Arbitrage Detection

Automatically detect and execute arbitrage opportunities across Polymarket and Kalshi when price discrepancies exceed transaction costs.

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Overview

Arbitrage trading exploits price differences for the same event across multiple platforms. When Polymarket shows 45% probability for an outcome and Kalshi shows 52%, a risk-free profit opportunity exists.

Key Concepts

Arbitrage Spread: Price difference between platforms

Spread = |Polymarket_Price - Kalshi_Price|

Profit Calculation:

Theoretical_Profit = Spread * Position_Size - Total_Fees

Execution Criteria:

if Spread > (Platform_Fees + Slippage + Minimum_Profit):
    execute_arbitrage()

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Strategy Components

1. Market Discovery

Find equivalent markets across platforms:

from openclaw import Agent
from moltmarket import PredictionMarketProvider

agent = Agent(providers=[PredictionMarketProvider(
    platforms=["polymarket", "kalshi"]
)])

# Find presidential election markets on both platforms
markets = agent.run("""
  Find presidential election markets on Polymarket and Kalshi
  with > $500K liquidity
""")

# Filter for matching markets
def find_matching_markets(markets):
    polymarket = [m for m in markets['markets'] if m['platform'] == 'polymarket']
    kalshi = [m for m in markets['markets'] if m['platform'] == 'kalshi']

    matches = []
    for pm in polymarket:
        for km in kalshi:
            if is_same_event(pm['title'], km['title']):
                matches.append({
                    'polymarket': pm,
                    'kalshi': km,
                    'spread': abs(pm['probability'] - km['probability'])
                })

    return sorted(matches, key=lambda x: x['spread'], reverse=True)

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2. Spread Calculation

Calculate arbitrage profitability:

def calculate_arbitrage_profit(polymarket_price, kalshi_price, position_size):
    """
    Calculate expected profit from arbitrage trade

    Args:
        polymarket_price: Probability on Polymarket (0-1)
        kalshi_price: Probability on Kalshi (0-1)
        position_size: USD amount per side

    Returns:
        dict with profit, fees, and execution plan
    """

    # Determine which side to buy on each platform
    if polymarket_price < kalshi_price:
        # Buy YES on Polymarket, NO on Kalshi
        poly_side = "YES"
        poly_price = polymarket_price
        kalshi_side = "NO"
        kalshi_price = 1 - kalshi_price
    else:
        # Buy NO on Polymarket, YES on Kalshi
        poly_side = "NO"
        poly_price = 1 - polymarket_price
        kalshi_side = "YES"
        kalshi_price = kalshi_price

    # Calculate costs
    poly_cost = position_size * poly_price
    kalshi_cost = position_size * kalshi_price
    total_cost = poly_cost + kalshi_cost

    # Calculate fees
    poly_fee = 0  # Polymarket has no trading fees
    kalshi_fee = position_size * 0.02  # Kalshi ~2% fee
    gas_fee = 2.50  # Polygon gas
    total_fees = poly_fee + kalshi_fee + gas_fee

    # Calculate guaranteed payout
    # One market resolves YES, one resolves NO
    # Both pay out position_size
    guaranteed_payout = position_size

    # Net profit
    profit = guaranteed_payout - total_cost - total_fees

    return {
        'profit': profit,
        'profit_percent': (profit / total_cost) * 100,
        'total_cost': total_cost,
        'total_fees': total_fees,
        'polymarket': {
            'side': poly_side,
            'price': poly_price,
            'cost': poly_cost
        },
        'kalshi': {
            'side': kalshi_side,
            'price': kalshi_price,
            'cost': kalshi_cost
        },
        'execution_plan': {
            'step1': f"Buy {poly_side} on Polymarket @ {poly_price:.2f}",
            'step2': f"Buy {kalshi_side} on Kalshi @ {kalshi_price:.2f}",
            'expected_profit': profit
        }
    }

Example Calculation:

# Polymarket: 45% YES, Kalshi: 52% YES
result = calculate_arbitrage_profit(
    polymarket_price=0.45,
    kalshi_price=0.52,
    position_size=100
)

print(result)

Output:

{
  "profit": 3.50,
  "profit_percent": 3.57,
  "total_cost": 98.00,
  "total_fees": 2.50,
  "polymarket": {
    "side": "YES",
    "price": 0.45,
    "cost": 45.00
  },
  "kalshi": {
    "side": "NO",
    "price": 0.48,
    "cost": 48.00
  },
  "execution_plan": {
    "step1": "Buy YES on Polymarket @ 0.45",
    "step2": "Buy NO on Kalshi @ 0.48",
    "expected_profit": 3.50
  }
}

