Udemy - Lazy Trading Part 7 Developing Self Learning Trading Robot

Posted in: Tutorials | By: voska89 | 4-01-2021, 13:23 | 0 Comments

Udemy - Lazy Trading Part 7 Developing Self Learning Trading Robot

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | SRT | Size: 2.60 GB | Duration: 3h 19m

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
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
MT4 Trading Platform, Demo Trading Account
GitHub Desktop Software for Version Control
GitHub Account for Version Control
"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
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


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