CUATS

Cambridge University
Algorithmic Trading Society

 

 

Events

Dr. Oliver Linton is Fellow of Trinity College and has been Professor of Political Economy at the University of Cambridge since 2011.  Prior to this, he was a Professor of Econometrics at the London School of Economics from 1999 to 2011 as well as a Professor in the Department of Economics and the Department of Statistics at Yale University, International Center for Finance from 1998 to 2000.  He holds a Ph.D. in Economics from UC Berkeley, and his research interests are in nonparametric and semiparametric methods, methods for estimating additive nonparametric regression, financial econometrics, and discrete time volatility models like GARCH (generalised autoregressive conditional heteroskedasticity).

 

He will be speaking with us on a topic that many of you will have heard of - high frequency trading - and specifically examining the evidence for the pros and cons of HFT and its effects on liquidity, transaction costs, volatility and finally some policy response recommendations to ensure well-functioninig security markets

 

The title of the talk is  "The Effect of High Frequency Trading on Security Markets and Some Policy Responses".

 

The event is scheduled for 25 November, 2020 at 17:00 PM GMT.

 

https://twitter.com/cuats_algos/status/1330986299986219012

About Us

The Cambridge University Algorithmic Trading Society (CUATS) is the first student society in Cambridge to promote the understanding of algorithms and their applications in financial trading. CUATS aims to provide interdisciplinary education on algorithm development and the basics of financial investment strategies.

Members

Yearly Events

Years Active

Hours of

Coding Sessions

What We Do

Speaker Series

 

We host outstanding, accomplished individuals across academia and industry in a series of talks and seminars held throughout the academic year. Academics discuss their research in quant finance, and industry experts share their experiences in real-life automated trading.

 

Coding Sessions

 

Our fortnightly Sunday coding sessions introduce mathematical concepts behind algorithmic trading, where you learn basic trading strategies and how to implement them in code.

These are hands-on, in-person sessions so a laptop is required.

Industry-Sponsored Workshops 

 

Our workshop events with our sponsoring companies and top industry employers enable members to participate in trading simluations, brain teasers, market making games, case studies, and to meet quant traders and recruitment leads.

Fireside Chat Series

 

To complement our Speaker Series, in November, 2021 we initiated the Fireside Chat Series to highlight the experiences of current students and recent graduates who have completed internships or are working in the quant finance industry. We organise a roundtable during Michaelmas term to coincide with the recruitment season.

 

Conference

 

CUATS will host its thid annual quant conference on 1 March 2024 bringing together leading experts in the quant fields of alpha generation, portfolio optimisation, execution, and risk management.  Full bio details of the speakers and the programme and abstracts of talks may be viewed at https://cuats.co.uk/conference

Details of the inaugural and last year's conferences may be viewed at

https://cuats.co.uk/conference2023 

https://cuats.co.uk/conference2022 

Gala

 

The third annual CUATS Gala will be on 14 June 2024.  This flagship event of the society, held in the Cambridge Union, is a formal event where members of the society are paired with industry sponsors for dinner, providing an exceptional opportunity for networking and career advancement.  Save the date.

The CUATS Gala 2023 may be viewed at https://cuats.co.uk/gala2023 and the CUATS Gala 2022 may be viewed at https://cuats.co.uk/gala2022

Challenge - Coding Competition

 

This event was desgined to showcase Cambridge University talent in developing the best trading algorithm!  Our second annual coding competition was launched on 5 November, 2023 and concluded on 5 January, 2024 with team presentations on 18 February, 2024.  https://cuats.co.uk/challenge 

Awards includ Amazon gift vouchers!

Insight Days

 

An initiative launched in November, 2022 to arrange in-person site visits to some of the leading quant firms in London and other cities, providing a real-life glimpse into the life of a quant trader, quant researcher, and quant developer and strengthening the society's connections to its sponsoring companies.   

Fed Day Social

 

Launched in February, 2023 this is an initiative to live trade around the eight FOMC interest rate announcements that occur per year, explain the rationale behind the executed trades, and further develop our abilities to codify opportunitistic trading strategies. These mini-competitions contrast with the annual CUATS Challenge - Coding Competition which is longer in duration and focuses on lower frequency algorithmic trading strategies.

Brain Teasers

 

Launched in October 2023, this initiative aims to hold regular practice sessions on a fortnightly basis to review and share different approaches to solving quant firm interview questions with a focus on probability and statistics questions.   

Future As Yet Undefined Activity

 

See a gap in our current events? Have an idea for a quant finance related activity you want to actualise?  We're always interested in constructive feedback and suggestions to develop new initiatives for the society to better serve its members.   

Committee

The Cambridge University Algorithmic Trading Society (CUATS) 2023/2024 Committee members are:

Contact Us

 

If you have any questions or suggestions, feel free to reach out to us

Address:

Cambridge University Algorithmic Trading Society

University Centre, Granta Place

Cambridge  CB2 1RU

United Kingdom

 

E-mail:

president@cuats.co.uk

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Contact Us

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If you have any questions or suggestions, feel free to reach out to us!

 

E-Mail: ____@cuats.co.uk

 

Post Box

Cambridge University Algorithmic Trading Society

SU Building, 17 Mill Lane

Cambridge, CB2 1RX

 

 

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Meet the Committee

Farouk Hadeed

President

Farouk is the president of the society

Christos Antonopoulos

Vice President

Christos is a second year enginneering student in Homerton College

Alex ...

Description

Description

Name..

Role

Description

About us 

 

The Cambridge University Algorithmic Trading Society (CUATS) is the first student society in Cambridge to promote the understanding of algorithms and their applications in financial trading. CUATS aims to provide interdisciplinary education of algorithm development and the basics of financial investment strategies. In the forms of workshops, networking sessions and tutorials (code writing of your own investment strategies), we wish to provide our members the necessary skills and the many opportunities to pursue a career in the financial industry as well as in the big data industry.

Vision

 

The Cambridge University Algorithmic Trading Society  is the first student society in Cambridge to promote the understanding of algorithms and their applications in financial trading. CUATS aims to provide interdisciplinary education of algorithm development and the basics of financial investment strategies. In the forms of workshops, networking sessions and tutorials (code writing of your own investment strategies), we wish to provide our members the necessary skills and the many opportunities to pursue a career in the financial industry as well as in the big data industry.

Mission

 

The Cambridge University Algorithmic Trading Society (CUATS) is the first student society in Cambridge to promote the understanding of algorithms and their applications in financial trading. CUATS aims to provide interdisciplinary education of algorithm development and the basics of financial investment strategies. In the forms of workshops, networking sessions and tutorials (code writing of your own investment strategies), we wish to provide our members the necessary skills and the many opportunities to pursue a career in the financial industry as well as in the big data industry.