Technical Analysis with Python for Algorithmic Trading
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)
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 ChartsInteractive Volume ChartsTrend, Support and Resistance LinesSimple Moving Average (SMA)Exponential Moving Average (EMA) Moving Average Convergence Divergence (MACD)Relative Strength Index (RSI)Stochastic Oscillator Bollinger BandsPivot 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!
Overview
Section 1: Getting Started
Lecture 1 What is Technical Analysis? / Course Overview
Lecture 2 Tips: How to get the most out of this course
Lecture 3 Did you know…? (what Data can tell us about Technical Analysis)
Lecture 4 Student FAQ
Lecture 5 *** LEGAL DISCLAIMER (MUST READ!) ***
Lecture 6 Course Materials / Download (Updated: 21 Dec 2022)
Section 2: Installing Python and Jupyter Notebooks
Lecture 7 Overview
Lecture 8 Download and Install Anaconda
Lecture 9 How to open Jupyter Notebooks
Lecture 10 How to work with Jupyter Notebooks
Section 3: Technical Analysis with Python – an Introduction
Lecture 11 Overview
Lecture 12 Installing and importing required Libraries/Packages
Lecture 13 IMPORTANT NOTICE
Lecture 14 Loading Financial Data from the Web
Lecture 15 Charting – Simple Line Charts
Lecture 16 Charting – Interactive Line Charts with Cufflinks and Plotly
Lecture 17 How to customize Plotly Charts
Lecture 18 Candlestick and OHLC Bar Charts
Lecture 19 Bar Size / Granularity
Lecture 20 Volume Charts
Lecture 21 Technical Indicators – Overview and Examples
Lecture 22 Trend Lines
Lecture 23 Support and Resistance Lines
Section 4: Technical Analysis – Theory and Use Cases
Lecture 24 Section Overview
Lecture 25 Technical Analysis vs. Fundamental Analysis
Lecture 26 Technical Analysis and the Efficient Market Hypothesis (EMH)
Lecture 27 Technical Analysis – Applications and Use Cases
Lecture 28 An Introduction to Currencies (FOREX) and Trading
Section 5: Simple Moving Averages (SMA) and Introduction to Backtesting
Lecture 29 Introduction
Lecture 30 Getting the Data
Lecture 31 A simple Buy and Hold “Strategy”
Lecture 32 Performance Metrics
Lecture 33 SMA Crossover Strategies – Overview
Lecture 34 Defining an SMA Crossover Strategy
Lecture 35 Vectorized Strategy Backtesting
Lecture 36 Finding the optimal SMA Strategy
Lecture 37 Generalization with OOP: An SMA Backtesting Class in action
Lecture 38 OOP: the special method __init__()
Lecture 39 OOP: the method get_data()
Lecture 40 OOP: the method set_parameters()
Lecture 41 OOP: the method test_strategy()
Lecture 42 OOP: the method plot_results()
Lecture 43 OOP: the method update_and_run()
Lecture 44 OOP: the method optimize_parameters()
Lecture 45 OOP: Docstrings and String Representation
Lecture 46 Trading Costs (Part 1)
Lecture 47 Trading Costs (Part 2)
Lecture 48 Trading Costs (Part 3)
Lecture 49 Special Case: Price/SMA Crossover
Section 6: Exponential Moving Averages (EMA)
Lecture 50 Introduction
Lecture 51 EMA Crossover Strategies – Overview
Lecture 52 Getting the Data
Lecture 53 EMA vs. SMA
Lecture 54 Defining an EMA Crossover Strategy
Lecture 55 Vectorized Strategy Backtesting
Lecture 56 OOP Challenge: Create the EMA Backtesting Class (incl. Solution)
Lecture 57 The EMA Backtesting Class in Action
Section 7: SMA / EMA Crossover Strategies (Coding Challenge)
Lecture 58 Introduction
Lecture 59 SMA / EMA Crossover Strategies – Overview
Lecture 60 Instructions & some Hints
Lecture 61 Solution
Section 8: Moving Average Convergence Divergence (MACD)
Lecture 62 Introduction
Lecture 63 MACD Strategies – Overview
Lecture 64 Getting the Data
Lecture 65 Defining an MACD Strategy (Part 1)
Lecture 66 Defining an MACD Strategy (Part 2)
Lecture 67 Vectorized Strategy Backtesting
Lecture 68 The MACD Backtesting Class in Action
Lecture 69 OOP Challenge: Create the MACD Backtesting Class (incl. Solution)
Lecture 70 Alternative MACD Strategies and Interpretations
Section 9: Relative Strength Index (RSI)
Lecture 71 Introduction
Lecture 72 RSI Strategies – Overview
Lecture 73 Getting the Data
Lecture 74 Defining an RSI Strategy (Part 1)
Lecture 75 Defining an RSI Strategy (Part 2)
Lecture 76 Vectorized Strategy Backtesting
Lecture 77 The RSI Backtesting Class in Action
Lecture 78 OOP Challenge: Create the RSI Backtesting Class (incl. Solution)
Lecture 79 Alternative RSI Strategies and Interpretations
Section 10: Working with two or many Indicators – MACD & RSI
Lecture 80 Introduction
Lecture 81 A combined MACD / RSI Strategy – Overview
Lecture 82 Backtesting and Optimizing the Strategies separately
Lecture 83 Combining MACD with RSI and Backtesting
