Lazy Trading Part 4: Trade Control With Reinforcement Learn

What you'll learn
Understand how to implement Reinforcement Learning in R for automated risk management
Learn how to use statistical analysis of performed trades to control trading systems
Setup Automated Decision Support Loop
Automate R scripts
Develop R code
Use Version control for your R project
Writing R functions
Perform data manipulations
Requirements
Knowledge on Forex Trading and it's pitfalls
You want to learn Data Science using Trading
PC Windows (min 4CPU 8Gb RAM). This machine should be left ON continuously for several weeks
R and R-Studio installed
Best with 1, 2, 3 courses of Lazy Trading Series
Description
"This is about a Robot that can control Robots!"About the Lazy Trading Courses:This series of courses is designed to to combine fascinating experience of Algorithmic Trading and at the same 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 perfog 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: Set up Automated Risk Management SoftwareThe fourth part of this series will enable automatic risk management of multiple Algorithmic Trading Systems. Algorithm will be capable to identify best and worse Trading Systems. This will allow to automate decision to start or stop Trading Robots. Course is featuring several methods of achieving this goal, provides functions allowing to apply or adapt this method for any situation including outside of trading.We will learn these Data and Computer Science concepts:Use R program to perform data analysis and generating output resultImport data from filesClean and select dataWriting and using functions in R'for' loopsData manipulation using 'pipe' operator and 'dplyr' package in RWrite data to filesCalculate Profit Factor in RUsing Reinforcement Learning in RReinforcement Learning ExampleCreating Adaptive Reinforcement Learning systemAutomating and Scheduling any R code"What is that ONE thing very special about this course" Application of Reinforcement Learning algorithm that is learning from very first observation!This project is containing several courses focused to help you managing Automated Trading Systems:Set up your Home Trading EnvironmentSet up your Trading Strategy RobotSet up your automated Trading JournalStatistical Automated Trading ControlReading News and Sennt AnalysisUsing Artificial Intelligence to detect market statusBuilding an AI trading systemDedicated R package 'lazytrade' is now published on CRAN to facilitate code sharing and improve code documentationIMPORTANT: all courses are very practical focusing to one specific topic with only essential theoretical explanations. These courses will help to focus on developing strats by automating boring but important processes for a trader.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 sennt analysis and web scrappingLearn Shiny to deploy any data project in hoursGet productivity hacksLearn to automate R programs and scheduling tht expandable examples of MQL4 and R codeWhat these courses are not:'Holy grail' or Automatic Trading Black BoxThese 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 performance results are not guarantee for the future.

Overview

Section 1: Introduction

Lecture 1 Specific Goals for this Course

Lecture 2 Disclaimer

Section 2: Quick Wins - Reproducible Examples

Lecture 3 Example 1: Detect trading systems with low performance

Lecture 4 Example 2: Selectively Enable Trading Systems using Profit Factor Monitoring

Lecture 5 Example 3: Selectively Enable Trading Systems based on Reinforcement Learning

Section 3: Statistically Control Trades. Basic Principles

Lecture 6 Link to Big Strategy and Profit Factor Monitoring

Lecture 7 Reinforcement Learning Q-Learning - P1

Lecture 8 Reinforcement Learning Q-Learning - P2

Section 4: Getting the code for the Course

Lecture 9 How to get the code

Lecture 10 R package 'lazytrade'

Section 5: Calculate basic statistics from Trading Results

Lecture 11 Goal of this Section

Lecture 12 Basics of data manipulation: select columns

Lecture 13 Basics of data manipulation: filter observations

Lecture 14 Data Manipulation: Arrange, Head, Tail...

Lecture 15 Data Manipulation: Mutate or create new columns

Lecture 16 Data manipulation: group by and summarise or calculating withing groups

Lecture 17 Data Manipulation: Code sample

Lecture 18 How to use Sample Data

Lecture 19 Cleaning Data example with Import Data function

Lecture 20 Function Check if Optimize

Lecture 21 Tell me when to optimize! Conclusion

Section 6: Trigger trades based on Profit Factor Monitoring

Lecture 22 Goal of this Section

Lecture 23 Review Base Algorithm

Lecture 24 Statistical Analysis of Trading Results and Control of Trading Robots

Lecture 25 Conclusion, if and how to apply this code

Section 7: Control Trades with Reinforcement Learning

Lecture 26 Goals of this Section

Lecture 27 Reinforcement Learning - Generic Example

Lecture 28 Reinforcement Learning - Adapting Example to Trading problem

Lecture 29 Reinforcement Learning -Trade Trigger P1

Lecture 30 Reinforcement Learning -Trade Trigger P2

Lecture 31 Reinforcement Learning - Function Get RL Policy

Lecture 32 Reinforcement Learning - Function Record Policy

Section 8: Adaptive Reinforcement Learning control parameters

Lecture 33 Objectives for this chapter

Lecture 34 Adapted Trade Trigger script. Reading R data files into R

Lecture 35 Writing best control parameters.

Lecture 36 Writing Optimal control parameters. P1 (Making 'for' loop your best friend)

Lecture 37 Writing Optimal control parameters. P2 (Log data within a function)

Lecture 38 Writing Optimal control parameters. P3 (Data manipulation using 'pipes')

Lecture 39 Summary Adaptive Reinforcement learning Control Parameters

Section 9: Practical Activity - Automate these scripts!

Lecture 40 Automate and schedule Statistical Analysis and Control

Section 10: Conclusion for Part 4

Lecture 41 Your special *BONUS*

Anyone who want to be more productive,Anyone who want to learn Data Science,Anyone who want to try Algorithmic Trading but have little ,Anyone interested in Self-Organizing systems,Data Scientists looking to have Reinforcement Learning in the knowledge tool box

HomePage:

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