Python for Financial Markets

This event will teach participants how to code in Python and demonstrate data analysis using libraries such as Pandas, NumPy and Scikit-learn.

Many market participants now want to use Python to move beyond Excel for the analysis of financial data. 

This online course will show attendees how to code in Python, making them familiar with basic concepts such as data structures and the Python standard library. 

Further sessions will discuss how to conduct data analysis using libraries such as Pandas, for dealing with time series, as well as Scikit-learn for machine learning. Attendees will be shown how to download market data from sources including Bloomberg and Quandl. 

The course will provide many use cases, including how to backtest trading strategies in Python, how to create web dashboards for financial analysis and also creating Excel add-ins using Python.

The course will take place over four days with technical content compressed into fast-paced 90-minute interactive sessions. Previous coding knowledge is useful but not a necessity. All participants should install Anaconda Python and the conda environment specified before the course starts (instructions included). 

What will you learn?
  • The basic concepts in Python and the standard Python library 

  • Using data analysis libraries like Pandas and NumPy, as well as visualisation libraries and an introduction to machine learning with Scikit-learn 

  • How to access market data in Python from many sources including Bloomberg and Quandl 

  • How to come up with basic trading strategies and backtest them in Python 

  • How to analyse high frequency price data around economic data events 

  • Creating Python based web dashboards to display market analytics 

  • Making Excel add-ins using Python 

Who should attend?
  • Trading desk
  • Risk management
  • Portfolio management
  • Middle office
  • Data scientists
Sessions include
  • Introduction to Python I
  • Introduction to Python II
  • Data analysis in Python I
  • Data analysis in Python II
  • Analysis of financial data I
  • Analysis of financial data II
  • Financial market case studies using Python I
  • Financial market case studies using Python II
Pricing options

We offer flexible pricing options for this course:

  • Early bird rates - save up to $500

  • Group booking rate - save over $1500

  • Subscribe to receive Risk Training updates and avoid missing out on additional savings

Saeed Amen

Founder, cuemacro and visiting lecturer

Queen Mary University of London

Saeed Amen is the founder of Cuemacro. Over the past fifteen years, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan) and is the co-author of The Book of Alternative Data (Wiley), due in 2020.

Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading. He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis. His clients have included major quant funds and data companies such as Bloomberg. He has presented his work at many conferences and institutions which include the ECB, IMF, Bank of England and Federal Reserve Board. He is also a co-founder of the Thalesians.

Live Virtual training courses


Our live virtual training courses have been designed to engage and inspire you. Much more than a webinar, our approach includes:

  • Technical content compressed into 60-minute interactive sessions and spread out over two, three or four days

  • Facilitated collaboration including Q&A, interactive polling and group workshops

  • Live interaction with subject matter experts – get your questions answered in real time

  • Receive comprehensive course materials and supporting content from to reinforce your learning

  • Stay connected with other learners and extend your network by joining our dedicated LinkedIn group for course participants

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