How to Build AI Crypto Trading Bots: A Step-by-Step Guide

I know there’s a long list of crypto trading bots available out there, but for one reason or another, they might not seem quite right for you. Maybe the applications don't offer the customization you need, or you simply don’t trust those tools. Whatever the case, it might be time to take matters into your own hands and learn how to build AI crypto trading bots yourself.

Knowing how to create such an application yourself lets you tailor its strategies, optimize its performance, and retain full control over your trading activities.

In this guide, I’ll walk you through everything you need to know about how to build an AI trading bot, from setting clear goals to deploying a bot that works for you and connecting it to your favorite exchanges like Bybit or Binance.

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How to Build AI Crypto Trading Bot Yourself?

A crypto trading bot is an automated program designed to execute cryptocurrency trades on behalf of its user. These bots operate based on predefined rules and strategies, enabling traders to make data-driven decisions without the need for constant monitoring. Some people may have specific goals, and that’s why they learn how to build AI crypto trading bots themselves.

Knowing how to set up this application yourself not only provides the freedom to customize it to suit your unique trading style but also helps you deeply understand how the crypto markets function.

Define Objectives and Goals

Before diving into the technical aspects of how to make AI trading bots, it’s crucial to clearly define the objectives. Understanding your trading goals, market scope, and risk tolerance is important because these decisions help shape how you design, train, and deploy your bot.

For example, if you want to focus on making small, frequent profits, you need a platform that can exploit minor price changes. The case will also be different if you want to build your own crypto trading bot that prioritizes long-term investment. Your software should focus on buying and holding assets for weeks, months, or even years.

After deciding on your goal, select your target market for optimal bot’s design and functionality by considering these factors:

• Cryptocurrencies to trade;
• Exchanges to operate on;
• Timeframes.

When you've just started learning how to make AI trading bots, I recommend choosing between established assets like Bitcoin (BTC) and Ethereum (ETH) or exploring emerging altcoins, depending on your strategy.

That said, established assets are generally more stable and liquid, making them a safer choice for most people. However, if you're looking for higher risk and potentially better rewards, smaller market cap coins might align with your goals, as they often exhibit higher volatility. Just ensure you're investing the amount you're comfortable with losing.

After that, select exchanges that support your chosen cryptocurrencies and provide API access (I'll delve deeper into the best options in the next sub-chapter).

The next step is to determine the frequency and timing of trading activity based on your objectives. Some bots are designed to operate 24/7, capitalizing on the global nature of crypto markets, while others may focus exclusively on high-volume trading hours.

For instance, if you prefer high-frequency strategies like scalping, a bot operating around the clock with rapid execution capabilities might be ideal. On the other hand, if you’re focusing on swing trading, software that executes trades during specific market conditions might be a better fit.

Gather Data

Data is the backbone of creating an AI-powered crypto trading bot, as it enables software to learn, adapt, and execute actions effectively. To create a well-functioning application, gather three main types of information: historical, real-time, and sentiment data.

Historical data is essential for backtesting and training your AI model. By analyzing past price movements, trading volumes, and market data (order book snapshots and trade history), the bot can identify trends, predict future movements, and refine strategies.

Meanwhile, real-time data is crucial for enabling the AI to execute trades based on current market conditions.

One of the sources of historical and real-time data is the exchange APIs. Most platforms, such as Bybit, Binance, and Coinbase, provide APIs for accessing historical data. So, which one is the best option to build your own crypto trading bot? The answer depends on what you need.

Bybit focuses on speed and reliability for Futures, Options, and Spot trading. REST API is great for automated trading and requests. Whereas, WebSocket API is for real-time and historical market data. The platform also provides market maker cashback for meeting trading requirements. The API is free to use for Bybit users, but regular trading and funding fees still apply to transactions.

Binance API offers access to Spot, Margin, Futures, and Options API trading for over 300 digital and fiat currencies. It also supports multiple programming languages like Python, Java, and Node.js, which makes the process overall process much easier. On the website, the team mentions that Binance API is designed for high-frequency and algorithmic trading.

In addition, like Bybit, Binance provides a free API that allows developers to create custom trading applications and interact with the Binance platform. However, you must follow some usage limits and policies, such as rate limiting and data caching.

