# Data science for algorithmic trading

We will create a algorithmic trading strategy that earns 50% annually.

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

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.

Course Content

• Introduction –> 6 lectures • 1min.
• Basics of python –> 6 lectures • 21min.
• Python for finance –> 9 lectures • 31min.
• Basics of trading –> 6 lectures • 7min.
• Statistics Stratégie for algorithmic trading –> 10 lectures • 38min.
• Backtesting –> 9 lectures • 23min.
• Data science for algorithmic trading –> 9 lectures • 41min.
• Machine Learning for algorithmic trading –> 6 lectures • 26min.
• BONUS –> 1 lecture • 1min. Requirements

• No knowledge required.
• Programming knowledge in python is advantageous.
• Statistics and algebra is a advantageous.

It’s with pride that I offer this data science course for algorithmic trading. It is the fruit of several years of work in the field in order 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 a algorithm of 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 to 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 reuse

Backtest the strategy

Get Tutorial