Algorithmic Trading with Python- Machine Learning strategies

Algorithmic Trading with Python: Machine Learning strategies, available at $69.99, has an average rating of 4, with 71 lectures, based on 175 reviews, and has 14915 subscribers.

You will learn about 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 This course is ideal for individuals who are Everyone who wants to learn MT5 live trading using python or Students in data science or Professional in data science or Professional in finance or Students in finance It is particularly useful for Everyone who wants to learn MT5 live trading using python or Students in data science or Professional in data science or Professional in finance or Students in finance.

Enroll now: Algorithmic Trading with Python: Machine Learning strategies

Summary

Title: Algorithmic Trading with Python: Machine Learning strategies

Price: $69.99

Average Rating: 4

Number of Lectures: 71

Number of Published Lectures: 71

Number of Curriculum Items: 71

Number of Published Curriculum Objects: 71

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Everyone who wants to learn MT5 live trading using python

Students in data science

Professional in data science

Professional in finance

Students in finance

Target Audiences

Everyone who wants to learn MT5 live trading using python

Students in data science

Professional in data science

Professional in finance

Students in finance

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.

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

Instructors

Lucas Inglese
Founder of Quantreo

Rating Distribution

1 stars: 8 votes

2 stars: 10 votes

3 stars: 24 votes

4 stars: 44 votes

5 stars: 89 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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