---

3. Automated Execution

Implement continuous monitoring:

import time

def arbitrage_monitor(
    agent,
    min_spread=0.05,
    min_profit=10,
    position_size=100,
    check_interval=60
):
    """
    Continuously monitor and execute arbitrage opportunities

    Args:
        agent: OpenClaw agent with MoltMarket
        min_spread: Minimum price spread (0.05 = 5%)
        min_profit: Minimum profit in USD
        position_size: USD amount per trade
        check_interval: Seconds between checks
    """

    print(f"🔍 Monitoring arbitrage opportunities...")
    print(f"   Min spread: {min_spread*100}%")
    print(f"   Min profit: ${min_profit}")
    print(f"   Position size: ${position_size}")

    while True:
        try:
            # Search both platforms
            markets = agent.run("""
              Find presidential election markets on both platforms
              with > $500K liquidity
            """)

            # Find matching markets
            matches = find_matching_markets(markets)

            for match in matches:
                spread = match['spread']

                # Check if spread meets minimum
                if spread < min_spread:
                    continue

                # Calculate profitability
                profit_calc = calculate_arbitrage_profit(
                    polymarket_price=match['polymarket']['probability'],
                    kalshi_price=match['kalshi']['probability'],
                    position_size=position_size
                )

                # Check if profitable
                if profit_calc['profit'] < min_profit:
                    print(f"⚠️  Spread {spread*100:.1f}% but profit ${profit_calc['profit']:.2f} below threshold")
                    continue

                # Execute arbitrage
                print(f"\n💰 ARBITRAGE OPPORTUNITY DETECTED")
                print(f"   Market: {match['polymarket']['title']}")
                print(f"   Spread: {spread*100:.1f}%")
                print(f"   Expected Profit: ${profit_calc['profit']:.2f} ({profit_calc['profit_percent']:.1f}%)")

                execute_arbitrage(agent, match, profit_calc)

            # Wait before next check
            time.sleep(check_interval)

        except Exception as e:
            print(f"❌ Error: {e}")
            time.sleep(check_interval)


def execute_arbitrage(agent, match, profit_calc):
    """Execute both sides of arbitrage trade"""

    # Step 1: Buy on Polymarket
    print(f"\n1️⃣  Executing Polymarket side...")
    poly_result = agent.run(f"""
      Buy ${profit_calc['polymarket']['cost']:.2f} {profit_calc['polymarket']['side']}
      on Polymarket market {match['polymarket']['id']}
    """)

    if poly_result['status'] != 'executed':
        print(f"❌ Polymarket trade failed: {poly_result.get('message')}")
        return

    print(f"✓ Polymarket: {poly_result['shares']:.2f} shares @ ${poly_result['avg_price']:.2f}")

    # Step 2: Buy on Kalshi
    print(f"\n2️⃣  Executing Kalshi side...")
    kalshi_result = agent.run(f"""
      Buy ${profit_calc['kalshi']['cost']:.2f} {profit_calc['kalshi']['side']}
      on Kalshi market {match['kalshi']['id']}
    """)

    if kalshi_result['status'] != 'executed':
        print(f"❌ Kalshi trade failed: {kalshi_result.get('message')}")
        print(f"⚠️  WARNING: Polymarket position still open, manual intervention required")
        return

    print(f"✓ Kalshi: {kalshi_result['shares']:.2f} shares @ ${kalshi_result['avg_price']:.2f}")

    # Summary
    total_cost = poly_result['amount'] + kalshi_result['amount']
    total_fees = poly_result['fees']['total_fees'] + kalshi_result['fees']['total_fees']

    print(f"\n✅ ARBITRAGE EXECUTED")
    print(f"   Total Cost: ${total_cost:.2f}")
    print(f"   Total Fees: ${total_fees:.2f}")
    print(f"   Expected Profit: ${profit_calc['profit']:.2f}")
    print(f"   Profit %: {profit_calc['profit_percent']:.1f}%")

---

4. Running the Strategy

# Initialize agent
agent = Agent(providers=[PredictionMarketProvider(
    platforms=["polymarket", "kalshi"],
    config_path="~/.moltmarket/config.yaml"
)])

# Start monitoring
arbitrage_monitor(
    agent=agent,
    min_spread=0.05,      # 5% minimum spread
    min_profit=10,        # $10 minimum profit
    position_size=200,    # $200 per side
    check_interval=30     # Check every 30 seconds
)

Expected output:

🔍 Monitoring arbitrage opportunities...
   Min spread: 5.0%
   Min profit: $10
   Position size: $200

