Polymarket Ladder Social Posts Trader

Trades distribution-sum violations in social media post-count range markets on Polymarket. Range bins for the same person and date range must sum to ~100% — when they do not, individual bins are mispriced. Also detects local anomalies where a single bin deviates sharply from its neighbors.

Ladder -- Social Posts Distribution Trader

This is a template. The default signal is distribution-sum violation detection across social media post-count range markets -- remix it with social media API feeds, posting frequency models, or cross-person correlation analysis. The skill handles all the plumbing (market discovery, distribution construction, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Polymarket lists multiple post-count range bins for the same person and date range:

• "Will CZ post 140-159 posts from March 1 to April 1?" = 25%
• "Will CZ post 160-179 posts from March 1 to April 1?" = 30%
• "Will CZ post 180-199 posts from March 1 to April 1?" = 20%

These bins form a probability distribution that must sum to ~100%. When they don't, individual bins are mispriced. This skill also detects individual bin anomalies where one bin deviates significantly from its neighbors.

The Edge: Distribution Arbitrage for Post-Count Markets

In traditional markets, discrete outcome probabilities must sum to 1.0 -- this is a fundamental axiom. On Polymarket, each post-count 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 bins for a (person, date_range) must sum to ~100%:

text

P(140-159) + P(160-179) + P(180-199) + P(200+) + ... = 100%

If the sum is 108%, at least one bin is overpriced -- sell NO on the highest-priced bin. If the sum is 92%, at least one bin is underpriced -- buy YES on the lowest-priced bin.

Violation Type 2: Neighbor Anomaly

Individual bins that are much higher or lower than their adjacent bins indicate local mispricing:

text

P(140-159) = 25%, P(160-179) = 55%, P(180-199) = 20%

The 160-179 bin at 55% is anomalously high relative to its neighbors at 25% and 20%.

Why This Works

1. Retail trades in silos -- most users view each post-count 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 persons, recurring periods -- CZ, Khamenei, Trump, Musk, and others create a broad opportunity surface across overlapping date ranges

Signal Logic

1. Discover all social media post-count range markets via keyword search + get_markets(limit=200) fallback
2. Parse each question: extract person, post-count range (low-high), and date range
3. Group into distributions by (person, date_range)
4. For each distribution with 2+ bins:
   • Check if bins sum to ~100% (tolerance configurable via SIMMER_MIN_VIOLATION)
   • If sum > 105%: sell NO on the highest-priced bin (most overpriced)
   • If sum < 95%: buy YES on the lowest-priced bin (most underpriced)
   • Check neighbor anomalies (bins deviating from adjacent bins by > 2x tolerance)
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

Remix Signal Ideas

• Twitter/X API v2: Pull real-time post counts per account — compare current posting velocity against the market's implied range to detect when a bin is stale
• Historical posting frequency model: Build a Poisson/negative-binomial model from 90 days of posting data per person — the model's CDF gives you a fair price for each bin, and any bin deviating >5% from model price is tradeable
• Cross-person correlation: CZ and Khamenei post frequencies may correlate with global events (crypto crashes, geopolitical escalation) — when one person's activity spikes, adjacent persons' bins may be underpriced
• Time-of-week patterns: Most public figures post more on weekdays — if you're halfway through the measurement period and the cumulative count is tracking toward a specific bin, bins far from that trajectory are overpriced
• Sentiment/topic analysis: Use NLP to detect if a person is in a "high-activity mode" (responding to controversy, product launch) — this predicts higher post counts and shifts the distribution

Supported Persons

CZ (Changpeng Zhao), Khamenei (Ali Khamenei), Trump (Donald Trump), Musk (Elon Musk), Vitalik (Vitalik Buterin).

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_MIN_VIOLATION	0.05	Min distribution deviation before trading (5%)

Edge Thesis

Social media post-count markets on Polymarket are structured as discrete probability distributions. Each range 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