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%:
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:
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
- Retail trades in silos -- most users view each post-count bin independently and don't cross-reference the full distribution
- No market maker enforcing consistency -- unlike bookmakers who balance their book, Polymarket has no mechanism to keep bins summing to 100%
- Mathematical, not opinion -- the violations are provable inconsistencies in the probability axioms
- Multiple persons, recurring periods -- CZ, Khamenei, Trump, Musk, and others create a broad opportunity surface across overlapping date ranges
Signal Logic
- Discover all social media post-count range markets via keyword search +
get_markets(limit=200) fallback
- Parse each question: extract person, post-count range (low-high), and date range
- Group into distributions by (person, date_range)
- 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)
- Rank violations by magnitude
- Trade only violations that also pass threshold gates (
YES_THRESHOLD / NO_THRESHOLD)
- 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)
