using intelligent systems and structural analysis to assure orderly operations of the modern trading...

Using intelligent systems and structural analysis to assure orderly operations of the modern trading and exchange platformsAnna Gromova, Olga Moskaleva, ExactproJuly 2016

Open Access Quality Assurance & Related Software Development for Financial Markets Tel: +7 495 640 2460, +1 415 830 38 49www.exactpro.com

http://exactpro.com/

Open Access Quality Assurance & Related Software Development for Financial Markets Tel: +7 495 640 24 60 , +1 415 830 38 49 www.exactpro.com 2

London Stock Exchange Group

• A leading diversified international exchange and infrastructure group with assets across the entire exchange value chain • The Group can trace its history back to 1698• Headquartered in the United Kingdom, with significant global operations, the Group employs approximately 3,500

people• Post trade and risk management services are a significant part of the Group’s business operations• Through FTSE Russell, the Group is a global leader in financial indexing, benchmarking and analytic services with

approximately $10 trillion benchmarked to its indexes

http://www.exactpro.com/


Software Quality Assurance experts for global financial markets enjoying steady growth thanks to expertise and unparalleled service

Exactpro is: Clients Worldwide:

• A specialist firm operating an open access model with focus on functional and operational testing of securities data distribution, trading systems, risk management, market surveillance and post-trade infrastructures.

• A UK company with operations in the US and four QA & software development centres in Russia

• Part of London Stock Exchange Group as of May 29, 2015

• Incorporated in 2009 with 10 people, our company has experienced significant growth as satisfied clients require

more services; now employing over 370 specialists



• Fraud detection - prevent market manipulation, money laundering and other illegal activities.

• Technical stability and search for possible software defects.

Two dimensions of our work



Detection of abusive behavior via monitoring and analysis of all market events

Surveillance systems



Main goals of surveillance systems:

• detection and prevention from market manipulations:

- insider trading;

- wash sales;

- layering the book;

- marking the close.




Defect management

Technical stability



Areas of research in defect management:

• automatic defect fixing

• automatic defect detection

• metrics and predictions of defect reports

• quality of defect reports

• triaging defect reports

Defect Management



Modern tools for surveillance can:

• Process a high frequency data flow.

• Monitor, store and retrieve:- trade data;- order data;- market data.

• Provide cross-market analysis.

• Allow market replay.




● Fast growing markets● Developing new tools and features● Increasingly complex regulatory requirements

Accumulating the huge volume of dataSurveillance systems need to be more adaptive




Methods of artificial intelligence in order to predict price movement:

• deep neural networks for financial market prediction;

• echo state networks in order to predict stock prices;

• self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting.




Examples of metrics:

• time to fix

• which defects get reopened

• which defects get rejected

• which defects get fixed

Defect Management



Defect clustering

• Understand weaknesses of software

• Improve testing strategy

Defect Management



Attributes for cluster analysis:

• Priority

• status

• resolution

• time to resolve

• count of comments

• area of testing

Defect Management



Defect Classification

Defect Management



Reasons of defect reopening:

• Bug report has insufficient information

• Developers misunderstand the root causes of defect

• Ambiguous requirements in specifications

Defect Management



Using metric allows:

• Define weaknesses in testing

• Characterize actual quality of the bug fixing process

• Define weaknesses in documentation

Defect Management



Conclusions:1. Intelligent procedures of the complex analysis need to be implemented in

the surveillance systems2. Clustering as way to understand and evaluate full array of bugs.

Linguistic information of bug-reports is important for analysis.

Future work:1. Predict manipulations by methods of machine learning2. Clustering of bug-reports and prediction of metrics “which defects get

reopened”.

Conclusions and future work



Thank you!


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