仅凭交易与价格数据，就能判断算法交易策略是吃流动性还是给流动性。
Liquidity-Based Audit of Algorithmic Trading Strategies
- 通过交易和价格历史，无需知道策略信号或优化目标，即可识别其对流动性的净需求。
- 在2016-2025年美股数据中，量化了疫情与加息冲击期间隐含买卖价差的变化。
- 揭示了多策略协同下流动性失衡导致的福利损失，规模随策略数量平方增长。
我们证明，仅从算法策略的交易与价格历史即可识别其对流动性的净需求，无需了解其信号或优化问题。精确的多期后悔分解表明，该统计量的符号可将线性策略分类为净流动性消费者或提供者，仅凭可观测数据就还原了Kyle（1985）的知情交易者/做市商二分法。在AR(1)成本过程下，该统计量等于策略规模与平方滚轮（Roll, 1984）隐含价差的乘积，使校正项直接成为当前市场不流动性程度的代理指标。扩展至内生价格影响并聚合N个相关策略后，得到流动性平衡条件；其违反将引发福利损失，规模与N²成正比，构成闭式表达的‘抛售外部性’。基于CRSP股票数据（2016–2025）进行校准，追踪了新冠疫情及2022年加息冲击期间的隐含价差变化，所提估计器计算复杂度为O(Tnd)。
原文摘要 · Abstract (English)
We show that net demand for liquidity by algo strategies is identifiable from its trade and price history alone, with no knowledge of its signal or optimization problem. An exact multi-period regret decomposition implies that the sign of this statistic classifies a linear strategy as a net liquidity consumer or provider, recovering the Kyle (1985) informed-trader/market-maker dichotomy from observables alone. Under an AR(1) cost process, the same statistic equals the product of strategy size and the squared Roll (1984) implied spread, making the correction a direct proxy for prevailing illiquidity. Extending to endogenous price impact and aggregating across N correlated strategies yields a liquidity-balance condition whose violation produces welfare loss scaling as N squared, a closed-form fire-sale externality. We calibrate to CRSP equity data (2016-2025), tracking implied spreads through the COVID-19 and 2022 rate-shock episodes, with an estimator computable in O(Tnd) time.
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