Cryptocurrency price prediction machine learning

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Kyriazis, Gunay, Samet, Adam Hayes, Full references including those not matched with items on IDEAS Most related items These are the items that most often cite the same works as this one and are cited by the same works as this one. Aurelio F. A survey based on hybrid analysis ," Papers Flori, Andrea, A network analysis using bibliometric methods ," International Review of Financial Analysis , Elsevier, vol.

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It then displays all that information in an easy to understand chart, so that you can assess the situation at a glance. No single tool should ever be the base of your decision making when entering any investment. Cryptocurrency is not an exception here. What you can trust is that in your due diligence process, Coinpredictor will help you speed up enormously your research by crunching numbers so you don't have to and present those numbers in an easy to understand format, giving you a visual representation of the markets as they are.

You will get an understanding of trends as they happen, together with an interpretation of what could happen if certain patterns repeat themselves as they many times do by using our tool. This is what we do and we do it very well. What we don't do is tell you the chart never lies. No one can, among other things because the trigger for the pattern might be different in nature. That's where your own research comes in. You get to see what could happen next, in a simple chart.

You also get to see where the algorithm has diverged from the actual price. By checking if any event triggered this divergence you gain insights into the way the cryptocurrency market works and how different factors affect it, as well as to which extent they do. So if you want a top aid in your research that saves you lots of time and helps you understand past and current price moves so that you can work out probabilities for profit, look no further.

Above everything else remember, the information on this website is exactly that, information. No part of this website should ever be constructed as financial advice. You should always seek help from a professional before committing to any investment. You should also know that cryptocurrency is a risky investment, not fit for most personal risk profiles.

If you're interested in learning more about machine learning for trading and investing, check out our AI investment research platform, the MLQ app. You can learn more about the MLQ app here or sign up for a free account here. This post may contain affiliate links. See our policy page for more information. A common method for price prediction are regression-based strategies. Such strategies use regression analysis to extrapolate a trend to derive a financial instruments direction of future price movement.

The problem to be solved, then, is understanding the relationship between historical data and future price prediction. In other words, we are looking for the relationship between the dependant and independent variable. Linear regression is appropriate for this problem as it analyzes two separate variables in order to find a single relationship. To solve this problem, I will first use a linear ordinary least squares OLS model and then a neural network regression model using Tensorflow and Keras.

In order to implement a data-driven investment approach we first need to acquire and understand our dataset, then we need to preprocess our data and implement the machine learning models, and finally we need to analyze the results against a benchmark and suggest improvements for the models. The equation for R 2 is as follows:.

It is important to note that the R 2 does have limitations as it simply provides an estimate of the relationship between price movements, but this does not necessarily indicate when the machine learning model is good or bad or if the predictions are biased.

The benchmark I will use is an R 2 score of 0. This number was chosen because financial asset including cryptocurrencies returns are often said to be unpredictable. An R 2 greater than 0 indicates that there is a relationship between the dependant and independent variable and an R 2 less than 0 suggests otherwise.

The time frame for the dataset will be from to , and the dataset have been downloaded from Quandl using the Bitfinex source [3]. After importing the data we see this dataset has 8 columns and entries. I will start by reviewing the daily close price, which is the Last column in the dataset. Also this dataset does have 3 NaN values, which will need to be dropped in the data preprocessing phase. The following graph displays the day simple moving average with the daily closing price for BTC-USD from Using machine learning for finance can be accomplished in many ways such as predicting the raw prices of our stocks, but as described in this Machine Learning for Finance DataCamp course, typically we will predict percent changes [4].

This makes it easier to create a general-purpose model for stock price prediction. The following graph displays a histogram of daily percent changes over time, which we see is right-skewed and has a nearly normal distribution. The 5-day percent changes of the daily price for the current day, and 5 days in the future are then created. After creating these columns, the correlation between our percent price changes present and future is calculated to see if previous price changes can predict future price changes.

We can see correlation is near 0 at 0. In this section we will review the algorithms that will be applied to the dataset: in particular, linear regression and a deep learning model. Linear regression is used to extrapolate a trend from the underlying asset. Linear regression and ordinary least squares OLS are decades-old statistical techniques that can be used to extrapolate a trend in the underlying asset and predict the direction of future price movement.

A simple example of linear regression trend extrapolation can be seen below from Ch. Finally, we will look a neural network regression algorithm to predict the future price of an asset. Loosely modeled after neurons in a biological brain, neural networks are connected by nodes that transmit a signal from one another through what are referred to as hidden layers.

The final layer in the neural network is our target prediction variable, which in our case will be a future price prediction. To solve this, this section will make use of the deep learning libraries Tensorflow with Keras running on top of it. Now that we are familiar with our data, it is time to prepare it for machine learning. The first step is to create features and targets. Our features are the inputs we use to predict future price changes with, in this case we are using historical data points as features, and our target is the 5-day future price change.

We want to incorporate historical data as our features, for example the price changes in the last 50 days. Instead of including each daily price percent changes, we can concentrate historical data into a single point using simple moving averages. A simple moving average SMA is the average of a value in the last n days. We can now use these indicators for prediction by creating a DataFrame that includes both a list of our features and the target 5-day future percent change.

This is done so that we can analyze if there are correlations between the features and targets before implementing our machine learning algorithms. The following plot uses the seaborn library to visualize a heatmap of the correlations:. Generally, we consider a correlation of 0. From this heatmap, however, we see that none of the variables fit this criteria in relation to our 5-day future price change target.

Now that we have prepared our data for machine learning, we will start implementing a linear model with our data. The purpose of this is to fit our model to the training data and then test on the most recent data to understand how our algorithm will perform on unseen data. Now that we have these two subsets we can fit a linear model by using the Ordinary Least Squares OLS function from statsmodels.

This is to be expected however, since linear models are one of the simplest machine learning models available. The use of neural networks has rapidly grown due to the exponential increase in GPU computational power over time, the size of data available, and improvements in software. Neural networks are similar to the previous models we have used in that they use features and targets to produce predictions, however neural networks have been shown to outperform other models because they capture variable interactions, have non-linearity, and are highly customizable.

Neural network models typically work better with standardized data, and one way to do this is by scaling our features. A histogram of the scaled data are plotted below:. Each layer our our neural network uses input, a weight, a bias and an activation function to add non-linearity.

We will use a common activation function called ReLu , or Rectified Linear Units, which is 0 for negative numbers and linear for positive numbers, to add non-linearity. Predictions are made by passing data through our neural network in the forward direction, and the final node yields the prediction - this is know as forward propagation. Once we have our predictions we use the loss function to compare our predictions and targets.

For regression, we often use the mean-squared-error loss function. We then use our error from the loss function and pass this backwards through the network, which updates weights and biases in order to improve our predictions - this is known as back propagation. To implement our neural network we use the Keras library with TensorFlow backend. Keras is a high level API that allows us to build neural networks with minimal code, but still allows for customization.

I then printed out the loss function of the model with the best R 2 score. In the model I used the ReLu activation function and the last layer is 1 node, and has a linear activation function. After creating the model, we then compile the model with an adam optimizer, and a loss function - in this case mse , or mean squared error. We then fit the model with our features and targets, and specify the number of epochs - or training cycles.

After training our model we plot the loss vs. After this I have calculated the R 2 metric and plotted the prediction vs. From these tests I see that epochs with [15, 20, 1] layers has the best loss function at 0.