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• Post category:Udemy (Sept 2021) ## We will create an algorithmic trading strategy that earns 50% annually.

What you will learn

Use numpy to do scientific calculation

Use pandas to import and organize data

Use Matplotlib to visualize data

Use, create, understand mathematical model

Features Engineering

Statistics for finance

Create an easy-to-reuse backtesting universe

Automatically take sales and buy positions

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Data import with a API

Description

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.

## English

Language

Content

Introduction

How to learn this course

Install Anaconda environnement

Install a librairy

How to have the personal key?

Ressources

Basics of python

Variable

list

for

if

for/if

Summary

Python for finance

libraires

data import

Variation price

standard deviation / mean

pandas visualization

Matplotlib visualization

autocorrelation

Bonus: Moving average

Summary

Stock market

Assets

cfd

leverage

Summary

Introduction

Data import

Reorganize

Set of data

Linear regression

Visualization

earning

Statistics

Summary

Backtesting

How understand this?

Starting

Initialization

Growth of investment

Statistics

Measures

Accuracy

Graphics

Summary

Data import

Variance Features Selection

Correlation features selection

Other features selection

Backtesting all variable

Backtesting best variable

Resume

Standardization

Summary

Data import

Preprocessing

Linear SVR

Decision Tree Regressor

Random Forest Regressor

Summary

BONUS

Code for create a backtesting environnemnt