Python Foundations → First Market Data

  • Introduction to Python
  • Python as a Calculator └ Lab
  • Variables └ Lab
  • Data Types └ Lab
  • Real Market Data with Pandas └ Lab

Charts and Returns

  • Your First Chart └ Lab
  • Comparing ETFs └ Lab
  • Measuring Returns └ Lab

Portfolio Performance

  • Building a Portfolio └ Lab
  • Risk: Volatility and Drawdowns └ Lab
  • Risk-Adjusted Return: The Sharpe Ratio └ Lab

Asset Allocation

Shipping Finance Work with AI

Course Navigation

Python Foundations → First Market Data

  • Introduction to Python
  • Python as a Calculator └ Lab
  • Variables └ Lab
  • Data Types └ Lab
  • Real Market Data with Pandas └ Lab

Charts and Returns

  • Your First Chart └ Lab
  • Comparing ETFs └ Lab
  • Measuring Returns └ Lab

Portfolio Performance

  • Building a Portfolio └ Lab
  • Risk: Volatility and Drawdowns └ Lab
  • Risk-Adjusted Return: The Sharpe Ratio └ Lab

Asset Allocation

Shipping Finance Work with AI

Labs

Python Foundations → First Market Data

  • Python as a Calculator
  • Variables
  • Data Types
  • Real Market Data with Pandas

Charts and Returns

  • Your First Chart
  • Comparing ETFs
  • Measuring Returns

Portfolio Performance

  • Building a Portfolio
  • Risk: Volatility and Drawdowns
  • Risk-Adjusted Return: The Sharpe Ratio
Open Full Labs
/ Overview
Course Resources
EN PT
Course Resources
EN PT

Tecnologias de Programação em Finanças

A comprehensive course covering Python programming fundamentals and their application to financial analysis, modeling, and quantitative finance.

5 modules
·
11 lessons
Start the course

Module 1

Python Foundations → First Market Data

From print() and arithmetic to your first yf.download() — the core Python you need to start reading real stock data.

Class notebook (Colab) Labs notebook (Colab)

  1. Introduction to Python

    Get started with Python: installation, setup, and your first program.

  2. Python as a Calculator

    Learn to use Python for basic arithmetic operations and financial calculations.

  3. Variables

    Learn to store and reuse values with variables.

  4. Data Types

    Understanding integers, floats, strings, and booleans.

  5. Real Market Data with Pandas

    Fetch real stock prices with yfinance and explore them with pandas. The Session 1 payoff.

Module 2

Charts and Returns

Pick up where Module 1 ended: take the DataFrame you just downloaded, chart it with Plotly, compute returns, and compare multiple tickers. Python primitives (lists, loops, dicts) are introduced just-in-time as the analysis needs them.

Class notebook (Colab) Labs notebook (Colab) In-class exercise (Colab)

  1. Your First Chart

    From a column of closing prices to an interactive Plotly line chart — the shortest path from DataFrame to visual.

  2. Comparing ETFs

    Download multiple tickers, plot them together, and normalize prices to make honest comparisons. Lists land here.

  3. Measuring Returns

    Daily returns, cumulative returns, total return, and annualized return — the numbers behind the charts. For loops land here.

Module 3

Portfolio Performance

Measure how a portfolio has actually performed: returns over time, drawdowns, Sharpe ratio.

Class notebook (Colab)

  1. Building a Portfolio

    Combine the three ETFs into one portfolio with weights, then measure its growth and annualized return (CAGR).

  2. Risk: Volatility and Drawdowns

    Measure portfolio risk: annualized volatility, the diversification effect, and drawdowns — the depth of the worst falls.

  3. Risk-Adjusted Return: The Sharpe Ratio

    Pull the ECB risk-free rate, compute the Sharpe ratio, and answer the real question: did diversification pay off?

Module 4

Asset Allocation

Build portfolios: mean-variance optimization, equal weighting (1/N), and the 60/40 benchmark.

Class notebook (Colab)

Module 5

Shipping Finance Work with AI

Take a notebook and turn it into a dashboard, a PDF report, and an Excel automation — directed by AI. Workflows you can use Monday morning.