Applying machine learning to the world of crypto
Anyone who’s been around the world of cryptocurrency the past few years is well aware of the volatile nature of the market and how quickly things can change on a day-to-day basis. This fluctuation has caused issues and unpredictability for traders trying to anticipate the direction of the market. The outlook can change on a dime, and the emotional toll for even the most stone-faced traders can be rather significant.
But what if we were able to effectively predict fluctuations in the market before they occurred? This is the specific aim of machine learning (ML) as it makes its way into the crypto landscape. If we can find ways for numbers and algorithms to take the place of our reactive emotional investing, we’ll drastically improve our likelihood of success.
Here’s how machine learning is helping shape the future of crypto to do just that.
What is machine learning?
Machine learning is the idea that there are generic algorithms that can analyze a data set without the user having to write any code. Instead, the user feeds the data into the algorithm and allows it to build its own logic off of what it sees.
A synchronous rise in both available crypto data and in computational power has led to a rise in machine learning within the cryptocurrency space. We’ve already seen ML make its way into many other industries, including the advent of self-driving cars and facial recognition technology. It’s been put to use on the stock market by institutional investors for some time now, and now crypto investors are following suit.
How can crypto make use of machine learning?
Crypto traders have begun to experiment with machine learning as a way of predicting when and how values will fluctuate, which is great because – as we mentioned – the volatile state of the industry can wreak havoc on investors’ emotions.
ML allows us to mitigate that risk and to let the numbers ultimately decide for us when to buy and when to sell. This is often done through the practice of long short-time memory – or LSTM.
Long short-time memory is, in short, a recurrent neural network (RNN) model design that analyzes past data to give a prediction. The model consists of a cell and three gates – an input gate, an output gate, and a forget gate – that regulate the flow of information in and out of the cell.
The more data able to be supplied to the cell, the more effectively a prediction is able to be made. For example, if you were asked to predict the next value in the series of 3, 6, …, you would probably give 9 as a prediction (as you’d assume the series is increasing in intervals of 3). But if the series were 3, 6, 12, …, you would assume the intervals were in fact doubling, and would predict the next value as 24.
Fortunately, there is a LOT of historic performance data available for cryptocurrencies, allowing predictions to be made on a rather well-informed basis.
To achieve the most accurate prediction possible, you’ll want to collect as much data as you can. In most cases, you will have to do some additional work to prep the data to be best received by the LSTM model. Once ready, the network will be able to identify small patterns in order to predict the next-day price, and so on.
Machine learning is real
The ultimate point of all this is that, yes, we can use machine learning to anticipate changes in the prices of crypto, which can in turn help us decide when to buy and when to sell. The more emotion we can remove from our trading practices, the more successful we’ll be.
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