Behavior based learning in identifying High Frequency Trading strategies

Yang, Steve Y.; Paddrik, Mark E.; Hayes, Roy; Todd, Andrew; Kirilenko, Andrei; Beling, Peter A.; Scherer, William T.

doi:10.1109/cifer.2012.6327783

Cited by 25 publications

(12 citation statements)

References 19 publications

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“…Once we have the order book at any given event tick, we take the market depth at five different levels as our base variables and then discretize these variables to generate an MDP model state space. This study extends the MDP model documented by Yang et al (2012) to obtain five variables, i.e. order volume imbalance between the best bid and the best ask prices, order volume imbalance between the 2nd best bid and the 2nd best ask prices, order volume imbalance between the 3rd best bid and the 3rd best ask prices, the order book imbalance at the 5th best bid and the 5th ask prices, and the inventory level/holding position (see figure 2(b)).…”

Section: Constructing An Mdp Model From Order Book Datamentioning

confidence: 96%

“…Consequently, strategies developed under certain value frameworks can be observed, learned and even reproduced in a different environment (such as a simulated financial market where impact of these strategies can be readily assessed). As documented by Yang et al (2012), Hayes et al (2012) and Paddrik et al (2012), the manipulative or disruptive algorithmic strategies can be studied and monitored by market operators and regulators to prevent unfair trading practices. Furthermore, new emerging algorithmic trading practices can be assessed and new regulations and policies can be evaluated to maintain the overall health of the financial markets.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian process-based algorithmic trading strategy identification

Yang

Qiao

Beling

et al. 2015

Quantitative Finance

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Many market participants now employ algorithmic trading, commonly defined as the use of computer algorithms, to automatically make certain trading decisions, submit orders and manage those orders after submission. Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators. Advanced data feeds and audit trail information from market operators now allow for the full observation of market participants’ actions. A key question is the extent to which it is possible to understand and characterize the behaviour of individual participants from observations of trading actions. In this paper, we consider the basic problems of categorizing and recognizing traders (or, equivalently, trading algorithms) on the basis of observed limit orders. These problems are of interest to regulators engaged in strategy identification for the purposes of fraud detection and policy development. Methods have been suggested in the literature for describing trader behaviour using classification rules defined over a feature space consisting of summary trading statistics of volume and inventory, along with derived variables that reflect the consistency of buying or selling behaviour. Our principal contribution is to suggest an entirely different feature space that is constructed by inferring key parameters of a sequential optimization model that we take as a surrogate for the decision-making process of the traders. In particular, we model trader behaviour in terms of a Markov decision process. We infer the reward (or objective) function for this process from observations of trading actions using a process from machine learning known as inverse reinforcement learning (IRL). The reward functions learned through IRL then constitute a feature space that can be the basis for supervised learning (for classification or recognition of traders) or unsupervised learning (for categorization of traders). Making use of a real-world data-set from the E-Mini futures contract, we compare two principal IRL variants, linear IRL and Gaussian Process IRL, against a method based on summary trading statistics. Results suggest that IRL-based feature spaces support accurate classification and meaningful clustering. Further, we argue that, because they attempt to learn traders’ underlying value propositions under different market conditions, the IRL methods are more informative and robust than the summary statistic-based approach and are well suited for discovering new behaviour patterns of market participants

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Section: Constructing An Mdp Model From Order Book Datamentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Gaussian process-based algorithmic trading strategy identification

Yang

Qiao

Beling

et al. 2015

Quantitative Finance

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show abstract

“…Hayes et al (2013) used supervised and unsupervised learning to reverse engineer fund allocation strategies for groups of human participants in simulated trading competition. As documented by Yang et al (2012) and Hayes et al (2012), algorithmic trading strategies can be monitored by market operators and regulators to prevent unfair trading practices and improve the health of the financial markets. Qiao and Beling (2013) proposed a general approach to behavior recognition in sequential decision problems that is based on Markov decision process (MDP) models and Gaussian process inverse reinforcement learning (cf.…”

Section: Trading Strategy Recognitionmentioning

confidence: 99%

Decision analytics and machine learning in economic and financial systems

Qiao

Beling

2016

Environ Syst Decis

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Decision analytics may be viewed as the combined use of predictive modeling techniques (e.g., forecasting and machine learning) and prescriptive decision frameworks (e.g., optimization and simulation) to create value in systems. Recent years have seen an explosion in decision analytics applications, driven by advances in machine learning methods and computational optimization and by massive increases in the data to which these techniques may be applied. Decision analytics has long been used in the domains of economic and financial systems, with credit scoring being an example of an early success, and the clear trend is to the development of ever more sophisticated methods and applications.This issue of Environment System & Decisions is devoted to decision analytics in financial and economic systems and environments. The papers in the issue each bear an influence from theoretical developments and financial and economic applications of statistical methods, machine learning, and decision and risk modeling techniques. Before introducing the papers, we review some of the influential literature in this regard.

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“…Furthermore, Naïve Bayes is a good classifier for predicting potential trades associated to market manipulation [16]. For the case of spoofing trading, detection can be done with the implementation of supervised learning algorithms [17], or can be identified by modelling trading decisions as MDPs and using Apprenticeship Learning to learn the reward function [18]. Though research is extensive in the area of market manipulation, few develop generative models of what encourages these economic agents to follow the disruptive strategies.…”

Section: Related Workmentioning

confidence: 99%

“…A model that fits the problem of manipulation under the representation Fig. 1 is that of a Markov Decision Process (MDP) [18], [22]. In general, an MDP is defined by the tuple {S, A, T, R}, where S and A are sets of states and actions, respectively (s ∈ S and a ∈ A), R is the set of rewards (r ∈ R), and T is a set of transition probabilities ({P (s |s, a)} ∈ T , where P (s |s, a) represents the probability of transitioning to state s from s after action a).…”

Section: A Spoofing As a Markov Decision Processmentioning

confidence: 99%

Learning unfair trading: A market manipulation analysis from the reinforcement learning perspective

Martínez-Miranda

McBurney

Howard

2016

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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Abstract-Market manipulation is a strategy used by traders to alter the price of financial assets. One type of manipulation is based on the process of buying or selling assets by using several trading strategies, among them spoofing is a popular strategy and is considered illegal by market regulators. Some promising tools have been developed to detect manipulation, but cases can still be found in the markets. In this paper we model spoofing and pinging trading from a macroscopic perspective of profit maximisation, two strategies that differ in the legal background but share the same elemental concept of market manipulation. We use a reinforcement learning framework within the full and partial observability of Markov decision processes and analyse the underlying behaviour of the manipulators by finding the causes of what encourages the traders to perform fraudulent activities. Procedures can be applied to counter the problem as our model predicts the activity of the manipulators.

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Behavior based learning in identifying High Frequency Trading strategies

Cited by 25 publications

References 19 publications

Gaussian process-based algorithmic trading strategy identification

Gaussian process-based algorithmic trading strategy identification

Decision analytics and machine learning in economic and financial systems

Learning unfair trading: A market manipulation analysis from the reinforcement learning perspective

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