Section 11: Stochastic Oscillator
Lecture 84 Introduction
Lecture 85 Getting the Data
Lecture 86 Defining an SO Strategy
Lecture 87 Vectorized Strategy Backtesting
Lecture 88 The SO Backtesting Class in Action
Lecture 89 OOP Challenge: Create the SO Backtesting Class (incl. Solution)
Lecture 90 Alternative SO Strategies and Interpretations
Section 12: Bollinger Bands
Lecture 91 Introduction
Lecture 92 Bollinger Bands – Overview
Lecture 93 Getting the Data
Lecture 94 Defining a Bollinger Bands Mean-Reversion Strategy (Part 1)
Lecture 95 Defining a Bollinger Bands Mean-Reversion Strategy (Part 2)
Lecture 96 Vectorized Strategy Backtesting
Lecture 97 The BB Backtesting Class in action
Lecture 98 OOP Challenge: Create the BB Backtesting Class (incl. Solution)
Section 13: Pivot Point Strategies
Lecture 99 Introduction
Lecture 100 Pivot Point – Overview and Data requirements
Lecture 101 Adding Pivot Point and Support and Resistance Lines
Lecture 102 Defining a simple Pivot Point Strategy
Lecture 103 Vectorized Strategy Backtesting
Lecture 104 Starting with raw Data
Lecture 105 Preparing the Data (1) – Timezone Conversion
Lecture 106 Preparing the Data (2) – Resampling to daily (NY Close)
Lecture 107 Preparing the Data (3) – OHLC Resampling
Lecture 108 Preparing the Data (4) – Merging Intraday and Daily Data
Lecture 109 Final Remarks – Now it´s your turn!
Section 14: Fibonacci Retracement Strategies
Lecture 110 Introduction
Lecture 111 Getting the Data
Lecture 112 A first Intuition on Fibonacci Retracement (Uptrend)
Lecture 113 A first Intuition on Fibonacci Retracement (Downtrend)
Lecture 114 Identifying Local Highs
Lecture 115 Identifying Local Lows
Lecture 116 Highs and Lows – an iterative approach
Lecture 117 Identifying Trends (Uptrend / Downtrend)
Lecture 118 Adding Fibonacci Retracement Levels
Lecture 119 A Fibonacci Retracement Breakout Strategy
Lecture 120 Vectorized Strategy Backtesting
Lecture 121 Final Remarks and alternative Strategies
Section 15: Appendix 1: Introduction to Time Series Data and Financial Analysis in Pandas
Lecture 122 Introduction
Lecture 123 Importing Time Series Data from csv-files
Lecture 124 Converting strings to datetime objects with pd.to_datetime()
Lecture 125 Indexing and Slicing Time Series
Lecture 126 Downsampling Time Series with resample()
Lecture 127 Coding Exercise 1
Lecture 128 Getting Ready (Installing required library)
Lecture 129 Importing Stock Price Data from Yahoo Finance
Lecture 130 Initial Inspection and Visualization
Lecture 131 Normalizing Time Series to a Base Value (100)
Lecture 132 The shift() method
Lecture 133 The methods diff() and pct_change()
Lecture 134 Measuring Stock Performance with MEAN Returns and STD of Returns
Lecture 135 Financial Time Series – Return and Risk
Lecture 136 Financial Time Series – Covariance and Correlation
Lecture 137 Coding Exercise 2
Lecture 138 Simple Returns vs. Log Returns
Lecture 139 Importing Financial Data from Excel
Lecture 140 Simple Moving Averages (SMA) with rolling()
Lecture 141 Momentum Trading Strategies with SMAs
Lecture 142 Exponentially-weighted Moving Averages (EWMA)
Lecture 143 Merging / Aligning Financial Time Series (hands-on)
Lecture 144 Helpful DatetimeIndex Attributes and Methods
Lecture 145 Filling NA Values with bfill, ffill and interpolation
Lecture 146 Timezones and Converting (Part 1)
Lecture 147 Timezones and Converting (Part 2)
Section 16: Appendix 2: Object Oriented Programming (OOP): The Financial Instrument Class
Lecture 148 Introduction
Lecture 149 Introduction to OOP and examples for Classes
Lecture 150 Required Packages
Lecture 151 The Financial Analysis Class live in action (Part 1)
Lecture 152 The Financial Analysis Class live in action (Part 2)
Lecture 153 The special method __init__()
Lecture 154 The method get_data()
Lecture 155 The method log_returns()
Lecture 156 String representation and the special method __repr__()
Lecture 157 The methods plot_prices() and plot_returns()
Lecture 158 Encapsulation and protected Attributes
Lecture 159 The method set_ticker()
Lecture 160 Adding more methods and performance metrics
Lecture 161 Inheritance
Lecture 162 Inheritance and the super() Function
Lecture 163 Adding meaningful Docstrings
Lecture 164 Creating and Importing Python Modules (.py)
Lecture 165 Coding Exercise 3: Create your own Class
Section 17: What´s next? (outlook and additional resources)
Lecture 166 Bonus Lecture
(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.
Course Information:
Udemy | English | 13h 27m | 5.31 GB
Created by: Alexander Hagmann
You Can See More Courses in the Developer >> Greetings from CourseDown.com