Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
3 Hour(s) 27 Minute(s)
Language
English
Taught by
Vladimir Zhbanko, Miguel Ferraz
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Learn to use Supervised Deep Learning modelling to detect patterns of Financial Assets
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
Course Content
- 10 section(s)
- 56 lecture(s)
- Section 1 Introduction
- Section 2 Idea of Market Status Detection with Artificial Intelligence?
- Section 3 About the code in this course
- Section 4 Collect the data needed for Deep Learning Model
- Section 5 Deploy Deep Learning Model capable to detect 6 market types
- Section 6 Deploy Deep Learning Model to Classify Market Type
- Section 7 Continuous improvement of Deep Learning Model
- Section 8 How to use Market Type information?
- Section 9 Choosing best Market Status for Trades with Reinforcement Learning
- Section 10 Conclusion for Part 6
What You’ll Learn
- 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
Skills covered in this course
Reviews
-
FFlorian Assous
Implement market type into the robot is very useful but also this course let us review many knowledge : deep learning with h2o, reinforcement learning, shiny app, MQL4. WOOW!! all is here and very useful to remind what we have seen in the previous courses. Course updated, make it even more interesting. Thanks Vladimir, It's excellent and brilliant!
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MMarco Sisfontes
It is ok
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CChirag Mirani
Great course!
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SShawn McKenzie
Another great course in the series. Section 7 (reinforcement learning) to the end of the course were particularly interesting!
