Artificial intelligence (AI) is changing the way that traders and investors use analytical tools to make informed decisions about which cryptocurrencies to buy and sell and when.
The launch of AI chatbot ChatGPT, in particular, has enabled developers to create various new algorithm-based trading tools and applications.
What Makes AI Useful in Cryptocurrency Investing?
The extreme volatility in cryptocurrency markets can make it difficult to analyze and identify profitable investment opportunities. Traditional market analysis can be time-consuming, particularly for new traders and investors, who may not get all the information they need to make accurate decisions. But AI systems are trained to collect and learn from vast sets of data using machine learning (ML) algorithms, natural language processing (NLP), and statistical models. These systems can then make predictions or even decisions based on the data.
Deep learning is a type of ML that uses multi-layered neural networks to learn from data in more sophisticated ways than human analysts. Tools based on this technology can help traders to analyze market data, confirm trends, and identify trading patterns they may not otherwise have seen to take advantage of market opportunities and maximize their returns. They can also provide alerts to potential market changes and threats that could negatively affect their positions so they can take steps to avoid losses.
AI tools can monitor markets in real-time, all the time, taking in content from crypto exchanges, news items, social media platforms, and other sources. This enables traders and investors to keep tracking markets – even at times when they are engaged in other tasks – and respond quickly to opportunities.
Furthermore, traders and investors can use AI tools to analyze the risk associated with different potential crypto investments and receive recommendations for how to manage and balance their portfolios.
AI systems can also automate the process of buying and selling cryptocurrencies, executing trades based on their monitoring of the market, saving time and effort and reducing the risk of human error.
In summary, the 4 most common use cases of AI in cryptocurrency investing are:
| Data analysis | Gain insights into market dynamics, identify patterns |
| Real-time market monitoring | Continuously track price fluctuations, news updates, social media sentiment, and other market drivers to respond quickly to opportunities and trends to inform trading |
| Risk assessment | Assess the risk levels associated with different cryptocurrencies and make recommendations for portfolio management |
| Automation | Real-time monitoring enables algorithms to execute trades automatically, saving time and limiting human error |
Types of AI Trading Tools
There are several different ways to incorporate AI into cryptocurrency trading tools. There are now trading bots powered by AI-based applications such as ChatGPT, investment portfolios compiled by AI bots, and market forecasting contests.
Trading Bots
Automated trading bots can be hosted on a cryptocurrency exchange or integrated with an exchange using an application programming interface (API).
Bots are typically used for short-term trading, and traders can set them to execute a specific strategy, watching for specific patterns in the available data. But there are also crypto bots available that can implement dollar-cost averaging (DCA) into a long-term investment. These are often much more simple but are also generally considered less risky.
ChatGPT brings new functionality to trading bots, as it can generate computer code. This means traders can use it to generate code for specific trading strategies, such as momentum trading Bitcoin, on certain exchanges, such as Binance. It will also explain which parameters in the code need to be adjusted and the source of its pricing data.
An example of a crypto trading bot is Omni, which runs on the Solana blockchain. The Omni algorithm has been trained with on-chain data to implement passive decentralized finance (DeFi) strategies such as coin and token staking.
Some trading platforms already offer “social trading features” that allow users to copy the trades of other traders that they follow. AI can take this a step further by continuously analyzing sentiment from social media platforms such as Twitter and Reddit, where cryptocurrency traders are most active. It can then determine whether market sentiment is bullish or bearish and make trading decisions accordingly.
Social investing platform eToro has introduced an AI-based cryptocurrency trading feature that brings in all the relevant tweets from Twitter about specific coins and tokens and will allow users to trade within Twitter directly.
Price Forecasting
Attempting to predict highly volatile cryptocurrency prices is notoriously difficult for human analysts. But AI’s ability to synthesize massive volumes of data and identify complex patterns is better suited to making predictions.
Machine learning allows users to an algorithm based on a combination of historical prices, trading volumes, market sentiment, macroeconomic indicators, and other market influences to make predictions about future price trends. Traders can use these insights into potential future price movements to inform their trading decisions. However, even the best, most complex AI models still get it wrong sometimes, so trade at your own risk.
Risk Management
Investors and traders can use AI to assess the risk of investing in various cryptocurrencies and use this information to build and rebalance their portfolios. For instance, Coinbase, the largest US-based cryptocurrency exchange, is testing ChatGPT to assist its risk analysis to screen coins or tokens before listing them for trading.
Individual investors may soon be able to enter prompts into AI applications like ChatGPT to assess whether a coin or token is an appropriate risk based on their age, portfolio, size, investing goals, and other factors.
AI tools like ChatGPT can also construct cryptocurrency portfolios based on their pricing and risk analysis to help traders and investors decide how to allocate their funds.
