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

ArXiv ID: 2512.22476 “View on arXiv”

Authors: Kaihong Deng

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.

Keywords: cryptocurrency, backtesting, execution costs, Bayesian optimization, microstructure frictions

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 5.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced Bayesian optimization and statistical diagnostics (CSCV/PBO) for rigorous validation, but its empirical testing is constrained to a limited set of assets and small-account simulations without market impact modeling.

  flowchart TD
    A["Research Goal:<br>Quantify backtest fragility & design auditable tuning"] --> B{"Inputs: 4 Crypto Perpetuals"}
    B --> C["Methodology: AutoQuant Framework"]
    C --> D["Execution: Bayesian Optimization<br>with T+1 semantics & costs"]
    D --> E{"Double Screening:<br>Rolling Windows + Cost Sensitivity"}
    E --> F["Outcomes: Overfitting Diagnostics<br>and Validated Configs"]
    F --> G["Key Findings:<br>Costs reduce returns; DCS lowers drawdowns"]