• Post category:StudyBullet-7
  • Reading time:9 mins read


Technical analysis, Machine Learning, Price Action, Backtest, MetaTrader 5 live trading.

What you will learn

Create an algorithmic trading strategy from A to Z (data import to live trading)

Put any algorithm in live trading using MetaTrader 5 and Python

Data Cleaning using Pandas

Guided tour thought the main algorithmic trading strategy (Technical Analysis, Price action, Machine Learning)

Manage financial data using Numpy, Pandas and Matplotlib

Python programming for algorithmic trading

Create scaling, intraday and swing trading strategies

Import stock price from Yahoo Finance and from your broker

Description

Do you want to create algorithmic trading strategies?

You already have some trading knowledge and you want to learn about quantitative trading/finance?

You are simply a curious person who wants to get into this subject to monetize and diversify your knowledge?

If you answer at least one of these questions, I welcome you to this course. All the applications of the course will be done using Python. However, for beginners in Python, don’t panic! There is a FREE python crash course included to master Python.


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In this course, you will learn how to use technical analysis, price action, machine learning to create robust strategies. You will perform quantitative analysis to find patterns in the data. Once you will have many profitable strategies, we will learn how to perform vectorized backtesting. Then you will apply portfolio techniques to reduce the drawdown and maximize your returns.

You will learn and understand  quantitative analysis used by portfolio managers and professional traders:

  • Modeling: Technical analysis (Moving average, RSI), price action (Support, resistance) and Machine Learning (Linear regression).
  • Backtesting: Do a backtest properly without error and minimize the computation time (Vectorized Backtesting).
  • Portfolio management: Combine strategies properly (Strategies portfolio).

Why this course and not another?

  • This is not a programming course nor a trading course or a machine learning course. It is a course in which statistics, programming and financial theory are used for trading.
  • This course is not created by a data scientist but by a degree in mathematics and economics specializing in mathematics applied to 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

READ ME
Install the environments

Python basis

Introduction
Type of object: Number
Type of object: String
Type of object: Logical operation / 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

Python for data science

Introduction
Numpy: Array
Numpy: Random
Numpy: Indexing / Slicing / Transformation
Pandas: Serie and DataFrame
Pandas: Cleaning and selection
Pandas: Conditional selection
Matplotlib: Graph
Matplotlib: Scatter
Matplotlib: Tools

Import and manage data

Introduction
Import & manage data from Metatrader 5
Import & manage data from Yahoo Finance

Algorithmic daily strategy: Technical Analysis

Introduction
Simple moving average
Strategy explanation
How to verify our trading position?
Compute the profit of a trading strategy
How to automate the strategy
Most important video: Performance depending of the broker!

Vectorized Backtesting

Introduction
Sortino ratio computation
Beta ratio computation (CAMP metric)
Alpha ratio computation (CAMP metric)
Drawdown function: creation
Drawdown function: application
Backtesting function (1)
Backtesting function (2)
Backtest your strategy

Algorithmic scalping trading: price action + SMA + RSI

Introduction
Import the data
Support & Resistance
Support & Resistance trading strategy
Support & Resistance + SMA trading strategy
Support & Resistance + SMA + RSI trading strategy
Automate the process
Scalping trading strategy + Portfolio management

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
Automatization of the features engineering process

Algorithmic intra-day strategy: Applied Machine learning

Introduction
Linear Regression: Theory
Import the data
Features engineering process
Split the dataset
Linear Regression: Practice
Predict stock prices using Machine learning predictions
Create trading strategies using Machine learning predictions
Automatize the process
Apply the process to hourly trading strategies

MetaTrader 5 live trading

Introduction
Install a library on Jupyter Notebook
Initialize the platform
Get data broker
Send orders on the market using Python
Get current positions
Run structure creation
Close all positions
Live trading application: random signal