Python for Financial Traders training course

Use Python and its statistical computing libraries to analyse and visualise your financial data and to gather some actionable insights

Next 1 February (Remote)
3 days £2,000.00 + VAT

JBI training course London UK

  • Explore the basics of the Python programming language
  • Earn the confidence to consume market data and extract features
  • Conduct real-world financial analysis in Python 
  • Explore many of the key libraries and tools commonly used in the financial services industry
  • Acquire knowledge on how to perform transformations on data
  • Understand how to work with financial time series data
  • Apply Python to build pricing and simple predictive models
  • Visualise your data with professional looking charts and graphs
  • Perform technical analysis and back-testing of models
  • Use Python to consume financial data from spreadsheets
  • Perform technical analysis to choose symbols between data ranges  
  • Use Python to build a trading strategy and to execute back-testing


Our three-day course is an introduction to data science and the Python programming language, exploring many of the key libraries and tools commonly used in the financial services industry.

Aimed at finance professionals, the course builds real solutions for data analysis using Pandas, visualisation using Matplotlib and Plot.ly, and n-dimensional matrix manipulation and linear algebra using Numpy.

The course is run by instructors with experience in applying Python in finance, therefore adding value with highly relevant Labs and models.

JBI training course London UK
JBI training course London UK

This course is of benefit to Financial Traders, business analysts, quantitative analysts, sales and marketing, financial software developers and any professional seeking to perform data analysis.

Prior knowledge of Python is not required.




Related Courses

Installation & Packaging

  • The Anaconda distribution as Python Data Science platform
  • Overview on Python virtual environment set-up
  • Running Python code in Jupyter notebook
  • Installation, packaging and virtualisation of Python using Conda.
  • We'll set up Python using the Anaconda distribution, a free and enterprise-ready Python distribution that includes hundreds of the most popular Python packages for science, math, engineering and data analysis.
  • Anaconda comes with Conda, a cross-platform tool for managing packages and virtual environments. We'll also set up Jupyter, a web-based interactive environment where users can organise, write and run their Python code in notebooks.

Environment Set-up

• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• Running Python code in Jupyter notebook

Python core concepts

• Built-in data types in Python
• Working with strings, numbers, lists, tuples and dictionaries
• Control flow statements
• Conditional execution with if statements
• Conditional loops with where statements
• Looping over a sequence with for statements
• Defining and using custom functions
• Working with dates and times
• Accessing data on file (CSV, JSON, ...)

Python Data Science libraries

• Working with data in pandas

  • Working with table-like data in pandas
  • Creating Series and DataFrame objects
  • Loading data from file into DataFrame objects
  • Adding, removing and updating databases

• Retrieving data in pandas

  • Querying data to extract specific rows and columns
  • Filtering data on specific conditions
  • Understanding pandas indexing

• Data manipulation in pandas

  • Data transformation
  • Applying functions to transform individual values
  • Applying functions to aggregate values by rows and columns

• Handling missing data in pandas

  • Identifying missing data points
  • Filtering out missing data
  • Filling missing data with given values, interpolation and other filling strategies

• Data Analysis in pandas

  • Extracting summary statistics over DataFrame objects
  • Performing data aggregation queries (groupby() method)
  • Aggregating multiple columns in the same query
  • Exploratory analysis of new datasets

• Data Visualisation in pandas

  • Plotting data from a Series or DataFrame object
  • Bar plots, line plots, scatter plots, histograms and other common charts
  • Basic customisation of charts

• Working with multiple tables

  • Concatenation of multiple tables based on structure/schema
  • Join/merge operations with DataFrame objects based on values
  • Reindexing operations, dealing with duplicate labels in the index
  • Dealing with duplicate records
  • Renaming columns

• Time Series with pandas

  • ◦ Working with date and time data types in pandas
  • Creating ranges of date/time data points
  • Indexing by time
  • Resampling: data aggregation over time
  • Moving window operations, e.g. moving average
  • Defining custom calendars, custom business days, custom holidays

• Working with text data in DataFrames

  • Using the str attribute in pandas objects
  • String manipulation functions in pandas
  • Filtering data with string pattern matching

• SQL databases

  • Connecting to SQL databases with SQLAlchemy
  • Loading data from SQL to pandas
  • Sending SQL queries to a database and retrieving the results in Python and pandas

• NumPy

  • Working with multi-dimensional arrays with NumPy
  • Arithmetic operations with NumPy arrays
  • Vectorised operations with NumPy arrays
  • Stats and linear algebra with NumPy
  • Slicing and indexing NumPy arrays

• Data Visualisation with matplotlib and plotly

  • Overview on the basic types of charts available with the Python libraries
  • Bar plots, line plots, histograms, scatter plots, pie charts
  • Customising the layout and format of a chart
  • Examples of static visualisation with matplotlib
  • Examples of interactive visualisation with plotly
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