Understanding the extremely volatile crypto market and executing cryptocurrency investments takes a lot of time and skill. With the emergence of more tech-driven tools, asset managers are increasingly looking to AI, Machine Learning (ML), and Natural Language Processing (NLP) to make sense of managing crypto investments in fund portfolios.

AI for crypto investing can provide:

  • More accurate predictions
  • Sentiment analysis on the crypto exchanges
  • Automated crypto trading
  • Better investment monetization

More Accurate Crypto Market Predictions with AI

One of the biggest problems in cryptocurrency trading is the volatility of the market and manual research, extraction, and analysis processes are not efficient when it comes to identifying investments and buy/sell signals. By collecting, cleaning, processing, and analyzing large sets of unstructured data, investors can predict and forecast significant events in the crypto market and make better informed investment decisions.

Additionally, AI with blockchain makes an even more powerful combination. Blockchain keeps a digital record of not only transactions but for anything of value. Since blockchain enables data to be stored and shared in a secure manner, AI can be used to analyze and generate insights from the historical and real-time blockchain data. Blockchain transactions can also reveal behavioral patterns to help make sense of the drivers in the crypto market.

AI Crypto Market Sentiment Analysis 

Sentiment analysis refers to the use of AI and natural language processing to analyze the sentiment or opinions of people about a certain topic. Within cryptocurrency investing, an overall positive sentiment on a digital currency can predict that the price will increase, while an overall negative sentiment would predict a price decrease. 

To determine the sentiment of the cryptocurrency market, news, blogs, articles, forums, social posts, stock message boards and even the comments related to them must be collected, processed, and analyzed to detect whether it is negative, neutral, or positive. Warnings of manipulations in the market can be detected through unusual behaviors observed in sentiment indicators.

Common types of sentiment analysis used to analyse the cryptocurrency market include: 

  • Polarity: Polarity analysis analyzes the statements and labels them based on whether they are positive, negative or neutral. Once the overall score is considered, analysts and investors can monitor trends and changes to the score.
  • Tone/emotion: NLP can be used to analyze the emotion or tone of the text. Insights are drawn by analyzing different kinds of emotions that appear. 
  • Aspect-based sentiment analysis: Aspect-based sentiment analysis categorizes data by the specific company or service and identifies the sentiment attributed to each one. This can include analyzing customer feedback by associating sentiments with a product or service. 

Automated Crypto Trading Strategies

Since AI can simulate human intelligence, investors and analysts often use AI in high-frequency trading strategies. Traders who are able to execute trades on cryptocurrency exchanges quickly are more profitable. High-frequency trading is a form of algorithmic trading used by investment and hedge funds where a computer will execute a large number of orders within fractions of a second. Algorithms based on mathematical computation data, predictive analytics, and forecast methodologies can analyze the markets and buy or sell cryptocurrencies within seconds.

Monetizing Crypto Insights

Crypto traders rely heavily on various signals. However, with unstructured data dominating the digital landscape, it can be nearly impossible to generate accurate signals manually. Natural Language Processing (NLP) allows data scientists and developers to build models that traders can use to obtain relevant and clean data. With AI’s NLP techniques, data can be classified and entities extracted by specific characteristics including the currency name, document type, currency founder, and more. Data scientists can then offer accurate trading insights in a manner that is understandable to non-tech-savvy traders or investors through an intuitive dashboard or interface. Investors and traders can then increase their profit using the insights gained. 

4 Steps to Deploying AI-Fueled Crypto Investing

With innovations in NoCode AI, these technologies can also be more accessible to non-technical users through a no-code environment. A no-code AI platform provides an intuitive user interface where data scientists and analysts can build and deploy AI models without writing any code.

No-code AI platforms, such as the Accern NoCdoeNLP Platform, are changing the arc of AI adoption for financial services companies by making AI far more accessible and with lower costs – both for set up and continuous operations.

To ensure a successful deployment of AI for crypto investing:

  1. Craft a strategic plan for bringing No-Code AI technologies into the company.
  2. Educate the team on how such platforms will be used, their benefits and potential challenges. 
  3. Set a clear process for assessing how the existing AI infrastructure and methodologies can be complemented with No-Code AI platforms to reduce risk and deliver more value.
  4. Ensure that all AI systems are well protected from data leaks and adhere to compliance and security issues.

Schedule a demo to learn more about the Accern NoCodeNLP Platform.