How to Use AI For Crypto Trading (Best Ways in 2025)

From ChatGPT to self-driving cars, artificial intelligence has entered many industries. AI crypto trading tools, for example, offer automation and data-backed predictions to execute trades and monitor the market 24/7. That said, since you're reading this article, chances are you want to know how to use AI for crypto trading to get maximum returns.

In this guide, you will find a variety of artificial intelligence use cases to maximize the potential of this technology, from automating trades to improving your understanding of the crypto markets. I'll also explain how to use AI to trade crypto with free tools offered by exchanges like Bybit and Binance.

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How to Use AI For Crypto Trading?

Many sectors are already using artificial intelligence for different purposes. That said, how do you use AI for crypto trading exactly? In this chapter, we'll explore some applications that offer such services.

Trading Automation

Automated trading is one of the most popular methods. It involves software and algorithms to execute trades automatically. So, how does this kind of AI help make money in crypto trading? The bots, powered by artificial intelligence, work by analyzing vast amounts of data in real-time, identifying patterns and executing trades quickly with minimum manual intervention and constant monitoring.

You can program this bot to follow specific strategies, such as trend following, mean reversion, or arbitrage. They can execute trades based on predefined parameters or machine learning algorithms. For example, a trend-following bot may buy when the price of a cryptocurrency is trending upward and sell when the trend reverses.

Moreover, unlike human traders, AI bots are not influenced by emotions like fear, greed, or impatience, leading to more rational decision-making.

If you already use exchanges like Bybit and Binance, you can take advantage of their free trading bots, eliminating the need to pay for additional tools. These platforms offer various bots, including options for dollar-cost averaging and spot grids. Some even have AI-powered features for crypto arbitrage to help you capitalize on price differences across markets efficiently.

Those using Binance can start trading with the automated bot by creating an account, funding an account, and selecting or customizing a strategy. For detailed instructions, refer to this Binance trading bot tutorial.

If you want to identify price discrepancies across exchanges, you may want to consider other options like Cryptohopper or 3Commas. These platforms offer advanced arbitrage tools and customizable trading strategies. Additionally, they provide features such as backtesting and detailed analytics to refine your approach and maximize returns.

Portfolio Management

In addition to directly trading crypto, AI can power crypto price and portfolio trackers to help you balance your holdings and mitigate risks. As a result, providing you with the ability to see the big picture of your overall assets, as it displays real-time data on portfolio value, asset allocation, and historical performance from multiple exchanges and wallets.

With such insights, traders can make informed decisions about their investments without manually tracking or reconciling their holdings.

In addition, the AI-powered trackers can detect and notify you if there are differences between the recorded transactions and actual blockchain data, such as discrepancies in fees, token amounts, or timing. This function helps ensure the accuracy of the portfolio records.

Besides a summary, automated labeling is another standout feature. It automatically classifies transactions into categories such as staking rewards, airdrops, or trades, saving time and reducing manual errors.

Some even act as tax calculators that provide insights and downloadable reports. This can be especially valuable during tax season to help you stay compliant with local regulations.

Note that learning how to use AI for crypto trading with these applications may cost. Some offer basic features for free, while advanced attributes like a tax-harvesting tool and premium services such as priority customer support may require a subscription. It's important to weigh the costs against the benefits to determine if a paid tool is worth the investment for your specific needs.

To use a crypto portfolio management application, traders typically need to create an account on the platform and enter the exchange API keys to grant the tool access to their portfolio data. This allows the tool to track holdings, transactions, and performance across multiple exchanges.

Predictive Analytics

Predictive analytics takes crypto trading to the next level by forecasting future trends. By analyzing historical data, market news, and social sentiment with AI-powered features, you can easily identify patterns and make predictions about price movements.

One of the key technologies is machine learning (ML), which empowers algorithms to learn from data. The way that this AI helps make money in crypto trading is pretty simple. ML models often outperform traditional statistical models, especially when dealing with complex relationships in data. The algorithms can notice hidden patterns and provide accurate predictions without relying on specific statistical assumptions.

