Polymarket Valuation Divergence Trader

---

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":["SIMMER_API_KEY"],"pip":["simmer-sdk"]},"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:

Quick Start

Setup

1. **Set your API key:**

```bash

export SIMMER_API_KEY=sk_live_...

```

2. **Check config:**

```bash

python valuation_trader.py --config

```

3. **Dry run (no trades):**

```bash

python valuation_trader.py

```

4. **Live trading:**

```bash

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

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

Troubleshooting

**"No signals found"**

**"Markets scanned: 0"**

**"Edge is negative but we're buying YES"**

**"Flip-flop warning: CAUTION"**

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.