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:

  • Why 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: retrieving real-time data from a REST 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 visualizing data easily:

  • Reading and writing essential data formats: CSV, Excel, SQL, time-series (others on request)
  • Indexing and selecting data in Pandas
  • Data fusion: joining & merging datasets
  • Summarization with “group by” operations; pivot tables
  • Visualization and statistical graphics with Seaborn
  • Automated reporting; interactive dashboards ipywidgets and voilà

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 as well as clustering and outlier detection:

  • Time-series analysis: parsing dates, resampling, handling time-zones, rolling-window operations
  • Introduction to NumPy for manipulating vector and matrix data: data types, powerful indexing, reshaping, ufuncs
  • Linear regression with statsmodels
  • Statistics; Monte Carlo simulation; density estimation
  • Clustering with scikit-learn
  • Outlier and anomaly detection with pyod

Day 4: Machine Learning

Day 4 gives you a practical and comprehensive introduction to machine learning for powerfully inferring complex models from data with examples selected from a range of industries, including time-series and spatial datasets:

  • Concepts of machine learning (ML)
  • Overview of the ML package ecosystem in Python
  • Nonlinear regression, with application to forecasting
  • Classification with scikit-learn: application to diagnosis, Al systems, and time-series prediction
  • Validation and model selection; diagnostic tools: yellowbrick
  • Feature engineering and selection; eli5
  • Overview of "classical" ML algorithms: Naive Bayes, logistic regression, SVMs, random forests
  • Deploying machine learning models in production

Day 5: Financial Time-Series in-depth

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

  • Rehaping of dataframes with stack/unstack/melt; hierarchical indices
  • Categorical, string, and datetime columns; binning continuous data
  • Joining of time series; searchsorted
  • Styling dataframes for reporting; interactive use via qgrid
  • Data cleaning; interpolation; imputing missing data
  • Common time-series models in Python: AR, ARIMA
  • Simulation examples: Monte Carlo risk analysis; Black-Scholes pricing
  • (Optional): 2-way integration of Python with Excel using xlwings
  • Visualizing time-series data interactively with Altair

Personal help

We are happy to offer on-the-spot problem-solving after each day of the training for you to ask one-on-one questions — whether about the course content and exercises or about specific problems you face in your work and how to solve them. If you would like us to prepare for this in advance, you are welcome to send us background info before the course.

Other information


Courses are conducted online via video meeting using Python Charmers' cloud notebook server for sharing code with the trainer(s).


Hardware: we recommend ≥ 8 GB of RAM and a webcam. Preferably also multiple screens and a quiet room (or headset mic).

Software: a modern browser: Chrome, Firefox, or Safari (not IE or Edge); and Zoom.

Coding: we have a cloud-based coding server that supports running code and sharing code with the trainer(s).


Most courses will run from 9:00 to roughly 17:00 (AEDT) each day, with breaks of 50 minutes for lunch and 20 minutes each for morning and afternoon tea.

Certificate of completion:

We will provide you a certificate if you complete the course and successfully answer the majority of the exercise questions.


You will have access to all the course materials via the cloud server.

We will also send you a bound copy of the course notes, cheat sheets, and a USB stick containing the materials, exercise solutions, and further resources.


“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