Can AI Help in Crypto Price Prediction? (Explained)

Can Artificial Intelligence really help in crypto price prediction? Artificial Intelligence today seems to be everywhere and the world of crypto is not to be left behind.

The use of AI in crypto price prediction can surely help you to make more profits and combined with machine learning it seems to be all the more effective.

All these may seem to be a bit more technical for any average crypto investor but with a little bit of understanding about AI, machine learning and Long Short-Term Memory or LSTM Neural Network will make things quite easier.

This is what this article aims at.

Over the years, the popularity of some of the common and major crypto coins has skyrocketed simply due to the reason that these have seen exponential growth for several months in their market cap.

So, crypto investors always wanted some tech support that would enable them to predict the prices of these coins and make even more profit since crypto investing and trading is all about speculation of the prices.

And, AI and machine learning have surely come to their rescue.

As you may know machine learning has proved its mettle in predicting the market prices of stocks.

For that, it uses a lot of different time series models.

However, application of machine learning for the same purpose in the world of crypto has been somewhat restrictive.

The obvious reason behind it is that the prices of crypto coins depend on a large number of other factors such as:

  • Internal competition
  • Technological advancements
  • Economic issues
  • Security problems
  • Demand on the markets to deliver
  • Political factors and lots more.

However, making profits from crypto due to its high volatility can only be possible when smart investing strategies are taken, which is what AI helps in.

Can Artificial Intelligence Really help in Crypto Price Prediction?

Can Artificial Intelligence Really help in Crypto Price Prediction

It is known to all that crypto lacks specific indices which make it quite difficult for the investors to predict the price movements when compared to predicting the prices of other traditional financial instruments such as stocks.

Typically, when it comes to predicting the prices of crypto coins, there are four particular steps to follow. These are:

  • Getting the real time data for crypto coins
  • Preparing the data for analyzing and testing
  • Predicting the price with the help of LSTM neural network and
  • Visualizing the results of the prediction.

However, this is not all that simple.

There are lots of aspects to judge in a given set of data for predicting the price of any crypto accurately, well almost.

Some of these vital aspects are:

  • The volume
  • The open values
  • The high values and
  • The low values.

All these need specific crypto tools that will help in proper analysis and thereby in precise prediction of the prices.

One such tool is an AI model that will help in analyzing the crypto market with an additional price predicting feature in it.

Typically, an effective AI model for this purpose should also come with other capabilities such as:

  • Natural language processing
  • Machine learning algorithms analysis and
  • Sentiment analysis.

This will make predicting crypto prices much easier and enable the user to maximize their profit making chances, whether it is a private investor or a professional company.

Add to that, if the solution even helps in other useful aspects of crypto trading and investing, then it would be further useful.

These aspects are:

  • Real time crypto market analysis
  • Risk management
  • Investments and
  • Portfolio management.

Such a solution can only be designed by implementing AI and it should be based on sentiment analysis and LSTM models.

Fortunately, now you have several such models powered by AI which can easily predict different types of prices for every type of crypto coin individually such as:

  • Open
  • High
  • Low and
  • Close

The price prediction process is typically enumerated continuously by AI Engine. With a continual update loop, you will get real time price calculations.

These AI powered solutions utilizes a lot of different parameters as its input and produces the desired output.

Depending on the particular model you choose, you can even add new parameters to the without needing to make any significant changes in the coding.

Apart from several flexible parameters embedded, some of the AI solutions also come with adjustable forecast intervals over and above their usual 7-day forecast based on a time step of one day.

No matter how advanced the features are in an AI powered solution, the eventual results are always conducive and nearly exact.

Trust Factor

The results produced by the AI powered models for price prediction of crypto coins are quite reliable.

This is because it takes into account a lot of different parameters and updates the results continuously.

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Therefore, what you get is almost exact.

As it is, cryptocurrency is extremely volatile in nature and therefore it is quite difficult, if not impossible for a human to predict the prices.

These AI powered solutions can forecast the prices for at least a week ahead, though there are some developers of some specific solutions who are trying to push it to four weeks.

Therefore, the AI powered solutions will surely make it easier for you to predict the prices of the volatile crypto coins irrespective of the fact that you are a novice or an experienced crypto investor.

Crypto price predictions using AI are more reliable because it eliminates the risks of making any human error while calculating the prices.

Sentiment Analysis

Use of AI will also help in sentiment analysis of the crypto market, which is much more volatile in comparison to a stock market.

The significant reason behind it is that the crypto market is still a new and emerging phenomenon.

