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Grant: Collaborative Research and Development Grants

Ryerson University

$100,000.00

Analytics for intraday trading data

EF 2017-18

Résumé
The Toronto Stock Exchange has chosen to partner with the Data Science Laboratory at Ryerson University and to provide access to most of its proprietary intraday trading data. This raw data contains information about trades, quotes and brokers occurring on a nanosecond time scale. Today, there is a real demand from customers not just to buy the raw data, but to buy analytics products that provide insights into the raw data. This demand for analytics comes from investing institutions, custodian banks, broker-dealers and other market participants that are building their analytics teams. The most sought after analytics product by TSE customers is analytic insights into trading strategies. Stock markets around the worlds such as the New York Stock Exchange (NYSE) are responding to this demand by developing analytics products. Our industrial partner - the Toronto Stock Exchange - is the ninth largest exchange in the world by market capitalization. In order to keep up with the pace of innovation, the Toronto Stock Exchange needs to develop analytics capabilities and analytics products. To respond to this demand, this proposal is a collaborative effort to convert raw intraday trading data into analytics insights about trading strategies. The Data Science Laboratory will apply novel algorithms such as evolutionary clustering to provide analytics insights into trading strategies. The end product will be a proof of concept showing the conversion of raw intraday trading data from the Toronto Stock Exchange into analytics insights of trading strategies using intelligent algorithms. Ultimately, the Toronto Stock Exchange will be in a position to provide analytics products to market participants and market regulators.
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
499983-2016
Name nom
Bener, Ayse
Department département
Mechanical and Industrial Engineering
Organization id
43
Institution établissement
Ryerson University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2016
Award amount
100000
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2700
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information technology
Research subject fr
Technologie de l'information
Partie
2
Nb partie
2
Application title
Analytics for intraday trading data
Keyword
NSERC
Application summary
The Toronto Stock Exchange has chosen to partner with the Data Science Laboratory at Ryerson University and to provide access to most of its proprietary intraday trading data. This raw data contains information about trades, quotes and brokers occurring on a nanosecond time scale. Today, there is a real demand from customers not just to buy the raw data, but to buy analytics products that provide insights into the raw data. This demand for analytics comes from investing institutions, custodian banks, broker-dealers and other market participants that are building their analytics teams. The most sought after analytics product by TSE customers is analytic insights into trading strategies. Stock markets around the worlds such as the New York Stock Exchange (NYSE) are responding to this demand by developing analytics products. Our industrial partner - the Toronto Stock Exchange - is the ninth largest exchange in the world by market capitalization. In order to keep up with the pace of innovation, the Toronto Stock Exchange needs to develop analytics capabilities and analytics products. To respond to this demand, this proposal is a collaborative effort to convert raw intraday trading data into analytics insights about trading strategies. The Data Science Laboratory will apply novel algorithms such as evolutionary clustering to provide analytics insights into trading strategies. The end product will be a proof of concept showing the conversion of raw intraday trading data from the Toronto Stock Exchange into analytics insights of trading strategies using intelligent algorithms. Ultimately, the Toronto Stock Exchange will be in a position to provide analytics products to market participants and market regulators.