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Curriculum / Phase 1 · Common Quantitative Core

Python for Quantitative Finance

Develop scientific Python and financial-data skills for building, testing and delivering quantitative models.

Why study Python for Quantitative Finance?

A notebook that runs once is a starting point. Quant work also needs reliable data alignment, tested calculations and outputs another person can reproduce. This programme develops Python around those demands.

Who this is for

For learners comfortable with functions and basic tables who want to build maintainable numerical research.

What you should be able to do

  • Work with arrays, tables and time-indexed financial data.
  • Design and test reproducible numerical models.
  • Connect data ingestion, persistent storage and model delivery.

An example of the work this prepares you for

A documented Python analysis with checked inputs, reusable model functions, numerical tests and a reproducible run.

Prerequisites and suggested preparation

You should be comfortable with the background covered in these programmes. Equivalent knowledge from university study or independent learning is also suitable.

A preview of the learning journey

The lessons build towards these kinds of reasoning and practical work.

Prepare financial data

Make dates, missing observations and array alignment explicit.

Build reusable models

Separate a numerical calculation from its data and presentation.

Deliver reliable work

Test, profile and document a model so another person can inspect it.

Members work through the detailed lessons, derivations, worked examples and solutions inside the learning dashboard.

Explore lesson previews by topic

Open a topic preview to see its introduction, learning objectives and prerequisites. Previous and next links let you explore the lessons in study order. Full lessons are available in the member dashboard; selected samples are free to read in full.

Topic previewScientific Python and data4 lessonsTopic previewModel and software design4 lessonsTopic previewData access and delivery4 lessonsTopic previewPython Foundations2 lessonsTopic previewControl Flow and Simple Numerical Programs6 lessonsTopic previewFunctions and Decomposition4 lessonsTopic previewBuilt-In Data Structures5 lessonsTopic previewFiles, Exceptions, and Modules4 lessonsTopic previewObject-Oriented Programming4 lessonsTopic previewTesting, Debugging, and Style3 lessonsTopic previewScientific Python Bridge2 lessons

Free resources to start learning

Use these alongside the programme or to prepare independently. Start with the introductory material, work through an example, then return to the research papers when the underlying ideas are familiar.

  1. Textbook · Free

    Python for Data Analysis

    Wes McKinney · third edition

    Use the free online edition for pandas, NumPy and time-series data handling.

  2. University course · Free

    CS50’s Introduction to Programming with Python

    Harvard · David Malan

    Review testing, exceptions and object-oriented programming where needed.

  3. University course · Free

    The Missing Semester of Your CS Education

    MIT · notes and video lectures

    Practise version control, debugging and profiling with the notes and videos.

  4. Documentation · Free

    QuantLib

    QuantLib contributors

    Explore a finance library as an implementation reference and potential benchmark.

Links reviewed 14 October 2026. Free refers to the linked reading, notes or videos; printed books and certificates may cost extra.

Find more papers and learning materials in the SelfMadeQuant Directory.

Where this programme fits

  • Quant Developer and Trading Systems · required programme

Ready to study Python for Quantitative Finance? Explore membership for access to the complete lessons.

Explore membership
Phase 1Financial Instruments, Markets and ConventionsStudy financial instruments, pricing conventions, funding and liquidity through a structured quant finance course.Phase 1Probability, Statistics and Financial DataConnect probability, statistical inference and regression to the analysis of financial data.Phase 1Linear Algebra, Optimisation and Numerical MethodsStudy matrix methods, optimisation and numerical techniques for quantitative finance models and calibration.Phase 1Econometrics and Financial Time SeriesStudy regression diagnostics, financial time series, volatility and forecast evaluation with applied econometrics.