Two Core Arbitrage Types Identified

1. Market Rebalancing Arbitrage (Intra-Market)
Occurs when the sum of ask prices for mutually exclusive outcomes in the same market/condition deviates from $1.00.

• Buy-side rebalancing: YES + NO asks sum < $1.00 → Buy both for guaranteed $1.00 payout.
• Sell-side rebalancing: YES + NO bids sum > $1.00 → Sell both for immediate profit.

This was the dominant source: ~$29M from market rebalancing + ~$10.6M from single-condition both-sides trades.

2. Combinatorial Arbitrage (Inter-Market)
Exploits pricing inconsistencies across logically dependent but separate markets (e.g., election winner vs. state outcomes, crypto price thresholds with overlapping conditions).

These are rarer but higher-conviction when detected. The paper identified thousands of potential opportunities, though only a fraction were executed due to complexity.

Key Empirical Findings

• Arbitrage opportunities appear frequently in volatile windows (first minutes of new rounds, major news events).
• Median deviation in mispriced conditions was significant (~$0.06–$0.40 below $1.00).
• Top 10 arbitragers captured 20.5% of total profits (~$8.1M), with the leading wallet earning over $2M from 4,000+ trades.
• Most profits came from systematic, high-frequency execution rather than one-off lucky bets.

Technical Blueprint for Building This into a Polymarket Trading Bot

Detection & Optimization Layer

def detect_rebalancing_opportunity(conditions):
    for cond in conditions:  # group by market/condition
        total_ask = sum(c.ask_price for c in cond.outcomes)
        if total_ask < 1.0 - MIN_EDGE:
            edge = 1.0 - total_ask
            size = kelly_sizing(bankroll, edge, variance)
            execute_multi_buy([o.id for o in cond.outcomes], size)

Combinatorial Layer (Advanced)
Use graph-based dependency detection + optimization solvers (integer linear programming or Bregman projection + Frank-Wolfe) to find profitable multi-leg bundles across related markets.

Execution Requirements

• Sub-second CLOB polling + WebSocket depth feeds
• Precise partial fill & slippage modeling
• Inventory tracking to avoid unintended net exposure
• Auto-redeem logic after resolution
• Risk limits on capital per opportunity

Why This Matters for Serious Polymarket Trading Bot Builders

The $40M figure proves that structural arbitrage is one of the most reliable, mathematically provable edges on Polymarket. While simple YES+NO sum checks are now competitive, builders who implement:

• Robust dependency graphing (LLM-assisted or heuristic)
• Convex optimization for bundle construction
• Realistic execution simulation
• Cross-market correlation engines

…will continue to capture the next wave of alpha, especially as multi-outcome political, sports, and RWA markets grow.

The paper confirms: prediction markets are not efficient by default — they are kept efficient by bots that systematically enforce probability rules. The winners are those with the best infrastructure for detection, optimization, and execution.

If you have more questions, please feel free to contact me at any time: https://t.me/FatherSon97

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