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Lazy Trading Part 6: Detect Market status with AI

3.5 hours on-demand video

$ 12.99

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Brief Introduction

Learn to use Supervised Deep Learning modelling to detect patterns of Financial Assets

Description

About the Lazy Trading Courses:
This series of courses is designed to to combine fascinating experience of Algorithmic Trading and at the same time to learn Computer and Data Science! Particular focus is made on building Decision Support System that can help to automate a lot of boring processes related to Trading and also learn Data Science. Several algorithms will be built by performing basic data cycle 'data input-data manipulation - analysis -output'. Provided examples throughout all 7 courses will show how to build very comprehensive system capable to automatically evolve without much manual input.

Inspired by:
“it is insane to expect that one system to work for all market types” // -Van K. Tharp

“Luck is what happens when preparation meets opportunity” // -Seneca (Roman philosopher)

About this Course: Use Artificial Intelligence in Trading
This course will cover usage of Deep Learning Classification Model to classify Market Status of Financial Assets using Deep Learning:

Learn to use R and h2o Machine Learning platform to train Supervised Deep Learning Classification Models
Easily gather and write Financial Asset Data with Data Writer Robot
Manipulate data and learn to build Classification Deep Learning Models
- Use random neural network structures
- Functions with examples in R package
Generate Market Type classification output for Trading Systems
Get Trading robot capable to consider Market Status information in your Strategies

This project is containing several short courses focused to help you managing your Automated Trading Systems:

Set up your Home Trading Environment
Set up your Trading Strategy Robot
Set up your automated Trading Journal
Statistical Automated Trading Control
Reading News and Sentiment Analysis
Using Artificial Intelligence to detect market status
Building an AI trading system

Update: dedicated R package 'lazytrade' was created to facilitate code sharing among different courses

IMPORTANT: all courses will have a 'quick to deploy' sections as well as sections containing theoretical explanations.

What will you learn apart of trading:

While completing these courses you will learn much more rather than just trading by using provided examples:

Learn and practice to use Decision Support System
Be organized and systematic using Version Control and Automated Statistical Analysis
Learn using R to read, manipulate data and perform Machine Learning including Deep Learning
Learn and practice Data Visualization
Learn sentiment analysis and web scrapping
Learn Shiny to deploy any data project in hours
Get productivity hacks
Learn to automate your tasks and scheduling them
Get expandable examples of MQL4 and R code

What these courses are not:

These courses will not teach and explain specific programming concepts in details
These courses are not meant to teach basics of Data Science or Trading
There is no guarantee on bug free programming

Disclaimer:

Trading is a risk. This course must not be intended as a financial advice or service. Past results are not guaranteed for the future. Significant time investment may be required to reproduce proposed methods and concepts

Requirements

Requirements
You should have a background knowledge on Trading and it's pitfals
You want to learn Data Science using Trading
PC Windows (min 4CPU 8Gb RAM). This machine should be left ON continuously for several weeks
MQL4 and R basic level
Best with 1, 2, 3, 4, 5 courses of Lazy Trading Series

Knowledge

Log data from financial assets to files
Prepare Time-Series data for Deep Learning Tasks
Detect Market Status of Financial Assets using Deep Learning
Learn to perform Supervised Classification with Deep Learning [with R and h2o]
Use Market Status in Financial Trading
Setup Automated Decision Support Loop
Automate R scripts
Develop R code
Use Version Control for R projects
Writing R functions
Perform data manipulations in R
Use H2O Machine Learning platform in R
Application of Reinforcement Learning to select best working Model

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