A major technological shift is continuing to dominate public discourse, fundamentally changing how we use computation to improve our lives. OpenAI’s ChatGPT is an incredibly useful instrument capable of affecting millions of jobs globally and introducing novel ways of interacting with digital goods and services for billions of people.
One of the more intriguing paradigm shifts is happening in the financial sector. Here, the introduction of Large Language Models has been relatively slow. It is being integrated into multiple areas at once, including solution such as an AI trading bot, yet the rate of adoption is comparatively low.
We strongly believe that all contemporary retail traders must focus on learning more about these innovative tools. Studying them and experimenting with them can be a way to improve your long-term chances of achieving success.
ChatGPT Trading Bot: Can AI Automate Your Trades?
In the fintech sector, LLM adoption has been cautious and incremental. Just like in other areas where expertise and experience are important (e.g., healthcare or education), we have to be very careful when trying to implement something that can affect our financial well-being!
Complementary to AI-driven insights, traders may implement a grid trading bot to systematically capture market oscillations or adopt a DCA trading bot for dollar-cost averaging strategies.
Here are some ways in which ChatGPT for trading is used in the world of finance and crypto:
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Customer support. Just like in many other sectors, platforms offering financial services are trying to cut corners by reducing expenses associated with providing client support. Robots can instantly respond to user inquiries and provide valuable educational materials, consultations, and tips. The vast majority of investment banks, centralized exchanges, and other platforms are implementing virtual assistants across their products.
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Data gathering and processing. Machines are extremely good at grabbing all the information they have access to and searching for correlations, patterns, and other neat things that often dodge the human eye. With tons of data on market history, price action deviations, and other information, machines can provide outstanding outputs based on technical analysis and other time-tested approaches.
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Processing textual information. It is possible to use these robots to gather social media posts, news stories, and other sources of useful information. Contemporary systems can understand the context and provide useful summaries to create a great overview of the current social sentiment around certain markets, assets, and even time frames.
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Implementing LLMs into automated trading systems (ATS). One of the most interesting applications for the technology is its integration into modern bots and automation platforms. While LLMs may not be very efficient at building such apps, they can be used in a variety of ways to enhance user experience. A good example that we will talk about later is the use of a ChatGPT-based system by Gunbot.
Large language models continue to improve rapidly; however, recent progress has begun to slow due to fundamental limitations in current neural network architectures. Nonetheless, they are still capable programs that should not be ignored.
At the same time, it is hard to implement them efficiently in many fields, including finance, crypto investing, and others.
Can ChatGPT Create a Trading Bot? A Step-by-Step Guide
The consensus of many experts is that currently available LLMs are not as good as average programmers at building software. However, they are definitely much faster. One of the trends among tech-centric companies in recent times has been the slow shift from traditional top-down management to horizontal structures with many senior developers simply combing through code generated by LLMs.
Many people believe that this approach is slightly more efficient in terms of how much time is spent on generating code. However, outputs are of worse quality. The same is true for many other areas currently affected by AI: creative writing, graphical design, video editing, and other areas can have passable quality, but human experts still deliver better end products.
Whether you, as an investor, want to rely on an LLM to create a program for you, knowing that the quality of the outcome will inevitably be lower compared to what a human can do, is a question that requires contemplation. All these doubts aside, the latest paid versions of OpenAI’s ChatGPT and Microsoft’s Copilot can be used to create any application in the vast majority of coding languages.
Note that we do not recommend using this particular approach. You must have at least some technical know-how to evaluate the quality of outputs and run them in relevant test environments before applying any applications written with the help of LLMs. Building and maintaining a robust AI trading system requires significant technical sophistication, and ChatGPT-generated code may have bugs. Integration with trading platforms can also be complex.
To build a trading bot using ChatGPT, you should be ready to study at least some aspects of app development, install libraries and test frameworks, test resulting programs, and know how to deploy them. While ChatGPT can assist in building a trading bot, it cannot independently execute trades or access real-time market data. The final product is typically a script that implements specific rules for trading, such as entry and exit criteria, stop-loss orders, and trading logic for different market conditions. These rules must be carefully defined and thoroughly tested. It can be a tedious process for someone without any technical expertise or prior experience. However, it is also a great opportunity to learn something new while making a tool for investing.
How to Build a Trading Bot with ChatGPT: Complete Guide
Since this particular LLM is a jack of all trades, it must be fine-tuned before you can ask it to build a specific program. Trading automation is not the simplest of applications out there. However, you can build something basic with some assistance from an LLM. Here’s a short guide on how to do it:
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Create an account with OpenAI and pay for the commercial version of the product.
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Go to Profile Settings and click the menu item “Customize ChatGPT”.
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Specify the prompt by describing what you are going to develop using the LLM.
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Provide detailed instructions on which libraries, frameworks, and target platforms.
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In the box below, enter the output format and ask the system to first provide a brief description of its development process.
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Ask the LLM to provide consistent yet simplified guidance on how to implement the code.
At this stage, the LLM will know what type of software you are planning to create and which tools it should use. As you see, to get started with the process, you need at least some knowledge of the development process. Interestingly enough, you can ask the system to provide detailed instructions on which tools and frameworks to use.
So, how to create a trading bot with ChatGPT? You need to experiment with prompts. Here’s a good example of how to create a good one:
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Choose a trading platform that has an API with detailed documentation. Binance or Coinbase are good choices.
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Install libraries that will be needed to test and run the code. For Python, you will need Pandas, Matplotlib, and NumPy.
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Define which strategy you plan to automate. The robot understands natural language and knows things like RSI reversals, Moving Average Crossovers, and more.