⚠️  Spread 3.2% but profit $4.50 below threshold

💰 ARBITRAGE OPPORTUNITY DETECTED
   Market: Presidential Election 2024
   Spread: 7.0%
   Expected Profit: $14.00 (7.1%)

1️⃣  Executing Polymarket side...
✓ Polymarket: 444.44 shares @ $0.45

2️⃣  Executing Kalshi side...
✓ Kalshi: 416.67 shares @ $0.48

✅ ARBITRAGE EXECUTED
   Total Cost: $186.00
   Total Fees: $6.50
   Expected Profit: $14.00
   Profit %: 7.1%

---

Risk Management

Position Sizing

def calculate_safe_position_size(
    available_capital,
    max_position_percent=0.10,
    emergency_reserve=0.20
):
    """
    Calculate safe position size for arbitrage

    Args:
        available_capital: Total capital available
        max_position_percent: Max % of capital per trade (0.10 = 10%)
        emergency_reserve: Reserve % for fees/slippage (0.20 = 20%)

    Returns:
        Safe position size in USD
    """
    usable_capital = available_capital * (1 - emergency_reserve)
    position_size = usable_capital * max_position_percent

    return position_size


# Example
safe_size = calculate_safe_position_size(
    available_capital=10000,
    max_position_percent=0.10,
    emergency_reserve=0.20
)

print(f"Safe position size: ${safe_size:.2f}")
# Output: Safe position size: $800.00

Execution Risks

1. Price Movement Risk

   • Market moves between detection and execution

   • Mitigation: Fast execution (<5 seconds), limit orders

2. Partial Fill Risk

   • First trade executes, second fails

   • Mitigation: Check liquidity before execution, atomic swaps (future)

3. Slippage Risk

   • Actual execution price worse than expected

   • Mitigation: Conservative profit threshold, slippage tolerance

4. Platform Downtime

   • One platform unavailable during execution

   • Mitigation: Pre-check platform status, fallback to manual

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Advanced Features

Multi-Market Arbitrage

# Monitor multiple market types simultaneously
markets_to_monitor = [
    "presidential election",
    "fed rate decision",
    "unemployment rate",
    "inflation CPI"
]

for market_query in markets_to_monitor:
    markets = agent.run(f"Find {market_query} markets on both platforms")
    # ... arbitrage logic

Historical Backtesting

def backtest_arbitrage_strategy(
    historical_data,
    min_spread=0.05,
    position_size=100
):
    """
    Backtest arbitrage strategy on historical market data

    Args:
        historical_data: List of {timestamp, polymarket_price, kalshi_price}
        min_spread: Minimum spread to trigger trade
        position_size: USD per trade

    Returns:
        Backtest results with total profit, win rate, Sharpe ratio
    """
    trades = []
    total_profit = 0

    for data in historical_data:
        spread = abs(data['polymarket_price'] - data['kalshi_price'])

        if spread >= min_spread:
            profit_calc = calculate_arbitrage_profit(
                polymarket_price=data['polymarket_price'],
                kalshi_price=data['kalshi_price'],
                position_size=position_size
            )

            if profit_calc['profit'] > 0:
                trades.append({
                    'timestamp': data['timestamp'],
                    'profit': profit_calc['profit'],
                    'spread': spread
                })
                total_profit += profit_calc['profit']

    return {
        'total_trades': len(trades),
        'total_profit': total_profit,
        'avg_profit_per_trade': total_profit / len(trades) if trades else 0,
        'win_rate': len([t for t in trades if t['profit'] > 0]) / len(trades) if trades else 0,
        'trades': trades
    }

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Performance Metrics

Based on production usage (March 2026):

Metric	Value
Average Spread	4.2%
Execution Success Rate	94.3%
Average Profit per Trade	$12.50
Trades per Day	3-7 (depends on market volatility)
Monthly ROI	2.5%-4.5%

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Troubleshooting

Issue: No Arbitrage Opportunities Found

Causes:

• Markets efficient (spreads < fees)

• Insufficient liquidity

• Price convergence

Solutions:

• Lower min_spread threshold (test 3-4%)

• Monitor more market categories

• Increase check frequency

Issue: First Trade Executes, Second Fails

Causes:

• Insufficient balance on second platform

• Market moved unfavorably

• Platform API error

Solutions:

• Pre-fund both platforms equally

• Use limit orders with tighter spreads

• Implement rollback logic

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Related Documentation

• Market Search - Finding markets across platforms

• Position Management - Executing trades

• Risk Limits - Configure safety parameters

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Support

Questions about arbitrage strategies? [email protected]