AutoQuant: An Auditable Expert-System Framework for Execution-Constrained Auto-Tuning in Cryptocurrency Perpetual Futures

TL;DR

AutoQuant is a framework that improves the reliability of cryptocurrency strategy backtests by incorporating realistic execution costs and strict semantics, reducing overfitting and enhancing validation.

Contribution

It introduces AutoQuant, a novel execution-aware, auditable auto-tuning framework that accounts for microstructure frictions and enforces strict trading semantics in crypto strategy evaluation.

Findings

Cost-aware backtests significantly differ from idealized ones.

Double screening reduces drawdowns without necessarily increasing returns.

AutoQuant highlights residual overfitting risks in strategy validation.

Abstract

Backtests of cryptocurrency perpetual futures are fragile when they ignore microstructure frictions and reuse evaluation windows during parameter search. We study four liquid perpetuals (BTC/USDT, ETH/USDT, SOL/USDT, AVAX/USDT) and quantify how execution delay, funding, fees, and slippage can inflate reported performance. We introduce AutoQuant, an execution-centric, alpha-agnostic framework for auditable strategy configuration selection. AutoQuant encodes strict T+1 execution semantics and no-look-ahead funding alignment, runs Bayesian optimization under realistic costs, and applies a two-stage double-screening protocol across held-out rolling windows and a cost-sensitivity grid. We show that fee-only and zero-cost backtests can materially overestimate annualized returns relative to a fully costed configuration, and that double screening tends to reduce drawdowns under the same strict semantics even when returns are not higher. A CSCV/PBO diagnostic indicates substantial residual overfitting risk, motivating AutoQuant as validation and governance infrastructure rather than a claim of persistent alpha. Returns are reported for small-account simulations with linear trading costs and without market impact or capacity modeling.