Lazy Trading Part 7 Developing Self Learning Trading Robot – Vladimir Zhbanko

Salepage link: At HERE. Archive:

What you’ll learn

• Log data from financial assets to files
• Learn to use Deep Learning with H2O
• Setup Automated Decision Support Loop
• Automate R scripts
• Develop R code
• Use Version control for your R project
• Writing R functions
• Perform data manipulations with pipes
• Use H2O Machine Learning platform in R
• Perform Deep Learning on Time-Series data
• Evaluate performance of Deep Learning models
• Backtest trading strategy in R Software

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 [Mac only with provided sample data]
• 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 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.

About this Course: Developing Self Learning Trading Robot with Statistical Modeling

This course will cover usage of Deep Learning Regression Model to predict future prices of financial asset. This course will blend everything that was previously explained to use:

• Use MQL4 DataWriter robot to gather financial asset data
• Use R Statistical Software to aggregate data to be ready for modeling
• Use H2O Machine Learning Platform to train Deep Learning Regression Models
  • Use random neural network structures
  • Functions with test and examples in R package
• Back-test trading strategy using Model prediction and historical data
• … update model if needed
• Use Model and New Data to generate predictions
• Use Model output in MQL4 Trading Robot
• Adding and using Market Type info [from course 6]
• Experiment by adding Reinforcement Learning to select best possible Market Type

“What is that ONE thing very special about this course?”

— Watch AI predicting the future!

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

1. Set up your Home Trading Environment
2. Set up your Trading Strategy Robot
3. Set up your automated Trading Journal
4. Statistical Automated Trading Control
5. Reading News and Sentiment Analysis
6. Using Artificial Intelligence to detect market status
7. Building an AI trading system

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

Who this course is for:

• Anyone who want to be more productive
• Anyone who want to learn Data Science using Algorithmic Trading
• Anyone who want to try Algorithmic Trading but have little time
• Anyone willing to learn Deep Learning and understand how to apply it to make predictions