Parameterised-Response Zero-Intelligence Traders

@article{Cliff2021ParameterisedResponseZT,
  title={Parameterised-Response Zero-Intelligence Traders},
  author={Dave Cliff},
  journal={CompSciRN: Industry Practical Application (Topic)},
  year={2021}
}
  • D. Cliff
  • Published 21 March 2021
  • Computer Science, Economics
  • CompSciRN: Industry Practical Application (Topic)
I introduce PRZI (Parameterised-Response Zero Intelligence), a new form of zero-intelligence trader intended for use in simulation studies of auction markets. Like Gode & Sunder's classic Zero-Intelligence Constrained (ZIC) trader, PRZI generates quote-prices from a random distribution over some specified domain of discretely-valued allowable quote-prices. Unlike ZIC, which uses a uniform distribution to generate prices, the probability distribution in a PRZI trader is parameterised in such a… 
BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling
  • D. Cliff
  • Computer Science, Economics
    ArXiv
  • 2021
TLDR
BBE is offered as a proof-of-concept simulator system that enables the generation of large high-resolution data-sets for automated discovery or improvement of profitable strategies for betting on sporting events via the application of AI/ML and advanced data analytics techniques.
Exploring Coevolutionary Dynamics of Competitive Arms-Races Between Infinitely Diverse Heterogenous Adaptive Automated Trader-Agents
TLDR
This work reveals that by taking only a small step in the direction of increased realism the authors move immediately into high-dimensional phase-spaces, which then present difficulties in visualising and understanding the coevolutionary dynamics unfolding within the system.

References

SHOWING 1-10 OF 107 REFERENCES
The predictive power of zero intelligence in financial markets.
TLDR
This work uses data from the London Stock Exchange to test a simple model in which minimally intelligent agents place orders to trade at random and demonstrates the existence of simple laws relating prices to order flows and suggests there are circumstances where the strategic behavior of agents may be dominated by other considerations.
Market Impact in Trader-Agents: Adding Multi-Level Order-Flow Imbalance-Sensitivity to Automated Trading Systems
TLDR
The 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.
BSE: A Minimal Simulation of a Limit-Order-Book Stock Exchange
  • D. Cliff
  • Business, Computer Science
    ArXiv
  • 2018
TLDR
The Bristol Stock Exchange is described, a novel minimal simulation of a centralised financial market, based on a Limit Order Book such as is common in major stock exchanges, which has been successfully used for teaching and research in a leading UK university since 2012.
Exhaustive Testing of Trader-agents in Realistically Dynamic Continuous Double Auction Markets: AA Does Not Dominate
TLDR
It is concluded that AA can indeed appear dominant when tested only against other AI-based trading agents in the highly simplified market scenarios that have become the methodological norm in the trading-agents academic research literature, but much of that success seems to be because AA was designed with exactly those simplified experimental markets in mind.
Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality
We report market experiments in which human traders are replaced by "zero-intelligence" programs that submit random bids and offers. Imposing a budget constraint (i.e., not permitting traders to sell
Minimal-Intelligence Agents for Bargaining Behaviors in Market-Based Environments
This report describes simple mechanisms that allow autonomous software agents to en gage in bargaining behaviors in market based environments Groups of agents with such mechanisms could be used in
Which Trading Agent is Best? Using a Threaded Parallel Simulation of a Financial Market Changes the Pecking-Order
This paper presents novel results generated from a new simulation model of a contemporary financial market, that cast serious doubt on the previously widely accepted view of the relative performance
Methods Matter: A Trading Agent with No Intelligence Routinely Outperforms AI-Based Traders
TLDR
It is shown that the best public-domain AI/ML traders in the published literature can be routinely outperformed by a “sub-zero-intelligence” trading strategy that at face value appears to be so simple as to be financially ruinous, but which interacts with the market in such a way that in practice it is more profitable than the well-knownAI/ML strategies from the research literature.
Adaptive-Aggressive Traders Don't Dominate
TLDR
It is demonstrated that Vytelingum's Adaptive-Aggressive algorithm is in fact routinely outperformed by another algorithm when exhaustively tested across a sufficiently wide range of market scenarios.
Agent-human Interactions in the Continuous Double Auction, Redux - Using the OpEx Lab-in-a-Box to explore ZIP and GDX
TLDR
The overall findings are that, both when competing against ZIP in pure agent vs. agent experiments and when competing Against human traders, GDX's performance is significantly better than the performance of ZIP.
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