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[Download] Technical Analysis with Python for Algorithmic Trading

3 years ago
Technical Analysis with Python for Algorithmic Trading

Download Files Size:

4.9 GB

Value:

$109

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 • 164 lectures • 13h 24m 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

  • Introduction

    00:43

  • Getting the Data

    01:45

  • Defining an SO Strategy

    08:41

  • Vectorized Strategy Backtesting

    02:54

  • The SO Backtesting Class in Action

    08:53

  • OOP Challenge: Create the SO Backtesting Class (incl. Solution)

    04:04

  • Alternative SO Strategies and Interpretations

    03:29

  • Introduction

    01:01

  • Bollinger Bands – Overview

    02:57

  • Getting the Data

    02:45

  • Defining a Bollinger Bands Mean-Reversion Strategy (Part 1)

    04:29

  • Defining a Bollinger Bands Mean-Reversion Strategy (Part 2)

    08:36

  • Vectorized Strategy Backtesting

    05:48

  • The BB Backtesting Class in action

    03:55

  • OOP Challenge: Create the BB Backtesting Class (incl. Solution)

    02:40

  • Introduction

    01:09

  • Pivot Point – Overview and Data requirements

    05:28

  • Adding Pivot Point and Support and Resistance Lines

    04:19

  • Defining a simple Pivot Point Strategy

    05:18

  • Vectorized Strategy Backtesting

    04:55

  • Starting with raw Data

    02:03

  • Preparing the Data (1) – Timezone Conversion

    03:07

  • Preparing the Data (2) – Resampling to daily (NY Close)

    04:23

  • Preparing the Data (3) – OHLC Resampling

    02:34

  • Preparing the Data (4) – Merging Intraday and Daily Data

    04:48

  • Final Remarks – Now it´s your turn!

    01:11

  • Introduction

    00:58

  • Getting the Data

    00:52

  • A first Intuition on Fibonacci Retracement (Uptrend)

    08:58

  • A first Intuition on Fibonacci Retracement (Downtrend)

    04:37

  • Identifying Local Highs

    06:08

  • Identifying Local Lows

    04:50

  • Highs and Lows – an iterative approach

    05:42

  • Identifying Trends (Uptrend / Downtrend)

    04:28

  • Adding Fibonacci Retracement Levels

    02:25

  • A Fibonacci Retracement Breakout Strategy

    07:57

  • Vectorized Strategy Backtesting

    04:04

  • Final Remarks and alternative Strategies

    01:52

  • Introduction

    00:47

  • Importing Time Series Data from csv-files

    08:16

  • Converting strings to datetime objects with pd.to_datetime()

    08:53

  • Indexing and Slicing Time Series

    07:25

  • Downsampling Time Series with resample()

    14:20

  • Coding Exercise 1

    05:10

  • Getting Ready (Installing required library)

    02:20

  • Importing Stock Price Data from Yahoo Finance

    09:29

  • Initial Inspection and Visualization

    05:32

  • Normalizing Time Series to a Base Value (100)

    06:31

  • The shift() method

    06:51

  • The methods diff() and pct_change()

    06:41

  • Measuring Stock Performance with MEAN Returns and STD of Returns

    08:49

  • Financial Time Series – Return and Risk

    08:30

  • Financial Time Series – Covariance and Correlation

    04:32

  • Coding Exercise 2

    00:04

  • Simple Returns vs. Log Returns

    09:18

  • Importing Financial Data from Excel

    11:25

  • Simple Moving Averages (SMA) with rolling()

    08:44

  • Momentum Trading Strategies with SMAs

    07:08

  • Exponentially-weighted Moving Averages (EWMA)

    04:32

  • Merging / Aligning Financial Time Series (hands-on)

    05:02

  • Helpful DatetimeIndex Attributes and Methods

    06:24

  • Filling NA Values with bfill, ffill and interpolation

    10:07

  • Timezones and Converting (Part 1)

    04:36

  • Timezones and Converting (Part 2)

    04:48

  • Introduction

    00:21

  • Introduction to OOP and examples for Classes

    10:58

  • The Financial Analysis Class live in action (Part 1)

    04:58

  • The Financial Analysis Class live in action (Part 2)

    03:42

  • The special method __init__()

    08:28

  • The method get_data()

    06:49

  • The method log_returns()

    03:21

  • String representation and the special method __repr__()

    03:41

  • The methods plot_prices() and plot_returns()

    05:21

  • Encapsulation and protected Attributes

    04:02

  • The method set_ticker()

    03:18

  • Adding more methods and performance metrics

    05:51

  • Inheritance

    09:01

  • Inheritance and the super() Function

    06:47

  • Adding meaningful Docstrings

    06:24

  • Creating and Importing Python Modules (.py)

    04:19

  • Coding Exercise 3: Create your own Class

    07:13

  • Get your special BONUS here!

    02:13

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.

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