It is for this reason the values of crypto coins, much unlike the stocks, do not correspond with the common factors such as availability of the assets and cash flow.

Rather, the value of crypto largely depends on the market sentiment.

This, typically, depends on the ‘herd instinct’ which signifies the behavior of the majority of the people in the market.

Ideally, in a crypto market the users tend to think and behave in the same way.

All of them are influenced by news headlines, current developments in the markets, latest events, social posts of general people and community, tweets of celebs and more.

All these factors include the price of a crypto coin or crypto coins making them move in a particular direction, north or south.

And, people react to it accordingly, almost all of them.

It is this sentiment that is taken into account and analyzed by the RNTN or the Recursive Neural Tensor Networks which helps in creating an AI bot that will help in crypto price prediction and trading.

Crypto Trading

Talking about crypto trading, AI solutions can also help in this particular aspect a lot as well.

As you may know, the crypto markets are open 24/7/365.

This means that, just as you, there are also several other crypto traders who are actively trading and are monitoring the prices of crypto coins closely at any given moment.

This eventually results in loads of data that you will need to analyze and an AI powered solution can do it with ease by using back-data finding.

This facilitates systematic trading since all historical market prices will be collected and analyzed to ensure an accurate price prediction of a crypto in the future.

It also makes the process much faster. It is this capability of AI that most of the major crypto trading firms of today are leveraging to make more profits with better price prediction made by using better crypto insights.

There are several different protocols that you will find today that use these insights to help even the small traders to predict about the market and the prices of crypto assets.

And, for that they do not need to do any extensive analysis on their own.

In order to ensure further accuracy in price prediction of crypto, these solutions use data related to the activity of a particular user and then recycle it back into the AI models.

All data collected by AI is made more useful by proper analysis which helps in signaling deals and providing indicators so that even the novice crypto traders can make their trades in a much more user-friendly setting.

This makes the entire process much more convenient and effective to them.

And, data scientists and experienced traders can sell their analysis in order to further monetize their skill sets.

In short, use of AI and deep learning to analyze huge data sets ensures further accuracy in price prediction by using spot germane patterns.

This helps the traders to make more productive and better trading decisions.

As for the crypto investors, they can also make better decisions and trim down the risks of uncertainty.

This does not only help them to make smarter investment decisions but also allows them to exploit the volatility of the crypto market to the fullest to maximize their profits.

Data and Code

Now, if you are to design such a useful AI model for crypto price prediction you should have some technical and programming skills.

Apart from that you should also know a few other things and implement your knowledge when you finally start designing your model.

A set of price data of a crypto coin that you will find in a website will typically comes with the following detail features:

  • The open price – It refers to the market open price of the crypto for that day
  • The high price – It refers to the highest price of the crypto for that day
  • The low price – It refers to the lowest price of the crypto for that day
  • The close price – It refers to the market close price of the crypto for that day
  • The volume – It refers to the volume of the crypto that has been traded for that day.
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Now, the code, on the other hand, will come with all the dependencies and libraries that are required along with the exchange rate and store the data obtained in the real time into a data frame.

For proper coding, the date time objects in the data file must be converted because it is read only as a string object.

Next, the data needs to be split into three different sets namely, the training set, the validation set, and the test set.

These sets should typically have 60%, 20%, and 20% of the entire data of the project respectively.

You should follow it up with a couple of functions in order to standardize the values.

This standardization or normalization is a process that is often used as a part of preparing data for machine learning.

The primary objective of this process is to modify the values of number columns of the dataset to a universal scale but not altering the differences in the ranges of these values.

A function is finally needed for extracting data of windows of small size to set up the data in the desired format.

It is this set of data that you will need to feed into the LSTM neural network.

Role of Long Short-Term Memory Neural Network

The Long Short-Term Memory or LSTM neural network uses special gates to let each of the layers of this network gather information from the previous layers as well as the current layer.

This data collected is then sent through several different gates such as input gate, forget gate, and others.

The data is also sent through several other activation functions such as Rectified Linear Unit or ReLU activation function, Hyperbolic Tangent or Tanh function, and others and finally is sent through the LSTM cells.

The primary purpose and the most significant benefit of it is that it allows each of the LSTM cells to remember the patterns for a specific period of time.

One important thing that you should keep in mind at this point is that the LSTM neural network can not only remember vital information but it also can forget the unrelated information at the same time.

Based on the LSTM architecture a sequential model is to be built that can be used to stack all different types of layers such as input layer, output layer, and hidden layer.

The neural network itself consists of a LSTM layer which is followed by a 20% dropout layer as well as a dense layer.

