About this Course: Read news and Sentiment Analysis

The fifthpart of this series will give you the ability to automatically read Forex Calendar for any specific event like US Non-Farm Payroll or when President Trump is going to have a speech. This will provide an ability to consider these events in your trading strategies in a simplest form of disabling the trading robots.

Additional research of this course will be about correlation of Asset's Text data Sentiment to the Asset's price in the future. This research will be conducted on two trading ideas*:

1. Sentiment difference of News Headers in the US, Canada, GB and it's their currency Pairs.

2. Sentiment of Twitter data relevant to Tesla Stock prices

As usual provided methods and ideas will help us to practice computer and data science skills:

• Webscrap news and analyse their sentiment for trading

• Setting up Version Control in our Projects

• Know how to automate our R code

• Text Sentiment analysis using basic Sentiment Analysis Polarity Scoring and NRC Sentiment Dictionary (8 emotions)

• Performing descriptive analysis of the Sentiment Polarity Scoring of the News Headers

• Getting Twitter data into R

• Deep regression learning to correlate Sentiment scores to the objective variable [performed in h2o deep learning environment]

*There isabsolutelyno guaranteethatproposed methods will work!!!

About the Lazy Trading Courses:

This series of courses is designed totocombine fascinating experience ofAlgorithmic Tradingand at the same time tolearnComputerandData Science! Particular focus is made on building foundation ofDecision Support Systemthat can help to automate a lot of boring processes related to Trading.

This project iscontaining several short coursesfocused to help you managing your Automated Trading Systems:

1. Set up your Home Trading Environment

2. Set up your Trading Strategy Robot

3. Set up your automatedTrading Journal

4. Statistical Automated Trading Control

5. Reading News and Sentiment Analysis

6. Using Artificial Intelligence to detect market status

7. Building anAI trading system

Update:dedicated R package 'lazytrade' was created to facilitate code sharing among different courses

IMPORTANT:

1. All courses will be short focusing to one specific topic with very shorttheoretical explanations. These courses will helpto focus on developingstrategies by automatingboring but important processes for a trader.

2. Best possible way to take the courses as a series is to reproduce all methods by re-creating automated trading system on PC Windows

What will you learn apart of trading:

While completing thesecourses 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 practiceData Visualization

• Learn sentiment analysis and web scrapping

• Learn Shiny to deploy any data project in hours

• Get productivity hacks

• Learn to automate your tasksand 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 onbug 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.