Alexander Hagmann

    Technical Analysis with Python for Algorithmic Trading

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    Technical Analysis · Online Course

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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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    Alexander Hagmann

    Industry

    Trading & Investing

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    Alexander Hagmann

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    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

    Online Course

    Learning format

    Technical Analysis

    Subcategory

    $74.99

    Price

    Price may change · updated within 1–2 weeks

    English

    Course language

    What You'll Learn

    • Make proper use of Technical Analysis and Technical Indicators.
    • Use Technical Analysis for (Day) Trading and Algorithmic Trading.
    • Convert Technical Indictors into sound Trading Strategies with Python.
    • Backtest and Forward Test Trading Strategies that are based on Technical Analysis/Indicators.
    • Create and backtest combined Strategies with two or many Technical Indicators.
    • Create interactive Charts (Line, Volume, OHLC, etc.) with Python and Plotly.
    • Visualize Technical Indicators and Trend/Support/Resistance Lines with Python and Plotly.
    • Use Pandas, Numpy and Object Oriented Programming (OOP) for Technical Analysis and Trading.
    • Load Financial Data from local files and the web.
    • Simple Moving Average (SMA) strategies
    • Exponential Moving Average (EMA) strategies
    • Moving Average Convergence Divergence (MACD) strategies
    • Relative Strength Index (RSI) strategies
    • Stochastic Oscillator strategies
    • Bollinger Bands strategies
    • Pivot Point strategies
    • Fibonacci Retracement strategies
    • mixed strategies (combining two or many indicators)

    Best For

    • Financial analysts and traders wanting to automate manual charting workflows using Python.
    • Developers looking to transition into systematic trading by implementing quantitative strategies.
    • Intermediate Python users who want to apply Pandas and NumPy to time-series financial datasets.
    • Traders seeking a rigorous, object-oriented approach to backtesting and validating technical indicators.

    Not For

    • Complete beginners with no prior experience in the Python programming language.
    • Individuals looking for 'get rich quick' algorithmic trading bots or 'black box' signals.
    • Investors interested solely in fundamental analysis or long-term macroeconomic asset valuation.
    • Those without basic knowledge of financial market concepts like OHLC data, trends, and support/resistance.

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