Comparison of double auction bidding strategies for automated trading agents

Comparison of double auction bidding strategies for automated trading agents Bc. Daniel Vach Abstrakt V této práci jsou porovnávány automatické obchodní strategie ZIP, GDX a AA v symetrických agent- proti-agentovi experimentech, kde se mění zastoupení jednotlivých strategií v populaci agentů. Také je představena nově vytvořená strategie ZIPOJA, která je založena na ZIP a obohacena Ojovým učícím pravidlem pro aktualizaci optimální ceny. ZIPOJA strategie je porovnávána proti ostatním strategiím, z čehož vychází, že se jí nedaří v porovnání s ostatními strategiemi. Dokonce původní ZIP ji také poráží. Dále je zjištěno, že dominance AA nad GDX a ZIP není robustní ve změnách složení populace agentů. Výsledek lze také silně ovlivnit vlastnostmi experimentu. GDX dominuje AA v mnoha experimentech, které jsou v této práci provedeny, což je v kontrastu s výsledky v předchozí literatuře. Nalezeny jsou také Nashovy rovnováhy ve smíšených strategiích. Dynamická analýza je použita pro nalezení spádových oblastí jednotlivých rovnováh.Comparison of double auction bidding strategies for automated trading agents Bc. Daniel Vach Absctract In this work, ZIP, GDX, and AA automated bidding strategies are compared in symmetric agent-agent experiments with a variable composition of agent population. ZIPOJA, a novel strategy based on ZIP with Oja's rule extension for updating its optimal price, is introduced. Then it is showed that ZIPOJA underperforms in competition against other strategies and that it underperforms even against the original ZIP. Dominance of AA over GDX and ZIP is questioned and it is showed that it is not robust to composition changes of agent population and that the experimental setup strongly affects the results. GDX is a dominant strategy over AA in many experiments in this work in contrast to the previous literature. Some mixed strategy Nash equilibria are found and their basins of attraction are shown by dynamic analysis.Institute of Economic StudiesInstitut ekonomických studiíFakulta sociálních vědFaculty of Social Science

Humans versus Agents: Competition in Financial Markets of the 21st Century

Information systems have revolutionized the nature of markets. Traditionally, markets inherently comprised the strategic interaction of human traders only. Nowadays, however, automated trading agents are responsible for at least 60% of the US trading volume on financial stock markets. In this respect, financial markets of the 21st century are different to markets of previous centuries. Fuelled by discussions on their possible risks, there is a need for research on the effects of automated trading agents on market efficiency and on human traders. In order to systematically investigate these issues, we introduce a market framework for human-computer interaction. This framework is then applied in a case study on a financial market scenario. In particular, we plan to conduct a NeuroIS experiment in which we analyze overall market efficiency as well as the trading behavior and emotional responses of human traders when they interact with computerized trading agents

Parameterised-Response Zero-Intelligence Traders

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 way that its probability mass function (PMF) is determined by a real-valued control variable s in the range [-1.0, +1.0] that determines the strategy for that trader. When s is zero, a PRZI trader behaves identically to the ZIC strategy, with a flat/rectangular PMF; but when s is close to plus or minus one the PRZI trader's PMF becomes asymptotically maximally skewed to one extreme or the other of the price-range, thereby enabling the PRZI trader to act in the same way as the "Shaver" strategy (SHVR) or the "Giveaway" strategy (GVWY), both of which have recently been demonstrated to be surprisingly dominant over more sophisticated, and supposedly more profitable, trader-strategies that incorporate adaptive mechanisms and machine learning. Depending on the value of s, a PRZI trader will behave either as a ZIC, or as a SHVR, or as a GVWY, or as some hybrid strategy part-way between two of these three previously-reported strategies. The novel smoothly-varying strategy in PRZI has value in giving trader-agents plausibly useful "market impact" responses to imbalances in an auction-market's limit-order-book, and also allows for the study of co-adaptive dynamics in continuous strategy-spaces rather than the discrete spaces that have traditionally been studied in the literature.Comment: 39 pages, 18 figures, 67 reference

High Frequency Trading in Financial Markets

Financial markets have undergone tremendous changes in the last decades. Next to the automation of the trading process and the improvement in market quality, High Frequency Trading (HFT) plays a major role in financial markets. This thesis provides a background on the evolution of financial markets and the role of HFT in price discovery and the nature of its interaction with human traders

Using Transfer Learning in Network Markets

Mechanism design is the sub-field of microeconomics and game theory, which considers agents have their own private information and are self-interested and tries to design systems that can produce desirable outcomes. In recent years, with the development of internet and electronic markets, mechanism design has become an important research field in computer science. This work has largely focused on single markets. In the real world, individual markets tend to connect to other markets and form a big “network market”, where each market occupies a node in the network and connections between markets reflect constraints on traders in the markets. So, it is interesting to find out how the structure of connected network markets impacts the performance of the resulting network markets and how we can optimize performance by varying the things that one could control in a network market. In this dissertation, I aim to find out whether we can apply transfer learning to other machine learning techniques like reinforcement learning in the design of network markets to help optimize the performance of the network markets. I applied transfer learning on both machine learning trading strategies and machine learning strategies for selecting which market to trade in. I found that, in most cases, by applying transfer learning to machine learning trading strategies or machine learning market selection strategies, we can improve the performance of the network market significantly