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.


Python for Financial Markets 

16-19 November, Online

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

This practical two-day 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.

There will also be tutorials for attendees to code themselves and it is recommended that participants bring laptops with Anaconda Python already installed on them.

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 visualization 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 analyze 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

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.

16 November 2020
2020-11-16 14:00:00 +0000