MIT Scientist Can Accurately Predict The Price Of Bitcoin

Photo credit: MIT
Photo credit: MIT

The Massachusetts Institute of Technology isn’t some ordinary grade school. It is world renown for being a global leader in technological expertise. When it comes to accurately predicting the Bitcoin price, an MIT researcher has gone where no man has gone before: developed an algorithm that can accurately predict the future price of Bitcoin.

However, before you get excited that this technology will be able to inform you what the Bitcoin price will be next month, next year, or 10 years in the future, you need to be aware that it can only predict 10 seconds into the future. reports:

A researcher at MIT’s Computer Science and Artificial Intelligence Laboratory and the Laboratory for Information and Decision Systems recently developed a machine-learning algorithm that can predict the price of the infamously volatile cryptocurrency Bitcoin, allowing his team to nearly double its investment over a period of 50 days.

Earlier this year, principal investigator Devavrat Shah and recent graduate Kang Zhang collected price data from all major Bitcoin exchanges, every second for five months, accumulating more than 200 million data points.

Using a technique called “Bayesian regression,” they trained an algorithm to automatically identify patterns from the data, which they used to predict prices, and trade accordingly.

Specifically, every two seconds they predicted the average price movement over the following 10 seconds. If the price movement was higher than a certain threshold, they bought a Bitcoin; if it was lower than the opposite threshold, they sold one; and if it was in-between, they did nothing.

Over 50 days, the team’s 2,872 trades gave them an 89 percent return on investment with a Sharpe ratio (measure of return relative to the amount of risk) of 4.1.

The team’s paper was published this month at the 2014 Allerton Conference on Communication, Control, and Computing.

“We developed this method of latent-source modeling, which hinges on the notion that things only happen in a few different ways,” says Shah, who previously used the approach to predict Twitter trending topics. “Instead of making subjective assumptions about the shape of patterns, we simply take the historical data and plug it into our predictive model to see what emerges.”

Shah says he was drawn to Bitcoin because of its vast swath of free data, as well as its sizable user base of high-frequency traders.

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