Paper: arXiv 2609.18019

Authors: Vincent Maciejewski

Abstract

Automated execution algorithms are organized into schedule-based and liquidity-seeking families. This paper concerns the first, whose members – Time-Weighted Average Price (TWAP), Volume-Weighted Average Price (VWAP), Percentage of Volume (POV) and Implementation Shortfall – are all model-based: each derives its decisions from an explicit model, forecast, schedule or control rule. We introduce Shadow-PPOV, a passive POV whose order-placement rate is set from observed order flow rather than from traded volume. Placing a passive order to fill efficiently conventionally involves an order-book model and a fill prediction. Shadow-PPOV replaces that prediction with tracking: on observing a third-party add, it may transmit its own limit order at the same price on the same venue, recording a single association between the observed order’s exchange identifier and its own. Cancellation is then identifier-driven – the shadow is withdrawn when the order it follows ends, at once on a cancel and after a brief grace window on a trade. The placement decision is thus model-free: price and venue are read off the observed order. Model-free is not information-free. Shadow-PPOV reads every order-book message and places only where a participant has just committed capital, while computing nothing from what it reads. Information is inherited from the flow rather than derived from a model. We evaluate Shadow-PPOV on a full calendar year of replayed Chicago Mercantile Exchange (CME) ES futures in a deterministic market-replay simulator, reporting its slippage and latency sensitivity and comparing it against the aggressive equivalent POV. We propose it as a model-free benchmark for passive-order placement, against which a predictive placement model can be scored. The algorithm and the order-book simulator are implemented in the open-source kaspar-hft project.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper presents a highly novel, model-free approach to passive execution with strong empirical validation using a full year of market replay data. While the mathematical complexity is moderate, the rigorous testing and open-source implementation contribute to a high overall score.

Research Flowchart

  flowchart TD
    A[Research Goal: Develop Model-Free Passive Execution] --> B{Key Methodology: Shadow-PPOV};
    B -- Order Placement Strategy --> C[Data/Inputs: Real-time Order Flow (Third-party Additions)];
    C -- Cancellation Strategy --> D[Computational Processes: Identifier-Driven Shadow Tracking];
    D -- Evaluation --> E[Evaluation Data: CME ES Futures (Full Year)];
    E -- Comparison --> F[Key Findings: Slippage & Latency Sensitivity, Comparison to Aggressive POV];
    F -- Outcome --> G[Outcome: Model-Free Benchmark for Passive-Order Placement];