Python applications in financial services
View AgendaKey reasons to attend
- Learn to use Python effectively in a variety of financial risk settings
- Explore practical use cases including data analysis and library creation
- Apply theory into practice with structured tutorials
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About the course
This expert-led learning event will equip participants with the practical skills and knowledge to effectively use Python in a variety of financial risk settings.
Participants will explore the many features of Python and practical use cases within financial markets, including data analysis and library creation. With a hands-on Python tutorial at the end of each day, this dynamic course will provide participants with the opportunity to apply the theory shared in the main sessions, as well as engage in live interaction and questions with the course expert.
By the end of the course, participants will have learned the necessary tools to facilitate successful programming, data considerations and backtesting in line with best market practice in the financial industry.
Pricing options:
- Early-bird rate: save up to $800 per person by booking in advance (refer to the booking section for the deadline)
- 3-for-2 rate: save over $2,000 by booking a group of three attendees (applicable to this course)
- Subscriber reward: save 30% off the standard rate if you are a Risk.net subscriber (use code SUB30)
- Season tickets: save over $1,000 per person by booking 10 or more tickets (available on selection of courses)
*The 30% subscriber reward discount is applicable only to current Risk.net subscribers. If this criteria is not met, we reserve the right to cancel the booking and issue an invoice for the correct rate. Discounts cannot be applied to already registered participants.
Learning objectives
- Master the basics and fundamentals of Python
- Visualise data libraries using different programmes
- Identify the types of financial data used for analysis
- Discuss the key considerations for creating a trading strategy
- Recognise the behaviour of foreign exchange around major data events
- Develop skills of Python use through tutorials and case studies
Who should attend
Relevant departments may include but are not limited to:
- Financial data
- Python
- Data analysis
- Machine learning
Agenda
December 3–6, 2024
Live online. Timezones: Emea/Americas
Sessions:
- Introduction to Python I
- Tutorial for introduction to Python I
- Data analysis in Python
- Tutorial for data analysis in Python
- Analysis of financial data
- Tutorial for analysis of financial data
- Financial market case studies using Python
- Tutorial for financial market case studies using Python
Tutors
Saeed Amen
Co-founder
Turnleaf Analytics
Saeed is a co-founder of Turnleaf Analytics, where they use innovative approaches including alternative data and machine learning to create macroeconomic forecasts, in particular for inflation which are available for client subscription, and have beaten the benchmark in over 60% of cases since live publication.
He has developed algorithmic trading strategies at Lehman Brothers (where he co-developed MarQCuS which had $2bn AUM), Nomura and independently. He has profitably run systematic trading models for market making trading desks. Clients have included Bloomberg, Accenture, CLS, Freepoint Commodities, RavenPack, Cytora, Investopedia, a Chicago prop firm, a large European asset manager and several UK quant hedge funds.
Saeed is an author and has written Trading Thalesians - What the ancient world can teach about trading today (Palgrave Macmillan) and has co-authored The Book of Alternative Data (Wiley). He is also the founder of Cuemacro, co-founder of Thalesians and a visiting lecturer at Queen Mary University of London.
Accreditation
This course is CPD (Continued Professional Development) accredited. One credit is awarded for every hour of learning at the event.
Pre-reading materials
The Risk.net resources below have been selected to enhance your learning experience:
- Adjoint differentiation for generic matrix functions - Read article | Risk.net
- Banks adopt Python for faster XVA data analysis and pricing - Read article | Risk.net
- Machine learning for categorization of operational risk events using textual description - Read article | Risk.net
To access some of the above articles you need to have a current subscription to Risk.net. If you don’t have one now, please subscribe to a free trial
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