polymarket-valuation-divergence
Trade Polymarket markets based on valuation divergence. When your probability model differs from Polymarket's price by >threshold, enter using Kelly sizing. Works with any probability model (Simmer AI consensus, user model, external API).
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/adlai88/polymarket-valuation-divergencePolymarket Valuation Divergence Trader
Trade markets where model probability diverges from Polymarket price.
This is a template. The default uses Simmer AI consensus as your probability model. Remix it with your own model, external APIs, or custom forecasting logic. The skill handles market scanning, Kelly sizing, and safe trade execution.
When to Use This Skill
Use this when:
- You have a probability model (AI, statistical, fundamental analysis)
- You want to trade when market prices diverge from your model
- You need Kelly criterion sizing for risk management
- You want to catch structural mispricings (mergers, earnings, regulatory events)
Quick Start
Setup
-
Set your API key:
export SIMMER_API_KEY=sk_live_... -
Check config:
python valuation_trader.py --config -
Dry run (no trades):
python valuation_trader.py -
Live trading:
python valuation_trader.py --live
Configuration
| Setting | Env Var | Default | Description |
|---|---|---|---|
| edge_threshold | SIMMER_VALUATION_EDGE | 0.015 | Min edge to trade (1.5%) |
| kelly_fraction | SIMMER_VALUATION_KELLY | 0.25 | Kelly criterion fraction (cap at 25%) |
| max_position_usd | SIMMER_VALUATION_MAX_POSITION | 5.00 | Max per trade |
| max_trades_per_run | SIMMER_VALUATION_MAX_TRADES | 5 | Max trades per cycle |
| probability_source | SIMMER_VALUATION_SOURCE | simmer_ai | "simmer_ai" or "user_model" |
Update settings:
python valuation_trader.py --set edge_threshold=0.02
How It Works
1. Scan Markets
Fetches active markets from Simmer. For each:
2. Get Model Probability
Default: Simmer AI consensus from /api/sdk/context/{market_id}
To use your own model: Edit get_model_probability() function.
3. Calculate Edge
edge = model_probability - market_price
If edge > threshold: trade signal ā
4. Kelly Sizing
kelly_fraction = edge / (1 - market_price)
kelly_fraction = min(kelly_fraction, MAX_KELLY)
position_size = kelly_fraction * bankroll
position_size = min(position_size, max_position_usd)
5. Execute Trade
- If edge > 0: Buy YES (model thinks it's underpriced)
- If edge < 0: Buy NO (model thinks it's overpriced)
- Include reasoning for transparency
- Execute with safeguards (flip-flop detection, slippage warnings)
6. Hold to Resolution
Positions held until market resolves. Check --positions to see current holdings.
Interpreting the Output
šÆ Valuation Trader Scan
Markets scanned: 50
Signals found: 3
Executed: 2
š° BTC hits $100k by EOY?
Model: 65% | Market: 52% | Edge: +13%
š° BUY YES $4.50 (Kelly: 2.25 shares)
š° Will Trump win in 2028?
Model: 58% | Market: 71% | Edge: -13%
š° BUY NO $3.75 (Kelly: 2.05 shares)
Customizing Your Model
Edit get_model_probability() to use any source:
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-adlai88-polymarket-valuation-divergence": {
"enabled": true,
"auto_update": true
}
}
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