Paper: arXiv 2609.21173

Authors: Vincent Maciejewski

Abstract

The actor model - state isolation, data-race freedom, and sequential single-message reasoning - has long been dismissed as unsuitable for high-frequency trading (HFT): actors seem to imply many threads, a mailbox per actor, and a heap-allocated message plus a context switch per interaction, overhead incompatible with a microsecond budget. This paper argues the dismissal is wrong for co-located actors, with a deployed, measured implementation: kaspar-hft, an open-source C++20 framework. Four extensions adapt the model for HFT: fast_send, a synchronous delivery mechanism in which the sending thread runs the receiver’s handler inline and returns the reply as a value; actor groups, which co-schedule actors on one thread behind a shared mailbox; per-actor selectable mailbox queues; and a memory pool. fast_send has receiver transparency: the handler is written identically for synchronous and asynchronous delivery and does not depend on which was used or which thread runs it. A grouped synchronous chain runs on one thread, cutting scheduler context switches from O(N) to O(1); a thread-local call-chain test detects cyclic invocation on one thread before any lock is taken, while a cycle spread across threads deadlocks. Microbenchmarks put the synchronous round trip at tens of nanoseconds. On a live CME market-data feed (ES, NQ, ZN futures), when the socket-reader thread decodes each packet and updates the book itself, socket-to-book medians for book updates are 0.8-1.1 microseconds, and a fast_send hop is about 1% of that. The shared-queue group also yields a production/simulation duality: the same actor code runs unchanged in live trading and deterministic backtest.

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 adaptation of the actor model for HFT, backed by strong empirical evidence from live market data. The methodology is clearly articulated, and the practical implementation in an open-source framework enhances its reproducibility and impact. The focus on microsecond latency budgets and real-world performance makes it highly relevant for quantitative finance practitioners.

Research Flowchart

  flowchart TD
    A[Research Goal: Adapt Actor Model for HFT Low-Latency] --> B{Methodology: kaspar-hft C++20 Framework Development};
    B --> C{Key Adaptations};
    C --> C1[fast_send: Synchronous Message Delivery];
    C --> C2[Actor Groups: Co-scheduling on one thread];
    C --> C3[Selectable Mailbox Queues];
    C --> C4[Memory Pool];
    C --> D[Computational Processes: Microbenchmarks & Live Market Data Test];
    D --> E[Data/Inputs: CME Market Data Feed (ES, NQ, ZN futures)];
    D --> F[Key Findings/Outcomes];
    F --> F1[fast_send round trip: tens of nanoseconds];
    F --> F2[Socket-to-book median latency: 0.8-1.1 microseconds];
    F --> F3[Fast_send hop: ~1% of latency];
    F --> F4[Production/Simulation Duality: same actor code for trading/backtest];
    F --> F5[Actor Model is suitable for HFT with adaptations];