When it comes to Coinbase API, this platform provides multiple connectivity options. For instance, if you want your bot to be designed for lower-frequency REST API is perfect for that. FIX Order Entry, on the other hand, is the opposite. Users can also access WebSocket Feed for market data and FIX Market Data for latency-sensitive information feeds.

Moreover, Coinbase API has advanced security features like two-factor authentication (2FA), biometrics, and address whitelisting. In terms of the market, it offers deep liquidity with access to over 550 markets and 237 USDC pairs. Also, there's no subscription fee for using Coinbase API.

Here are the key differences shown in a table:

Table: Comparison of exchange APIs

An alternative is to use charting tools like TradingView and CryptoCompare, which aggregate data from multiple exchanges, providing a centralized and efficient solution for data collection. These platforms can significantly simplify the process of gathering and standardizing information across various markets, saving both time and effort.

However, a key drawback is the cost, as advanced features are often locked behind paid subscriptions. The free plans, while useful, tend to be limited in scope. For instance, TradingView provides access to only 7 years of historical data, which may be insufficient for strategies requiring deeper analysis.

In contrast, the paid subscriptions offer up to 20 years of historical data, allowing for more comprehensive backtesting and improved accuracy.

Additionally, the free plan doesn’t support Deep Backtesting, which is crucial for evaluating complex strategies across a wide range of market conditions.

The cost of alternative tools for accessing historical data should be carefully considered. This is because you also need to account for news and sentiment data when learning how to build AI crypto trading bots. Both aforementioned variables are crucial for gathering information on the current attitude or mood of investors regarding certain cryptocurrencies.

That said, market sentiment can impact trading decisions in the highly volatile market. Therefore, it is crucial to have.

Understanding how traders and the general public feel about an asset can help your bot anticipate trends more accurately. So, how do we get and “quantify” this data? Sentiment scores are derived from news headlines and the volume of mentions for certain cryptocurrencies across social media platforms.

Some charting applications simplify the process of building AI trading bots by integrating historical, real-time, and sentiment data into a single interface. While these platforms can save time and effort, they often come at a premium price, making it essential to weigh the cost against the benefits of your specific trading strategy.

When it comes to sources of sentiment data, there are several options to explore during your process of learning how to build AI crypto trading bots.

You can gather insights from crypto news platforms or pull data from websites that do not forbid it in their ToS, short for the term of service. Social media such as X, Reddit, and Telegram also offer valuable sentiment data.

You can use APIs to collect posts, tweets, and discussions, focusing on crypto-specific forums like r/Cryptocurrency or hashtags like #Bitcoin for targeted analysis. However, keep in mind that all platforms have their own rules about collecting data, so make sure to study them thoroughly.

For a more automated approach, third-party sentiment analysis APIs like Lexalytics, AYLIEN, or Google Cloud NLP can process large text datasets and provide detailed insights.

There are several tools to collect and analyze sentiment data. For websites, libraries like Beautiful Soup or Scrapy can extract structured statistics from unstructured web pages. Meanwhile, Python libraries such as NLTK, SpaCy, or TextBlob are useful for analyzing the information.

Effectively combining historical and sentiment information helps you improve your AI trading bot’s ability to predict market movements and adapt to changing conditions.

Design the Strategy

In this step, it’s time to think about the right strategy that aligns with your objectives. Below are some of the most common trading strategies:

• Scalping
• Swing trading
• Arbitrage
• Trend following
• Dollar-cost averaging (DCA)

Traders who aim to learn how to build an AI trading bot that makes small, consistent profits can use the scalping technique – a high-frequency strategy that involves taking advantage of little price changes in the market. It then requires high-speed execution and real-time data analysis in your bot and target markets with high liquidity and minimal slippage.

Meanwhile, those who want their bots to seek profit from price "swings" over days or weeks would be more interested in swing trading. This method involves less frequent trades, focusing on significant trends or reversals. Compared to scalping, it relies more on fundamental analysis. Therefore, when you dive into how to create a crypto trading bot, you need to look for macroeconomic trends and market cycles.

Now, you can also use the arbitrage strategy that takes advantage of price differences for the same asset across different markets or exchanges.^[1] For instance, if Bitcoin trades at $30,000 on one platform and $30,100 on another, a bot can purchase for cheaper and sell for more, this ways profiting from the spread.

That said, arbitrage requires real-time data from multiple exchanges and ultra-fast execution to capture price differences before they disappear. In such a case, using a charting tool instead of individual exchange APIs may better streamline the process and improve efficiency.

