The impact of AI on the crypto industry – can neural networks be useful in trading?

In this article, Kevin Plumpton, a leader in the field of anti-money laundering and counter-terrorism financing, the pioneer of KYCMATIC, and the CEO of the global financial crypto-fiat ecosystem Kyrrex covers the impact of AI on the crypto industry and examines the potential of neural networks in cryptocurrency trading.

The rise of cryptocurrency is a fascinating phenomenon of our day. Digital currencies remain a very attractive investment tool, with the potential to generate enormous profits on cryptocurrency exchanges or when investing in these assets over the long term. Despite their increasing popularity, investing in cryptocurrencies is not without its challenges, one of which is the complexity of the technology involved. Major financial institutions, including hedge funds, banks, pension funds and family offices, are joining the cryptocurrency daily. Typically, these companies use trading models that are significantly more complicated than those used by individual investors. The use of AI is no longer limited to corporations. Crypto assets investment requires a high level of knowledge and diligence. As more data-driven resources become accessible, investors and traders are increasingly turning to AI and machine learning to make sense of the markets. These advances have led to the development of sophisticated trading bots and predictive models that have the potential to provide traders with a competitive advantage.

Data analysis and pattern recognition

Cryptocurrency markets operate around the clock and generate an enormous amount of data, making it challenging for traders to stay abreast of trends and make informed decisions. AI-powered neural networks excel at processing and analyzing large data sets, enabling traders to identify patterns and correlations that may not be visible to the human eye. By recognizing historical price patterns and market trends, these networks can help predict potential price movements and make more strategic trading choices. AI-powered sentiment analysis tools can scour social media platforms, news articles and forums to gauge public perception and emotions around specific cryptocurrencies. By gaining a better outlook on market sentiment, traders can make better informed decisions about their trades.

The cryptocurrency market can be analyzed using common types of sentiment analysis, such as:

In a polarity study, the statements are looked at and labeled based on whether they are reasonable, harmful, or neutral. Analysts and investors will determine the trend that similar information and labels are viewed in the same way once the overall score is taken into account. Aspect-based sentiment analysis. Aspect-based sentiment analysis groups data based on the business or service and determines how people feel. One way to do this is to look at customer feedback by posting a feeling with a result or service. Tone and emotion. NLP can determine how a text makes you feel or how it sounds. We can learn more by looking at the different types of feelings that arise.

Neural networks have also found extensive application in algorithmic trading strategies. Traders can develop complex trading algorithms that execute buy or sell orders based on predetermined conditions. AI-powered trading bots can efficiently manage multiple trading pairs simultaneously and react to market fluctuations in real-time, ensuring faster and more precise executions.

The role of neural networks in trading

Neural networks excel in predictive analytics because of their ability to identify patterns in large amounts of historical data. By training a neural network on historical cryptocurrency price data, it can learn to recognize patterns associated with different market conditions and predict potential price movements. These predictions can help traders anticipate trends and adjust their strategies accordingly.

One of the problems that traders face is that there is not a lot of money in the cryptocurrency market. Due to their high volatility, digital currencies are not suitable for business payments. Sometimes the difference between supply and demand is made up by the high fees charged by exchanges and banks. To solve the problem, machine learning platforms are used. Side-matching technology is used to help clients find the right broker and bank, allowing them to work directly with the broker and bank and pay the least amount of fees. Moreover, this fast-paced industry presupposes effective risk management. Neural networks can help traders optimize their risk management strategies by analyzing past data to identify patterns associated with potential losses.

Nowadays, cryptocurrency markets are also known for their volatility, which can pose challenges for traders looking to make profitable trades. Neural networks can process and respond to market data quickly, enabling traders to capitalize on short-term opportunities and adapt to rapidly changing market conditions.

Other than that, AI systems can track and compare changes in the exchange rate and find patterns. They can then use this information to make accurate predictions about how the exchange rate will change in the future.

Challenges and limitations

While the use of neural networks in cryptocurrency trading offers numerous advantages, it also comes with some challenges and limitations: Here are some of them: it starts from overfitting. AI often memorizes historical data instead of identifying common patterns, which in turn leads to poor performance in live trading environments. Another problem is the general lack of regulation. The industry is relatively unregulated in many regions, so AI trading bots raise concerns about market manipulation. Of course, this leads to another problem – in uncertain market conditions, AI algorithms can be affected by major news events or regulatory changes.

To conclude, we can say that AI has clearly brought drastic changes to the crypto industry by improving data analysis, predictive modeling and algorithmic trading strategies. As technology advances and AI algorithms become more sophisticated, the role of neural networks in cryptocurrency trading is likely to expand. However, it is essential to recognize that AI-driven trading also presents risks and challenges that need to be carefully managed. As with any trading approach, AI-based strategies must be complemented by comprehensive risk management, continuous monitoring and human oversight. Ultimately, while neural networks offer valuable insights and opportunities in the crypto industry, human expertise remains indispensable to interpret results, assess risks, and make prudent trading decisions.

Kevin Plumpton is the CEO of Kyrrex Group.