRESHOLD	0.62	Sell NO only if market probability >= this
SIMMER_SUM_TOLERANCE	0.05	Allowed deviation from 100% sum before trading

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