Technical Analysis with Python for Algorithmic Trading

Course Curriculum

Chapter 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: May 2023)

Chapter 2: Installing Python and Jupyter Notebooks

Lecture 1: Overview

Lecture 2: Download and Install Anaconda

Lecture 3: How to open Jupyter Notebooks

Lecture 4: How to work with Jupyter Notebooks

Chapter 3: Technical Analysis with Python – an Introduction

Lecture 1: Overview

Lecture 2: Installing and importing required Libraries/Packages

Lecture 3: IMPORTANT NOTICE

Lecture 4: Loading Financial Data from the Web

Lecture 5: Charting – Simple Line Charts

Lecture 6: Charting – Interactive Line Charts with Cufflinks and Plotly

Lecture 7: How to customize Plotly Charts

Lecture 8: Candlestick and OHLC Bar Charts

Lecture 9: Bar Size / Granularity

Lecture 10: Volume Charts

Lecture 11: Technical Indicators – Overview and Examples

Lecture 12: Trend Lines

Lecture 13: Support and Resistance Lines

Chapter 4: Technical Analysis – Theory and Use Cases

Lecture 1: Section Overview

Lecture 2: Technical Analysis vs. Fundamental Analysis

Lecture 3: Technical Analysis and the Efficient Market Hypothesis (EMH)

Lecture 4: Technical Analysis – Applications and Use Cases

Lecture 5: An Introduction to Currencies (FOREX) and Trading

Chapter 5: Simple Moving Averages (SMA) and Introduction to Backtesting

Lecture 1: Introduction

Lecture 2: Getting the Data

Lecture 3: A simple Buy and Hold "Strategy"

Lecture 4: Performance Metrics

Lecture 5: SMA Crossover Strategies – Overview

Lecture 6: Defining an SMA Crossover Strategy

Lecture 7: Vectorized Strategy Backtesting

Lecture 8: Finding the optimal SMA Strategy

Lecture 9: Generalization with OOP: An SMA Backtesting Class in action

Lecture 10: OOP: the special method __init__()

Lecture 11: OOP: the method get_data()

Lecture 12: OOP: the method set_parameters()

Lecture 13: OOP: the method test_strategy()

Lecture 14: OOP: the method plot_results()

Lecture 15: OOP: the method update_and_run()

Lecture 16: OOP: the method optimize_parameters()

Lecture 17: OOP: Docstrings and String Representation

Lecture 18: Trading Costs (Part 1)

Lecture 19: Trading Costs (Part 2)

Lecture 20: Trading Costs (Part 3)

Lecture 21: Special Case: Price/SMA Crossover

Chapter 6: Exponential Moving Averages (EMA)

Lecture 1: Introduction

Lecture 2: EMA Crossover Strategies – Overview

Lecture 3: Getting the Data

Lecture 4: EMA vs. SMA

Lecture 5: Defining an EMA Crossover Strategy

Lecture 6: Vectorized Strategy Backtesting

Lecture 7: OOP Challenge: Create the EMA Backtesting Class (incl. Solution)

Lecture 8: The EMA Backtesting Class in Action

Chapter 7: SMA / EMA Crossover Strategies (Coding Challenge)

Lecture 1: Introduction

Lecture 2: SMA / EMA Crossover Strategies – Overview

Lecture 3: Instructions & some Hints

Lecture 4: Solution

Chapter 8: Moving Average Convergence Divergence (MACD)

Lecture 1: Introduction

Lecture 2: MACD Strategies – Overview

Lecture 3: Getting the Data

Lecture 4: Defining an MACD Strategy (Part 1)

Lecture 5: Defining an MACD Strategy (Part 2)

Lecture 6: Vectorized Strategy Backtesting

Lecture 7: The MACD Backtesting Class in Action

Lecture 8: OOP Challenge: Create the MACD Backtesting Class (incl. Solution)

Lecture 9: Alternative MACD Strategies and Interpretations

Chapter 9: Relative Strength Index (RSI)

Lecture 1: Introduction

Lecture 2: RSI Strategies – Overview

Lecture 3: Getting the Data

Lecture 4: Defining an RSI Strategy (Part 1)

Lecture 5: Defining an RSI Strategy (Part 2)

Lecture 6: Vectorized Strategy Backtesting

Lecture 7: The RSI Backtesting Class in Action

Lecture 8: OOP Challenge: Create the RSI Backtesting Class (incl. Solution)

Lecture 9: Alternative RSI Strategies and Interpretations

Chapter 10: Working with two or many Indicators – MACD & RSI

Lecture 1: Introduction

Lecture 2: A combined MACD / RSI Strategy – Overview

Lecture 3: Backtesting and Optimizing the Strategies separately

Lecture 4: Combining MACD with RSI and Backtesting

Chapter 11: Stochastic Oscillator

Lecture 1: Introduction

Lecture 2: Getting the Data

Lecture 3: Defining an SO Strategy

Lecture 4: Vectorized Strategy Backtesting

Lecture 5: The SO Backtesting Class in Action

Lecture 6: OOP Challenge: Create the SO Backtesting Class (incl. Solution)