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:10.2139/ssrn.1955965

Cited by 14 publications

(21 citation statements)

References 26 publications

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“…An Inverse Reinforcement Learning (IRL) algorithm was applied to the learning and classifying of traders' behaviours. The experiments were conducted on a simulated S&P 500 futures market through a multi-agent approach [10] and achieved more than 90% classification accuracy. An empirical study of the relationship between the market efficiency and the market close price manipulation, defined as ramping, was carried out and showed a raise in execution costs of completing large trades when experiencing the market close ramping [11].…”

Section: Review Of Related Literaturementioning

confidence: 99%

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Detecting price manipulation in the financial market

Cao

Coleman

et al. 2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Abstract-Market abuse has attracted much attention from financial regulators around the world but it is difficult to fully prevent. One of the reasons is the lack of thoroughly studies of the market abuse strategies and the corresponding effective market abuse approaches. In this paper, the strategies of reported price manipulation cases are analysed as well as the related empirical studies. A transformation is then defined to convert the time-varying financial trading data into pseudostationary time series, where machine learning algorithms can be easily applied to the detection of the price manipulation. The evaluation experiments conducted on four stocks from NASDAQ show a promising improved performance for effectively detecting such manipulation cases.

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Section: Review Of Related Literaturementioning

confidence: 99%

“…An effective classification algorithm was shown in [10] but it was based only on simulated markets where the traders and their strategies were clearly defined.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Detecting price manipulation in the financial market

Cao

Coleman

et al. 2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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“…Especially in the case of dark pools, they apply a censored exploration algorithm to the problem of smart order routing (SOR). Yang, Paddrik, Hayes, Todd, Kirilenko, Beling, and Scherer () examine an IRL algorithm for the separation of HFT strategies from other algorithmic trading activities. They also apply the same algorithm to the identification of manipulative HFT strategies (i.e., spoofing).…”

Section: Machine Learning For Hft and Lobmentioning

confidence: 99%

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

Ntakaris

Magris

Kanniainen

et al. 2018

Journal of Forecasting

115

106

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Managing the prediction of metrics in high‐frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high‐frequency limit order markets for mid‐price prediction. We extracted normalized data representations of time series data for five stocks from the Nasdaq Nordic stock market for a time period of 10 consecutive days, leading to a dataset of ∼4,000,000 time series samples in total. A day‐based anchored cross‐validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state‐of‐the‐art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large‐scale dataset can serve as a testbed for devising novel solutions of expert systems for high‐frequency limit order book data analysis.

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“…Finance literature employs several avenues when studying HFTs activity. Agents-based simulation is one of the most widely used method (Leal, Napoletano, Roventini, & Fagiolo, 2016;Paddrik et al, 2012;Yang et al, 2012), which requires detailed trading data from each trader to simulate traders' behaviors. Similarly, Brogaard (2010) and Kirilenko et al (2017) rely on financial data sets containing specific trader information that allow to distinguish between HFTs and non-HFTs.…”

Section: Introductionmentioning

confidence: 99%

Extracting information from the limit order book: New measures to evaluate equity data flow

Florescu

2019

High Frequency

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In this research, we develop a set of new measures to evaluate the data flow in the U.S. equity exchanges using Level I order book data. The quantities we develop and use to summarize trading activity are as follows: the activity‐weighted spread and the activity‐weighted return. We study the distribution of these two quantities and observe that there are significant changes in their behavior when equity markets are impacted by an external event. We focus the study on three exchanges: New York Stock Exchange (NYS), NASDAQ InterMarket (THM), and NYSE Arca (PSE) since they exhibit the largest impact to the proposed measures. We also study two different financial events which happened suddenly without any prior warning. These events are as follows: May 6, 2010, the “Flash Crash,” and April 23, 2013, the “Hoax tweet” event. Based on the results we obtain, the order flow dynamic is disturbed during these rare events. We quantify this change by measuring the number of detected “anomalies” for each exchange and equity studied. We believe the methodology we propose may be capable of detecting a potential unforecasted event as it is impacting the equity markets.

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Behavior Based Learning in Identifying High Frequency Trading Strategies

Cited by 14 publications

References 26 publications

Detecting price manipulation in the financial market

Detecting price manipulation in the financial market

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

Extracting information from the limit order book: New measures to evaluate equity data flow

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