Programmatic Dutch-Book Strategies, Bots, and Portfolio Optimization
Dutch-book arbitrage on Polymarket remains a solid edge—buy a complete set of mutually exclusive outcomes for under 1 USDC, guaranteeing profit when one resolves to 1. But in 2026, success hinges on execution, fees, and robustness amid evolved APIs and fiercer competition.
Key Updates for 2025–2026
- Data Pipeline & APIs: Use Gamma API for market metadata and CLOB for real-time prices/depth.
- New endpoints like get-book(s) include min sizes, ticks, and risk flags—perfect for bots. Higher rate limits enable efficient scanning.
- Scanner Setup: Fetch active events via Gamma, store in a Postgres DB (with JSON fields for flexibility).
- Filter by liquidity metrics (e.g., 24h volume, open interest) to avoid thin markets.
- Embeddings for Smarter Arb: Leverage models like e5-large-v2 with tools like Chroma to spot near-duplicates or linked markets (e.g., variant election phrasings).
- This unlocks cross-event hedging and combinatorial opportunities.
Arbitrage Execution TipsCore Logic:
Sum best-ask prices across outcomes;
if <1 (net of fees/slippage), it's arb-positive.
For multi-outcome or linked markets, model combinatorial coverage carefully.
Real-World Constraints:
Size trades by order book depth, not fixed amounts.
Use WebSockets for instant updates—mispricings vanish in seconds due to HFT bots.
Focus on liquid events like elections or macros.
Fees & Risk:
Account for taker fees in calculations.
Pure arb is low-risk but low-reward; shift to portfolio views for sustainability.
Evolving to a Portfolio Betting AgentChasing tiny edges is risky—think "pennies before a steamroller." Instead, build a system that embeds markets, analyzes correlations, and optimizes bets via Kelly criterion for log-growth under uncertainty.
This turns arb into quantitative trading with hedging across related events.Ideal for devs: Start with open-source bots, integrate embeddings, and prioritize atomic multi-leg trades.
Questions? Drop a reply!
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