An Open-Source Limit-Order-Book Exchange for Teaching and Research

Cliff, Dave

doi:10.1109/ssci.2018.8628760

Cited by 19 publications

(25 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the work reported here we used neither OpEx nor ExPo, but instead BSE [1,5] which is another open-source ExpEcon market simulator, initially developed as a teaching aid but subsequently used as a platform for research (see, e.g. [15]).…”

Section: On Laboratory Models Of Marketsmentioning

confidence: 99%

Adaptive-Aggressive Traders Don’t Dominate

Snashall

Cliff

2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

For more than a decade Vytelingum's Adaptive-Aggressive (AA) algorithm has been recognized as the best-performing automated auction-market trading-agent strategy currently known in the AI/Agents literature; in this paper, we demonstrate that it is in fact routinely outperformed by another algorithm when exhaustively tested across a sufficiently wide range of market scenarios. The novel step taken here is to use large-scale compute facilities to brute-force exhaustively evaluate AA in a variety of market environments based on those used for testing it in the original publications. Our results show that even in these simple environments AA is consistently outperformed by IBM's GDX algorithm, first published in 2002. We summarize here results from more than one million market simulation experiments, orders of magnitude more testing than was reported in the original publications that first introduced AA. A 2019 ICAART paper by Cliff claimed that AA's failings were revealed by testing it in more realistic experiments, with conditions closer to those found in real financial markets, but here we demonstrate that even in the simple experiment conditions that were used in the original AA papers, exhaustive testing shows AA to be outperformed by GDX. We close this paper with a discussion of the methodological implications of our work: any results from previous papers where any one trading algorithm is claimed to be superior to others on the basis of only a few thousand trials are probably best treated with some suspicion now. The rise of cloud computing means that the compute-power necessary to subject trading algorithms to millions of trials over a wide range of conditions is readily available at reasonable cost: we should make use of this; exhaustive testing such as is shown here should be the norm in future evaluations and comparisons of new trading algorithms.

show abstract

Section: On Laboratory Models Of Marketsmentioning

confidence: 99%

Adaptive-Aggressive Traders Don’t Dominate

Snashall

Cliff

2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…For all experiments performed in this paper, we use the Bristol Stock Exchange (BSE), a teaching and research platform designed for running controlled financial trading experiments (for details, see [9]). BSE is a minimal, discrete-time simulation of a centralised financial market, containing a single exchange with public limit order book (LOB).…”

Section: Modelling Reaction Timementioning

confidence: 99%

“…Here, we address the gap between research and reality by exploring reaction speed and trading urgency on a suite of reference algorithms (AA, GDX, GVWY, SHVR, ZIC, and ZIP) using the Bristol Stock Exchange simulation platform (see [9]). In Section 2, we review related work and introduce key economic concepts and technical details of the financial trading agents and simulation platform used in this study.…”

Section: Introductionmentioning

confidence: 99%

Agents and Artificial Intelligence

2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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.

show abstract

“…Turquoise Lit is a LOB-based exchange in which the LOB is made visible to all participants, and hence Turquoise Lit can be simulated by the existing BSE functionality as documented by Cliff (2018aCliff ( , 2018b and does not need to be discussed further here. However, BSE does not have any dark-pool functionality, nor any reputation-scoring system, and so the focus in this section is on describing those two aspects of LSE Turquoise.…”

Section: Reputational Dark Pool Accessmentioning

confidence: 99%

A simulator for studying automated block trading on a coupled dark/lit financial exchange with reputation tracking

Church¹,

Cliff²

2019

The European Modeling and Simulation Symposium

View full text Add to dashboard Cite

We describe a novel simulation of a contemporary realworld financial exchange: London Stock Exchange (LSE) Turquoise, and we also introduce a newly-created adaptive automated trading strategy called ISHV, which exhibits realistic behavior in situations where large orders can radically shift prices before transactions occur. LSE Turquoise is a recently-introduced platform where buying and selling takes place on a pair of coupled trading pools: a lit pool that is visible to all traders; and a dark pool where large "block" orders are hidden from sight until they are automatically matched with a counterparty, after which the transaction is then revealed. Orders from traders are routed to the lit or dark pool depending on their size, and on the reputation of the trader issuing the order. Unlike all other public-domain adaptive trading strategies, ISHV can alter the prices it quotes in anticipation of adverse price changes that are likely to occur when orders for block-trades are publicly visible: so-called market impact. LSE Turquoise is intended to reduce the negative effects of market impact; something that we test with our simulator. We extend the existing BSE open-source exchange simulator to incorporate coupled lit and dark pools, naming the new system BSELD. We show ISHV exhibiting market impact in a lit-only pool, and discuss how a Turquoisestyle coupled dark pool reduces or eliminates that impact. We also show results from a Turquoise-style reputationtracking mechanism, which can be used for modulating trader access control to the dark pool.

show abstract

An Open-Source Limit-Order-Book Exchange for Teaching and Research

Cited by 19 publications

References 13 publications

Adaptive-Aggressive Traders Don’t Dominate

Adaptive-Aggressive Traders Don’t Dominate

Agents and Artificial Intelligence

A simulator for studying automated block trading on a coupled dark/lit financial exchange with reputation tracking

Contact Info

Product

Resources

About