Polymarket 24h Geopolitics Cluster Trader

Trades logical inconsistencies in geopolitical event clusters on Polymarket. Geopolitical markets form clusters where probabilities must satisfy constraints...

Security Scan

The skill's requests and code are consistent with a Polymarket trading bot that uses the Simmer SDK and a single required SIMMER_API_KEY; nothing in the provided materials suggests hidden or unrelated credential access or unexpected install behavior.

Purpose & Capability

Name/description (arbitrage on Polymarket geopolitical clusters) match the requested items: a single SIMMER_API_KEY credential and the simmer-sdk pip dependency declared in clawhub.json. The requested env vars, tunables, and SDK dependency are proportionate to a trading skill.

Instruction Scope

SKILL.md and trader.py describe market discovery, parsing, clustering, and trade execution. The instructions default to paper trading and require an explicit --live flag for real trades. Note: the doc calls this a template and suggests remixing with external feeds (news, satellite) — if a user extends the skill to ingest arbitrary external data, scope and risk increase; the provided instructions themselves do not perform unrelated file reads or credential harvesting.

Install Mechanism

No arbitrary downloads or extract steps. The manifest declares a pip dependency on 'simmer-sdk' which is an expected package for interacting with the Simmer platform. No install URLs, shorteners, or personal servers were used.

Credentials

Only SIMMER_API_KEY is required (declared in clawhub.json and SKILL.md). Tunables are exposed via env vars for risk parameters. No unrelated secrets, cloud credentials, or config paths are requested.

Persistence & Privilege

always:false and autostart:false (cron:null) — the skill will not run automatically without user configuration. The automaton entrypoint is set but managed and not autostarting, which is expected for a trading bot template.

Assessment

This skill appears internally consistent and behaves like a trading template: keep your SIMMER_API_KEY secret and test exclusively in paper mode (no --live) until you're confident in behavior. Review the full trader.py source and simmer-sdk documentation before providing the API key to confirm the SDK's network endpoints and permissions. If you or a third party add external data feeds (news, satellite, scraping), re-evaluate scope and credentials — those integrations can introduce new risks. Finally: run initial tests in an isolated environment, monitor outgoing network calls, and limit live trading caps before enabling --live.

24h Geopolitics Cluster Trader

This is a template. The default signal is geopolitical cluster consistency checking — remix it with additional constraint types, news sentiment, or satellite data feeds. The skill handles all the plumbing (market discovery, cluster grouping, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Polymarket lists many geopolitical markets that form logical clusters:

• Strike-count markets: "Will Israel strike 5 countries?", "...6 countries?", "...7 countries?"
• Daily military action: "Will Israel take military action in Gaza on March 21?", "...in Lebanon on March 20?"
• Bilateral escalation: "Will Iran conduct military action against Israel?"
• Ceasefire markets: "Will there be a ceasefire in Gaza?"

These markets are logically constrained. Striking 7 countries requires striking 6 first, so P(7) <= P(6) always. Escalation in one region affects others. When retail trades these markets in isolation, the constraints break — and that is the edge.

The Edge: Logical Consistency Arbitrage

Three Violation Types

1. Monotonicity violations (strike-count markets):

   • P(strike 7) must be <= P(strike 6) <= P(strike 5)
   • If P(strike 7) = 8% but P(strike 6) = 5%, the 7-market is overpriced or the 6-market is underpriced

2. Correlation violations (daily military action):

   • If Israel has 100% probability of action in Gaza on a given date, correlated regions (Lebanon, Syria) should reflect heightened risk
   • A very low probability in a correlated region on the same date may be underpriced

3. Prerequisite chain violations:

   • If P(Iran military action against Israel) = 8%, then P(Israel strikes 6+ countries) — which likely requires Iranian theater escalation — should not be much higher
   • The downstream event cannot greatly exceed its prerequisite

Example

Market	Probability
Israel strike 5 countries	20%
Israel strike 6 countries	15.9%
Israel strike 7 countries	18%

Violation: P(strike 7) = 18% > P(strike 6) = 15.9%. The 7-market is overpriced. Trade: sell NO on the 7-market (or buy YES on the 6-market).

Why This Works

1. Retail trades in silos — users view each market independently without cross-referencing logically related markets
2. Logical constraints are structural — cumulative events, prerequisite chains, and correlation are mathematical facts, not opinions
3. Resolution forces convergence — as resolution approaches, the market must price consistently or create guaranteed arbitrage
4. Low-liquidity geopolitical markets — these niche markets have less market-maker coverage, so mispricings persist longer

Signal Logic

1. Discover geopolitical markets via keyword search (Israel, military, strike, Iran, Gaza, Lebanon, war, ceasefire)
2. Parse each question: extract event type, region, actor, threshold, date
3. Group into clusters (strike-count, daily-action, bilateral, ceasefire)
4. Check consistency constraints:
   • Strike counts: monotonically decreasing probabilities
   • Daily action: cross-region correlation on same date
   • Prerequisites: downstream events bounded by upstream probability
5. Rank violations by magnitude
6. Trade only opportunities that 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.

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	35	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_MIN_VIOLATION	0.04	Min cluster consistency violation to trigger a trade

Edge Thesis

Geopolitical events are not independent coin flips. They form causal chains: escalation in one theater raises risk in adjacent theaters; striking N countries is a prerequisite for striking N+1. Prediction markets price each event individually, but the joint probability distribution must be internally consistent.

When it is not, the inconsistency is a free edge. This skill systematically detects and trades these logical violations, acting as an automated consistency enforcer for geopolitical prediction markets.

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

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