Although the phrase “volatility in the crypto market” may sound repetitive, its truth remains undeniable. Coins can experience dramatic fluctuations within hours or even minutes. For example, after Mt. Gox moved substantial BTC as part of compensation, the price of Bitcoin plummeted from $60,000 to $53,717 on July 5, 2024, or the most recent market crash on August 5, 2024, when the price of Bitcoin fell below $50,000 at some point under the impact of macro-economic data and fears of an escalation due to geopolitical factors.

The amount of information surrounding the crypto market is enormous, and it is not only difficult for traders to fully utilize such volumes of information, but perhaps not even possible. Among other things, news is sometimes unpredictable. That is why it was inevitable and logical for traders to combine trading with artificial intelligence algorithms and models that identify specific market patterns and use them to profit. In our new material, we will try to understand what AI trading is, how AI helps traders maximize their results, and what the prospects for AI trading are.
What is AI trading?
Let’s start by understanding the concepts, precisely the distinctions between AI, algorithmic trading, and machine learning trading, as these terms often overlap.
Algorithmic trading involves using computer algorithms to automate trading activities. These algorithms follow pre-programmed instructions based on timing, price, and quantity. For instance, a trader might set a strategy to buy a coin when its 50-day moving average rises above the 200-day moving average and sell it when the 50-day moving average falls below the 200-day mark. The computer program will then automatically monitor the coin’s price and moving averages, placing buy and sell orders when these conditions are met.
Machine learning, a subset of artificial intelligence research, focuses on developing statistical algorithms that enable AI to mimic human learning, improving its accuracy over time. In trading, machine learning involves analyzing historical data to identify patterns and trends that can predict future price movements.
AI trading uses advanced artificial intelligence techniques such as algorithmic predictions, Natural Language Processing (NLP), and big data analysis. These systems analyze vast amounts of data, identify patterns, and make predictions to inform trading strategies.
In summary, algorithmic trading is a broad term encompassing any automated trading system with predefined rules. Machine learning-based trading is a specific type of AI trading that builds models to predict market movements and optimize trades based on data. AI trading uses advanced AI techniques to process and learn from large data sets, make predictions, and adapt to new information.
The current state of AI trading
In 2023, AI trading became a significant focus, paralleling the rise of AI chatbots like OpenAI’s ChatGPT. In just two months, it reached 100 million users, surpassing platforms like TikTok.
As we look to 2024, has the landscape of AI in trading changed? According to a survey by NVIDIA, a remarkable 91% of financial services companies are either exploring AI or have already implemented it. This shift is evident in several significant integrations within the crypto market.
For instance, Pluto’s AI now offers Robinhood traders personalized investment strategies, data analysis tools, and real-time information, helping them make quick decisions. This AI tool optimizes portfolios by evaluating each user’s risk tolerance, investment goals, and past trades.
Similarly, Bybit, a well-known cryptocurrency exchange, has launched TradeGPT, a personalized AI trading assistant. This tool provides traders with AI-driven insights and specific tips, such as analyzing certain cryptocurrencies and suggesting tailored trading strategies with entry prices and explanations.

Another significant development is the Bitbot presale, which raised over $1 million in under eight weeks. This surge reflects the growing interest in AI and trading technology and highlights this innovative approach to trading.
The rise of AI trading has also caught the attention of the United States Commodity Futures Trading Commission (CFTC). The agency has emphasized that while AI can enhance trading strategies, it cannot predict the future. They caution against guarantees of high yields using bots, trade signal algorithms, crypto-asset arbitrage algorithms, and other AI-assisted technologies.
Different ways to use AI tools in crypto trading
While the cryptocurrency market is primarily dominated by AI bots, AI’s potential in cryptocurrency trading extends beyond these automated tools. Here are some examples of how it can be used in cryptocurrency trading:
- AI x data analytics
Accurate crypto market data is crucial for understanding trends and making informed investment decisions. However, fake transactions like wash trading can inflate volumes and distort market activity.
To address this, companies use AI to analyze large datasets and detect irregular trading patterns, ensuring more reliable market insights. For example, BitsCrunch, an AI-enabled, decentralized blockchain data platform that identifies suspicious trades in real time for accurate data.
- AI x trends
AI tools can analyze extensive historical and real-time data to identify trends and patterns in the cryptocurrency market. This helps traders anticipate market movements more accurately, which is crucial in the volatile crypto market. For example, a research platform, TokenMetrics, claims on its website that it can analyze historical coin data to gain insights into trends using its built-in artificial intelligence. Its algorithms consider various factors such as fundamental reports, sentiment analysis, moving averages, and support/resistance levels to identify potential trends and predict future price movements.
- AI x predictions
AI tools use complex data interpretation and predictive modeling to forecast price trends, detect anomalies, and seize profitable opportunities in real-time. This capability allows traders to make informed decisions about buying, selling, or holding cryptocurrencies.
Financial services platform IntoTheBlock offers a Predictions service that can forecast the direction in which the price is expected to move over the next hour. Currently, IntoTheBlock has prediction models for Bitcoin, Ethereum, Litecoin, and Bitcoin Cash. The price can move in one of four ways: Up, Down, Neutral, or No Prediction. These indicators’ development results from deep learning models created by IntoTheBlock’s team of data scientists over many months.

