Advanced Crypto strategies for Algorithmic trading 2022

Advanced Crypto strategies for Algorithmic trading 2022

Advanced Crypto strategies for Algorithmic trading 2022

Technical analysis, machine learning and risk management for crypto algorithmic strategies. MT5 bots included

Language: english

Note: 4.5/5 (36 notes) 8,102 students

Instructor(s): Lucas Inglese

Last update: 2022-01-12

What you’ll 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

 

Requirements

  • None. You have to be motivated to learn the techniques of quantitative analysts for crypto currencies. That’s it!

 

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.


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.

 

Who this course is for

  • Everyone

 

Course 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 (CAPM metric)
    • Alpha ratio computation (CAPM 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

 

Advanced Crypto strategies for Algorithmic trading 2022Advanced Crypto strategies for Algorithmic trading 2022

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