Lazy Trading Part 6: Detect Market status with AI

Lazy Trading Part 6: Detect Market status with AI, available at $49.99, has an average rating of 4.4, with 56 lectures, 7 quizzes, based on 23 reviews, and has 508 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone interested to practice Deep Learning Supervised Modelling (Regression and Classification) or Anyone who want to be more productive or Anyone who want to learn Data Science or Anyone who want to try Algorithmic Trading but have little time It is particularly useful for Anyone interested to practice Deep Learning Supervised Modelling (Regression and Classification) or Anyone who want to be more productive or Anyone who want to learn Data Science or Anyone who want to try Algorithmic Trading but have little time.

Enroll now: Lazy Trading Part 6: Detect Market status with AI

Summary

Title: Lazy Trading Part 6: Detect Market status with AI

Price: $49.99

Average Rating: 4.4

Number of Lectures: 56

Number of Quizzes: 7

Number of Published Lectures: 56

Number of Published Quizzes: 7

Number of Curriculum Items: 63

Number of Published Curriculum Objects: 63

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Anyone interested to practice Deep Learning Supervised Modelling (Regression and Classification)
Anyone who want to be more productive
Anyone who want to learn Data Science
Anyone who want to try Algorithmic Trading but have little time

Target Audiences

Anyone interested to practice Deep Learning Supervised Modelling (Regression and Classification)
Anyone who want to be more productive
Anyone who want to learn Data Science
Anyone who want to try Algorithmic Trading but have little time

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 Computerand 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 Curriculum

Chapter 1: Introduction

Lecture 1: Specific Goals for this Course

Lecture 2: Disclaimer

Lecture 3: How to follow this course?

Chapter 2: Idea of Market Status Detection with Artificial Intelligence?

Lecture 1: Why to detect Market Status

Lecture 2: How to detect Market Status with Artificial Intelligence

Lecture 3: Deep Learning architecture in R [h2o.ai]

Chapter 3: About the code in this course

Lecture 1: Introduction to this Section

Lecture 2: R package 'lazytrade'

Lecture 3: How to install R package 'lazytrade'

Lecture 4: How to reproduce Examples in the R packages

Lecture 5: How to get the source code of 'lazytrade' package?

Lecture 6: How to understand R functions inside 'lazytrade' package?

Lecture 7: Get the code

Chapter 4: Collect the data needed for Deep Learning Model

Lecture 1: Goals of this Section

Lecture 2: Logging data from financial Assets

Lecture 3: Note about History and how to use Data Writer for special symbols

Lecture 4: Which indicator to use? Note about Frequently Asked Questions

Lecture 5: Interactive data collection

Lecture 6: Visualize data matrix as 3D

Lecture 7: Visualize prepared dataset

Lecture 8: How to load and inspect dataset?

Chapter 5: Deploy Deep Learning Model capable to detect 6 market types

Lecture 1: Goal of this Section

Lecture 2: Build the Classification Model

Lecture 3: Important Note when updating h2o package in R

Lecture 4: Deep Dive function mt_make_model

Lecture 5: Schedule a task to build the model

Chapter 6: Deploy Deep Learning Model to Classify Market Type

Lecture 1: Goal of this Section [Deploy]

Lecture 2: How to adapt this script Score Data?

Lecture 3: Deploy Script to 'Score Data'

Lecture 4: Reviewing our results… how accurate are our classifications?

Lecture 5: Automate script with Task Scheduler

Lecture 6: Deep Dive function mt_evaluate

Lecture 7: Collect more data for future model update

Lecture 8: How to check documentation and examples?

Chapter 7: Continuous improvement of Deep Learning Model

Lecture 1: Motivation for this Chapter

Lecture 2: How to create User Interface? Create new / delete ShinyApp

Lecture 3: User Interface to check data

Lecture 4: Updating the model

Lecture 5: Algorithm Blueprint

Chapter 8: How to use Market Type information?

Lecture 1: Objectives of this chapter

Lecture 2: Consuming Market Type in MQL4 – Read MarketType function

Lecture 3: Market Type 'Confidence' or how to read 'double' values from files?

Lecture 4: Code of the test script…

Lecture 5: Robot Falcon F2

Lecture 6: Trading Robot example with Market Type facility

Chapter 9: Choosing best Market Status for Trades with Reinforcement Learning

Lecture 1: Motivation for this Chapter

Lecture 2: Jumping Monkey Simulation – Theory

Lecture 3: Jumping Monkey Simulation – implementation in R

Lecture 4: What is next? Way from simulation to real application

Lecture 5: Combine Market Status Data with Trading Results

Lecture 6: Perform Reinforcement Learning to define the best state for the Trading System

Lecture 7: Adaptive Reinforcement Learning Control

Lecture 8: Apply the policy decision to the Trading Robot in Terminal 3

Lecture 9: Concluding the chapter

Chapter 10: Conclusion for Part 6

Lecture 1: Summary of this course

Lecture 2: What is our next step?

Instructors

Vladimir Zhbanko
Senior Engineering Specialist and Instructor
Miguel Ferraz
Economist/Programmer

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 2 votes
4 stars: 4 votes
5 stars: 15 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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