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面向成本的加密货币永续期货回测与可审计调参

文章 arXiv papers · 作者: Kaihong Deng
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总结

本文介绍 AutoQuant，这是一种基于规则的决策支持框架，用于选择加密货币永续期货的交易配置。框架明确执行时点、资金费用处理、手续费、滑点和可行性要求，然后依据不同评估窗口和成本假设进行贝叶斯搜索与分阶段筛选。确定性输出和会计核对旨在使预先定义信号族内的选择过程可追溯、可复现。

在 BTC、ETH、SOL 和 AVAX 合约上，研究发现，忽略资金费用和滑点会使结果看起来显著优于计入全部成本的模拟。分阶段流程并不保证更高收益；在 BTC 基准案例和部分重复实验中，它反而会在相同执行规则下识别出回撤较低或结果不那么极端的替代方案。比较、消融、诊断、推断检验和回放评估支持将其作为验证流程。证据仅限于使用线性成本的小账户模拟，未考虑市场冲击或机构规模的容量限制。

核心观点

  • 明确的执行和资金费用规则使配置选择假设清晰可见。
  • 贝叶斯搜索与跨窗口、跨交易成本情景的筛选相结合。
  • 忽略资金费用和滑点可能大幅高估模拟表现。
  • 分阶段筛选可以倾向于选择较低回撤的替代方案，但不保证收益更高。
  • 结果来自小账户模拟，未考虑市场冲击和大规模容量限制。

标签

加密资产永续合约回测交易执行风险管理仓位规模
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# 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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。