Technical Analysis with Python for Algorithmic Trading
Use Technical Analysis and Indicators for (Day) Trading. Create, backtest and optimize TA Trading Strategies with Python
This course clearly goes beyond rules, theories, vague forecasts, and nice-looking charts. (These are useful but traders need more than that.) This is the first 100% data-driven course on Technical Analysis. We´ll use rigorous Backtesting / Forward Testing to identify and optimize proper Trading Strategies that are based on Technical Analysis / Indicators. This course will allow you to test and challenge your trading ideas and hypothesis. It provides Python Coding Frameworks and Templates that will enable you to code and test thousands of trading strategies within minutes. Identify the profitable strategies and scrap the unprofitable ones!
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
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