Deep Learning for algorithmic trading using Python

Deep Learning for algorithmic trading using Python

Deep Learning for algorithmic trading using Python

Artificial intelligence, Backtesting, optimization for algorithmic trading with MetaTrader 5. Bot included

Language: english

Note: 4.3/5 (28 notes) 4,513 students

Instructor(s): Lucas Inglese

Last update: 2022-03-10

What you’ll learn

  • Create an algorithmic trading strategy based on deep learning algorithms
  • Put any algorithm in live trading using MetaTrader 5 and Python
  • Manage financial data using Numpy, Pandas and Matplotlib
  • Data cleaning using Pandas
  • Python programming
  • Deep learning implementation using TensorFlow 2.0
  • Understand and implement the Deep Neural Networks (DNN)
  • Understand and implement the Recurrent Neural Networks (RNN)
  • Import stock price from Yahoo Finance and from your broker

 

Requirements

  • Basis in Machine Learning or Deep Learning are very well welcome

 

Description

You already know python, and you want to monetize and diversify your knowledge?

You already have some trading knowledge, and you want to learn about artificial intelligence in algorithmic trading?

You are simply a curious person who wants to get into this subject?


If you answer at least one of these questions, I welcome you to this course. For beginners in python, don’t panic! There is a python course (small but condensed) to master this python knowledge.

In this course, you will learn how to program strategies from scratch. Indeed, after a crash course in Python, you will learn how to implement a system based on Deep Learning (Deep neural network, Recurrent neural network).

Once the strategies are created, we will backtest them using python. So that we know better this strategy using statistics like Sortino ratio, drawdown the beta… Then we will put our best algorithm in live trading.


You will learn about tools used by both portfolio managers and professional traders:

  • Artificial intelligence algorithm

  • Apply Deep Learning in Live Trading

  • Predict stock prices using Deep Learning

  • Live trading implementation

  • Import financial data using MetaTrader 5 or Yahoo finance

  • DNN Algorithm

  • RNN algorithm to analyze and predict time series behavior

  • How to do a backtest a strategy using the programming language Python

  • Numpy, Pandas, Matplotlib

  • Sharpe, Sortino ratios

  • Alpha, Beta coefficients



Why this course and not another?

  • It is not a programming course nor a trading course. It is a course in which programming is used for trading.

  • A data scientist does not create this course, but a degree in mathematics and economics specialized in Machine learning for 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 who wants to learn advanced AI techniques to algorithmic trading

 

Course content

  • Introduction
    • READ ME
    • Install the environments
  • 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: Scatter
    • Matplotlib: Toolbox
  • Import and manage the data
    • Introduction
    • Import & manage data from Metatrader 5
    • Import & manage data from Yahoo Finance
  • Features engineering
    • Introduction
    • Get stock prices
    • Create a simple moving average (SMA)
    • Create a moving standard deviation (MSD)
    • Use the Technical analysis library to compute the RSI indicator
    • Automatization of the features engineering process
  • Deep Neural Networks apply to algorithmic trading
    • Introduction
    • Quick recap of the DNN theory
    • Data import & Features engineering
    • Train / Test set split (to fit the DNN model)
    • Why and how to standardize the features
    • Create a DNN using Tensorflow 2.0
    • Use the DNN predictions to create a trading strategy
    • Automate the process
    • The stochastic initialization problem
    • How to fix the stochastic initialization problem
    • Bagging method using the different ANNs
  • 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)
    • Backtest a trading strategy based on DNN
  • Recurrent Neural Networks for algorithmic trading
    • Introduction
    • Theory behind RNNs
    • Recap from the DNN chapter
    • How to transform 2-dimensional data into 3-dimensional data
    • How to create a RNN using TensorFlow 2.0
    • Dropout Layer
    • RNN prediction to create a trading strategy
    • Automate the process
    • Find the best models throughout all the stochastic initialization
  • MetaTrader 5 live trading using Python
    • 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 signals
    • Live Trading strategy based on ANN
    • Live Trading strategy based on RNN

 

Deep Learning for algorithmic trading using PythonDeep Learning for algorithmic trading using Python

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