These come with a linear activation function.

While designing the model, you must remember that it should come with a proper optimizer and a loss function.

It is also required to set up a few predetermined parameters in your model that you will need to use later. Some of these parameters are:

  • The length of the window
  • The random number seed
  • The test set size
  • The number of neurons in LSTM layer
  • The batch size
  • The epochs and
  • The dropouts.

Next, you will need to train the model by using x-train inputs and y-train labels and test it for its abilities.

Mean Absolute Error

You will also need to use an evaluation metric in your model. The most commonly used evaluation metric is the Mean Absolute Error or MAE.

The main objective behind choosing this specific evaluation metric instead of the RMSE or Root Mean Squared Error deviation metric is that the MAE evaluation metric is more interpretable.

The RMSE will not be able to illustrate average error individually and therefore the users will have much more difficulty in understanding as compared to the MAE evaluation metric which can be explained easily even to a non-technical user.

MAE is also a much better option to choose because it is able to measure the average extent of the errors in a given set of price predictions irrespective of their current direction.

All the individual differences will have the same weight since it is the average over the test sample of the absolute differences among the predicted and actual observations.

The Model Testing

Now that you have almost finished creating the model, instead of hoping for the best, you should test it for its accuracy.

While doing so, you should upload the data, save it, and then use it in another new experiment.

Also, you will need to select everything required apart from the date and the next-day positive as your input and target respectively.

While viewing the model, look at the patience epoch parameter, which should be ideally set at a low, but you may increase it up a bit in order to run your model a bit longer.

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Things may not be right or just as expected at the first time, even then, if you get more than 50% accuracy in price prediction for the next day, you may consider a job well done.

However, if the majority of the day is positive, and quite a high one, you may consider that your model is not performing well.

If the model is guessing only the true value every day, it can be concluded that it is not ‘learning’ anything really.

If you represent this in a graph and it shows a steep rise in the beginning but a flat line then after, your model is actually guessing blindly.

Therefore, the graph will move sideways all through.

If such a thing happens, consider it is time to focus on a few more theories so that you can improve your model.

First, you may consider that your model cannot handle a large number of inputs. In that case you should try with fewer inputs.

Check if the performance is the same, worse or better and work on it accordingly.

Secondly, it may be that you are using the wrong type of problem.

Consider changing your model from tabular classification to tabular regression, depending on its current state.

In most cases you will see a better performance when this change is done even though you may not notice any significant changes in the modeling view.

Remember, if your model manages to achieve an accuracy of higher fifties, consider it to be much better than simply guessing true all the time.

From this point on, you should try different settings and try to find out different things such as:

  • The dropout ratio
  • The performance of the model with lower value
  • Overfitting and underfitting and
  • Class weights.

If none of these tweaks seem to work out and result in any considerable improvement in your model, consider your model to be basically flawed and is not learning anything.

Remember, you will need much more than the curve getting unsystematic upper peaks or moving higher or lower just a little bit on the y-axis.

The Step Forward

In such a situation when you cannot find any proper solution, you should take a bold step forward and consider a complete overhaul for your model.

One effective solution could be using minute candles rather than daily.

This may typically solve the biggest problem in your model by eliminating the majority of the external factors from the value accomplishment.

As said, earlier, the price of a crypto coin is typically influenced by the market sentiment instead of the historical price.

Therefore, it is elementary that your model should not work on the basis of the historical price.

However, for a lower period of time, the external events may be fewer which may make it quite easier to spot any specific pattern in the historical price at that time.

This may eventually help your model to download and use a large number of data.

Another useful way to make your price prediction model based on Ai and machine learning work well is to normalize the data.

This may make things a bit harder for the model but this is a good thing eventually.

Finally, you may also try out using a few of the previous candles, of course, normalized ones in every row

All these three tweaks in combination will make your model work really well and make it look like a real AI model.

It will perform well, learn, split the data set into half between true and false for next day positives, and the graph will look like a log(x) graph. And, most importantly, the model will get a staggering accuracy as well.

In the end it can be said that in order to predict crypto prices you do not need to have brilliant engineers or deep pockets.

All you have to do is download the data available publicly and a suitable platform in order to train an AI on the data.

This is not hard at all as it will turn out even if you are an AI novice.

With AI backing you up, you will not be afraid even by Wall Street, leave alone crypto.

Conclusion

As you can see from this article, using AI for crypto price prediction is a good move.

It will interpret crypto price data in real time using different parameters and elements and different useful neural network architectures for more accuracy.