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

Abstract

Backtests of cryptocurrency perpetual futures are sensitive to execution timing, funding alignment, trading costs, and reuse of evaluation windows during parameter search. In high-friction markets, attractive results may therefore reflect hidden implementation choices as much as signal quality. Using BTC/USDT, ETH/USDT, SOL/USDT, and AVAX/USDT perpetual contracts, this study examines whether an auditable execution-aware configuration-selection pipeline can reduce performance overestimation and expose parameter fragility more clearly than naive one-stage tuning. This paper proposes AutoQuant, an expert-system-style decision-support framework for configuration selection. AutoQuant encodes strict execution timing, funding visibility, cost realism, and feasibility constraints as explicit rules; combines Bayesian search with two-stage screening across windows and cost scenarios; and exports deterministic artifacts with accounting-invariant checks for traceability. The resulting governance protocol selects and documents configurations within a pre-specified signal family under strict semantics. Empirically, fee-only and zero-cost backtests materially inflate apparent performance relative to fully costed runs with funding and slippage. Two-stage screening does not guarantee higher returns; in the BTC anchor case and several replications, it more often surfaces lower-drawdown or less extreme alternatives under identical strict semantics. Same-budget optimizer comparison, module and screening-policy ablations, funding-rule diagnostics, inferential checks, cross-asset replications, and third-party replay checks position AutoQuant as auditable validation infrastructure for configuration selection under explicit execution and cost assumptions. The experiments use small-account simulations under linear costs and exclude market impact and institutional capacity constraints.