Algorithmic Trading with Python: Machine Learning strategies

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: FREE E-book!

Chapter 2: Basics of python

Lecture 1: Introduction

Lecture 2: Type of object: Number

Lecture 3: Type of object: String

Lecture 4: Type of object: Logical Operations and Boolean

Lecture 5: Type of object: Variable assignment

Lecture 6: Type of object: Tuple and List

Lecture 7: Type of object: Dictionary

Lecture 8: Type of object: Set

Lecture 9: Python structures: If / Elif/ Else

Lecture 10: Python structures: For

Lecture 11: Python structures: While

Lecture 12: Functions: Basics of function

Lecture 13: Functions: Local variable

Lecture 14: Functions: Global variable

Lecture 15: Functions: Lambda function

Chapter 3: Basics of Python for Data Science

Lecture 1: Introduction

Lecture 2: Numpy: Array

Lecture 3: Numpy: Random

Lecture 4: Numpy: Indexing / slicing /Transformation

Lecture 5: Pandas: Serie and DataFrame

Lecture 6: Pandas: Cleaning and selection data

Lecture 7: Pandas: Conditional selection

Lecture 8: Matplotlib: Graph

Lecture 9: Matplotlib: Scatter

Lecture 10: Matplotlib: Tools

Chapter 4: Import financial data

Lecture 1: Introduction / Install library on google colab

Lecture 2: Import the data

Lecture 3: Strengths and weaknesses of yfinance

Chapter 5: Financial features engineering

Lecture 1: Introduction

Lecture 2: Get stock prices

Lecture 3: Create a simple moving average (SMA)

Lecture 4: Create a moving standard deviation (MSD)

Lecture 5: Use the technical analysis library to create a RSI indicator

Lecture 6: Automatization of the features engineering process

Chapter 6: Linear regression algorithm

Lecture 1: Introduction

Lecture 2: Linear Regression: Theory

Lecture 3: Import the data

Lecture 4: Split the dataset

Lecture 5: Linear Regression: Practice

Lecture 6: Predict stock prices using Machine learning predictions

Lecture 7: Create trading strategies using Machine learning predictions

Lecture 8: Automatize the process

Chapter 7: Vectorized Backtesting

Lecture 1: Introduction

Lecture 2: Sortino ratio computation

Lecture 3: Beta ratio computation (CAPM metric)

Lecture 4: Alpha ratio computation (CPAM metric)

Lecture 5: Drawdown function: creation

Lecture 6: Drawdown function: application

Lecture 7: BackTesting Function

Lecture 8: Backtesting Function: Customize

Lecture 9: Application: Machine learning

Chapter 8: Support vecteur machine

Lecture 1: Introduction

Lecture 2: SVR: Therory

Lecture 3: Features engineering: Create technical indicators

Lecture 4: Features engineering: Standardization

Lecture 5: Features engineering: Principal component analysis

Lecture 6: SVR: Practice

Lecture 7: Backtest the strategy

Lecture 8: Automatization

Chapter 9: MetaTrader 5 Live Trading using Python

Lecture 1: Introduction

Lecture 2: Install a library on Jupyter Notebook

Lecture 3: Initialize the platform

Lecture 4: Get data from your broker

Lecture 5: Send orders on the market using Python

Lecture 6: Get current positions

Lecture 7: Run structure creation

Lecture 8: Close all positions

Lecture 9: Live Trading application: random signals

Lecture 10: Live Trading application: SVR