Deep Learning for algorithmic trading using Python

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  • Reading time:9 mins read


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

What you will learn

Create a 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

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).


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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.

English
language

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
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