Lazy Trading Part 7: Developing Self Learning Trading Robot
- 4.3
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Brief Introduction
Learn to assemble Smart Learning Algorithms. Predict future price change based on financial data patternsDescription
"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:
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
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 [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
Knowledge
- 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
Vladimir Zhbanko
4.3 Raiting
Lazy Trading Part 4: Reinforcement Learning Trading Control
- 4.1
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Learn to build trading risk management software for your Trading Robots using Reinforcement Learning example! "This is about a Robot that can control Robots!"...
Lazy Trading Part 7: Developing Self Learning Trading Robot
- 4.3
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Learn to assemble Smart Learning Algorithms. Predict future price change based on financial data patterns "No one can promise that this will work, at least it will work by itself!"...
