Python for Finance

Python is a general programming language which can be used to build web applications, websites or even complex applications. The most important part of Python is its syntax which is considered to be close to the original mathematics syntax. This makes it quite flexible in playing around with numbers, making it a very useful tool for data analysis, risk management, automatic trading and other financial applications.

During the course, the delegates will focus on practical applications of the programme in the area of finance and risk through workshops and working examples.

No programming experience is required.

This course is primarily aimed at those working in financial institutions, regulatory bodies, advisory firms and technology vendors. Specific job titles may include but are not limited to:

• Trading
• Portfolio management
• Asset allocation
• Data science
• Financial engineering
• Quantitative analytics
• Quantitative modelling
• Infrastructure and technology

• Learn the capabilities of Python regarding financial applications
• Become familiar with the programming language and the system of modules and tools
• Understand the various data structures in Python
• Learn about Jupyter Notebooks
• Get to grips with the various applications of Python – graphs, automated reports, financial data
• Discover how to create & edit spreadsheets with Python
• Become familiar with environment management in Python
• Be introduced to the machine learning library (scikit-learn) of Python

• Basic notions of statistics
• Good working knowledge of Excel
• Elementary knowledge of a programming language (Matlab, VBA,…) helps but no knowledge of Python is required

Introduction to Python

The core of the language and the large eco-system of modules and toolboxes.

Python DNA

• UFuncs (universal function)
• List comprehension
• Broadcasting
• Data structures in Python for the data scientist
  • Pandas
  • Numpy
  • Dictionaries
  • List
  • Others

Jupyter Notebooks

• Introduction: all the development will be done in Jupyter notebooks. A notebook is a browser based tool to develop & debug Python programs. Jupyter is an interactive web tool known as a computational notebook, which researchers can use to combine software code, computational output, explanatory text and multimedia resources in a single document. Computational notebooks have been around for decades, but Jupyter in particular has exploded in popularity over the past couple of years. This rapid uptake has been aided by an enthusiastic community of Python.  

• Papermill: we will illustrate how to use Jupyter notebooks into an environment such as a trading room where several programs are to be scheduled and run every single day.

Applications

• Graphs: Matplotlib is the basic library to construct graphs in Python. The delegates will get familiar with the different graphic tools and will see how extra modules such as Plotly and Seaborn turn Python graphs into very powerful instructive tools.

Workshop: Creating a linear regression in python and plotting the output

• Automated Research Reports: building further on the pandas toolbox, we will illustrate using a practical example how profit / loss attribution for a portfolio becomes very straightforward. Python virtually can put an end to the long and tedious pivot tables one is running in Excel.

Workshop: Creating a ytd p&l and risk attribution report for a fund starting from large data-dump

• Downloading Financial Data: every data vendor (Reuters, BBG, FactSet, etc.) is offering to its client base an API to retrieve data. In this course the Quandl will be used for data retrieval.

Workshop: Retrieving data from Quandl and applying some time-series analysis on the dataset

Excel and Python

How to construct impressive spreadsheets from scratch with Python: formatting, styles, embedded graphs, multiple sheet handling, etc.

Workshop: Creating on-the-fly spreadsheet containing a risk report

Working as a Team

How to deal in an efficient way with the continuous inflow of new modules into the Python programming language.

Workshop: Creating 2 different environments on a single computer

Object Oriented Programming with Python in Practice

An introduction to the machine learning library of Python (SKLearn) with practical examples.

• Least squares
• Principal components analysis
• Gaussian mixtures
• K-nearest neighbours