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Development Programming Languages Python

Technical Analysis with Python for Algorithmic Trading

Use Technical Analysis and Indicators for (Day) Trading. Create, backtest and optimize TA Trading Strategies with Python
Bestseller
Rating: 4.7 out of 54.7 (253 ratings)
4,137 students
Created by Alexander Hagmann
Last updated 11/2021
English
English [Auto]

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)

Course content

17 sections • 165 lectures • 13h 25m total length

  • Preview05:16
  • Tips: How to get the most out of this course
    05:27
  • Preview01:57
  • Student FAQ
    02:07
  • *** LEGAL DISCLAIMER (MUST READ!) ***
    00:37
  • Course Materials / Download
    01:32

  • Overview
    00:39
  • Download and Install Anaconda
    08:08
  • How to open Jupyter Notebooks
    09:29
  • How to work with Jupyter Notebooks
    14:00

  • Overview
    01:15
  • Installing and importing required Libraries/Packages
    03:03
  • Loading Financial Data from the Web
    06:43
  • Charting - Simple Line Charts
    05:00
  • Preview04:28
  • How to customize Plotly Charts
    04:07
  • Candlestick and OHLC Bar Charts
    06:09
  • Bar Size / Granularity
    08:19
  • Volume Charts
    03:35
  • Technical Indicators - Overview and Examples
    03:32
  • Trend Lines
    04:09
  • Support and Resistance Lines
    05:11

  • Section Overview
    00:58
  • Technical Analysis vs. Fundamental Analysis
    05:58
  • Preview04:53
  • Technical Analysis - Applications and Use Cases
    09:15
  • An Introduction to Currencies (FOREX) and Trading
    07:26

  • Introduction
    03:07
  • Getting the Data
    03:56
  • A simple Buy and Hold "Strategy"
    05:20
  • Performance Metrics
    06:33
  • Preview04:05
  • Defining an SMA Crossover Strategy
    07:00
  • Vectorized Strategy Backtesting
    08:16
  • Finding the optimal SMA Strategy
    11:24
  • Generalization with OOP: An SMA Backtesting Class in action
    10:19
  • OOP: the special method __init__()
    04:02
  • OOP: the method get_data()
    09:06
  • OOP: the method set_parameters()
    06:20
  • OOP: the method test_strategy()
    04:58
  • OOP: the method plot_results()
    03:12
  • OOP: the method update_and_run()
    04:40
  • OOP: the method optimize_parameters()
    03:10
  • OOP: Docstrings and String Representation
    04:51
  • Trading Costs (Part 1)
    06:11
  • Trading Costs (Part 2)
    06:42
  • Trading Costs (Part 3)
    02:52
  • Special Case: Price/SMA Crossover
    02:12

  • Introduction
    00:48
  • EMA Crossover Strategies - Overview
    02:40
  • Getting the Data
    00:47
  • EMA vs. SMA
    05:22
  • Defining an EMA Crossover Strategy
    03:36
  • Vectorized Strategy Backtesting
    05:46
  • OOP Challenge: Create the EMA Backtesting Class (incl. Solution)
    02:53
  • The EMA Backtesting Class in Action
    05:13

  • Introduction
    00:36
  • SMA / EMA Crossover Strategies - Overview
    01:30
  • Instructions & some Hints
    00:52
  • Solution
    06:09

  • Introduction
    00:43
  • MACD Strategies - Overview
    05:05
  • Getting the Data
    00:55
  • Defining an MACD Strategy (Part 1)
    05:59
  • Defining an MACD Strategy (Part 2)
    02:49
  • Vectorized Strategy Backtesting
    04:29
  • The MACD Backtesting Class in Action
    09:19
  • OOP Challenge: Create the MACD Backtesting Class (incl. Solution)
    05:37
  • Alternative MACD Strategies and Interpretations
    05:57

  • Introduction
    00:50
  • RSI Strategies - Overview
    03:47
  • Getting the Data
    00:30
  • Defining an RSI Strategy (Part 1)
    07:54
  • Defining an RSI Strategy (Part 2)
    05:49
  • Vectorized Strategy Backtesting
    03:12
  • The RSI Backtesting Class in Action
    08:11
  • OOP Challenge: Create the RSI Backtesting Class (incl. Solution)
    03:03
  • Alternative RSI Strategies and Interpretations
    04:26

  • Introduction
    00:57
  • A combined MACD / RSI Strategy - Overview
    02:26
  • Backtesting and Optimizing the Strategies separately
    02:48
  • Combining MACD with RSI and Backtesting
    04:29

Requirements

  • A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
  • An internet connection capable of streaming HD videos.
  • Basic Python Coding Skills (Variables, Data Types, Lists, For Loops, Functions) -> This is not a Course for complete Python Beginners.
  • Basic Coding Skills in Pandas, Numpy and Matplotlib
  • Basic Knowledge of Trading / Investing would be great (not mandatory, but it helps)

Description

"(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:

  • Interactive Line Charts and Candlestick Charts

  • Interactive Volume Charts

  • Trend, Support and Resistance Lines

  • Simple Moving Average (SMA)

  • Exponential Moving Average (EMA)       

  • Moving Average Convergence Divergence (MACD)

  • Relative Strength Index (RSI)

  • Stochastic Oscillator

  • Bollinger Bands

  • Pivot Point (Price Action)

  • Fibonacci Retracement (Price Action)

  • 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!

Who this course is for:

  • (Day) Traders and Investors who want to make proper use of Technical Analysis.
  • (Day) Traders and Investors who want to professionalize their Business.
  • Technical Analyst and Chartist who want to improve their work/analysis with powerful Python Coding
  • Everyone who wants to do more with Technical Analysis than just telling vague stories and creating pretty charts.

Instructor

Alexander Hagmann
Data Scientist | Finance Professional | Entrepreneur
Alexander Hagmann
  • 4.7 Instructor Rating
  • 5,862 Reviews
  • 53,973 Students
  • 10 Courses

Alexander is a Data Scientist and Finance Professional with more than 10 years of experience in the Finance and Investment Industry.

He is also a Bestselling Udemy Instructor for

- Data Analysis/Manipulation with Pandas

- (Financial) Data Science

- Python for Business and Finance

- Algorithmic Trading

Alexander started his career in the traditional Finance sector and moved step-by-step into Data-driven and Artificial Intelligence-driven Finance roles. He is currently working on cutting-edge Fintech projects and creates solutions for Algorithmic Trading and Robo Investing. And Alexander is excited to share his knowledge with others here on Udemy. Students who completed his courses work in the largest and most popular tech and finance companies all over the world.

Alexander´s courses have one thing in common: Content and concepts are practical and real-world proven. The clear focus is on acquiring skills and understanding concepts rather than memorizing things.   

Alexander holds a Master´s degree in Finance and passed all three CFA Exams (he is currently no active member of the CFA Institute).   

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