Fools Rush In: Competitive Effects of Reaction Time in Automated Trading

TL;DR

This paper investigates how reaction time influences automated trading strategies in simulated markets, revealing that faster isn't always better and that simple strategies can outperform complex ones when computation time is considered.

Contribution

It introduces realistic models of trading speed and profiles various algorithms, showing that reaction time impacts performance and challenging assumptions about the dominance of complex strategies.

Findings

Faster trading isn't always better for performance.

Simple strategies can outperform complex ones when accounting for computation time.

Reaction time significantly influences trading success in simulated markets.

Abstract

We explore the competitive effects of reaction time of automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. A large body of research conducted over several decades has been devoted to trading agent design and simulation, but the majority of this work focuses on pricing strategy and does not consider the time taken for these strategies to compute. In real-world financial markets, speed is known to heavily influence the design of automated trading algorithms, with the generally accepted wisdom that faster is better. Here, we introduce increasingly realistic models of trading speed and profile the computation times of a suite of eminent trading algorithms from the literature. Results demonstrate that: (a) trading performance is impacted by speed, but faster is not always better; (b) the Adaptive-Aggressive (AA) algorithm, until recently considered the most dominant trading strategy in the literature, is outperformed by the simplistic Shaver (SHVR) strategy - shave one tick off the current best bid or ask - when relative computation times are accurately simulated.