Traditionally, traders have relied on human-based pattern recognition and technical analysis, looking at the company’s financial health, competitors, and other methods for determining what trades to make on an asset.
However, with the growth of artificial intelligence (AI), there are additional ways that traders can analyze the markets, using the data gathered via machine learning. Both methods have their place in the industry, but it is best to understand how they both work and their benefits and drawbacks.
AI plays a crucial role in cryptocurrency trading by providing insights and predictions based on vast amounts of data. Cryptocurrency markets are highly volatile and operate 24/7, making it challenging for traders to keep up with the constant fluctuations.
AI algorithms can analyze and interpret complex market data in real time, enabling traders to make informed decisions and maximize their chances of profitable trades.
AI utilizes advanced data analysis techniques and pattern recognition to understand and predict market trends.
By employing AI-based trading algorithms and platforms, traders can gain insights, automate trading strategies and potentially improve their overall trading performance in the cryptocurrency markets.
Human intuition involves making decisions based on instinct, gut feelings, and personal judgment. It plays a significant role in decision-making processes across various domains, including trading.
Intuition involves tapping into unconscious knowledge, experience, and emotions to make judgments.
Traditional human-based trading methods include technical and fundamental analysis. Technical analysis involves studying historical price and volume data to identify patterns, trends, and indicators to guide trading decisions. Traders using technical analysis rely on charts, graphs, and mathematical tools to predict future price movements and make buy or sell decisions.
Fundamental analysis focuses on evaluating the intrinsic value of an asset by analyzing relevant financial, economic, and qualitative factors. This approach involves studying financial statements, company news, industry trends, and macroeconomic indicators to assess an asset’s value and potential growth.
Anthony Cerullo, chief communications strategist at Walbi — an AI-powered decentralized finance platform — told Cointelegraph, “We can all agree that AI lacks human intuition. It lacks that ‘gut feeling’ that says when something is right or wrong. In terms of quantitative analysis in trading, that gut feeling is useful.”
Cerullo continued, “Human intuition helps to provide a subjective understanding of market dynamics, investor sentiment, and potential opportunities that are not captured solely through numerical data.”
However, the benefits of human intuition don’t make AI obsolete, according to Cerullo. Instead, a relationship combining the two may be beneficial: “This is not to say human intuition is better than AI — just that it can do things AI cannot do.”
“Furthermore, AI can do things humans are not capable of either. That’s why a relationship between the two — and not a competition — is the best possible outcome.”
AI signals offer distinct advantages in trading, including speed, scalability, and the ability to reduce emotional bias.
AI algorithms excel at processing and analyzing large volumes of data in real time. This enables traders to swiftly respond to market changes and execute trades at optimal times.
In highly volatile markets, where prices can fluctuate rapidly, the speed advantage of AI signals can be particularly valuable. Traders can capitalize on timely opportunities and make informed decisions without being hindered by delays in data analysis.
Scalability is another notable advantage of AI signals. These algorithms can be scaled to analyze multiple cryptocurrencies or markets simultaneously. This scalability empowers traders to monitor and trade across various markets, expanding their trading opportunities and potential profits.
AI signals also offer the benefit of reducing emotional bias in trading decisions. Human traders are often influenced by emotions such as fear, greed, or overconfidence, which can cloud judgment and lead to suboptimal decision-making.
In contrast, AI algorithms operate based on data-driven analysis, relying on objective information rather than emotional factors.
Cerullo told Cointelegraph, “AI algorithms leverage advanced data analysis techniques and pattern recognition. For example, they analyze historical price data, trade volumes, news sentiment, social media trends, and other relevant data points to identify patterns and correlations,” continuing:
“Humans can do this, but not nearly as quickly as AI algorithms. Furthermore, humans may miss patterns that AI picks up 100% of the time. By learning from past market behavior, AI algorithms recognize potential trading opportunities or patterns that indicate future price movements.”
AI algorithms have limitations that must be considered when relying on them for decision-making. One such limitation is the lack of contextual understanding.
These algorithms primarily depend on historical data and patterns to make predictions, which means they may struggle when faced with complex or unique market situations that lack historical precedents or require a deep understanding of contextual factors. In these cases, AI algorithms may not provide accurate or reliable signals.
Gracy Chen, managing director at crypto exchange Bitget, said, “It is crucial for traders to continuously validate the accuracy and stability of AI signals in real-world conditions. Regular monitoring and iteration are necessary to ensure the AI system’s signals remain reliable.”
“Should any issues arise, updating the data used by the AI system becomes crucial to enhance the stability and accuracy of the signals it generates.”
Another limitation of AI algorithms is their susceptibility to data biases. The quality of AI signals depends on the data they are trained on. The AI signals may be flawed if the training data is incomplete, biased, or not representative of current market conditions.
Biases in the training data can influence the accuracy and reliability of the predictions made by AI algorithms. Therefore, ensuring that the training data is comprehensive, unbiased, and relevant to the current market conditions is crucial.
Over-reliance on AI signals can also pose potential risks, such as technical failures, that AI systems may encounter. These failures can result in inaccurate or misleading signals.
It is, therefore, important to have human involvement and supervision to mitigate the risks associated with technical failures.
One advantage of human intuition is its flexibility. Traders can quickly adapt to changing market conditions and make decisions based on their unique perspectives. Unlike AI algorithms, which primarily rely on numerical data, human intuition allows traders to consider a broader range of factors.
They can consider qualitative information, market sentiment, and industry knowledge, which can significantly influence trading decisions. This flexibility enables traders to incorporate a more comprehensive market understanding into their decision-making process.
This adaptability is crucial in dynamic markets where unforeseen events or sudden shifts in trends can occur.
Critical thinking is an essential component of human intuition. It involves combining rational analysis with subconscious information processing. Traders with developed intuition can assess the credibility and reliability of information. They can question assumptions, identify potential biases and make judgments based on a holistic market view.
However, limitations of human intuition can also impact decision-making in trading. One limitation of human intuition is cognitive limitations.
Humans have limited memory, attention span, and information processing capacity. In complex and data-intensive trading environments, these cognitive limitations can impact the accuracy and reliability of intuitive judgments.
Traders may struggle to process and analyze vast amounts of information efficiently, leading to potential errors or oversights in decision-making.
Combining AI signals with human intuition can be particularly effective. AI can provide data-driven insights, identify patterns and process large volumes of information efficiently. Human intuition, on the other hand, adds contextual understanding, adaptability, and critical thinking to the decision-making process.
