Udemy

Algorithmic Stock Trading and Equity Investing with Python

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  • 7,540 Students
  • Updated 12/2025
4.3
(457 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
6 Hour(s) 29 Minute(s)
Language
English
Taught by
Alexander Hagmann
Rating
4.3
(457 Ratings)

Course Overview

Algorithmic Stock Trading and Equity Investing with Python

Interactive Brokers (IBKR) for Algorithmic Trading & Portfolio Investing | Stock Market Analysis, ETF Trading & Theory

Welcome to the most comprehensive and complete course on (automated) Stock Trading and Equity Investing!

This course covers

  • Automated Stock Trading for Income Generation (Algo Trading, Day Trading & more)

  • Automated ETF & Equity Portfolio Investing for long-term Wealth Accumulation (passive, semi-active, and active Investing)

with Python and Interactive Brokers (IBKR).


At the end of the course, you´ll have mastered all four aspects required for long-term success:

  1. Theory (Finance & Investing 101): What you really need to know before you trade/invest in stocks.

  2. Data: Successful Investment and Trading Strategies are data-driven.

  3. API Trading with Interactive Brokers: Automated Paper Trading and Live Trading with low Spreads and Commissions (no inactivity fees)

  4. Python: The right tool that integrates Theory, Data, and API Trading. This course explains the code and covers everything you need to know in a Python Crash Course (for beginners).


Some Highlights:

  • Load and analyze Stock Market Data (historical prices, financial statements, ratios, valuation multiples) for thousands of stocks

  • Trade stocks on various exchanges and from various world regions (North America, Europe, India, Australia, etc.)

  • Fundamental Analysis, Equity Valuation Methods, Technical Indicators, and Optimization Techniques explained.

  • Trading Strategies with multiple Tickers/Instruments at once

  • Test and improve your skills in various Keystone Projects (new concept)


What else should you know about me and the course?

  • The course shows how to do things right. But equally important, it highlights the most commonly made mistakes in Trading & Investing. There is hardly any other business where beginners make so many mistakes. Why is that? A lack of skills, expertise, and experience. And: Overconfidence and overreliance on intuition. As a finance professional with an extensive academic background (MSc in Finance, CFA) my clear message is: For Trading and Investing, intuition and common sense are not your best friends. Very often, the most intuitive solution is not the correct solution!

  • This course is "not only" a Stock trading and Equity investing course but also an in-depth Python Course that goes beyond what you can typically see in other courses. Create hands-on Applications with Python and use it for your Trading & Investing Business!


What are you waiting for? Join now!

Thanks and looking forward to seeing you in the Course!

Course Content

  • 38 section(s)
  • 460 lecture(s)
  • Section 1 Getting started
  • Section 2 PART 1: Basics and Prerequisites
  • Section 3 Equity Markets and Stock Trading/Investing
  • Section 4 Installing Python and Jupyter Notebooks
  • Section 5 Equity Analysis with Python (Part 1)
  • Section 6 Excursus: How to avoid and debug Coding Errors (don´t skip!)
  • Section 7 Equity Analysis with Python (Part 2)
  • Section 8 Keystone Project - Loading Data and Stock Analysis
  • Section 9 Introduction to Interactive Brokers (IKBR) and API Trading
  • Section 10 Keystone Project - Algorithmic Trading with IBKR
  • Section 11 Financial Data Analysis and Performance Evaluation
  • Section 12 Keystone Project: Stock Performance Analysis & Comparison
  • Section 13 PART 2: ETF Trading & Equity Portfolio Investing with Python and IBKR
  • Section 14 How to build and analyze a Stock Index
  • Section 15 ETF Investing and Index Replication / Tracking
  • Section 16 Keystone Project - Creating and Implementing a customized Investment Strategy
  • Section 17 Equity Portfolio Optimization and Analysis
  • Section 18 Portfolio Optimization: Theory and practical Pitfalls
  • Section 19 Reverse Optimization and the Black-Litterman model
  • Section 20 Asset Pricing (CAPM) - Theory and practical Implications
  • Section 21 Keystone Project: Portfolio Optimization, CAPM & Black-Litterman
  • Section 22 PART 3: Algorithmic Stock Trading with Python and IKBR
  • Section 23 Trading Strategies - Overview
  • Section 24 Backtesting multiple Tickers Strategies (Momentum/Contrarian)
  • Section 25 Technical Analysis with Python - Introduction
  • Section 26 Stock trading with Technical Indicators - Backtesting
  • Section 27 Keystone Project - Technical Trading with IBKR
  • Section 28 PART 4: Advanced VIP Topics
  • Section 29 Equity Valuation Concepts
  • Section 30 Advanced Data Sources - EOD Historical Data
  • Section 31 Data Streaming & Algorithmic Day Trading with IBKR
  • Section 32 APPENDIX: Python Crash Course
  • Section 33 Appendix 1: Python (& Finance) Basics
  • Section 34 Appendix 2: User-defined Functions
  • Section 35 Appendix 3: Numpy, Pandas, Matplotlib and Seaborn Crash Course
  • Section 36 Appendix 4: Advanced Pandas Time Series Topics
  • Section 37 Appendix 5: Object Oriented Programming (OOP)
  • Section 38 What´s next? (outlook and additional resources)

What You’ll Learn

  • Algorithmic Stock Trading with Python and the Interactive Brokers (IBKR) API, Automated ETF & Equity Portfolio Investing, Passive (ETF), Semi-Active and Active Investing, Stock Trading Strategies with multiple Tickers, Equity Portfolio Optimization, Management & Rebalancing, Backtesting and Implementation of Trading & Investment Strategies, Technical Analysis and Indicators, Equity Valuation Methods (DDM and Multiples), Fundamental Analysis, Stock Indices and Index Tracking/Replication, How to measure, benchmark & improve the Performance of your Equity Portfolio, Loading and analysing Stock Data from (free) Web Sources, API Trading with Interactive Brokers, Python Basics & Numpy, Pandas, Matplotlib, Truly Data-driven Trading and Investing, Asset-Pricing Models (CAPM), Black-Litterman Model


Reviews

  • R
    Rafael Ángel Campos Vargas
    5.0

    Excelente curso para el interesando en el Trading Algorítmico. Se encuentra orientado a evitar que el estudiante no cometa toda clase de falacias y errores a los que se expone si no analiza los problemas adecuadamente. No es apropiado para principiantes y deberá complementarse con otros cursos. Sin embargo, es importante que el estudiante no pierda dinero con métodos que no sirven y que podrían funcionar en un análisis superficial. Además, debe tener la capacidad de adaptarse al mercado cambiante. Por eso, si te interesa el Trading Algorítmico deberías llevar este curso o alguno equivalente.

  • O
    Olalekan Solomon Awoyemi
    4.5

    Superb, and detailed.

  • K
    KM Lee
    1.0

    You really should split the code into separate lessons instead of dumping everything into one file. This is crazy, honestly.

  • A
    Ajit Bhalchandra Joshi
    5.0

    Happy that I chose this course

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