| Course for : | Intermediate |
|---|---|
| Course Deuration : | 4 hours on-demand video |
| In Course Have : | Video, Articles |
| Course Fee : | $9.99 |
What you'll learn
Description
Learn to assemble Smart Learning Algorithms. Predict future price change based on financial data patterns
Requirements
- It would help if you had in Trading and its pitfalls.
- You would like to know more about Data Science using Trading.
- PC Windows (min 4CPU 8Gb RAM). This machine needs to be turned on continuously for some time [Mac only with sample data PC Windows (minimum 4CPU 8Gb RAM)
- Java installation on Computer
- R Statistical Software
- R-Studio
- MT4 Trading Platform, Demo Trading Account
- GitHub Desktop Software for Version Control
- GitHub Account for Version Control
Description
“No one can promise that this will work. At least it will work by itself! “
About the Lazy Trading Courses:
This course series is designed to provide an exciting experience with Algorithmic Trading and, at the same time, master computer and Data Science! The focus is on developing a Decision Support System that can assist in automating a variety of the tedious processes involved in Trading and learning Data Science. The various algorithms will be constructed using the basic data cycle, namely data input-data manipulation – analysis-output’. Examples throughout all seven lessons will help you develop a comprehensive system that can evolve automatically without manual input.
This Course’s Content: Building a Self Learning Trading Robot with the use of statistical models
This course will teach the Deep Learning Regression Model to forecast future prices for financial assets. This course will integrate everything previously taught to make it easier to apply:
- Make use of the MQL4 DataWriter robot to collect information about financial assets.
- Make use of R Statistical Software to aggregate data to prepare to model.
- Use H2O Machine Learning Platform to train Deep Learning Regression Models.
- Use random neural network structures.
- Functions that include tests and examples in the R package
- Back-testing trading strategies with Model predictor and historical data
- … update model if needed
- Make use of Models and new Data to make forecasts.
- Use Model output in MQL4 Trading Robot
- Making use of Market Type data [from course 6[from course 6
- Experiment with using reinforcement learning to determine the most effective Market Type
“What is that ONE thing very special about this course? “
Check out AI predicting the future!
This project includes several classes designed to assist in the management of Automated Trading Systems:
- Create the Home Trading Environment
- Create the Trading Strategy Robot
- Automate your Trading Journal
- Statistical Automated Trading Control
- Reading News and Sentiment Analysis
- Utilizing Artificial Intelligence to detect the market state
- The development of the foundations of an AI trading system
Important: all courses will include a ‘quick-to-install section, along with sections that provide theoretic explanations.
What can you expect to learn from Trading?
After completing these classes, you will learn about Trading using the examples provided.
- Practice and learn to use the Decision Support System
- Get organized and efficient by using Version Control and Automated Statistical Analysis
- Learn with R to manipulate, read and perform Machine Learning including Deep Learning
- Study and practice Visualization of Data Visualization
- Learn about sentiment analysis, scraping websites
- Get Shiny and deploy any project involving data in a matter of hours
- Find productivity tips
- Automate your tasks, and schedule them
- See examples of MQL4 that can be expanded and R code.
These courses are not:
- The courses won’t instruct and explain particular programming principles in detail
- These classes are not designed to introduce the basics in Data Science or Trading
- There is no assurance of programming that is bug-free.
Disclaimer:
The Trading of securities is risky. This course is not considered a financial advisory or service. The past results cannot be warranted shortly. A significant amount of time may be required to replicate the ideas and methods that have been proposed.
Who should this course be intended for:
- Anyone who would like to become more productive
- Anyone interested in learning Data Science using Algorithmic Trading
- Anyone who wants to experience Algorithmic Trading but have little time
- Anyone who wants to study Deep Learning and understand how to use the technique to predict
