Algorithmic Stock Trading and Equity Investing with Python
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Unclaimed ProfileAre you looking to bridge the gap between financial theory and automated execution? The 'Algorithmic Stock Trading and Equity Investing with Python' course, created by data science and finance expert Alexander Hagmann, offers an incredibly thorough, hands-on path to mastering quantitative finance. Throughout this comprehensive 38-hour program, learners transition from basic Python programming to deploying complex, automated trading strategies via the Interactive Brokers (IBKR) API. The curriculum seamlessly blends passive ETF strategies with active, data-driven equity trading. You will explore critical financial theories like the Capital Asset Pricing Model (CAPM) and the advanced Black-Litterman model, while learning practical skills in portfolio optimization, rebalancing, and risk assessment. By utilizing essential Python libraries such as Pandas, NumPy, and Matplotlib, you will learn to retrieve historical market data, perform technical and fundamental analysis, and backtest your strategies with historical records before going live. Designed for retail investors, quantitative developers, and financial analysts alike, this course provides the exact tools needed to construct robust, automated trading systems and scientifically manage modern equity portfolios.
About the creator
Alexander Hagmann
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Reviews
Alexander Hagmann is an expert instructor specializing in the intersection of finance, data science, and programming. With a deep passion for systematic trading and cryptocurrency, Alexander has dedicated his career to demystifying algorithmic trading strategies for students worldwide. His teaching philosophy centers on highly practical, hands-on learning, where complex mathematical concepts and programming APIs are translated into clear, executable Python code. Through his courses, Alexander guides learners from the foundational elements of financial data analysis to deploying fully automated trading bots on major platforms like Binance. He places a strong emphasis on risk management, rigorous backtesting, and avoiding common pitfalls…Show more
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Program Overview
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Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
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
Best For
- Financial analysts and developers looking to automate trading workflows using Python
- Retail investors who want to build and backtest quantitative equity strategies
- Students of quantitative finance seeking practical experience with the IBKR API
- Professionals aiming to integrate modern asset-pricing models into portfolio management
Not For
- Absolute beginners who have never programmed or used Python before
- Traders looking for 'get rich quick' automated trading bots or signals
- Users without an Interactive Brokers account who strictly want a paper-trading simulation without API connectivity
- Investors focused exclusively on discretionary trading with no interest in systematic or data-driven approaches
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