How to Create an AI Trading Bot

Trading bots with artificial intelligence (AI) integration are increasingly being used to automate trading and mitigate risk. For example, according to the 2024 survey from JP Morgan, 61% of the 4,000+ institutional traders surveyed believe that AI adoption will be the deciding factor in trading between 2024 and 2027. Trading bots can be used to make trades in any asset, from stocks and indices to cryptocurrencies. The development algorithm will be similar. No matter whether an investor plans to create software on his own or purchase a custom bot, he needs to study the niche and take into account the pitfalls of the development.

What is AI trading bot?

An AI-enabled trading bot is software for automating the trading process. Using AI development, the software analyzes the market condition, manages and maintains the "weight" of the investment portfolio and conducts transactions with selected assets based on established strategies (arbitrage, scalping, mean reversion and others).

AI и ML

An important nuance is that AI, machine learning (ML) and automation are different concepts, even though they are sometimes used interchangeably. AI is the broadest scope, which includes all types of intelligent software modeling. Automation, on the other hand, involves setting up an automatic decision-making scheme, while machine learning focuses on the ability of machines to gain experience and improve their efficiency and performance. AI-integrated trading bots combine all 3 concepts: they use machine learning + AI techniques to automate trading and increase profitability.

Principle of work and AI bots' advantages

The general principle of operation can be described as follows: the software collects data from various sources, then identifies trading opportunities based on the analysis of price dynamics, market sentiment, and trading volume. After that, it places buy/sell orders and locks in profits. The main advantages of using AI-based bots for trading:

• Fast analysis of large amounts of data. The software analyzes both asset price data and news reports in different languages 24/7. It can be customized for both free and paid sources. The bot can assess and forecast market sentiment, identify current drivers and market patterns;


• Elimination of the human factor. The advantage of automated trading is that the risk of making unsuccessful deals based on emotions is reduced to zero. Irrational financial decisions and intuitive emotional are among the TOP mistakes of novice traders that cause maximum losses. The software works according to strictly defined algorithms taking into account the risk level set by the trader;


• Self-learning. Thanks to the support of the machine learning option, the software is able to improve its trading tactics and show more efficient results. This is possible due to the use of artificial neural networks;


• Speed of concluding trades. Buy/sell orders are placed on exchanges instantly when conditions are favorable and the software sees an opportunity for profit. Placing orders manually is a slower process. This is especially important when trading in highly volatile markets, such as cryptocurrency markets.


• Autonomous operation. Transactions are made automatically without the trader's participation. This makes it possible to trade 24/7, capitalizing on all market fluctuations.

AI trading bots segment analysis

AI trading bots are a prospective niche for investment. From the JP Morgan survey, we can see that a growing number of investors are praising the importance of incorporating artificial intelligence into trading: in 2022, only 25% of respondents cited AI as a major factor in shaping trading, and in 2024, 61% already do.

That said, more investors are favoring more classic markets over cryptocurrency in 2024: 78% of institutional traders surveyed said they do not intend to trade coins/tokens in 2024.




Thus, a wider target audience will get trading bots that give access to different types of assets, not just cryptocurrencies: stocks, commodities, G10 rates, indices, and more.

AI and blockchain in trading

Interest in blockchain has also declined from 25% to 7% in the last 3 years, according to a JP Morgan survey. However, not everyone holds this opinion. Ethereum creator Vitalik Buterin sees prospects in combining the capabilities of AI and blockchain technology. Blockchain platforms with AI trading bots may become a solution for those who need decentralization and transparency of operations.




According to his comments, the main danger of the AI model is the fact that the mechanisms responsible for internal work and decision-making are hidden, and this leads to high centralization. If you make the model more open, it increases the risk of malicious attack and hacking. But, in general, Buterin considers the use of AI for decision-making in trading to be an interesting but risky solution for traders.

AI bots' minuses

In addition to centralization, traders using AI bots should consider other risks:

• Possible technical failures of the software;


• AI systems are prone to overfitting, i.e., focusing too much on training and historical data and less on the current state of the market. This makes trading bots less effective in unpredictable situations;


• The likelihood of hacking.

So potential investors and developers of AI trading bots should pay attention to security and software quality issues. Multi-stage testing before the release of the project will help, which usually includes: backtesting based on the analysis of historical data, testing for correctness of algorithm execution, protection against attacks and efficiency/profitability of operation.

