Data science for algorithmic trading



It is with pride that I offer this data science course for algorithmic trading. It is the fruit of several years of work in the field to truly understand all the subtleties of the world of quantitative finance.

Using libraries will allow you to do complex mathematical calculations applied to finance in just a few lines of code. We will see how to create an algorithm for trading from data import to automatic positions. You will create an algorithm that will yield more than 50% annually on the Nasdaq 100 using an algorithm.


In summary, we will study:

The numpy library to do scientific calculations

The pandas library organize and visualize data

The Matplotlib library to make powerful graphics

Features engineering

Linear regression for finance

Machine vector support

Decision tree

Random Forest

Apply and understand the Sharpe ratio

Apply and understand the Sortino ratio

Understanding the volatility of a stock market asset

Understand and create a backtesting universe that is easy to use

Backtest the strategy using the most know metrics in trading

How to choose the best features for Machine Learning models

The basics in python language to follow the course and to do your own projects after the course.

The basics in python language to follow the course and to do your own projects after the course.

Who this course is for:

  • Everyone

What you’ll learn

  • Use numpy to do scientific calculation
  • Use pandas to import and organize data
  • Use Matplotlib to visualize data
  • Use, create, understand mathematical model
  • Machine learning for algorithmic trading
  • Features Engineering
  • Statistics for finance
  • Create an easy-to-reuse backtesting universe
  • Automatically take sales and buy positions
  • Data import with a API


Data science for algorithmic trading, Free Tutorials Download

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Author: Ho Quang Dai

I am Ho Quang Dai, from Vietnam – A country that loves peace. I share completely free courses from major academic websites around the world. Hope to bring free knowledge to everyone who can’t afford to buy

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