How Reaction Speed and Urgency Affect Automated Traders

Summary

The study examines how reaction speed and trading urgency affect automated strategies in simulated markets with one exchange, a public limit order book, and continuous double-auction matching. Reaction speed means the time a strategy needs to calculate a response to market events; urgency describes how strongly its behavior changes as a deadline approaches. These dimensions extend analysis that often compares pricing strategies while leaving time effects aside.

In the simulations, incorporating reaction speed changes earlier comparisons: the simple SHVR strategy can outperform the more complex adaptive algorithm AA. Giving ZIP traders a pace parameter, producing the ZIPP variant, adds deadline sensitivity and significantly improves profitability in the reported setting. These findings concern the described simulated market and do not by themselves show that the same rankings or gains hold in live markets or under other exchange conditions.

Key ideas

• Automated trading performance depends on how quickly a strategy processes market events.
• Urgency captures how strategy behavior responds to an approaching deadline.
• Accounting for reaction speed can change the relative performance of trading algorithms.
• In the simulations, SHVR can outperform AA, and a pace parameter improves ZIP trader profitability.

Tags

# Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders


# Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders









We consider issues of time in automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. In particular, we explore two effects: (i) reaction speed - the time taken for trading strategies to calculate a response to market events; and (ii) trading urgency - the sensitivity of trading strategies to approaching deadlines. Much of the literature on trading agents focuses on optimising pricing strategies only and ignores the effects of time, while real-world markets continue to experience a race to zero latency, as automated trading systems compete to quickly access information and act in the market ahead of others. We demonstrate that modelling reaction speed can significantly alter previously published results, with simple strategies such as SHVR outperforming more complex adaptive algorithms such as AA. We also show that adding a pace parameter to ZIP traders (ZIP-Pace, or ZIPP) can create a sense of urgency that significantly improves profitability.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.