Lazy Trading Part 6: Detect Market Status With Ai

Learn to use Supervised Deep Learning modelling to detect patterns of Financial Assets

What you'll 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
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
MQL4 and R basic level
Best with 1, 2, 3, 4, 5 courses of Lazy Trading Series
Description
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.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 TradingThis 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 ModelsEasily gather and write Financial Asset Data with Data Writer RobotManipulate data and learn to build Classification Deep Learning ModelsUse random neural network structuresFunctions with examples in R packageGenerate Market Type classification output for Trading SystemsGet 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 EnvironmentSet up your Trading Strategy RobotSet up your automated Trading JournalStatistical Automated Trading ControlReading News and Sentiment AnalysisUsing Artificial Intelligence to detect market statusBuilding an AI trading systemUpdate: dedicated R package 'lazytrade' was created to facilitate code sharing among different coursesIMPORTANT: 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 SystemBe organized and systematic using Version Control and Automated Statistical AnalysisLearn using R to read, manipulate data and perform Machine Learning including Deep LearningLearn and practice Data VisualizationLearn sentiment analysis and web scrappingLearn Shiny to deploy any data project in hoursGet productivity hacksLearn to automate your tasks and scheduling themGet expandable examples of MQL4 and R codeWhat these courses are not:These courses will not teach and explain specific programming concepts in detailsThese courses are not meant to teach basics of Data Science or TradingThere is no guarantee on bug free programmingDisclaimer: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

Overview

Section 1: Introduction

Lecture 1 Specific Goals for this Course

Lecture 2 Disclaimer

Lecture 3 How to follow this course?

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

Lecture 4 Why to detect Market Status

Lecture 5 How to detect Market Status with Artificial Intelligence

Lecture 6 Deep Learning architecture in R [h2o.ai]

Section 3: About the code in this course

Lecture 7 Introduction to this Section

Lecture 8 R package 'lazytrade'

Lecture 9 How to install R package 'lazytrade'

Lecture 10 How to reproduce Examples in the R packages

Lecture 11 How to get the source code of 'lazytrade' package?

Lecture 12 How to understand R functions inside 'lazytrade' package?

Lecture 13 Get the code

Section 4: Collect the data needed for Deep Learning Model

Lecture 14 Goals of this Section

Lecture 15 Logging data from financial Assets

Lecture 16 Note about History and how to use Data Writer for special symbols

Lecture 17 Which indicator to use? Note about Frequently Asked Questions

Lecture 18 Interactive data collection

Lecture 19 Visualize data matrix as 3D

Lecture 20 Visualize prepared dataset

Lecture 21 How to load and inspect dataset?

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

Lecture 22 Goal of this Section

Lecture 23 Build the Classification Model

Lecture 24 Important Note when updating h2o package in R

Lecture 25 Deep Dive function mt_make_model

Lecture 26 Schedule a task to build the model

Section 6: Deploy Deep Learning Model to Classify Market Type

Lecture 27 Goal of this Section [Deploy]

Lecture 28 How to adapt this script Score Data?

Lecture 29 Deploy Script to 'Score Data'

Lecture 30 Reviewing our results… how accurate are our classifications?

Lecture 31 Automate script with Task Scheduler

Lecture 32 Deep Dive function mt_evaluate

Lecture 33 Collect more data for future model update

Lecture 34 How to check documentation and examples?

Section 7: Continuous improvement of Deep Learning Model

Lecture 35 Motivation for this Chapter

Lecture 36 How to create User Interface? Create new / delete ShinyApp

Lecture 37 User Interface to check data

Lecture 38 Updating the model

Lecture 39 Algorithm Blueprint

Section 8: How to use Market Type information?

Lecture 40 Objectives of this chapter

Lecture 41 Consuming Market Type in MQL4 - Read MarketType function

Lecture 42 Market Type 'Confidence' or how to read 'double' values from files?

Lecture 43 Code of the test script…

Lecture 44 Robot Falcon F2

Lecture 45 Trading Robot example with Market Type facility

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

Lecture 46 Motivation for this Chapter

Lecture 47 Jumping Monkey Simulation - Theory

Lecture 48 Jumping Monkey Simulation - implementation in R

Lecture 49 What is next? Way from simulation to real application

Lecture 50 Combine Market Status Data with Trading Results

Lecture 51 Perform Reinforcement Learning to define the best state for the Trading System

Lecture 52 Adaptive Reinforcement Learning Control

Lecture 53 Apply the policy decision to the Trading Robot in Terminal 3

Lecture 54 Concluding the chapter

Section 10: Conclusion for Part 6

Lecture 55 Summary of this course

Lecture 56 What is our next step?

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