polymarket-valuation-divergence

name	polymarket-valuation-divergence
displayName	Polymarket Valuation Divergence
description	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).
metadata	{"clawdbot":{"emoji":"💹","requires":{"env":"[Truncated]","pip":"[Truncated]"},"cron":null,"autostart":false,"automaton":{"managed":true,"entrypoint":"valuation_trader.py"}}}
authors	["Simmer (@simmer_markets)"]
version	1.0.0
published	false

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

1. Set your API key:

   export SIMMER_API_KEY=sk_live_...
   

2. Check config:

   python valuation_trader.py --config
   

3. Dry run (no trades):

   python valuation_trader.py
   

4. 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

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

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Start small. Test with dry-run first. Verify your model against historical outcomes. Then go live with small position sizes.