Udemy – MQL5 DATA ANALYSIS: Developing a Weekly Percentage Change EA

In every ecosystem, there are different organisms with different strategies for survival. These strategies all depend on members of an ecosystem occupying particular roles causing dependencies to arise. These strategies however, depend on patterns based on how other organisms survive and behave. These patterns of survival and behaviour are prone to changes as other organisms evolve and change their strategies, causing those organisms whose strategies do not align with the new environment to be extinct if they do not adapt to the changes.

The financial markets can be likened to a natural ecosystem, where market participants interact together while occupying different roles and implementing different strategies to satisfy those roles. Just like the organisms in an ecosystem, market participants identify patterns in the markets based on the behaviour of other participants and use them to develop strategies. However, as every participant seeks to better achieve their objective for participating in the financial markets, their strategies change, causing the market to change in behavior and character, invalidating some patterns while encouraging the manipulation of new ones.

In this course, I will be teaching you how to isolate certain market characteristics and behavior to develop a speculative edge and implement it in an algorithmic trading system using the MQL5 language. This course is project based and we are going to achieve our objectives by analysing data on the weekly percentage changes in price. You will learn how to code an MQL5 script that will collect any desired financial market data and store it as a csv file, allowing you to organize, manipulate and analyse it from any historical period of interest.

In this course, we shall identify patterns in the weekly percentage price changes within a period of 18 years. We shall achieve this by visualizing data on a scatter plot and identifying useful data clusters that may represent price patterns of interest. We shall then develop a hypothesis based on patterns we can identify in the visualized data and test it by developing an algorithmic trading strategy based on it. The patterns we shall identify shall give us an edge in finding protocols for making trade entries and most of all, in developing exit strategies. We shall test our hypothesis using out of sample data to ensure that the trading strategy logic success is not due to over fitting but due to a true edge in the markets.

So hit hard on the enroll button, now, and join me on this exciting journey of coding a script to collect data and analyze it, providing us with a hypothesis to test within an algorithmic trading strategy

DOWNLOAD LINK: MQL5 DATA ANALYSIS: Developing a Weekly Percentage Change EA

MQL5_DATA_ANALYSIS_Developing_a_Weekly_Percentage_Change_EA.part1.rar – 1000.0 MB
MQL5_DATA_ANALYSIS_Developing_a_Weekly_Percentage_Change_EA.part2.rar – 1000.0 MB
MQL5_DATA_ANALYSIS_Developing_a_Weekly_Percentage_Change_EA.part3.rar – 1000.0 MB
MQL5_DATA_ANALYSIS_Developing_a_Weekly_Percentage_Change_EA.part4.rar – 550.8 MB

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