Lazy Trading Part 2: Setting up and deploying Trading System

Lazy Trading Part 2: Setting up and deploying Trading System, available at $54.99, has an average rating of 3.95, with 88 lectures, 2 quizzes, based on 41 reviews, and has 416 subscribers.

You will learn about Using Automated Trading System in MQL4 Develop methodology to test and analyse Trading Strategy Using version control to manage complex projects Learn to set up Automated Decision Support Systems using R Statistical Software Learn how to adapt Trading System Robot to specific Market Type Replicate Decision Support System concept on other areas rather than Trading This course is ideal for individuals who are Anyone who want to be more productive or Anyone who want to learn Data Science and Trading at the same time or Anyone who want to try Algorithmic Trading but have little time It is particularly useful for Anyone who want to be more productive or Anyone who want to learn Data Science and Trading at the same time or Anyone who want to try Algorithmic Trading but have little time.

Enroll now: Lazy Trading Part 2: Setting up and deploying Trading System

Summary

Title: Lazy Trading Part 2: Setting up and deploying Trading System

Price: $54.99

Average Rating: 3.95

Number of Lectures: 88

Number of Quizzes: 2

Number of Published Lectures: 82

Number of Published Quizzes: 2

Number of Curriculum Items: 90

Number of Published Curriculum Objects: 84

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Using Automated Trading System in MQL4
Develop methodology to test and analyse Trading Strategy
Using version control to manage complex projects
Learn to set up Automated Decision Support Systems using R Statistical Software
Learn how to adapt Trading System Robot to specific Market Type
Replicate Decision Support System concept on other areas rather than Trading

Who Should Attend

Anyone who want to be more productive
Anyone who want to learn Data Science and Trading at the same time
Anyone who want to try Algorithmic Trading but have little time

Target Audiences

Anyone who want to be more productive
Anyone who want to learn Data Science and Trading at the same time
Anyone who want to try Algorithmic Trading but have little time

About this Course: Setting up and deploying Trading System

The second part of this series will cover setting up our Expert Advisor or Trading Robot. At the end of this course we will have complete and ready to be used Algorithmic Trading System integrated with our Decision Support System:

Programming environment
Setting up Version Control Project
Overview of robot functions
How to customize to target market inefficiency
Integrate robot with Decision Support System (start/stop trading system from external commands)
Customize and record trades results
Rolling Optimization, automatic robot backtest

The same robot template will be used in other courses of the Lazy Trading Series

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 foundation of Decision Support System that can help to automate a lot of boring processes related to Trading.

This project is containing several short courses focused to help you managing your Automated Trading Systems:

Set up your Home Trading Environment
Setting up and deploying Trading Systems
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 be short focusing to one specific topic. You will not get lost in various sections and deep theoretical explanations. These courses will help you to focus on developing strategies 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 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Specific Goals for this Course

Lecture 2: Disclaimer

Lecture 3: Ask questions or join Discord Channel!

Chapter 2: Tools and Habits of a Trader and a Programmer

Lecture 1: Goals of This Section

Lecture 2: Get updated and read about the programing language

Lecture 3: Get ideas about Trading Strategies and Resources

Lecture 4: All above seems too complex or complicated? No problem!

Lecture 5: DSS_Public repository

Chapter 3: Intruduction to MQL4

Lecture 1: Introduction to this chapter

Lecture 2: Getting to know our Programming Environment MQL4 Editor

Lecture 3: Create new Script in MQL4

Lecture 4: Types of Variables

Lecture 5: Get value from function in MLQ4

Lecture 6: Use Condition [If] statement

Lecture 7: How to make a function

Lecture 8: Create your own functions catalog [Include]

Lecture 9: How to debug in MQL4

Lecture 10: For Loops

Lecture 11: Arrays

Chapter 4: Types of Trading Systems

Lecture 1: Introduction to this Chapter

Lecture 2: Types of Algorading systems: Rule, Model, Hybrid based

Lecture 3: Rule-based robot FALCON_B

Lecture 4: Model Based robot FALCON_F2

Lecture 5: Hybrid robot Falcon A

Chapter 5: What is really an Expert Advisor?

Lecture 1: Introduction of this Chapter

Lecture 2: Robot main structure

Lecture 3: External/Internal parameters

Lecture 4: Initialization/Deinitialization functions

Lecture 5: Start function

Lecture 6: User-Defined Functions

Chapter 6: Adapting Robot Template

Lecture 1: Get the code

Lecture 2: R package 'lazytrade'

Lecture 3: Our Robot Template

Lecture 4: Logging trading results to file

Lecture 5: Reading commands from Decision Support System

Lecture 6: How to understand and modify this robot?

Lecture 7: Adding option to close all positions on Friday evening

Lecture 8: Adding option to close all positions on Friday evening – video

Chapter 7: Practical Activity

Lecture 1: Optional challenge: Modify this bot

Lecture 2: Deploy to learn!

Lecture 3: Using MetaTrader Terminal Profiles to manage different setups

Lecture 4: Keeping MT Terminals Profiles under Version Control

Chapter 8: How to evaluate Trading [Strategy] Robot?

Lecture 1: Goals of this Section

Lecture 2: FALCON_D – simple trading robot

Lecture 3: Why Periodic Optimization?

Lecture 4: Optimization Method P1. Settings overview

Lecture 5: Optimization Method P2. Collecting data during Trades Simulation

Lecture 6: Analyse simulated trades

Lecture 7: Evaluation of results

Lecture 8: Activity to practice

Chapter 9: Automatic 'Backtest' in MT4

Lecture 1: Goal of the Section

Lecture 2: Quick overview – automatic optimization

Lecture 3: Initialization script and available options

Lecture 4: Review Main Script

Lecture 5: Practical test – Optimization

Lecture 6: Practical Test – Backtest

Lecture 7: Dynamically update parameters after Optimization

Lecture 8: Results

Chapter 10: Automatic Optimization, Update Parameters and Backtest!

Lecture 1: Goal of this Section

Lecture 2: Detailed Plan

Lecture 3: MT4: Write Parameters Log

Lecture 4: MT4: Perform Optimization

Lecture 5: Windows: Update Environmental Variables

Lecture 6: R: Read Files with Settings, Trading Results, Parameters Logs

Lecture 7: R: Overwrite new settings [function]

Lecture 8: R: Join Parameters and Trading Results

Lecture 9: R: Find the best parameters

Lecture 10: R: Overwrite new settings

Lecture 11: R: Backtest new settings

Lecture 12: Evaluate results from Backtest

Lecture 13: Write Decision to control Trading Robot

Lecture 14: MT4: How to update robot parameters programmatically!

Lecture 15: Demo: What did we achieve in this section?

Lecture 16: Automate Part 1: Run all developed code from R script

Lecture 17: Automate Part 2: Use Task Scheduler to periodically execute R scripts

Lecture 18: Concluding this section

Chapter 11: Deploying MT4 Robots Programmatically

Lecture 1: Goals of this Section

Lecture 2: Script Deploy Robots

Lecture 3: Deploy one Robot on the Terminal Programmatically

Chapter 12: Conclusion for Part 2

Lecture 1: Summary of this course

Lecture 2: Bonus Lecture: YOUR SPECIAL ENTRY TO NEXT COURSE

Lecture 3: Results and preview for Next Course!

Instructors

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

Rating Distribution

1 stars: 3 votes
2 stars: 2 votes
3 stars: 7 votes
4 stars: 11 votes
5 stars: 18 votes

Frequently Asked Questions

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