Building a Polymarket Trading Bot: Order Book Monitoring, Slippage Handling, and Position Sizing

 

Most first-attempt Polymarket bots place a market order and call it done. That works fine on liquid, high-volume markets, but Polymarket has thousands of long-tail markets with thin order books and wide bid-ask spreads, where a naive market order can fill at a price far from the quote seen just a moment earlier.

Polymarket's API is split into two services. The Gamma API handles public, read-only market discovery. The CLOB API handles order books, prices, and actual trading. Reading market data requires no authentication, while placing orders does.

A solid bot workflow starts by discovering a market and pulling its order book, then calculating edge by comparing a model's estimated probability against the market's implied probability. Position sizing should use a fractional-Kelly approach, capped by a hard maximum exposure per market, since that cap matters more for risk control than the Kelly fraction itself. Before filling an order, checking remaining order book depth prevents chasing slippage with repeated market orders on a thin book.

Lessons worth passing on: poll less and cache more rather than hammering the API on a flat interval, log every trade the bot decided not to make since that data reveals whether the edge threshold is actually calibrated, and treat correlated markets as one combined position rather than separate independent bets, since they often share the same underlying real-world event.
https://github.com/casatrick/polymarket-arbitrage-bot-python

Comments

Post a Comment

Popular posts from this blog

How a Polymarket Arbitrage Bot Actually Works: 5 Strategies Explained

A Polymarket arbitrage bot scans prediction markets for pricing inefficiencies and executes trades to capture the resulting spread. That's the concept in one sentence, but arbitrage on Polymarket isn't one strategy, it's several distinct mechanisms, each exploiting a different kind of mispricing. Here's a breakdown of how each one works, based on an open-source implementation. Intra-market arbitrage is the core mechanic. In a binary Polymarket market, YES and NO token prices should always sum to $1.00. When combined pricing falls below that after fees, buying both outcomes locks in a profit that doesn't depend on which outcome actually happens. Buy YES and NO, merge the tokens into a complete set, redeem for $1.00 per share. This is the closest thing to genuinely risk-free arbitrage available in prediction markets. Combinatorial arbitrage extends the same logic to multi-outcome markets, like price-range or bracket-style markets with three or more possible outcomes...
Read more

Why Every Polymarket Trading Bot Needs to Adapt to TWAP

When building a Polymarket trading bot, it's easy to focus on prediction models, latency, and order execution. However, platform infrastructure changes can have an even greater impact on a trading system than a new strategy. With Polymarket introducing TWAP (Time-Weighted Average Price) settlement for crypto Up/Down markets, I spent the last few days reviewing how my bot behaves near market resolution. Supporting TWAP wasn't simply replacing one price calculation-it required rethinking assumptions around settlement, execution timing, and trade evaluation. This update reminded me that trading bots aren't static software. They're living systems that must evolve alongside the markets they interact with. Over the coming weeks, I'll share a detailed technical breakdown covering the architecture changes, implementation decisions, and lessons learned while updating my Polymarket trading bot for the new TWAP model.
Read more