Trading and machine learning were made for each other. Both activities are rooted in analyzing noisy data for patterns and using those patterns for making predictions. The relationship is so much so that you can even take graduate-level college courses focused entirely on the problem of applying machine learning to trading. Surely many an engineer has felt the allure of this "dark side."
Generally, in AI stock trading machine learning is used to build models based on financial data, of which there is no shortage of: moving averages of stock highs and lows, moving averages of trade volume, overall market trends, market volatility, etc. This stuff is all right there and easy enough to translate into features useful for training machine learning models, but stock prices themselves react to more than just historical financial data. The market is ultimately subject to the real-world
It seems like a trivial statement, but when you start looking at trading as a math problem it's easy to forget that stocks are tied to companies that do and sell things IRL. And this IRL-ness is much harder for an algorithm to comprehend, let alone use for making future predictions. Nonetheless, an investment management firm called Triumph Asset Management is making a go of it with a new system that parses large volumes of news articles for indicators that can be incorporated into predictive models.
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