Users need to consider several factors to build effective ML models for crypto trading: input data, model selection, and output type.^[1] Input data includes historical prices, technical indicators, sentiment analysis, and on-chain data. Next, they need to choose the right ML model, like neural networks or decision trees.

The former is an ML technique that teaches computers to process data in a way that is inspired by the human brain. It is made up of layers of interconnected nodes. You can also call them artificial neurons. That said, those nodes pass data to each other, similar to how neurons in the brain pass electrical impulses to each other.

Meanwhile, decision trees refer to a visual representation of the potential outcomes of a series of choices. It's a popular tool in machine learning and data science that helps people and organizations weigh the costs, probabilities, and benefits of different actions.

Research also suggests to use the support vector machines (SVM) because the model provides the most accurate results.^[2] SVM can handle complex, non-linear datasets with high-dimensional features, such as price patterns, trading volumes, and market sentiment indicators. Unlike many machine learning models, it’s less prone to overfitting in smaller datasets, a common occurrence in niche or low-volume crypto assets.

After inputting data and selecting a model, it’s time to decide the output type. This step means deciding whether to predict the exact price (regression) or the direction of price movement (classification).

Sentiment Analysis

Sentiment analysis is the process of using AI to analyze opinions, emotions, and attitudes expressed in text data, such as news articles, social media posts, and forums. I know that many users who are using AI to trade crypto aim to make emotion-free decisions. However, sentiment analysis actually helps you measure the overall market mood, whether it is bullish or bearish.

As a result, knowing how to use AI for crypto trading during the sentiment analysis process can help you predict how trends may influence prices. Given the speculative and sentiment-driven nature of the crypto market, this type of analysis is particularly valuable for identifying short-term opportunities.

One of the common AI-powered features people often rely on is Natural Language Processing (NLP). It helps interpret textual data by:

• Analyzing social media and community posts to see the overall sentiment about a particular cryptocurrency.
• Processing news articles related to cryptocurrencies.
• Understanding forum discussions.

From all these data, NLP identifies keywords, sentiment scores, and trends so that traders can decide whether they have to buy, sell, or hold their funds.

If there's a sudden surge of positive sentiment around a cryptocurrency, it may signal a potential price increase and vice versa. For example, a positive news story, such as a major company adopting Bitcoin, can trigger a rapid increase. Similarly, rumors or regulatory announcements can cause panic selling. Traders who use sentiment analysis can anticipate these shifts and position themselves accordingly.

For instance, platforms such as LunarCrush and Santiment specialize in providing such features.

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Coding Assistance

Artificial intelligence has become a valuable resource for developers working on trading algorithms. This is because it is much easier to create, test, and debug complex code.

Some of the most popular applications for that are GitHub Copilot and ChatGPT. Both platforms are capable of generating high-quality code, and even experienced programmers use them. Copilot and ChatGPT can help you create a trading bot by integrating APIs or implementing machine-learning models for predictions.

For instance, a developer can ask one of the platforms to generate Python code for connecting to the Binance API or build a function for calculating moving averages, saving hours of manual effort.

Lastly, as noted above, artificial intelligence is also helpful for debugging and optimization. With the ability to analyze code and detect potential issues, AI tools help developers identify syntax errors, logical flaws, or inefficiencies in their algorithms. Platforms like DeepCode and TabNine provide suggestions to improve the quality and performance of the code, reducing the chances of costly bugs during live trading.

Educational Purposes

Besides being able to code, artificial intelligence provides learning assistance for traders, helping them understand the fast-paced cryptocurrency market.

AI aids in personalizing the learning process. Tools like ChatGPT can serve as interactive tutors, answering questions about trading concepts, explaining technical indicators, or offering tailored advice on how to use AI to trade crypto according to user queries. These AI learning tools can even recommend educational content, such as articles, videos, or courses, aligned with a trader’s skill level and goals.

