• Post category:StudyBullet-5
  • Reading time:9 mins read


What you will learn

Get data from your broker

Create crypto trading strategies from scratch

Create crypto strategies using Machine Learning

Plot financial data

MT5 live trading using Python

Vectorized Backtesting

Manage financial data using Pandas

Quantify the risk of a strategy

Combine Trading strategies

Understand and implement different drawdown break strategies (risk management)

Manage the risk of the crypto-currencies

Data cleaning using Pandas

Find the best increase of the crypto-currencies to optimize your returns

Description

Do you want to create quantitative CRYPTO strategies to earn up to 79%/YEAR ?

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.

In this course, you will learn how to use technical analysis and machine learning to create robust crypto 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 risk management techniques to control the volatility in your crypto investment plan.

 


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

  • Modeling: Technical analysis (Support & resistance, Ichimoku), Machine Learning (Random Forest Classifier).

  • Backtesting: Do a backtest properly without error and minimize the computation time (Vectorized Backtesting).

  • Risk management: Manage the drawdown(Drawdown break strategy), combine strategies properly (Crypto 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, financial theory, and machine learning 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

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

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

Your first crypto algorithmic trading strategy

Introduction
Manage the data
Import data from MT5 platform
Support & Resistance
Strategy intuition
Code the strategy
Verification graph
Compute the profit
Automatization
Apply to a crypto assets portfolio
How to improve this strategy?

Vectorized Backtesting

Introduction
Sortino ratio computation
Beta ratio computation (CPAM metric)
Alpha ratio computation (CPAM metric)
Drawdown: function creation
Drawdown: application
Backtesting Function (1)
Backtesting Function (2)
Application: crypto backtesting

Advanced crypto strategies: Machine Learning classifier

Introduction
Features engineering
Target engineering
Train / Test set
Standardization
Principal component analysis
Fit the model
Make predicitons
Compute the profit
Automatization + Other example

Portfolio & Risk management apply to crypto algorithmic trading

Introduction
Drawdown break strategy: The theory
Drawdown break strategy: The practice
Crypto strategies portfolio
Drawdown break strategy: Apply to portfolio
Drawdown break strategy and Stop loss: Complementary or substitutable

MetaTrader 5 live Trading

Introduction
Install a library on Jupyter
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
Live trading application: Breakout strategy
Enroll for Free

 

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