Twitter Cadence Model Trader
This is a template.
The default signal is a Poisson model based on historical posting rates — remix it with Twitter/X API v2 real-time post counts, time-of-day distributions, or NLP topic-burst detection.
The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.
Strategy Overview
Post-count bin markets (e.g., "Will Elon Musk post 190-214 tweets from March 30 to April 1?") are perfect for statistical modeling because the number of posts in a time interval follows a Poisson distribution.
Retail traders price these bins by gut feeling. This skill prices them with math.
Signal Logic
Poisson Model
- Parse the person, bin range, and time period from the market question
- Compute lambda = person's historical daily rate x period days
- Calculate P(bin) = sum of Poisson PMF from bin_lower to bin_upper
- Compare model probability to market price
- Trade when model diverges from market AND price is in threshold band
Person Baselines
| Person | Daily Rate | Platform |
|---|
| Elon Musk | ~65 tweets/day | X/Twitter |
| Donald Trump | ~23 posts/day | Truth Social |
| Vitalik Buterin | ~8 posts/day | X/Twitter |
| CZ | ~12 posts/day | X/Twitter |
Model Bias
The Poisson model acts as a conviction multiplier:
- If model says bin is 2x more likely than market → 2x conviction boost
- If model says bin is 0.5x less likely → signal vetoed (don't trade against math)
Remix Ideas
- Twitter API v2: Replace baseline daily_rate with real-time 7-day rolling average
- Time-of-day model: Elon posts 60% of tweets between 6PM-2AM Pacific — use hourly Poisson
- Negative binomial: Better than Poisson for overdispersed counts (bursty posters)
- Topic burst NLP: Detect thread-mode (3x rate) vs normal mode
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 bins |
Installation & Setup
clawhub install polymarket-twitter-cadence-model-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.
Review the source before providing live credentials if you require full auditability.