Popular AI bots in 2024

Cloud platforms, which provide access to different types of AI bots including crypto trading bots on a subscription basis, are the most popular among traders in 2024. Traders pay special attention to such indicators: available types of assets, exchanges, price of use and tools for analytics and risk mitigation (stop-loss option, backtesting, trading indicators and others). Platforms with training sections and 24/7 support are also an advantage. The top 2024 platforms include Dash2Trade, Perceptrader, Coinrule, Learn2Trade and Kryll.

Kryll

Web3 AI project aimed at cryptocurrency trading. Trading strategies can be applied on popular exchanges: Bybit, Crypto.com, Gate.io, Binance, Kraken and others. Claimed success rate: up to 103% income per year. Hundreds of bots, analytical tools and technical indicators are available. Popular options:

• Smartfolio. AI-based feature for balancing a cryptocurrency portfolio (coins, NFTs, DeFi protocols);


• Gem Detector. A tool that allows you to identify new and potentially profitable coins before they become popular and their price increases significantly. This is possible thanks to the built-in machine learning option and advanced algorithms;


• X-Ray. An AI-based tool for sentiment analysis: it gathers information from social media, blogs and news reports in different languages 24/7;


• Harpoon. Thanks to this option, the AI provides up-to-date data on trades and strategies of leading traders.

It is not available in free version, AI bots are available by subscription. But there is a unique reward system for community members. Rewards are accrued in the native KRL project coin.

Perceptrader

This is an AI-based expert advisor targeting the Forex market. Waka Waka EA trading system is used, which automatically creates buy/sell orders when the market fluctuates. Efficiency is improved by implementing a machine learning option, analyzing charts and signals using neural networks and advanced forecasting models. Thanks to Perceptron technology, the software eliminates unpromising deals.

There is a risk management option (you can set the highest allowable drawdown, account load and so on), and you can also select settings for a certain type of trading. There is a free 14-day period. A lifetime license for use costs $2,400.

Dash2Trade

Cloud-based automated trading platform offers access to 10,000+ strategies, 400 cryptocurrencies and 2 AI bots: GRID and DCA. Backtesting option is available. Integration with top CEXs is provided. AI is also used to track technical indicators. 26 guides have been released to work with them more efficiently. The launch of additional trading bots has also been announced for 2024.




A free version is provided, but it does not give access to AI bots. A subscription costs $18 per month or $120 per year. The project has its own native D2T token.

Coinrule

A cloud-based platform aimed at automated cryptocurrency trading. Both AI and regular trading bots are available. Users get access to 150+ strategies. Coinrule works on a monthly subscription basis, minimum price: $29.99 per month. Trading bots connect to the APIs of major crypto exchanges: Binance, Kraken, Kucoin and others. Notifications about changes in the market, automatically completed orders and received profits are sent to Telegram.

Learn2Trade

The platform is aimed at 2 types of trading: Forex stocks and cryptocurrencies. The trader chooses the type of subscription. On premium subscriptions for Forex or cryptocurrencies, the user receives up to 5 signals daily. The success rate is estimated at 76%. L2T algorithms are also available (up to 70 signals per month, trade copying option, 24/7 market access) and Formula 1 training program with 5 video courses. More than 70,000 traders are registered on the platform in 2024.

Basic stages of AI trading bots development

Launching a trading bot based on Artificial Intelligence requires both an understanding of trading strategies and programming knowledge (from writing algorithms to working with graphic editors to create UX/UI). The process is similar to the development of conventional trading software, but with the addition of one more step - integration of the AI model. Let's look at the main nuances of the main stages below.

Choosing a language for writing code

The most commonly used languages are Python, C# and JavaScript. When choosing one, you should focus on speed (the bot should react quickly to changes in the market and place orders lightning fast), available tools and libraries. An active community will also help to simplify the development process. Features of popular languages:

• JavaScript. Advantage when developing trading bots - gives the ability to easily handle requests from multiple APIs. But it is inefficient if complex calculations are required;


• Python. A simple and functional language that is suitable for AI bot tasks. Many libraries are available, which simplifies the development process. The best choice if the option of machine learning is provided. But the weak point is performing tasks where memory usage needs to be intensive;


• С#. Effective in tasks where high performance is required. This is an important advantage, as the bot needs to process data from exchanges in 24/7 mode. Minuses: difficult programming language to learn;


• Rust and Go. Both languages are characterized by high performance. Rust emphasizes more on memory safety. But the community of these languages is less developed than for Python or C#.

