Polymarket Twitter Cross Contagion Trader
v0.0.3Trades post-count bin markets by detecting cross-person contagion where one public figure posting heavily causes correlated figures to increase their rate. R...
Twitter Cross-Contagion Trader
This is a template.
The default signal detects cross-person posting correlation from market prices — remix it with real-time Twitter API monitoring, topic co-occurrence tracking, or network graph analysis of reply/quote-tweet chains.
The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.
Strategy Overview
Public figures don't post in isolation. When Elon Musk enters a political debate, Trump often responds on Truth Social. When crypto crashes, both CZ and Vitalik spike their posting rates.
Markets price each person's bins independently. This skill exploits the cross-person correlation that markets miss.
Signal Logic
Phase 1: Detect Contagion
- Group all post-count bin markets by person
- Compute each person's "heat" — are the market prices shifted toward higher bins?
- Positive heat = market expects more posts than baseline (person is "hot")
Phase 2: Apply Contagion Coefficients
| Source | Target | Beta | Why |
|---|---|---|---|
| Elon | Trump | 0.15 | Political overlap, mutual commentary |
| Trump | Elon | 0.12 | Elon reacts to executive orders |
| Elon | Vitalik | 0.08 | Crypto topic overlap |
| Vitalik | Elon | 0.06 | Elon sometimes responds to ETH news |
| Elon | CZ | 0.05 | Crypto/exchange overlap |
Phase 3: Trade with Adjusted Conviction
When person A is hot and beta(A->B) > 0, boost conviction on person B's higher bins.
Remix Ideas
- Real-time monitoring: Track actual post counts in real-time instead of inferring from market prices
- Reply chain analysis: @-mention graphs show direct contagion paths
- Topic clustering: NLP on recent posts to detect shared topics driving correlation
- Dynamic beta: Re-estimate contagion coefficients weekly from observed data
Risk Parameters
| Parameter | Default | Notes |
|---|---|---|
| Max position size | $40 USDC | Per market |
| Min market volume | $1,000 | Standard filter |
| Max bid-ask spread | 10% | Default threshold |
| Min days to resolution | 0 | Post-count markets are short-lived |
| Max open positions | 8 | Diversify across persons |
Installation & Setup
clawhub install polymarket-twitter-cross-contagion-trader
Requires: SIMMER_API_KEY environment variable.
Cron Schedule
Cron is set to null — the skill does not run on a schedule until you configure it in the Simmer UI.
Safety & Execution Mode
The skill defaults to paper trading (venue="sim"). Real trades only execute when --live is passed explicitly.
| Scenario | Mode | Financial risk |
|---|---|---|
python trader.py | Paper (sim) | None |
| Cron / automaton | Paper (sim) | None |
python trader.py --live | Live (polymarket) | Real USDC |
The automaton cron is set to null — it does not run on a schedule until you configure it in the Simmer UI. autostart: false means it won't start automatically on install.
Required Credentials
| Variable | Required | Notes |
|---|---|---|
SIMMER_API_KEY | Yes | Trading authority — keep this credential private. Do not place a live-capable key in any environment where automated code could call --live. |
Tunables (Risk Parameters)
All risk parameters are declared in clawhub.json as tunables and adjustable from the Simmer UI without code changes. They use SIMMER_-prefixed env vars so apply_skill_config() can load them securely.
| Variable | Default | Purpose |
|---|---|---|
SIMMER_MAX_POSITION | 40 | Max USDC per trade (reached at 100% conviction) |
SIMMER_MIN_VOLUME | 1000 | Min market volume filter (USD) |
SIMMER_MAX_SPREAD | 0.10 | Max bid-ask spread (0.10 = 10%) |
SIMMER_MIN_DAYS | 0 | Min days until market resolves |
SIMMER_MAX_POSITIONS | 8 | Max concurrent open positions |
SIMMER_YES_THRESHOLD | 0.38 | Buy YES if market price ≤ this value |
SIMMER_NO_THRESHOLD | 0.62 | Sell NO if market price ≥ this value |
SIMMER_MIN_TRADE | 5 | Floor for any trade (min USDC regardless of conviction) |
Dependency
simmer-sdk is published on PyPI by Simmer Markets.
- PyPI: https://pypi.org/project/simmer-sdk/
- GitHub: https://github.com/SpartanLabsXyz/simmer-sdk
- Publisher: hello@simmer.markets
Review the source before providing live credentials if you require full auditability.