Polymarket Valuation Divergence

export SIMMER_API_KEY=sk_live_...

Configuration

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

1. Structural mispricings: Merger arb, earnings surprises, regulatory news
2. Consensus divergence: Market hasn't caught up to known information
3. Model > crowd: Your probability model is better than crowd estimates
4. Short-dated markets: Errors correct faster

When This Doesn't Work

1. Crowd is right: Market prices reflect true probabilities
2. Model is calibrated wrong: Your probability estimates are worse than market
3. Latency: By the time you execute, price moved against you
4. 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.