Selection of assets and exchanges

The most common AI-based trading bots are for the cryptocurrency and Forex markets, but the software can also work with binary options software, CFDs, indices, metals and commodities. Based on the selected market, exchanges will also be selected, with which the trading bot will interact via their APIs. Important criteria when choosing platforms for trading are: availability of an open API, trading volume, list of available assets, commissions and the ability to create an account from the trader's country. Popular options of cryptocurrency exchanges: Kucoin, OKX, Binance, Kraken, Bybit and Uniswap.

Strategies for trading

Next, you need to choose a trading strategy, from this will depend on the algorithms and the principle of action of the software to make deals and profit. Popular options:

• Scalping. Earnings on insignificant changes in asset prices;


• Position trading. Long-term observation of the trend in order to profit from significant changes in the price of an asset;


• Reversion to the mean. The deviation of the asset rate from the moving average is taken into account;


• Arbitrage. Making a profit by buying assets at a lower price and selling them at a higher price. Common variations: using an arbitrage bot for intra-exchange exchange (taking into account the difference in the price of one asset on different exchanges) and triangular arbitrage (using the difference in the exchange rate of three currency/cryptocurrency pairs).

At this stage, it is important to have knowledge in trading and the ability to correctly assess trends in the markets. Cloud platforms are particularly popular, which give access to a large number of different strategies (150+).

Selection and implementation of trading indicators

Based on the chosen trading strategy, which will be followed by AI bot, you should also select technical indicators. Thanks to them, changes in the market will be tracked. Popular options are:

• MACD to assess the strength of the trend;


• ATR to assess the volatility of the asset;


• ADX to assess the intensity of the trend;


• MA, which shows the average price of an asset;


• RSI, which helps to assess the probability of a trend change.

It is possible to develop your own unique indicators that will analyze the market according to certain indicators. The foundation is based on mathematical models. Creating such indicators requires knowledge not only in programming, but also in trading.

Choosing an AI framework

Frameworks give you the tools to build software faster. Popular options for developing AI bots are TensorFlow with high-level API interfaces, the compact and extensible Keras environment, and PyTorch, which is the most effective for fast learning tasks. The largest number of projects, according to GitHub statistics, are launched using TensorFlow.

When choosing, you should consider whether the framework is compatible with the chosen programming language, as well as evaluate its flexibility and ability to process large amounts of information. An active community and tutorials will be an additional plus.

Infrastructure selection

One of the development steps is choosing an environment for deploying the bot. Sometimes a dedicated server is chosen, but more often cloud services are used. Important criteria: scalability for effective work in different trading conditions, fault tolerance and availability. It is better not to choose free subscription services, as they do not give the necessary level of security. Popular cloud providers:

• AWS;


• Digital Ocean;


• Google Cloud;

This stage involves setting up the virtual machine, connecting to the APIs of the exchanges from which data will be received, and setting up strategies for trading.

ML model selection

We need to select and evaluate the performance of the machine learning model that will be used by the trading bot. The main goal is to teach the AI software to solve trading tasks more efficiently and accurately based on data analysis. At this stage, the development is handled by a Data Science specialist (skills in Scala, SQL, Java and ability to work with databases are required). Popular ML models:

• Logistic regression;


• Decision Tree;


• Linear regression;


• Random forest;

UI/UX development

To ensure that a trader can effectively use the capabilities of a trading bot, it is necessary to create a convenient and understandable user interface. You can use ready-made templates (some of them are free) or develop from scratch for specific trader's needs. Full-fledged development requires the ability to work with prototyping tools. For example, Proto.io, Invision Studio, Adobe XD, Webflow, Axure RP 10 and Sketch.

Testing of AI trading bot

An important final stage of development is software testing. First of all, it is necessary to make sure that the algorithms are correct and there are no bugs in the bot's operation. Then a multi-stage security check is conducted, as it is necessary to make sure that there are no vulnerabilities. The software must be protected from hacking and hacker attacks.

Also, the development team evaluates the profitability of the bot - such backtesting is carried out on the basis of historical data. Such indicators can be used:

• ROI to determine the profitability of trading;


• Profitability ratio;


• Average time to execute a trade;


• Risk-adjusted return assessment.

For a clearer understanding of the software's efficiency and to improve the strategy, you can use visualization (graphs, charts, etc.), which will show the profit/loss ratio, types of assets involved, etc.

Conclusion

Artificial intelligence technology complements the functionality of trading bots well and increases their efficiency. Analyzing competitors, it is possible to conclude that cloud platforms with AI bots that can use a large number of strategies and have access to the APIs of the largest exchanges are most in demand. The development of such projects costs from $40,000 to $80,000. The price of simpler AI bots ranges from $10,000 to $40,000.