Finance Programming

Python for Finance

Build practical financial analysis, modelling, and portfolio workflows with Python

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Quick Course Facts
20
Self-paced, Online, Lessons
20
Videos and/or Narrated Presentations
7.2
Approximate Hours of Course Media
About the Python for Finance Course

Python for Finance is a practical online course for learners who want to use Python to analyse markets, measure risk, value assets, and build portfolio workflows. You will move from finance-focused Python foundations to applied projects that help you make cleaner, more repeatable financial decisions.

Build Practical Finance Workflows With Python

  • Learn Python essentials for financial calculations, data handling, and market analysis.
  • Build practical financial analysis, modelling, and portfolio workflows with Python using NumPy, pandas, and visualisation tools.
  • Apply core Finance concepts including returns, compounding, volatility, drawdowns, CAPM, valuation, and risk measures.
  • Complete a capstone project that brings professional Python for Finance skills into a reusable analysis workflow.

A hands-on Python for Finance course covering financial data, risk, valuation, portfolio analytics, forecasting, simulation, and strategy testing.

This course gives you a structured path into Finance programming, starting with a Python workspace designed for financial analysis. You will learn the Python, NumPy, and pandas skills needed to import, clean, transform, and analyse market data with confidence.

As the course progresses, you will calculate returns, log returns, compounding, volatility, drawdowns, correlations, and performance metrics. You will also visualise prices, distributions, and portfolio behaviour so that financial results become easier to interpret and communicate.

You will explore portfolio analysis, diversification, mean-variance optimisation, CAPM, fixed income basics, equity valuation models, responsible time series forecasting, Monte Carlo simulation, Value at Risk, scenario analysis, and backtesting without common pitfalls. By the end, you will be able to build practical financial analysis, modelling, and portfolio workflows with Python and apply them in a professional Finance context.

Course Lessons

Full lesson breakdown

Lessons are organized by topic area and each includes descriptive copy for search visibility and student clarity.

Foundations

3 lessons

Lesson 1: Setting Up a Finance-Focused Python Workspace

18 min Preview
In this lesson, students set up a practical Python workspace for finance work: a clean project folder, an isolated environment, core analytical packages, and a repeatable notebook workflow. The focus …

Lesson 2: Python Essentials for Financial Calculations

20 min
This lesson establishes the Python building blocks needed for practical financial calculations: variables, numeric types, arithmetic expressions, lists, dictionaries, functions, and simple control flo…

Lesson 3: Working with NumPy for Vectorised Finance

19 min
This lesson introduces NumPy as the core numerical engine behind many Python finance workflows. Students learn how vectorised arrays make return calculations, compounding, risk measures, and scenario …

Data Analysis

2 lessons

Lesson 4: Financial Data Handling with pandas

22 min
In this lesson, students learn how to handle financial datasets with pandas in a way that supports reliable analysis and modelling. The focus is on practical workflows: importing price and fundamental…

Lesson 5: Importing and Cleaning Market Data

21 min
In this lesson, students learn how to import market data into Python and prepare it for reliable financial analysis. The focus is on practical workflows using pandas: reading price data from CSV files…

Core Financial Metrics

2 lessons

Lesson 6: Returns, Log Returns, and Compounding

20 min
This lesson introduces the return measures used throughout financial analysis in Python: simple returns, log returns, cumulative returns, and compounded growth. Students learn what each metric means, …

Lesson 7: Visualising Prices, Returns, and Distributions

18 min
In this lesson, students learn how to turn raw price and return series into practical visual diagnostics. The focus is on plotting adjusted prices, simple and log returns, rolling volatility, and retu…

Risk and Performance

1 lesson

Lesson 8: Volatility, Drawdowns, and Risk Measures

22 min
This lesson teaches practical risk measurement for financial return series in Python, focusing on volatility, downside risk, drawdowns, Value at Risk, and Conditional Value at Risk. Students learn how…

Portfolio Analysis

3 lessons

Lesson 9: Correlation, Diversification, and Portfolio Behaviour

21 min
This lesson explains how correlation shapes portfolio behaviour, why diversification can reduce risk without necessarily reducing expected return, and how Python can be used to measure these effects f…

Lesson 10: Building Portfolio Performance Analytics

23 min
In this lesson, students build a practical portfolio performance analytics workflow in Python. The focus is on turning asset prices and portfolio weights into portfolio returns, cumulative growth, vol…

Lesson 11: Mean-Variance Optimisation in Python

24 min
This lesson teaches mean-variance optimisation as a practical Python workflow for building portfolios from expected returns, volatility, covariance, and constraints. Students will learn how to formula…

Asset Pricing

1 lesson

Lesson 12: CAPM, Beta, and Factor-Based Thinking

21 min
In this lesson, students learn how the Capital Asset Pricing Model connects expected return to systematic risk, why beta is the central risk measure in the model, and how to estimate beta using Python…

Asset Classes

1 lesson

Lesson 13: Fixed Income Basics with Python

20 min
This lesson introduces fixed income as an asset class and shows how Python can turn bond cash flows, prices, yields, and duration into repeatable calculations. Students learn the core mechanics behind…

Valuation

1 lesson

Lesson 14: Equity Valuation Models in Python

23 min
In this lesson, students build practical equity valuation models in Python, focusing on discounted cash flow, dividend discount, and comparable multiples workflows. The emphasis is not on pretending v…

Forecasting

1 lesson

Lesson 15: Forecasting Financial Time Series Responsibly

22 min
This lesson teaches a disciplined workflow for forecasting financial time series in Python without overstating what models can know. Students learn why financial forecasts are fragile, how to define t…

Simulation and Risk

2 lessons

Lesson 16: Monte Carlo Simulation for Finance

24 min
This lesson introduces Monte Carlo simulation as a practical tool for financial uncertainty. Students learn how to simulate many possible future price paths, portfolio outcomes, and risk scenarios usi…

Lesson 17: Value at Risk and Scenario Analysis

23 min
This lesson introduces Value at Risk as a practical way to summarize portfolio downside risk over a defined horizon and confidence level. Students learn how to compute historical VaR, parametric VaR, …

Strategy Testing

1 lesson

Lesson 18: Backtesting Trading Strategies Without Common Pitfalls

25 min
This lesson teaches how to backtest trading strategies in Python without fooling yourself with biased data, unrealistic execution assumptions, or overfit parameters. Students learn how to separate sig…

Professional Practice

2 lessons

Lesson 19: Automating Reports and Reusable Finance Workflows

20 min
This lesson shows how to turn finance notebooks into repeatable reporting workflows that can be rerun, reviewed, and shared. Students learn how to separate data loading, calculations, charts, and outp…

Lesson 20: Capstone: Building a Python Finance Analysis Project

25 min
In this capstone lesson, students bring together the core Python finance skills from the course into a professional analysis project. The goal is not to build a perfect trading system, but to create a…
About Your Instructor
Professor Daniel Martin

Professor Daniel Martin

Professor Daniel Martin guides this AI-built Virversity course with a clear, practical teaching style.