Algorithmic Stock Trading and Equity Investing with Python
Last updated on October 21, 2026 6:38 pmAlgorithmic Stock Trading and Equity Investing with Python
★★★★★
$699.00 in stock
Udemy.com
as of October 21, 2026 6:38 pm
Interactive Brokers (IBKR) for Algorithmic Trading & Portfolio Investing | Stock Market Analysis, ETF Trading & Theory
Created by:
Alexander Hagmann
Data Scientist | Finance Professional | Entrepreneur
Data Scientist | Finance Professional | Entrepreneur
Rating:4.62 (394reviews)
6607students enrolled
What Will I 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
Requirements
- No Python experience needed. This course provides a Python Crash Course.
- No Finance/Investment knowledge required. You will learn everything you need to know.
- A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
- An internet connection capable of streaming HD videos.
- Some high school level math skills would be great (not mandatory, but it helps).
Target audience
- Beginners who want to start with Stocks/equities and want to do it right straight way (avoiding common mistakes).
- Stock Traders and Investors who want to professionalize and automate their Business.
- Finance & Investment Professionals who want to step into Data-driven Finance.
- Data Scientists and Machine Learning Professionals with an interest in Investing into Stocks/Equities.


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