Auditable Auto-Tuning for Cost-Aware Crypto Perpetual Futures Backtests
Summary
The document presents AutoQuant, a rule-based decision-support framework for selecting trading configurations in cryptocurrency perpetual futures. It makes execution timing, funding treatment, fees, slippage, and feasibility requirements explicit, then uses Bayesian search and staged screening across evaluation windows and cost assumptions. Deterministic outputs and accounting checks are intended to make selection traceable and reproducible within a predefined signal family.
Across BTC, ETH, SOL, and AVAX contracts, the study finds that omitting funding and slippage can make results look materially better than fully costed simulations. The staged process does not ensure stronger returns; in the BTC reference case and some replications, it instead identifies alternatives with lower drawdowns or less extreme outcomes under the same execution rules. Comparisons, ablations, diagnostics, inferential checks, and replay assessments support its role as a validation process. Its evidence is limited to small-account simulations using linear costs, without market impact or institutional-scale capacity constraints.
Key ideas
- Explicit execution and funding rules make configuration selection assumptions visible.
- Bayesian search is combined with screening across windows and transaction-cost scenarios.
- Ignoring funding and slippage can substantially overstate simulated performance.
- Staged screening can favor lower-drawdown alternatives without guaranteeing higher returns.
- Results come from small-account simulations and omit market impact and large-scale capacity limits.
Tags
Full text
# 2512.22476 # AutoQuant: An Auditable Expert-System Framework for Execution-Constrained Auto-Tuning in Cryptocurrency Perpetual Futures 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.
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