AI to Pick Stocks

AI to Pick Stocks

Introduction: AI to Pick Stocks

AI to pick stocks that can win? Is this possible? It was recently reported that three of the developers responsible for the first AI system to beat humans at head-up, no-limit poker have left DeepMind to launch a new startup that purports to dominate the stock market.

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This new venture, called Equilibre Technologies , will employ algorithms to point out where the best stocks and cryptocurrency to invest in are. 

Machine Learning for Algorithmic Trading: Predictive models for alternative data for systematic trading strategies with Python.
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02/19/2024 07:43 am GMT

There have been other companies who have used AI to pick stocks –

Source: YouTube

It is difficult to “solve” the stock market with AI, but the leadership of the team appears to be up to the task. Predicting financial markets is always a big challenge with the ever changing landscape of ups and downs. 

Rudolf Kadlec, Martin Schmid, and Matej Moravcik, the company’s founders, all worked together at IBM and DeepMind. It makes sense to keep a successful team together, a team together with good conviction can solve any problem.

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Equilibre Technologies isn’t out with much information yet, but we can be sure they’ll stick close to the vest when it comes to developing algorithmic trading technology.

While they’re not the first to use artificial intelligence to demystify the stock market, they are taking a slightly different approach than their competitors.

Based on historical trends, the typical stock-picking AI tries to predict what will happen next in the market.

Self taught AI will be the end of us

The Equilibre team is known for creating the most complex algorithms to solve problems without a lot of information. In order to gain a theoretical advantage over other methods of trading, they combine game theory with artificial intuition.

Taking part in poker is much like successfully playing the stock market, according to a DeepStack paper:

Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.

We introduce DeepStack, an algorithm for imperfect information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning.

In a study involving 44,000 hands of poker, DeepStack defeated with statistical significance professional poker players in heads-up no-limit Texas hold’em.

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There is no such thing as a guarantee in tech, but this sounds like a promising proposition. From the founders’ history and connections, a solid foundation can be built and can help predict stocks.