Python Training

Python for Finance

This is a course for financial analysts, traders, risk analysts, fund managers, researchers, data scientists, statisticians, and software developers.


Some familiarity with programming concepts (in any language) is assumed.

Expected Outcomes

By the end of the course, you will have all the knowledge you need to start using Python competently for processing, analysing, modelling, and visualising financial data, with a focus on time series. You will have had experience with using Python for various scripting, data-manipulation and visualisation tasks with data in a variety of formats, including SQL databases, CSV, Excel spreadsheets, JSON, and API endpoints. You will know how to slice, dice, merge, aggregate, pivot, clean, munge, resample, and plot financial time-series data with ease. You will understand the elegance and power of the Python language and its powerful ecosystem of packages for finance and data analytics, and you will be well-placed to continue learning more as you use it day-to-day.

Course Syllabus

Day 1: Python Basics

Day 1 covers how to use Python for basic scripting and automation tasks, including tips and tricks for making this easy. The syllabus is as follows:

  • Why use Python? What’s possible?
  • The Jupyter notebook for rapid prototyping
  • Modules and packages
  • Python concepts: an introduction through examples
  • Essential data types: strings, tuples, lists, dicts
  • Worked example: fetchind and ranking real-time data from a web API
  • Raising and handling exceptions

Day 2: Handling, analyzing and presenting data in Python

Python offers amazingly productive tools like Pandas for working with different kinds of data. Day 2 gives a thorough introduction to analyzing and vis- ualizing data easily. You will learn how to slice, dice, merge, aggregate, pivot, clean, munge, resample, and plot data with ease:

  • Reading and writing essential financial data formats: CSV, Excel, SQL, JSON, time-series (others on request)
  • Indexing and selecting data in Pandas
  • Data fusion: joining & merging datasets
  • Summarizing data with "group by" operations; pivot tables
  • Visualization and statistical graphics with Seaborn
  • Creating automated reports from Jupyter notebooks

Day 3: Time-series, simulation, inference and modelling

Day 3 demonstrates more advanced features of Pandas for working with data, including time-series data. It then describes Monte Carlo simulation methods and walks you through using powerful methods of inference and modelling for different kinds of data in Python:

  • Time-series analysis: parsing dates, resampling
  • Introduction to NumPy for manipulating vector and matrix data: data types, powerful indexing, reshaping, ufuncs
  • Classical statistics: Monte Carlo simulation and linear regression with scipy.stats and statsmodels
  • Density estimation with scikit-learn
  • Dimensionality reduction with PCA

Day 4: Machine learning

Day 4 introduces a more automated approach to modelling real-world data with several powerful machine learning algorithms using scikit-learn. The da- tasets are selected from a range of industries: financial, geospatial, medical, and social sciences:

  • Concepts of machine learning (ML)
  • Overview of the ML package ecosystem in Python
  • Classification with scikit-learn: Naive Bayes, logistic regression, SVMs, random forests Application to diagnosis, AI systems, and time-series prediction
  • Nonlinear regression, with application to forecasting
  • Clustering data with DBScan, with application to outlier detection
  • Validation and model selection; feature selection
  • Deploying machine learning models in production

Day 5: Handling financial time-series in depth

Day 5 teaches you in-depth about munging, modelling, and visualization of financial time-series data in Python:

  • Secret weapons in Pandas: handling time-zones, searchsorted, hierarchical indices, unstack, styles
  • Common time-series models in Python: AR, ARIMA
  • Simulation examples: Monte Carlo risk analysis; Black-Scholes pricing
  • Handling missing data and outliers; outlier detection with clustering
  • Visualizing time-series data interactively with Altair
  • 2-way integration of Python with Excel using xlwings
  • Examples: running Monte Carlo risk simulations with Python and Excel
We also encourage you to bring your own data sets to the course where relevant.

Supplemental materials

We will supply you with printed course notes, cheat sheets, and a USB stick containing kitchen-sink Python installers for multiple platforms, solutions to the programming exercises, several written tutorials, and reference documentation on Python and the third-party packages covered in the course.

Other information

Personal help:
Your trainer(s) will be available after the course each day for you to ask any one-on-one questions you like — whether about the course material and exercises or about specific problems you face in your work and how to use Python to solve them.
Food and drink:
We will provide lunch, morning and afternoon tea, and drinks.
The course will run from 9:00 to roughly 17:00 each day, with breaks of 45 minutes for lunch and 15 minutes each for morning and afternoon tea.

Upcoming Courses


Python for Finance:
23 Mar – 27 Mar 2020

Level 1, 440 Collins Street, Melbourne CBD

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Python for Finance:
20 Apr – 24 Apr 2020

Level 13, 60 Margaret Street, Sydney CBD

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Python for Finance:
01 Jun – 05 Jun 2020

Level 1, 440 Collins Street, Melbourne CBD

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Other Locations

Python for Finance:
Dates TBA

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“The course was delivered by trainers who were extremely knowledgeable in their field. It was really good to learn from the best!”

- Marius Roman

“Great course. Enjoyed it and will follow up with some practical implementation of some of the work.”

- Adam Grace

“One of the best training courses I've been on.”

- David Scurrah

“I loved it. Ed was inspiring.”

- Onoriode Coast

“Really impressed by Python's capability and excited to use as alternative to MatLab, as is free and better supported.”

- Carsten Hofmann

“Very comprehensive intro to every aspect of python. Highly qualified trainer. Beyond my expectation on every aspect.”

- Baichuan Sun

“The VM setup and USB is great. Ed is an excellent instructor - he presents well and welcomes any questions. He is clearly a super smart guy who has a great grasp on what he is teaching - able to just prototype on the fly and the course overall really opened my eyes to python.”

- Jack Hendy

“Course content was well presented and easily digested. Practical exercises were an essential part of the course – a good ratio of lecture/play was achieved. Well done Ed and Henry!”

- Steve Zegelin

“Simply awesome!!”

- James Park

“Both Ed and Henry presented well…. The course structure was adjusted to suit the participants quickly and easily.”

- Jenet Austin

“Excellent training course, excellently presented. Perhaps the best that I have had in the area of IT / programming.”

- George Grozev

“One of the best programming courses I have attended - thanks!”

- Giant Billen

“This course has shown me how I could have done the work I was doing just last week 10x more efficiently in Python.”

- Maruf Rahman

“It was a pleasure ... Shared feedback from all involved was that it’s been one of the most beneficial and well delivered training courses we’ve been a part of.”

- Dylan Matthews

“Was the most fulfilling and rewarding class I have taken since "general relativity" at uni. Was extremely well run. Excellent all round!”

- Dr Millicent Maier

“Very impressed with the course, delivery. And depth of knowledge of Ed and Henry. Far exceeded my expectations and has greatly improved my core skills as well as inspired so many new ideas for my current work / projects. Thank you!”

- Kelsey Druken