Algorithmic Trading with Python: Machine Learning strategies

Algorithmic Trading with Python: Machine Learning strategies

Algorithmic Trading with Python: Machine Learning strategies

Artificial intelligence / Machine Learning for algorithmic trading. MetaTrader 5 bots included!

Language: english

Note: 4.1/5 (106 notes) 12,476 students

Instructor(s): Lucas Inglese

Last update: 2021-12-16

What you’ll learn

  • Machine learning skills
  • MT5 live trading
  • Create algorithmic trading strategies using Machine Learning
  • Manage data using Pandas
  • Data Cleaning using Pandas
  • Python programming
  • Compare / choose trading strategies
  • Understand and implement a Linear Regression
  • Understand and implement a SVM
  • Understand and implement a PCA
  • Import stock prices from your broker
  • Import stock prices from Yahoo Finance
  • Put your strategy on a VPS



  • Some python knowledge are welcoming but not necessary



You already know python, and you want to monetize and diversify your knowledge?

You already have some trading knowledge, and you want to learn about artificial intelligence in algorithmic trading?

You are simply a curious person who wants to get into this subject?

If you answer at least one of these questions, I welcome you to this course. For beginners in python, don’t panic! There is a python course (small but condensed) to master this python knowledge.

In this course, you will learn how to program strategies from scratch. Indeed, after a crash course in Python, you will learn how to implement a system based on Machine Learning (Linear regression, Support Vector Machine).

Once the strategies are created, we will backtest them using python. So that we know better this strategy using statistics like Sortino ratio, drawdown the beta… Then we will put our best algorithm in live trading.

You will learn about tools used by both portfolio managers and professional traders:

  • Artificial intelligence algorithm

  • Apply Machine Learning in Live Trading

  • Predict stock prices using Machine Learning

  • Live trading implementation

  • Import financial data

  • Linear Regression Algorithm

  • Support Vector Machine (SVM)

  • How to do a backtest

  • The risk of a stock

  • Python

  • What is a long and short position

  • Numpy

  • Pandas

  • Matplotlib

  • Sharpe ratio

  • Sortino ratio

  • Alpha coefficient

  • Beta coefficient

Why this course and not another?

  • It is not a programming course nor a trading course. It is a course in which programming is used for trading.

  • A data scientist does not create this course, but a degree in mathematics and economics specialized in Machine learning for finance.

  • You can ask questions or read our quantitative finance articles simply by registering on our free Discord forum.

Without forgetting that the course is satisfied or refunded for 30 days. Don’t miss an opportunity to improve your knowledge of this fascinating subject.


Who this course is for

  • Everyone who wants to learn MT5 live trading using python
  • Students in data science
  • Professional in data science
  • Professional in finance
  • Students in finance


Course content

  • Introduction
    • Introduction
  • Basics of python
    • Introduction
    • Type of object: Number
    • Type of object: String
    • Type of object: Logical Operations and Boolean
    • Type of object: Variable assignment
    • Type of object: Tuple and List
    • Type of object: Dictionary
    • Type of object: Set
    • Python structures: If / Elif/ Else
    • Python structures: For
    • Python structures: While
    • Functions: Basics of function
    • Functions: Local variable
    • Functions: Global variable
    • Functions: Lambda function
  • Basics of Python for Data Science
    • Introduction
    • Numpy: Array
    • Numpy: Random
    • Numpy: Indexing / slicing /Transformation
    • Pandas: Serie and DataFrame
    • Pandas: Cleaning and selection data
    • Pandas: Conditional selection
    • Matplotlib: Graph
    • Matplotlib: Scatter
    • Matplotlib: Tools
  • Import financial data
    • Introduction / Install library on google colab
    • Import the data
    • Strengths and weaknesses of yfinance
  • Financial features engineering
    • Introduction
    • Get stock prices
    • Create a simple moving average (SMA)
    • Create a moving standard deviation (MSD)
    • Use the technical analysis library to create a RSI indicator
    • Automatization of the features engineering process
  • Linear regression algorithm
    • Introduction
    • Linear Regression: Theory
    • Import the data
    • Split the dataset
    • Linear Regression: Practice
    • Predict stock prices using Machine learning predictions
    • Create trading strategies using Machine learning predictions
    • Automatize the process
  • Vectorized Backtesting
    • Introduction
    • Sortino ratio computation
    • Beta ratio computation (CAPM metric)
    • Alpha ratio computation (CPAM metric)
    • Drawdown function: creation
    • Drawdown function: application
    • BackTesting Function
    • Backtesting Function: Customize
    • Application: Machine learning
  • Support vecteur machine
    • Introduction
    • SVR: Therory
    • Features engineering: Create technical indicators
    • Features engineering: Standardization
    • Features engineering: Principal component analysis
    • SVR: Practice
    • Backtest the strategy
    • Automatization
  • MetaTrader 5 Live Trading using Python
    • Introduction
    • Install a library on Jupyter Notebook
    • Initialize the platform
    • Get data from your broker
    • Send orders on the market using Python
    • Get current positions
    • Run structure creation
    • Close all positions
    • Live Trading application: random signals
    • Live Trading application: SVR


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