Market Impact in Trader-agents: Adding Multi-level Order-flow Imbalance-sensitivity to Automated Trading Systems
Zhen Zhang, Dave Cliff
Abstract
Financial markets populated by human traders often exhibit so-called “market impactâ€, where the prices quoted by traders move in the direction of anticipated change, before any transaction has taken place, as an immediate reaction to the arrival of a large (i.e., “blockâ€) buy or sell order in the market: traders in the market know that a block buy order is likely to push the price up, and that a block sell order is likely to push the price down, and so they immediately adjust their quote-prices accordingly. In most major financial markets nowadays very many of the participants are “robot tradersâ€, autonomous adaptive software agents, rather than humans. This paper addresses the question of how to give such trader-agents a reliable anticipatory sensitivity to block orders, such that markets populated entirely by robot traders also show market-impact effects. This is desirable because impact-sensitive trader-agents will get a better price for their transactions when block orders arrive, and because such traders can also be used for more accurate simulation models of real-world financial markets. In a 2019 publication Church & Cliff presented initial results from a simple deterministic robot trader, called ISHV, which was the first such trader-agent to exhibit this market impact effect. ISHV does this via monitoring a metric of imbalance between supply and demand in the market. The novel contributions of our paper are: (a) we critique the methods used by Church & Cliff, revealing them to be weak, and argue that a more robust measure of imbalance is required; (b) we argue for the use of multi-level order-flow imbalance (MLOFI: Xu et al., 2019) as a better basis for imbalance-sensitive robot trader-agents; and (c) we demonstrate the use of the more robust MLOFI measure in extending ISHV, and also the well-known AA and ZIP trading-agent algorithms (which have both been previously shown to consistently outperform human traders). Our results demonstrate that the new imbalance-sensitive trader-agents introduced in this paper do exhibit market impact effects, and hence are better-suited to operating in markets where impact is a factor of concern or interest, but do not suffer the weaknesses of the methods used by Church & Cliff. We have made the source-code for our work reported here freely available on GitHub.
DownloadPaper Citation
in Harvard Style
Zhang Z. and Cliff D. (2021). Market Impact in Trader-agents: Adding Multi-level Order-flow Imbalance-sensitivity to Automated Trading Systems.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 426-436. DOI: 10.5220/0010391004260436
in Bibtex Style
@conference{icaart21,
author={Zhen Zhang and Dave Cliff},
title={Market Impact in Trader-agents: Adding Multi-level Order-flow Imbalance-sensitivity to Automated Trading Systems},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={426-436},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010391004260436},
isbn={978-989-758-484-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Market Impact in Trader-agents: Adding Multi-level Order-flow Imbalance-sensitivity to Automated Trading Systems
SN - 978-989-758-484-8
AU - Zhang Z.
AU - Cliff D.
PY - 2021
SP - 426
EP - 436
DO - 10.5220/0010391004260436