Model Predictive Control For Trade Execution

Battle Count: 77

This paper is directly and centrally relevant to quantitative trading. It addresses the core problem of algorithmic order execution in continuous double-auction markets, which is fundamental to institutional trading desks, market makers, and quantitative funds. The MPC framework provides a production-grade, modular solution with demonstrated 40-50% improvement in schedule shortfall over spread-crossing benchmarks. The work bridges academic optimal execution theory (Almgren-Chriss, Bertsimas-Lo) with practical implementation constraints (speed, modularity, rich action spaces).

The core optimization is a standard quadratic program solvable in ~1ms with Clarabel, making the mathematical implementation straightforward. However, the full production system requires multiple modular components (scheduler, fill probability model, fill covariance model, trading cost model, rollout cost model, constraint controller, candidate order generator), each of which can be independently complex. The modular architecture reduces coupling but increases integration complexity. Real-time data feeds, order management systems, and multi-venue connectivity add significant engineering overhead beyond the algorithmic core.

The algorithm is described in full mathematical detail with explicit hyperparameters (Table 1), solver choice (Clarabel), and simulation parameters. However, the NASDAQ L3 ITCH data is proprietary and not publicly available. The simulation environment details (1200 instruments, $10K orders, 10ms latency) are specified but cannot be exactly replicated without access to the same data feed. The modular architecture and component descriptions are thorough enough for independent reimplementation given equivalent data.