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Technical Analysis with Python for Algorithmic Trading
Use Technical Analysis and Indicators for (Day) Trading. Create, backtest and optimize TA Trading Strategies with Python
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)
(Latest course update and full code review in May 2023!)
“(How) Can I use Technical Analysis and Technical Indicators for Trading and Investing?” – This is one of the most frequently asked questions in trading and investing.
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!
The course covers the following Technical Analysis Tools and Indicators:
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Interactive Line Charts and Candlestick Charts
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Interactive Volume Charts
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Trend, Support and Resistance Lines
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Simple Moving Average (SMA)
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Exponential Moving Average (EMA)
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Moving Average Convergence Divergence (MACD)
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Relative Strength Index (RSI)
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Stochastic Oscillator
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Bollinger Bands
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Pivot Point (Price Action)
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Fibonacci Retracement (Price Action)
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combined/mixed Strategies and more.
This is not only a course on Technical Analysis and Trading. It´s an in-depth coding course on Python and its Data Science Libraries Numpy, Pandas, Matplotlib, Plotly, and more. You will learn how to use and master these Libraries for (Financial) Data Analysis, Technical Analysis, and Trading.
Please note: This is not a course for complete Python Beginners (check out my other courses!)
What are you waiting for? Join now and start making proper use of Technical Analysis!
As always, there is no risk for you as I provide a 30-Days-Money-Back Guarantee.
Thanks and looking forward to seeing you in the Course!
Installing Python and Jupyter Notebooks
Technical Analysis with Python - an Introduction
Technical Analysis - Theory and Use Cases
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11
Overview -
12
Installing and importing required Libraries/Packages
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13
IMPORTANT NOTICE -
14
Loading Financial Data from the Web
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15
Charting - Simple Line Charts
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16
Charting - Interactive Line Charts with Cufflinks and Plotly
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17
How to customize Plotly Charts
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18
Candlestick and OHLC Bar Charts
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19
Bar Size / Granularity
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20
Volume Charts
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21
Technical Indicators - Overview and Examples
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22
Trend Lines
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23
Support and Resistance Lines
Simple Moving Averages (SMA) and Introduction to Backtesting
Exponential Moving Averages (EMA)
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29
Introduction
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30
Getting the Data
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31
A simple Buy and Hold "Strategy"
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32
Performance Metrics
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33
SMA Crossover Strategies - Overview
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34
Defining an SMA Crossover Strategy
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35
Vectorized Strategy Backtesting
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36
Finding the optimal SMA Strategy
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37
Generalization with OOP: An SMA Backtesting Class in action
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38
OOP: the special method __init__()
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39
OOP: the method get_data()
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40
OOP: the method set_parameters()
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41
OOP: the method test_strategy()
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42
OOP: the method plot_results()
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43
OOP: the method update_and_run()
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44
OOP: the method optimize_parameters()
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45
OOP: Docstrings and String Representation
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46
Trading Costs (Part 1)
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47
Trading Costs (Part 2)
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48
Trading Costs (Part 3)
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49
Special Case: Price/SMA Crossover
SMA / EMA Crossover Strategies (Coding Challenge)
Moving Average Convergence Divergence (MACD)
Relative Strength Index (RSI)
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62
Introduction
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63
MACD Strategies - Overview
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64
Getting the Data
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65
Defining an MACD Strategy (Part 1)
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66
Defining an MACD Strategy (Part 2)
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67
Vectorized Strategy Backtesting
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68
The MACD Backtesting Class in Action
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69
OOP Challenge: Create the MACD Backtesting Class (incl. Solution)
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70
Alternative MACD Strategies and Interpretations
Working with two or many Indicators - MACD & RSI
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71
Introduction
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72
RSI Strategies - Overview
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73
Getting the Data
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74
Defining an RSI Strategy (Part 1)
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75
Defining an RSI Strategy (Part 2)
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76
Vectorized Strategy Backtesting
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77
The RSI Backtesting Class in Action
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78
OOP Challenge: Create the RSI Backtesting Class (incl. Solution)
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79
Alternative RSI Strategies and Interpretations
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!