• Post category:StudyBullet-7
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Figures detection, Patterns detection, Backtest, using MetaTrader 5 and Python. Bots included

What you will learn

🚀Create an algorithmic trading strategy using patterns detection or figures detection from scratch

🤖Put any algorithm in live trading using MetaTrader 5 and Python

Data Cleaning using Pandas

Manage financial data using Numpy, Pandas and Matplotlib

Python programming for algorithmic trading

Combine price action and technical analysis to optimize your performance

Detect trading figures through the candlestick

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 price action 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: price action (Support, resistance) patterns detection (trading figures detection)
  • Backtesting: Make 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

Basis of the programming language Python

Introduction
Type of object: Number
Type of object: String
Type of object: Logical operation / Boolean
Type of object: Variable assignent
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 transformation
Pandas: Conditional selection
Matplotlib: Graph
Matplotlib: Scatter
Matplotlib: Tools

Import and manage the financial data

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

Figures detection: Trading strategy using the Engulfing figure

Introduction
Import / Transform data
Bullish Engulfing figure
Verification signal creation
Bearish Engulfing figure
Compute the profit
Automate the process

Vectorized Backtesting

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

Combine price action with technical analysis to optimize your profits

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

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 positions
Close all positions
Live trading application: random signal
Live trading application: Engulfing figure strategy
Live trading application: Support & resistance + SMA + RSI strategy