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Python & Financial Software DevelopmentEnrollment opening soon

Advanced Python for Quant Developers

Write fast, well-structured and well-tested Python for quantitative work: performance profiling, vectorization, concurrency, typing, packaging and design patterns.

  • Duration6 weeks
  • LevelIntermediate
  • Modules6
  • Lessons24
Enroll nowView curriculum
Advanced Python for Quant Developers
Proposed fee$699USD · 6 weeks

Course overview

This course is for developers who already write Python and now need code that is fast, maintainable and safe to run in research and production pipelines. You will profile and optimize numerical code, use NumPy and Numba effectively, apply concurrency where it helps, and design clean object-oriented and functional components. The course also covers type hints, property-based testing, packaging and continuous integration so your quant code can be trusted and reused by a team.

  • Profiling and optimization
  • Advanced NumPy and Numba
  • Concurrency and async
  • Typing and design patterns
  • Testing strategies
  • Packaging and CI

What you will learn

  • Profile Python code to locate bottlenecks and apply targeted optimizations with measured results
  • Vectorize numerical routines with NumPy broadcasting and accelerate loops with Numba where appropriate
  • Choose between multiprocessing, threading and asyncio for CPU-bound and I/O-bound workloads
  • Design typed, modular components using dataclasses, protocols and common design patterns
  • Package a library with tests, type checks and a continuous integration pipeline

Curriculum

6 modules · 24 lessons · 6 weeks. Curriculum preview; the final syllabus may change before enrollment opens.

  • How CPython executes code and manages memory
  • Profiling with cProfile, line profilers and timeit
  • Memory profiling and avoiding unnecessary copies
  • Setting up reproducible benchmarks

  • NumPy broadcasting, strides and views
  • Replacing loops with vectorized operations
  • Accelerating hot paths with Numba
  • Efficient pandas: dtypes, categoricals and avoiding apply

  • The GIL and when it matters
  • multiprocessing and concurrent.futures for CPU-bound tasks
  • asyncio for I/O-bound data collection
  • Rate limiting, retries and backoff

  • Type hints, mypy and protocols
  • Dataclasses, enums and immutable data
  • Design patterns useful in quant code
  • Configuration, logging and error handling

  • Unit and integration tests with pytest fixtures
  • Property-based testing with Hypothesis
  • Testing numerical code with tolerances
  • Golden datasets and regression tests

  • Project layout and pyproject.toml
  • Dependency management and reproducible environments
  • Continuous integration with linting, typing and tests
  • Containerizing a Python tool with Docker

Practical projects

01

Optimize a slow rolling-statistics library and document benchmark results before and after each change

02

Build an async data downloader with rate limiting, retries and structured logging against a mock API

03

Package a typed quant utilities library with property-based tests and a CI workflow

Skills you will practice

  • NumPy
  • Numba
  • asyncio
  • Type hints
  • pytest
  • Packaging

Requirements

Who it's for

  • Python developers moving into quantitative or trading technology teams
  • Researchers whose notebooks need to become reliable libraries
  • Engineers preparing for algorithmic trading and data engineering tracks

Prerequisites

  • Python Programming for Financial Markets or equivalent intermediate Python experience
  • Familiarity with pandas and command-line tools

Learning format

  • Self-paced lessons with focused coding exercises
  • Hands-on labs with benchmark harnesses
  • Code reviews on weekly assignments
  • Live architecture and refactoring sessions during cohort runs

Quantzivo certificates are completion credentials issued by Quantzivo, not accredited qualifications. Courses teach technical and research skills; they do not promise investment returns or provide investment advice.

Frequently asked questions

Self-paced lessons with focused coding exercises Hands-on labs with benchmark harnesses Code reviews on weekly assignments

Python Programming for Financial Markets or equivalent intermediate Python experience. See the full list under Requirements.

Yes. Completing the lessons and assessed projects earns a Quantzivo certificate of completion. It is a completion credential issued by Quantzivo, not an accredited qualification.

The $699 USD fee is a proposed launch price. Final fees, schedules, refund terms and any regional options are confirmed before any payment is taken.

Quantzivo teaches technical, data and research skills. It does not provide investment advice, trading signals or any promise of returns. Trading involves risk, including loss of capital.
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