Algorithmic Trading with Python: Machine Learning strategies

  • Post category:StudyBullet-4
  • Reading time:9 mins read


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

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 programmation

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

Description

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.


Get Instant Notification of New Courses on our Telegram channel.


 

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.

English
language

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
Automatisation 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
Enroll for Free

 

💠 Follow this Video to Get Free Courses on Every Udemy Topics! 💠

 

Tags: ,