The company’s website states that, for example, if a model is 70% accurate, on average, it has accurately predicted 7 out of 10 forecasts.
- AI x portfolio optimization
AI-powered trading platforms execute trades efficiently and swiftly, utilizing real-time data and advanced algorithms. These platforms offer features for portfolio optimization, such as rebalancing strategies and tax optimization, to maximize returns and mitigate risks.
For instance, Swiss startup Aisot, which integrates different ML approaches, AI, and quantitative finance, provides AI-curated crypto portfolios.
According to the startup’s website, the company launched a fully AI-driven cryptocurrency portfolio in early October. In the first three months after launch, it achieved a 72% return after fees, beating Bitcoin’s 53% return over the same period.

- AI x risk management
AI tools assess and mitigate risks by analyzing market data, identifying potential threats, and implementing proactive risk-mitigation strategies. This proactive approach helps traders safeguard their investments against market fluctuations and adverse events.
Lockchain is an example of such a platform. It aggregates on-chain data and open-source intelligence.
Users receive alerts through email, Slack, or Telegram, notifying them of potential risk events relevant to their portfolios. These events could include breaches affecting their coins or risks associated with custody wallets.
- AI x fraud detection
AI can help detect fraudulent activity in cryptocurrency exchanges by monitoring suspicious patterns of behavior, such as wash trading or impersonation. Using machine learning models, AI can continuously learn and evolve to identify and prevent emerging threats, making the trading environment safer. For example, Merkle Science’s machine learning-powered tools claim to be able to monitor transaction behavior and prevent Web3 crime and cross-chain attacks.
What is the future of AI trading?
Exploring the future of AI trading, we sought insights from influential voices in the crypto industry. Here’s what emerged as the most intriguing perspectives from our discussions.
Discussing trading strategies, the CFO of ChicksX pointed out, “One issue with trading strategies is that if too many people use the same or similar strategies, it will reduce any potential profitability. This may be why there isn’t easy or cheap access to competent AI crypto trading bots, and this may remain the case for some time. It’s also worth considering whether AI will ever be able to completely overtake humans in all aspects of trading, especially as it is such a multi-faceted discipline”.
Dennis Shirshikov, head of growth at Gosummer, highlighted, “AI will democratize access to advanced trading strategies. Traditionally, sophisticated trading tools and high-frequency trading algorithms were exclusive to large financial institutions. With AI-powered platforms, individual traders and smaller firms can leverage these tools to compete on a more level playing field”.
Becky Leighton from Coininsider emphasized, “The tools become more sophisticated to pick up on fraudulent activity, market manipulation, and possible attacks. It’ll be easier for AI-implemented tools to identify suspicious activity and increased awareness about new threats will mitigate platforms facing hacks and breaches.”
Alari Aho, CEO and Founder of Toggl, envisioned, “AI-powered bots that utilize reinforcement learning to continuously improve their trading strategies will become increasingly prevalent. These bots will learn from their successes and failures, adapting to changing market conditions autonomously. Over time, they will develop highly refined trading tactics that optimize returns”.
What is also interesting is the data from a JPMorgan survey that shows artificial intelligence technology will play a leading role in trading over the next three years.
The survey polled 4,010 institutional traders from 65 countries. According to the results, 61% of traders predict that AI/machine learning will be the most influential in shaping the future of trading over the next three years.

Final words
The influence of new technologies on trading is unmistakably evident, enabling investors to optimize their strategies, mitigate risks, and discover more trading opportunities. The emergence of new startups, investments by companies integrating OpenAI, and growing public confidence all indicate a solid and enduring intersection of trading and artificial intelligence.
However, asserting that an investor can confidently entrust their portfolio entirely to a machine is premature. While AI has shown remarkable capabilities in processing vast amounts of data and making complex decisions, the unpredictability of cryptocurrency markets, regulatory uncertainties, and unforeseen events still pose significant challenges. Human judgment, adaptability to changing conditions, and the ability to interpret nuanced market signals remain crucial factors that cannot yet be fully replicated by AI alone.
FAQ
How do AI algorithms help crypto traders?
Besides AI bots that are available on the crypto market, artificial intelligence services can enhance crypto trading by detecting irregular patterns, analyzing real-time data for market predictions, optimizing portfolios, and managing risks.
What is the future of AI crypto trading?
AI’s role in crypto trading will continue to grow, focusing on preventing scams and improving trading strategies for better returns. JP Morgan reports that 61% of traders believe AI and machine learning will shape the future of trading in the next three years.