In addition, AI can analyze blockchain data such as transactions, user information, and contract details to gauge its behavior and performance. This can help to identify which blockchains and native cryptocurrencies are seeing the most adoption and which have potential threats and vulnerabilities traders and investors need to know about.
It’s important to remember that even ChatGPT, one of the most impressive AI models ever created, still makes mistakes, a phenomenon called AI hallucinations. It will make these mistakes just as confidently as it gets things right, so be careful.
How AI-Powered Trading Automation Works
Automated trading employs computer programs to execute trades on the trader’s behalf based on preset criteria. The aim is to make trading more efficient and minimize human involvement. This removes the influence of emotions on trading decisions, which can cause traders to panic or become overconfident and make irrational trades.
AI algorithms advance this capability by learning from data and adapting to new information to make trading decisions. AI-based trading bots can incorporate reinforcement learning as well as machine learning and deep learning in different ways:
- Machine learning trains an AI algorithm from historical datasets to enable it to make predictions about what future data will look like;
- Deep learning uses multi-layered neural networks to learn and make predictions from data;
- Reinforcement learning (RL) trains AI algorithms to make decisions through a system of rewards for correct decisions and penalties for incorrect decisions.
Once an algorithm has been trained to collect and analyze data, it can be backtested to ensure it makes correct decisions about past data and is reliable enough to use in forecasting future prices.
The AI algorithm can then execute trades based on its price predictions. For instance, if an AI trading bot predicts bitcoin’s price will rise, it can execute a trade to buy a specified quantity of the coin. If it predicts that bitcoin’s price will fall, it can execute a trade to sell all or part of a position.
How AI Sentiment Analysis Works
The ability to process large volumes of data quickly is one of AI’s strong suits. It is especially useful for analyzing market sentiment about a particular crypto coin or token from social media platforms, news websites, forum discussions, blogs, and other internet sources.
AI algorithms can use natural language processing to identify whether the content of these sources is positive or negative based on the language they use.
This sentiment analysis helps traders to gain insights into the overall attitude of the market and determine whether most participants are bullish, bearish, or neutral. Sentiment reflects market fundamentals and can often affect prices in ways that are not clear from technical analysis alone.
If there is a significant number of positive references to a particular cryptocurrency from various sources, the AI would take this to indicate bullish sentiment, while a large number of negative references would indicate bearish sentiment. Based on its data analysis, the algorithm can provide traders with a sentiment score to contribute to decision-making.
Examples of AI-Based Cryptocurrency Trading Tools
There is a growing number of AI-based tools for real-time market monitoring and identifying opportunities. As the technology develops, trading bots will increasingly become available on cryptocurrency exchanges, open-source software repositories like GitHub, and new AI marketplaces.
- SingularityNET is a decentralized network that allows any user to create and monetize AI-based services on its marketplaces, including bots for market analysis;
- TensorCharts is a cryptocurrency-focused day trading software application that uses AI to provide live market data visualization;
- Cryptoindex is a trading platform that provides AI-based analytical tools, such as an index that tracks the top 100 cryptocurrencies by market capitalization and rebalances continuously;
- Kryll.io is an algorithmic crypto trading platform that allows users to create or rent bitcoin and altcoin AI bots to automate their trading;
- HypeIndex is an AI platform that scrapes the internet to analyze sentiment on cryptocurrencies as well as stocks, helping traders to act before market movements.
3 Tips For Using AI Tools In Cryptocurrency Trading
AI algorithms clearly have useful applications in crypto trading and investing. However, as with any form of investing, there are some precautions you should take when using them to buy and sell assets with real money.
- Verify information. AI can analyze and provide a wealth of information, but you should always verify that it is accurate before finalizing trades or investments. AIs make mistakes too!
- Do your own research. AI trading tools should not replace other forms of research and analysis. You should use them as a part of a broader investment strategy that includes fundamental and technical analysis rather than blindly following whatever AI outputs.
- Be aware of security risks. Like any online application, AI tools can be vulnerable to malicious attacks and other security risks. Also, some trading bot platforms misrepresent the effectiveness or risks of their AI tools. You should make sure to use secure tools from reputable sources.
By following best practices for trading and investing, you can use AI tools to enhance your strategies and avoid unnecessary losses.
The Bottom Line
Artificial intelligence can analyze massive amounts of data, forecast market movements, and detect potential risks – which traders can use to inform their decision-making and enhance their trading strategies. There are currently AI tools available for automated trading, sentiment analysis, and risk management, and as AI technology develops, more sophisticated tools will continue to emerge.
By taking human emotion out of the equation, AI has the potential to help traders and investors limit their losses. It is important to keep in mind that AI-based tools should be used in conjunction with other forms of analysis and should not be your only source of research.
