How to Predict Cryptocurrency Prices With Machine Learning?

Machine learning can be used in real-world applications. You can even use it for predicting cryptocurrency prices with the use of LSTM neural networks. Since cryptocurrency prices change so frequently, it becomes challenging to trade in cryptocurrency.

Several traders and companies use machine learning to predict cryptocurrency prices as it makes it easier for them to earn profits. Similarly, if you want to earn daily profits, you can start trading in cryptocurrency. Visit the site for more information and create your free trading account today.

1. Getting cryptocurrency data using trading features

To predict prices of these digital currencies with machine learning, you would require trading features from the database that includes open price, close price, high price, low price, and volume of the respective cryptocurrency.

You need to get real-time trading data to predict an accurate cryptocurrency price. There are few websites that provide datasets of almost every cryptocurrency with accurate data. In order to find real-time data of any cryptocurrency, you can get the information easily. Also, these websites are verified and have all the data you need.

The database must include 5 trading features. These features are as follows:

• Open price – Open price is the value of a currency at the starting of the day.
• Close price – Close price is the value of a currency at the end of the day.
• High price – High price is the maximum value of the day a currency can rise.
• Low price – Low price is the lowest value of the day a currency can fall.
• Volume – Volume is the total number of currency being in trade on a day.

This is all the data you need to predict cryptocurrency prices with machine learning. Ensure that the source of data is reliable and accurate.

2.  The use of coding with machine language for training and testing

Once you have collected all the data, now it’s time to write some codes to get started with machine learning. Firstly, start with installing all the libraries and dependencies needed. Now select the US dollar exchange rate and store the real-time cryptocurrency data you collected into a pandas data frame.

You also have to convert string date time into a python date-time object. This is important because operations are easier on a string instead of a Date Time object. Now use the trading features to formulate the dataset with cryptocurrency values.

Now divide the data into two sets for training and testing. To be more accurate, you can even divide the data into training, validation, testing. Now put the real-time data of cryptocurrency prices in US dollars as a function while writing the codes. Once you have plotted all the data into coding, you can see the change in cryptocurrency prices in a graphical representation.

In this graphical representation of cryptocurrency prices, you can study the changing trend in values. Although, this dataset is just a general representation of changes in the price of a particular cryptocurrency. Determining the future price of cryptocurrencies is never so easy.

Now, you need to create certain functions to normalize these values because it is part of preparing the data as a part of machine learning. Normalization is useful to alter the numerical values to a common scale without altering the difference in the range of values.

Now, again create a function to extract windows in the size of 5 and then again, continue to create functions to create a format to be used later in the LSTM neural network.

3.  Using LSTM neural network to predict the cryptocurrency prices

LSTM or long short-term memory networks are used in machine learning for long-term dependencies using special gates to get information from the previous layer and the current layer as well as using the LSTM layer into the network. The data passes from several gates and multiple activation functions, and finally, it goes from the LSTM cells.

LSTM cells help you to learn patterns for a specific period that is why LSTM neural networks are used in machine learning. Also, you can remove irrelevant information from LSTM cells. This helps you to sort out the data more accurately.

Now, build a sequential model to stack all the layers in logical order. There are three layers with a linear activation function that includes the LSTM layer which is the input layer, the next comes to the Dropout layer also the hidden layer and at last, a Dense layer/ the output layer. To build this model, you can use Adam to optimize the data and Mean Squared Deviation to estimate the loss function.

Now, create the parameters that include random number seed, window length, size of the test set, amount of neurons in LSTM cells batch size, dropouts, optimizer, loss, and epochs for later use. Once you are done creating the parameters, now you have to train the model with inputs and labels.

To evaluate the metric, you can apply the Mean Absolute Error method. The MAE method can be easily interpreted, and since the model needs to be readily explained, MAE is an ideal choice.

4.  Mean Absolute Error method

This method is suitable for calculating the average magnitude of errors for predicting future outcomes. It provides an average test sample that makes it easier to identify changes between actual and predicted values where each difference has equal weight.

Once you obtain the MAE value using the formula, you can place the actual and predicted values with the use of coding. You can see a double graphical representation showing the actual value and the predicted value of cryptocurrency.

The Bottom-line

Predicting cryptocurrency prices can be really difficult and challenging. With the help of machine learning, you can predict the price, but that doesn’t give complete assurance that it will be accurate. If you have the knowledge of coding and following these steps, you can somewhat predict future cryptocurrency prices.

Ensure that the data you collect and input is accurate and verified from a reputable source. Using LSTM neural networks predicting cryptocurrency prices can become a little easy with machine learning.