Algorithmic trading for beginners: from zero to hero

Algorithmic trading for beginners: from zero to hero

Algorithmic trading for beginners: from zero to hero

Technical indicators, risk management, Python programming, maths for trading, MetaTrader 5 live trading. Bots included

Language: english

Note: 4.2/5 (32 notes) 6,800 students

Instructor(s): Lucas Inglese

Last update: 2022-02-26

What you’ll learn

  • Create a trading strategy from scratch, backtest it and optimize it
  • Basics in Python and Maths for algorithmic trading
  • Advanced algo trading concepts like Hurst exponent and how to adapt your strategy to your data
  • Understand how to create and use technical indicators with Python
  • Backtest your strategy without error using vectorized backtesting
  • Learn many financial metrics: Sortino ratio, alpha, beta,…
  • Learn how to analyze your drawdown
  • Add a stop loss on your strategies
  • Combine different technical indicators to double your earnings
  • MetaTrader 5 live trading using Python



  • None. You have to be motivated to learn the techniques of quantitative analysts. You just need a computer and Internet!



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.

In this course, you will learn how to use technical analysis 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 and risk management 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) and condition combination.

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

  • Risk management: Manage the drawdown(Stop loss), combine strategies properly (Strategies portoflio).

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.


Who this course is for

  • Everyone


Course content

  • Introduction
    • READ ME
    • Install the environments
  • How to break into algorithmic trading field?
    • Introduction
    • Algorithmic trading vs quantitative trading
    • Create trading system
    • Contract for Difference (CFD)
    • Entry & Exit signals
    • Risk return couple
    • Prerequisites
  • Python basics
    • Introduction
    • Type of object: Number
    • Type of object: String
    • Type of object: Logical operations / 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: Toolbox
    • Matplotlib: Scatter
  • Basics statistics
    • Introduction
    • Population versus sample
    • Application: create google stock price sample
    • Central tendency measure: The mean
    • Application: Compute mean Google return + Annualization of returns
    • Central tendency measure: The median
    • Extreme value problem? Compute the median
    • Central tendency measure: The percentile
    • Application: Understand Google return distribution
    • Dispersion measure: The variance
    • Application: Compute variance returns + Variance annualization
    • Dispersion measure: The standard deviation
    • Application: Compute the volatility + Annualize the volatility
    • Relationship measure: Covariance / covariance matrix
    • Application: Assets covariance
    • Relationship measure: Correlation
    • Application: Assets correlation
    • DOWNLOAD summary sheet about descriptive statistics
  • Import and manage the data
    • Introduction
    • Import & manage data from Metatrader 5
    • Import & manage data from Yahoo finance
  • Your first algorithmic trading strategies
    • 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 data!
  • 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 our strategy
  • Intermediate trading strategies: Combine different signals
    • Introduction
    • Recap
    • Compute the RSI
    • Add multiple conditions to take a position
    • Verify if the positions are correctly implemented
    • Compute the profits
    • Apply a stop loss (SL) on your returns
    • Automate the strategy
    • Compare the same strategy using different data sources
    • Create a portfolio of trading strategies
  • Advanced concepts to optimize you strategies
    • Introduction
    • What is the Hurst exponent?
    • How to find if is this asset is Trending or not?
    • How to find if is this asset is Mean reverting or not?
    • How to find if is this asset follows a random walk or not?
    • Adapt your strategy to your data!
  • 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: moving averages + rsi
  • How to go deeper into the algorithmic trading
    • Bonus lecture


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