Using AI and Neural Networks to Analyze Cryptocurrency Prices

Cryptocurrency Price Prediction Using Neural Networks and AI

With the spread of neural networks it’s become more and more popular to rely on artificial intelligence, and this can also be applied to the field of crypto. Let’s take a look at what modern tools are available for our forecasts and how we can more accurately predict the crypto landscape.

Cryptocurrency Price Prediction Using Neural Networks and AI

What Are Neural Networks?

A neural network is a computing model whose layered structure resembles the networked structure of neurons in the brain. It features interconnected processing elements called neurons that work together to produce an output function. Neural networks are made of input and output layers/dimensions, and in most cases, they also have a hidden layer consisting of units that transform the input into something that the output layer can use.

While traditional computers are ready to go out of the box, neural networks must be ‘trained’ over time to increase their accuracy and efficiency. Fine-tuning these learning machines for accuracy pays rich dividends, giving users a powerful computing tool in artificial intelligence (AI) and computer science applications. In essence, a neural network embodies artificial intelligence and each time gives better results, making less mistakes with every new instance.

AI Applications

Where can artificial intelligence be used? There are a number of fields where AI will be most welcomed.

Banking

First of all, AI is widely used in banking and finance. The adoption of AI solutions in banking has grown more common. According to the report ‘The impact of artificial intelligence in the banking sector & how AI is being used in 2022,’ the majority of financial service companies say they have implemented the technology in business domains like risk management (56%) and revenue generation through new products and processes (52%). 

AI in Banking

Source: Business Insider Intelligence

Credit Decision Making

Many AI-powered solutions help banks assess potential borrowers by analyzing historical data and calculating various factors. With a robust credit scoring model, lenders are able to develop innovative lending systems even for individuals or entities with limited credit history.

Chemical Industry

AI is being used more and more by chemists to perform various tasks. Originally, research in AI applied to chemistry has largely been fueled by the need to accelerate drug discovery and reduce its huge costs and the time to market for new drugs. So far, AI has made significant progress towards the acceleration of drug discovery R&D.

Speech Recognition and Imitation

The Internet is rich with software that offers speech recognition. However, just recently Microsoft researchers announced a new text-to-speech AI model called VALL-E that can closely simulate a person’s voice when given a three-second audio sample. Once it learns a specific voice, VALL-E can synthesize audio of that person saying anything, in a way that attempts to preserve the speaker’s emotional tone, so this field continues to develop.

Computer Chess

Computer chess includes both hardware and software capable of playing chess. Chess provides opportunities for players to practice even in the absence of human opponents, and also provides opportunities for analysis, entertainment, and training. Computer chess applications that play at the level of a chess master or higher are available on hardware from supercomputers to smartphones. 

Fraud Detection

Being highly prone to fraudulent transactions, banking and financial institutions need reliable systems to detect and prevent these in a timely manner. Unfortunately, however attentive, human employees are unable to supervise all the transactions and manage malicious content or suspicious payments. Machine learning algorithms, on the other hand, can examine hundreds of data points in real time and identify questionable or fraudulent transactions.

Design

AI is rapidly changing the digital marketing industry. AI simulates human intelligence processes in machines and computer-controlled robots. This allows computer systems to perform laborious tasks, helping humans focus on more important things. Today, businesses have been utilizing AI tools to optimize their processes and hit their goals faster. Similarly, AI is learning how to design and perform creative tasks in a quick span of time, making the life of designers easier.

AI in Supply Chain

A supply chain is a process of taking an item from raw materials into a finished product with many middlemen along the way. For example, anything we order online goes through a supply chain with manufacturers, distributors, drivers, shipments, etc. All these steps are typically managed by different companies, requiring armies of expensive planners and complex engineers. Below are a few examples of AI applications in the supply chain.

Healthcare

Artificial intelligence simplifies the lives of patients, doctors, and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost.  

AI in Crypto Trading

These are just a few fields where AI can make (and makes) a huge difference. Other sectors include economy, market analysis, etc., but the field that interests us the most is price prediction of cryptocurrencies using technical trade indicators. We have already reviewed technical analysis in this article, so let’s find out that AI has to offer.

Financial market forecasting is a well-established discipline of financial research. Regarding the predictability and efficiency of financial markets, a contradictory body of information exists. Regression analysis of probable signals with the aim of explaining asset returns is a well-established method of analyzing return-predictive signals and it has been used for many years. Various characteristics may be included in linear regressions, but they are not flexible in their incorporation and they impose strict assumptions on the functional form of how signals suggest market movements. 

All in all, the crypto market has many good characteristics including high market data availability and no closed trading periods. However, it suffers from its high price volatility and relatively smaller capitalization. In crypto financial trading, data can be available to all traders. However, the analysis and the selection of this data makes the difference between executing good trades and bad trades. Therefore, one of the main challenges in financial trading is to develop methods/approaches to extract meaningful knowledge and insights from the data. Furthermore, due to the high price volatility of cryptocurrencies, forecasting their future growth becomes more challenging.

Many traders have created bots that trade for them based on a set number of market strategies. However, this takes a lot of investment, work, and time, and the result is not always guaranteed as the exchange can change its parameters and thus alter the set mechanisms, which you may never recover.

Cryptocurrency Price Prediction Using Neural Networks and AI

Neural Network Cryptocurrency Trading: Methods

At the moment, there is no set method to establish the future price of digital assets, however, the basic theory is to feed the neural network old crypto data, so that it could forecast market trends accordingly. As neural networks learn easily, this is a valid approach even for such a turbulent market. The data used in a neural network has to be paired with human decision-making. Neural networks can also provide a comprehensive investment strategy for both your short-term and long-term investment goals.

Companies are using a neural network to input their investment and trading ideas that are time-tested. After implementation, you gain data that will help you figure out how effective your idea is likely to be; traditional AI models are unable to do it. A neural network allows you to modify your idea by changing the parameters for the data inputs you want to consider.

There have been numerous attempts to predict future cryptocurrency prices. For instance, in this thesis of a German student, a framework has been laid for such a network. Some crypto enthusiasts have also tried to predict Bitcoin prices on their own, and it’s quite possible that very soon there will be a valid method of doing that.

Conclusion

Every investor needs to know about crypto market trends. However, due to the dynamic nature of the cryptocurrency market, it is a big challenge to predict the prices of various digital assets. Hopefully, with the development of neural networks and the establishment of the crypto market, one day we will be able to predict crypto prices more accurately.


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Don’t forget to do your own research before buying any crypto. The views and opinions expressed in this article are solely those of the author.

Tags: AI Artificial Intelligence crypto price prediction Neural Networks price analysis
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