Still, if you want to know how to build an AI trading bot with a more structured and less time-sensitive approach, the trend-following strategy can be your option. It focuses on identifying the market trade and whether it's bullish or bearish. This method often uses technical indicators like Moving Averages to confirm trends.

For those looking for long-term investment, you can learn how to set up an AI trading bot with a Dollar Cost Averaging (DCA) strategy. Invest a fixed amount regularly (e.g., weekly or monthly) regardless of market conditions. The aim of using this strategy on your bot is to reduce the risk of making large purchases at unfavorable prices and help average out the cost over time.

Implement and Test AI Models

Once you've got your objectives, the necessary data, and a trading strategy, it's time to implement the AI model that powers your bot. But first, select the right AI model. The choice purely depends on the complexity of your trading strategy.

A popular option includes machine learning models like regression, decision trees, random forests, and support vector machines. To be more specific, they are suitable for predicting price movements based on historical data.

For strategies that rely on understanding patterns over time. For example, deep learning models like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are ideal for predicting future price movements based on past trends.

Once you've selected an AI model, the next step is to train and implement it, which involves several steps. Firstly, you need to prepare your data by cleaning it to remove any noise or inconsistencies. Then, you will need to extract relevant features, such as technical indicators or sentiment analysis and normalize the data to ensure all features are on the same scale.

After preparing the data, train the model using an optimization algorithm like gradient descent. This process involves iteratively adjusting the AI's parameters. This step is needed to minimize errors in the training data.

Finally, evaluate the model's performance, which can be done using various metrics like accuracy, precision, recall, and F1-score. Additionally, you can use backtesting to simulate the AI's performance on historical data and assess its potential profitability and risk.

There are several popular tools and frameworks used in the machine learning space that can help in the process of how to build AI crypto trading bots.

Among all the options, TensorFlow seems to stand out. Developed by Google, this application is an open-source machine learning framework widely used for building deep learning models. It provides an extensive set of tools, libraries, and resources for training neural networks, which are useful for tasks like price prediction and pattern recognition in crypto trading.

PyTorch is another deep-learning framework that emphasizes flexibility and speed. It’s commonly used for natural language processing (NLP) tasks and computer vision but is also highly effective for time series analysis and forecasting in crypto trading. PyTorch could be used to build custom models for predicting market trends or generating buy/sell signals based on past data.

Once you've implemented your trading bot, the next step is to determine whether it functions as intended. Extensive testing is crucial before deployment to minimize errors and identify bugs or inefficiencies.

That said, if you want to fine-tune hyperparameters, refine its algorithms, and optimize performance metrics such as accuracy, latency, and risk-adjusted returns, it's highly advised to do so in controlled environments. It's worth noting that deploying an untested model may have significant risks, including unintended actions that could lead to financial losses or operational failures.

Testing an AI crypto trading bot generally involves three main phases: backtesting, paper trading, and stress testing.

Backtesting analyzes the bot’s performance by running it against historical market data.^[2] The goal is to determine if the trading strategy would have been profitable in the past and identify areas for improvement. During the backtesting, the metrics to measure the success of your AI crypto bot include the Sharpe ratio, accuracy, and profit/loss.

The Sharpe ratio is a key indicator of risk-adjusted return. To be more precise, it measures how much excess return you're earning for the additional risk taken. A higher Sharpe ratio indicates better performance.

Accuracy measures the percentage of profitable trades executed by the bot, providing insight into the strategy’s reliability.

Finally, the overall profit/loss calculates the net earnings (or losses) generated by the bot.

On the other hand, paper trading is the process of simulating live trades in a virtual environment, using real-time or delayed market data. Unlike backtesting, which focuses on past statistics, this method enables you to observe how the bot performs in current market conditions without risking actual capital. Some features to test include:

• Order execution logic (e.g., limit VS market orders);
• Reaction to real-time price movements and volatility;
• Interaction with exchange APIs and error handling.

Stress testing assesses a trading bot's robustness under extreme and unpredictable market conditions to ensure it can handle cases without huge failures. Key scenarios include sudden price spikes or crashes, such as flash crashes, high trading volumes that may cause slippage or order delays, and downtime of exchange APIs or connectivity issues.