In case you're a visual learner, consider learning how to use Writesonic's Photosonic or Midjourney's image generators to make infographics from text prompts.

Some exchanges also leverage chatbots to improve user experience and support, increasing their learning experience. For instance, Coinbase has recently applied this tool and delivered better search relevance through its AI-powered help center.

Using AI for Crypto Trading: Pros and Cons

Traditional trading methods, relying on manual analysis and decision-making, struggle to keep up with the speed and complexity of the cryptocurrency market. So, I think it’s important to learn how to use AI for crypto trading since the technology brings various benefits (although there are some drawbacks and risks, too).

In my opinion, one of the notable advantages is constant monitoring. Unlike traditional assets, crypto markets are highly volatile, making it challenging for humans to monitor and react effectively. With AI-powered features, users can automate trade execution with precision and speed, maximizing profits even while they sleep.

What you need to do is define your risk tolerance and preferred strategies to help the software navigate.

Understanding how to make money with AI during crypto trading can also benefit from its ability to process and analyze huge amounts of data in real-time. Artificial intelligence identifies patterns and irregularities in market behavior that may be impossible for human traders to detect.

For instance, it can simultaneously analyze historical price movements alongside live order book data, social media sentiment, and macroeconomic trends. Using that information assists users in getting the right timing for their trades and identifying entry and exit points effectively.

Naturally, AI doesn't have emotional judgment since it always sticks to predefined algorithms and strategies, ensuring decisions are based solely on data and logic. As a result, the software doesn't execute impulsive actions like panic selling or overtrading.

When you try to understand how to use AI for crypto trading, you may notice some changes over time because AI can continuously learn and improve. This is because it collects more data, ensuring it remains effective even in changing market conditions.

Despite those benefits, using AI can have disadvantages. First, it often requires a substantial upfront investment in tools, software, and infrastructure, especially when you decide to build it yourself.

Besides financial resources, building your own AI bot requires a level of technical expertise that not all people have. Some of the skills you need to have are programming, data science, and machine learning. Of course, learning them or hiring specialists is an option, but the process can be time-consuming and expensive, which makes it a barrier for beginners or small-scale traders.

That said, the effectiveness of AI models also depends heavily on the quality of the data they’re trained on and the reliability of the infrastructure they operate within. Incomplete or biased datasets can lead to flawed analysis and erroneous trading signals.

Additionally, system failures, such as server downtime or API errors, can disrupt automated processes and result in missed opportunities or financial losses. Regular system maintenance and monitoring are required to mitigate this risk, adding to the complexity of using AI in trading.

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Tips on Using AI for Crypto Trading

Successfully learning how to use AI for crypto trading wisely requires not only technical know-how but also a thoughtful approach to planning, execution, and adjustment. So, here are key strategies to ensure you maximize your potential profit while minimizing risks.

Build a Strong Foundation in Crypto and AI

Before using AI to trade crypto, it's essential to develop a solid understanding of both cryptocurrency markets and artificial intelligence fundamentals. While the software automates many processes, the human brain is still critical for interpreting complex scenarios, assessing market dynamics, and making final decisions. AI tools only execute your set strategies.

Learn how blockchain technology, exchanges, wallets, and trading mechanisms work. Also, familiarize yourself with concepts like market orders, liquidity, and volatility. The reason for this is because they directly impact trading decisions. For example, knowing how sudden regulatory changes affect prices helps you better interpret the AI's outputs.

Besides understanding crypto basics, learning how to use AI for crypto trading, such as the principles behind machine learning and predictive analytics, gives valuable insight into how AI tools operate and their limitations. As a result, you know how to troubleshoot issues effectively and set realistic expectations for your AI systems.

I also recommend you search for legal and ethical considerations of using AI to trade crypto in your area. Because it often operates in a space that’s increasingly scrutinized by regulators. Therefore, ensure that your trades comply with local laws, including tax obligations and anti-money laundering regulations.

