AI and machine learning help traders manage execution costs

Artificial intelligence and machine learning are increasingly being used by retail traders to identify and manage hidden trading costs such as spreads, slippage, and execution delays. Machine learning models can analyze large datasets to compare order submission prices with final execution prices, helping traders identify patterns where liquidity drops or volatility increases costs.

In specialized environments like Polymarket, managing slippage is critical for automated trading bots. Because Polymarket prices represent implied probabilities, even small differences between expected and actual fill prices can impact profitability. Effective strategies involve estimating order-book depth, setting maximum slippage rules, and splitting large orders to minimize price impact within the Central Limit Order Book (CLOB).