During stress testing, focus on latency in order execution, the bot’s ability to recover from unexpected errors or disconnections, and its performance under varying liquidity conditions. This process ensures the bot is resilient and reliable in challenging market environments.

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Deploy the Trading Bot

Once you complete the previous step of how to build AI crypto trading bots, we can proceed to the real game: deploying your trading bot. If you use Python, using Python-based frameworks like Backtrader, Zipline, or PyAlgoTrade can help make the development process more efficient. These algorithms provide essential tools for backtesting, live trading, and automating various strategies.

For those using TradingView as the data source, consider learning how to set up AI trading bot using TradingView Pine Script. It’s a scripting language for creating trading strategies directly on the platform.

Besides using Python and TradingView Pinescript, there are several other applications that offer pre-built tools and services to simplify the process on how to create a crypto trading bot yourself.

Google Cloud Platform (GCP) is one of the options. It offers a range of artificial intelligence and machine learning services. This includes Vertex AI, which provides tools for building, training, and deploying models. You may also want to check Amazon Web Services (AWS), which provides a comprehensive suite of services for AI and machine learning, or Microsoft Azure, which has a drag-and-drop interface for creating and training models.

Whatever method you end up choosing, make sure your bot has these key components:

• Signal generation;
• Execution engine;
• Risk management.

Signal generation is the core of your bot's decision-making process. The AI model, trained on historical data and real-time market information, analyzes patterns and trends to generate buy, sell, or hold signals. They can be based on a variety of factors, such as technical indicators, fundamental analysis, or sentiment analysis.

Meanwhile, as the name suggests, the execution engine is responsible for executing trades based on the signals generated by the AI model. It interacts with cryptocurrency exchanges through APIs to place orders, adjust positions, and manage risk. This component must be highly efficient and reliable to minimize slippage and latency.

Lastly, risk management is crucial for preserving capital and maximizing profits. It implements strategies like stop-loss and take-profit orders to limit potential losses and secure gains. Another risk management strategy includes position sizing techniques to allocate capital wisely across different trades.

After completing the checklist for how to build an AI crypto trading bot above, you should be ready to launch it. I recommend starting with small investments, such as penny trades, to minimize risk while gaining valuable insights into real-world behavior.

You’ve Built a Crypto Trading Bot, Now What?

Congratulations, you launched your bot! That said, the journey doesn’t end here. After you learn how to set up AI trading bots, monitoring its performance is a critical ongoing process, ensuring its adaptability in changing market conditions.

First, track key performance metrics, such as profit and loss, win rate, drawdown, and execution speed, to assess its effectiveness.

Then, keep an eye on market conditions, including volatility and major news events, to help ensure the bot remains compatible with the current environment. Regular checks for technical issues are also important. Sometimes, API failures, order execution errors, or algorithm bugs can disrupt performance.

Jot down everything you found to uncover patterns and highlight areas for refinement. To avoid oversights, set up real-time alerts and checklists for performance updates, errors, or breaches of risk thresholds. From this performance analysis, update your strategies and software to ensure the software remains aligned with the market trends and technical requirements.

Once everything you’ve learned about how to make a crypto trading bot has proven successful, consider scaling up by increasing your capital gradually and diversifying strategies or expanding to new markets to reduce dependency on a single approach. If your bot becomes more complex, consider automating monitoring to reduce manual effort as you scale.

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Conclusions

Learning how to build AI crypto trading bots is an exciting journey that combines programming skills, financial knowledge, and strategic thinking. By following a structured process, from defining your goals to testing the bot, you can create a tool that trades intelligently in fast-moving crypto markets.

Platforms like Bybit, Binance, and Coinbase play a critical role in the process of how to make AI trading bots more effective. Both are highly popular cryptocurrency exchanges offering robust APIs and advanced trading features for deploying your bot.

However, implementing what you've learned about how to make a crypto trading bot needs more than just initial creation. Continuous improvement and adaptation are key. Start small, think about strong risk management techniques, and scale cautiously to minimize risk and maximize profits.

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. L. Zhang, Z. Zhang, T. Li: 'A Consensus-Based Solution for Cryptocurrencies Arbitrage Bots in Intelligent Blockchain';

2. F. Dakalbab, M. A. Talib, Q. Nasir, et al: 'A Consensus-Based Solution for Cryptocurrencies Arbitrage Bots in Intelligent Blockchain'.