Additionally, use AI ethically by avoiding manipulative practices like pump-and-dump schemes – ensure that your strategies are transparent and fair!

Once you’ve mastered these foundational topics, you may have the necessary framework to identify your own trading styles and risk tolerance, ultimately allowing you to choose the most suitable tools that work within your jurisdictions.

The success of AI trading relies heavily on selecting the right platform. Exchanges like Bybit and Binance are excellent for beginners, offering pre-built strategies and a user-friendly interface. On the other hand, advanced users may prefer customizable platforms, enabling them to build and refine their own bots for more tailored trading strategies.

Start Small and Scale Gradually

When diving into how to use AI for crypto trading, you may notice that the software can handle tasks at scale, no matter the market volatility or complexity of the strategy. However, starting small is crucial to minimizing risks and building confidence in your approach.

Consider trying out demo accounts or paper trading first. These features allow you to test your strategies in simulated environments, which is particularly useful for familiarizing yourself with the behavior of AI bots and fine-tuning your settings before using real money.

For more confidence, consider backtesting. It involves running your AI strategy on historical data to evaluate its performance to identify potential weaknesses and refine parameters before deploying the strategy in live markets.

When you’ve done those tests, I suggest you begin with straightforward strategies like dollar-cost averaging or trend following instead of jumping into complex setups. These are easier to monitor and evaluate while providing a foundation for scaling up later.

Once you’ve validated your strategy with small investments, gradually increase your position sizes and complexity. Scaling up provides an opportunity to observe how AI handles real-time market volatility at different investment levels. Still, avoid scaling too quickly, as this can increase risks if the strategy fails to perform as expected.

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Regularly Review and Adjust

Always note that AI trading systems are not set-it-and-forget-it solutions. Regular evaluation and adjustment are important to ensure they remain effective in ever-changing market conditions.

Therefore, it’s essential to periodically evaluate your AI system’s performance against key metrics such as profitability, accuracy, and trade execution speed. Analyze trends over time to determine whether the system consistently meets your objectives or requires some tinkering.

Typically, such platforms come with performance dashboards with historical data on your trades, allowing you to track progress and refine strategies (a feature worth considering when selecting an application).

Beyond evaluation, the post-deployment process involves regular updates. It includes continuously training AI models with new market data and adapting them to new trends, ensuring they remain relevant and effective. Additionally, optimize parameters, such as risk thresholds and trading frequencies, on a routine basis to improve the system’s overall performance.

While AI automates much of the process, staying actively engaged remains crucial. Market conditions, tools, and regulations change frequently, and proactive monitoring of these developments is key to maintaining and refining your trading strategy for long-term success.

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Conclusions

Learning how to use AI for crypto trading helps the folk automate their strategies, manage portfolios, and gain knowledge through personalized learning assistance.

Platforms like Bybit, Binance, and KuCoin make it easier for new users to begin their AI journey. These exchanges offer accessible features such as pre-built bots for strategies, as well as AI-powered tools for monitoring and automation. Beginners can benefit from their user-friendly interfaces, while more experienced users can leverage customizable solutions.

Still, understanding how to use AI to trade cryptocurrencies involves more than just picking the right platform. It requires a solid grasp of cryptocurrency fundamentals and regular performance evaluations. Starting with small, backtesting your approaches, and staying proactive in adjusting your systems are crucial to long-term success.

The content published on this website is not aimed to give any kind of financial, investment, trading, or any other form of advice. BitDegree.org does not endorse or suggest you to buy, sell or hold any kind of cryptocurrency. Before making financial investment decisions, do consult your financial advisor.

Scientific References

1. O. Omole, D. Enke: 'Deep Learning for Bitcoin Price Direction Prediction: Models and Trading Strategies Empirically Compared';

2. G. Babaei, P. Giudici, E. Raffinetti: 'Explainable Artificial Intelligence for Crypto Asset Allocation'.