Order Book Microstructure and Slippage Simulation: How Fixed Assumptions Hide Execution Ruin
馃挕 TL;DR - Order Book & Slippage Simulation Executive Summary (BLUF) Empirical Reality of Slippage: Standard backtesting engines that assume instant, guaranteed limit fills or rely on static, flat slippage parameters heavily distort the empirical reality of execution friction. In live cryptocurrency markets, ignoring non-linear Market Impact Costs can easily erode over $20$% of net algorithmic profits. Virtual Queue & Power-Law: Quantitative developers must incorporate dynamic power-law slippage modeling conditioned on localized pool depth and implement fill-probability filters ($P(\text{fill})$) that simulate queue position and time-to-fill priority under various volatility regimes. Execution Integrity: Incorporate defensive prompt constraints (Adaptive Sizing) to dynamically scale down entry sizes or perform split executions when trade volumes exceed 10% of the active order book depth. The Limit Order Mirage: The Flaw of 100% Fill Certainty When quantitative developers instruct AI coding engines (such as ChatGPT, Claude, or Copilot) to generate backtesting scripts using limit orders, the systems typically produce basic execution logic that looks like this: ...