Polymarket 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 ✓
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
🎯 Valuation Trader Scan
Markets scanned: 50
Signals found: 3
Executed: 2
📰 BTC hits $100k by EOY?
Model: 65% | Market: 52% | Edge: +13%
💰 BUY YES $4.50 (Kelly: 2.25 shares)
📰 Will Trump win in 2028?
Model: 58% | Market: 71% | Edge: -13%
💰 BUY NO $3.75 (Kelly: 2.05 shares)
Customizing Your Model
Edit get_model_probability() to use any source:
Option 1: User-provided probabilities (config)
def get_model_probability(market_id, market_question):
# Return fixed probability from config
return USER_PROBABILITIES.get(market_id)
Option 2: External API (e.g., Synth, Metaculus)
def get_model_probability(market_id, market_question):
# Fetch forecast from external API
resp = fetch_json(f"{EXTERNAL_API}/forecast?q={market_question}")
return resp["probability"]
Option 3: Rules-based model
def get_model_probability(market_id, market_question):
if "crypto" in market_question.lower():
return 0.55 # Crypto has historical upside
elif "election" in market_question.lower():
return 0.50 # No strong signal on elections
return None # Skip if no model
Commands
# Dry run (simulate trades, no real execution)
python valuation_trader.py
# Live trading
python valuation_trader.py --live
# Show current positions
python valuation_trader.py --positions
# Show config
python valuation_trader.py --config
# Update config
python valuation_trader.py --set edge_threshold=0.02
# Quiet mode (cron-friendly)
python valuation_trader.py --live --quiet
Risk Management
- Position size cap:
max_position_usd limits each trade
- Kelly fraction cap: Limited to 25% of bankroll (never bet >25% on one trade)
- Trade limit:
max_trades_per_run prevents over-trading
- Safeguards: Simmer SDK checks for flip-flop warnings, slippage, expiring positions
- --no-safeguards: Skip checks (use only if you know what you're doing)
Troubleshooting
"No signals found"
- Model probabilities and market prices are close
- Increase
edge_threshold in config if too conservative
- Check your model source — is it generating probabilities?
"Markets scanned: 0"
- No active markets available
- Try during peak market hours
"Edge is negative but we're buying YES"
- This is correct! Edge < 0 means market is overpriced. Buy NO instead (which we do).
"Flip-flop warning: CAUTION"
- You recently changed direction on this market
- Discipline tracking prevents over-trading. Wait or use
--no-safeguards.
Performance Metrics
Check your performance:
python valuation_trader.py --positions
Shows: realized P&L, win rate, average edge per trade, largest positions.
When This Works Best
- Structural mispricings: Merger arb, earnings surprises, regulatory news
- Consensus divergence: Market hasn't caught up to known information
- Model > crowd: Your probability model is better than crowd estimates
- Short-dated markets: Errors correct faster
When This Doesn't Work
- Crowd is right: Market prices reflect true probabilities
- Model is calibrated wrong: Your probability estimates are worse than market
- Latency: By the time you execute, price moved against you
- Liquidity: Slippage eats the edge on illiquid markets
Start small. Test with dry-run first. Verify your model against historical outcomes. Then go live with small position sizes.
