Alexander Hagmann

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The 'Technical Analysis with Python for Algorithmic Trading' course, created by finance educator Alexander Hagmann, offers an immersive, hands-on bridge between traditional market charting and automated quantitative trading. Designed for intermediate traders, financial analysts, and Python developers, this program dismantles the manual effort of technical analysis by automating it through code. Students learn to implement, optimize, and backtest classic indicators—including Simple Moving Averages (SMA), Relative Strength Index (RSI), MACD, and Bollinger Bands—using powerful libraries like Pandas and NumPy. Hagmann emphasizes a structured, Object-Oriented Programming (OOP) approach, ensuring that your trading algorithms are clean, modular, and highly scalable. Beyond simple coding, the course focuses heavily on rigorous backtesting and forward testing methodologies to prove strategy viability before risking live capital. Additionally, you will master interactive financial visualization using Plotly to build professional-grade OHLC and volume charts. Whether you are looking to refine your day trading setup or transition into systematic quantitative trading, this resource provides the exact blueprint needed to translate complex market theories into executable Python strategies.

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Automated Cryptocurrency Portfolio Investing with Python A-Z, created by Alexander Hagmann, offers a comprehensive, data-driven approach to modern digital asset management. This course is specifically designed for developers, quantitative analysts, and retail investors who want to move beyond emotional trading and build their own automated Crypto Robo-Advisor. Throughout this extensive 32.5-hour program, students learn how to programmatically connect to major global cryptocurrency exchanges like Binance, Coinbase, and Kraken using the powerful CCXT library. The curriculum covers key financial engineering concepts, such as Mean-Variance Portfolio Optimization, asset diversification, and algorithmic rebalancing strategies to maximize returns while managing risk. By leveraging Python libraries like Pandas, NumPy, and Matplotlib, learners gain hands-on experience pulling market data from CoinGecko, analyzing performance metrics, and creating custom crypto indices. Whether you are a beginner looking to master Object-Oriented Programming (OOP) through practical financial applications or an experienced programmer aiming to build a production-grade automated investing bot, this course provides the exact mathematical and coding framework needed to succeed in the volatile cryptocurrency market.

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This comprehensive training program equips quantitative developers and retail traders alike with the end-to-end skills required to construct, test, and deploy automated cryptocurrency trading bots. Led by industry expert Alexander Hagmann, students dive deep into the mechanics of the Binance API, mastering both Spot and Futures trading frameworks. Throughout the curriculum, learners leverage the power of Python, utilizing core libraries such as Pandas, NumPy, and CCXT to interface with leading exchanges and process massive datasets of historical and real-time market data. Crucially, the course emphasizes rigorous mathematical verification, guiding you through detailed backtesting, forward testing, and paper trading methodologies to isolate profitable strategies while managing risks like slippage, spread, and commissions. Beyond strategic formulation, you will gain hands-on cloud computing experience by hosting and scheduling your fully autonomous trading algorithms on virtual servers via Amazon Web Services (AWS). Whether you aim to optimize your personal trading portfolio, understand the mathematical realities of margin trading, or launch robust cloud-based software architectures, this course provides a clear, step-by-step pathway from basic scripting to professional algorithmic execution.

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Are 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.