Discussing the article: "Overcoming The Limitation of Machine Learning (Part 1): Lack of Interoperable Metrics"
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Check out the new article: Overcoming The Limitation of Machine Learning (Part 1): Lack of Interoperable Metrics.
There is a powerful and pervasive force quietly corrupting the collective efforts of our community to build reliable trading strategies that employ AI in any shape or form. This article establishes that part of the problems we face, are rooted in blind adherence to "best practices". By furnishing the reader with simple real-world market-based evidence, we will reason to the reader why we must refrain from such conduct, and rather adopt domain-bound best practices if our community should stand any chance of recovering the latent potential of AI.
Imagine you’re in a lottery-style competition. You and 99 other people are randomly selected to play for a $1,000,000 jackpot. The rules are simple; you must guess the heights of the other 99 participants. The winner is the person with the smallest total error across their 99 guesses.
Now, here’s the twist: for this example, imagine the average global human height is 1.1 meters. If you simply guess 1.1 meters for everyone, you might actually win the jackpot, even though every single prediction is technically wrong. Why? Because in noisy, uncertain environments, guessing the average tends to produce the smallest overall error.
Author: Gamuchirai Zororo Ndawana