[DOWNLOAD] Technical Analysis with Python for Algorithmic Trading {4.9GB}

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.

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 course05:27
  • Preview01:57
  • Student FAQ02:07
  • *** LEGAL DISCLAIMER (MUST READ!) ***00:37
  • Course Materials / Download01:32

  • Overview00:39
  • Download and Install Anaconda08:08
  • How to open Jupyter Notebooks09:29
  • How to work with Jupyter Notebooks14:00

  • Overview01:15
  • Installing and importing required Libraries/Packages03:03
  • Loading Financial Data from the Web06:43
  • Charting – Simple Line Charts05:00
  • Preview04:28
  • How to customize Plotly Charts04:07
  • Candlestick and OHLC Bar Charts06:09
  • Bar Size / Granularity08:19
  • Volume Charts03:35
  • Technical Indicators – Overview and Examples03:32
  • Trend Lines04:09
  • Support and Resistance Lines05:11

  • Section Overview00:58
  • Technical Analysis vs. Fundamental Analysis05:58
  • Preview04:53
  • Technical Analysis – Applications and Use Cases09:15
  • An Introduction to Currencies (FOREX) and Trading07:26

  • Introduction03:07
  • Getting the Data03:56
  • A simple Buy and Hold “Strategy”05:20
  • Performance Metrics06:33
  • Preview04:05
  • Defining an SMA Crossover Strategy07:00
  • Vectorized Strategy Backtesting08:16
  • Finding the optimal SMA Strategy11:24
  • Generalization with OOP: An SMA Backtesting Class in action10:19
  • 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 Representation04:51
  • Trading Costs (Part 1)06:11
  • Trading Costs (Part 2)06:42
  • Trading Costs (Part 3)02:52
  • Special Case: Price/SMA Crossover02:12

  • Introduction00:48
  • EMA Crossover Strategies – Overview02:40
  • Getting the Data00:47
  • EMA vs. SMA05:22
  • Defining an EMA Crossover Strategy03:36
  • Vectorized Strategy Backtesting05:46
  • OOP Challenge: Create the EMA Backtesting Class (incl. Solution)02:53
  • The EMA Backtesting Class in Action05:13

  • Introduction00:36
  • SMA / EMA Crossover Strategies – Overview01:30
  • Instructions & some Hints00:52
  • Solution06:09

  • Introduction00:43
  • MACD Strategies – Overview05:05
  • Getting the Data00:55
  • Defining an MACD Strategy (Part 1)05:59
  • Defining an MACD Strategy (Part 2)02:49
  • Vectorized Strategy Backtesting04:29
  • The MACD Backtesting Class in Action09:19
  • OOP Challenge: Create the MACD Backtesting Class (incl. Solution)05:37
  • Alternative MACD Strategies and Interpretations05:57

  • Introduction00:50
  • RSI Strategies – Overview03:47
  • Getting the Data00:30
  • Defining an RSI Strategy (Part 1)07:54
  • Defining an RSI Strategy (Part 2)05:49
  • Vectorized Strategy Backtesting03:12
  • The RSI Backtesting Class in Action08:11
  • OOP Challenge: Create the RSI Backtesting Class (incl. Solution)03:03
  • Alternative RSI Strategies and Interpretations04:26

  • Introduction00:57
  • A combined MACD / RSI Strategy – Overview02:26
  • Backtesting and Optimizing the Strategies separately02:48
  • Combining MACD with RSI and Backtesting04:29

  • Introduction00:43
  • Getting the Data01:45
  • Defining an SO Strategy08:41
  • Vectorized Strategy Backtesting02:54
  • The SO Backtesting Class in Action08:53
  • OOP Challenge: Create the SO Backtesting Class (incl. Solution)04:04
  • Alternative SO Strategies and Interpretations03:29

  • Introduction01:01
  • Bollinger Bands – Overview02:57
  • Getting the Data02:45
  • Defining a Bollinger Bands Mean-Reversion Strategy (Part 1)04:29
  • Defining a Bollinger Bands Mean-Reversion Strategy (Part 2)08:36
  • Vectorized Strategy Backtesting05:48
  • The BB Backtesting Class in action03:55
  • OOP Challenge: Create the BB Backtesting Class (incl. Solution)02:40

  • Introduction01:09
  • Pivot Point – Overview and Data requirements05:28
  • Adding Pivot Point and Support and Resistance Lines04:19
  • Defining a simple Pivot Point Strategy05:18
  • Vectorized Strategy Backtesting04:55
  • Starting with raw Data02:03
  • Preparing the Data (1) – Timezone Conversion03:07
  • Preparing the Data (2) – Resampling to daily (NY Close)04:23
  • Preparing the Data (3) – OHLC Resampling02:34
  • Preparing the Data (4) – Merging Intraday and Daily Data04:48
  • Final Remarks – Now it´s your turn!01:11

  • Introduction00:58
  • Getting the Data00:52
  • A first Intuition on Fibonacci Retracement (Uptrend)08:58
  • A first Intuition on Fibonacci Retracement (Downtrend)04:37
  • Identifying Local Highs06:08
  • Identifying Local Lows04:50
  • Highs and Lows – an iterative approach05:42
  • Identifying Trends (Uptrend / Downtrend)04:28
  • Adding Fibonacci Retracement Levels02:25
  • A Fibonacci Retracement Breakout Strategy07:57
  • Vectorized Strategy Backtesting04:04
  • Final Remarks and alternative Strategies01:52

  • Introduction00:47
  • Importing Time Series Data from csv-files08:16
  • Converting strings to datetime objects with pd.to_datetime()08:53
  • Indexing and Slicing Time Series07:25
  • Downsampling Time Series with resample()14:20
  • Coding Exercise 105:10
  • Getting Ready (Installing required library)02:20
  • Importing Stock Price Data from Yahoo Finance09:29
  • Initial Inspection and Visualization05:32
  • Normalizing Time Series to a Base Value (100)06:31
  • The shift() method06:51
  • The methods diff() and pct_change()06:41
  • Measuring Stock Performance with MEAN Returns and STD of Returns08:49
  • Financial Time Series – Return and Risk08:30
  • Financial Time Series – Covariance and Correlation04:32
  • Coding Exercise 200:04
  • Simple Returns vs. Log Returns09:18
  • Importing Financial Data from Excel11:25
  • Simple Moving Averages (SMA) with rolling()08:44
  • Momentum Trading Strategies with SMAs07:08
  • Exponentially-weighted Moving Averages (EWMA)04:32
  • Merging / Aligning Financial Time Series (hands-on)05:02
  • Helpful DatetimeIndex Attributes and Methods06:24
  • Filling NA Values with bfill, ffill and interpolation10:07
  • Timezones and Converting (Part 1)04:36
  • Timezones and Converting (Part 2)04:48

  • Introduction00:21
  • Introduction to OOP and examples for Classes10:58
  • 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 Attributes04:02
  • The method set_ticker()03:18
  • Adding more methods and performance metrics05:51
  • Inheritance09:01
  • Inheritance and the super() Function06:47
  • Adding meaningful Docstrings06:24
  • Creating and Importing Python Modules (.py)04:19
  • Coding Exercise 3: Create your own Class07:13

  • 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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