Brain Emotional Learning Based Models

We use models developed based on how emotions are processed in mammalian brains. These computational models have been shown to predict non-linearity and chaos, better than any traditional artificial intelligence models.

Emotional Machine Trading

We use algorithms to build intelligent machines. What makes us different is that our machines are designed in such a way that they are not only rational, but also emotional.

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Technical Indicators

In markets, prediction strategies are useful in determining the trends. Various technical tools and indicators are used by traders to predict the market. More famous technical tools are RSI and MACD. We do research on finding better technical tools.

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How to build more intelligent machines? The answer is in emotions...

So far, it's safe to say that humans are smarter than machines, although Watson beats humans in various tasks. How about in markets? Can machines be better in investing than a seasoned investment banker or a hedge fund? The answer is YES! The best performing hedge fund in terms of return on investment in the last few years is Renaissance Capital that uses algorithmic trading. Do they have information that we don't have? hmmmm... probably YES! Now the question is if with fair play, a machine developed by a company outside of the establishment be better than those from Wall Street, and humans? That's where we come in, and we want to play the game fair and square. OneLove Investments is a firm specialized in theoretical science behind new prediction models. These prediction are commonly used in Wall Street to trade different assets. We don't do any trading and we don't have a super computer. We don't have an office close to NASDAQ or NYSE to be closer to the data. We don't use high frequency trading. We simply theorize about better predicting models, based on years of trials and errors, and we're using emotional machines. They are faster, more reliable and better performing than those millions of lines of codes... End of statement!

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