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Ask the system to provide an in-depth explanation of the code and how it works. Ask for the best ways to test it.
The output will look something like this:
An example output may include dozens of lines of code. The program, written with instructions outlined previously, is capable of collecting data from the Alpaca trading platform, generating a signal using technical indicators, placing an order on the platform, and looping the process to fully automate it.
It is a very simple solution that can be used if you are not interested in using specialized services or purchasing ready-made robots from third-party developers. It is also a good alternative to developing everything from scratch.
ChatGPT AI Trading Bot: How It Works & Key Benefits
Contemporary neural networks are designed to be dynamic and adaptive. Some problems encountered by these models, such as data quality, the availability of computational resources, and quality assurance, prevent them from achieving peak performance. Nonetheless, these are already impressive pieces of technology.
The biggest issue with them is that they are incapable of processing some data and providing adequate outputs. For instance, they can produce code that has gibberish in it. The system often cannot explain how it arrived at a given output. Fixing such errors without understanding the coding language is a futile endeavor.
A big benefit is that you can build novel tools. For instance, it is possible to add some AI enhancements to the final product by implementing unique ChatGPT trading algorithms or asking the system to provide adjustments based on changing market circumstances. You will have to do these adjustments manually and schedule your inquiries, but it can be beneficial if you are planning to utilize the full power of LLMs.
The vast majority of companies that are implementing various forms of artificial intelligence in their products use proprietary models trained on very specific sets of data. Unfortunately, you won’t have the same luxury.
Here are some examples of excellent AI-powered products in the fintech sector:
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WunderTrading has a powerful AI-assisted statistical arbitrage system that can deliver great results when applied to portfolios that rely on a diversified asset composition to work. The system can adjust these positions depending on market conditions and protect your capital from unforeseen risks. It is a great product that many people love.
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3Commas is another automation provider that tries to implement AI agents across multiple products. The attempts have been questionable so far, with many users criticizing this vendor for underperforming ATS and poor implementation of automated customer support chats. This company is at the forefront of AI integration.
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Gunbot has a very interesting way of using a ChatGPT-powered system. They have a virtual assistant that can understand user inquiries written in natural language. These prompts are used to write code compatible with their platform. You can ask the system to create any algorithm and launch it on the Gunbot platform immediately.
These are great tools for newcomers who are interested in exploring the world of crypto while using the guiding hand of an expert AI system. However, we have to warn you about the novelty of the technology and the risks associated with using an untested product.
How to Use ChatGPT for Trading: Strategies and Automation
LLMs are great for executive summaries of textual information, providing basic educational materials, or assisting with data processing. They should not be used as decision-making tools that can solve your problems. Strategizing on a higher level is still the main job of an end user. You must understand that these models are not a panacea for all the world’s problems. These are glorified search engines with some innovative features.
What does it mean for a retail trader who wants to use LLMs to enhance their financial activities? Well, you can use them for a wide range of interesting tricks:
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Strategy evaluation. If you do not want to spend hours reading about various strategies, you can ask the bot about the effectiveness of different analytical approaches and general investment strategies. Make sure to add a line that specifies that the output must contain links to relevant research papers, sources, and publications.
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Strategy optimization. By using advanced customization, you can provide the bot with all the necessary information about the type of strategies and assets you are focused on. Then, use prompts to enhance your current positions and asset acquisition approaches. It is a good way of receiving a second opinion.
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Code improvements. Many retail traders like tools developed by enthusiasts and professionals. GitHub is full of various applications that can be extremely helpful for capital holders interacting with centralized and decentralized exchanges. You can use the code and review it using the capabilities of ChatGPT.
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Gathering social sentiment. The court of public opinion often decides the fate of a token. It is useful to know what people are thinking about target digital assets. You can ask the bot to analyze different news sources and social media platforms. While it may take some time, it is a great way of identifying the general direction of some trends within the crypto community.
Again, we have to remind you that these LLMs should not be used for high-level decision-making. Use them for data gathering, processing, summaries, and other mundane tasks. It is hugely important to make all serious capital allocation decisions personally based on verifiable information and insights from respected sources.
Trading Bots and Risk Management
Trading bots have revolutionized the way investors approach the market, offering the ability to automate trades and execute strategies around the clock. However, while a trading bot can help you capitalize on market opportunities, it also introduces new risks that require careful management. Coding errors, overfitting to past data, and sudden market shifts can all lead to unexpected losses if not properly addressed.
To safeguard your capital, it’s essential to implement robust risk management techniques alongside your trading bot. Position sizing is a fundamental strategy—by controlling the amount of capital allocated to each trade, you can limit the impact of any single loss. Incorporating stop loss orders is another critical tool, automatically closing positions if the market moves against you beyond a set threshold. This helps prevent small losses from turning into catastrophic ones.
Portfolio diversification is equally important. By spreading your investments across different assets or strategies, you reduce the risk that a single market event will negatively affect your entire portfolio. Remember, even the most sophisticated bots can’t predict every market movement, so combining automation with disciplined risk management is key to long-term trading success.
Backtesting and Evaluating Trading Performance
Before deploying a trading bot in live markets, it’s crucial to understand how it might perform under real-world conditions. This is where backtesting comes in. By running your trading strategy on historical data, you can evaluate its effectiveness, identify potential weaknesses, and optimize parameters for better results.
Backtesting allows you to see how your bot would have handled different market scenarios, from bull runs to sudden crashes. This process not only helps you gauge potential profitability but also exposes the risks and limitations of your chosen strategy. By analyzing the results, you can refine your approach, adjust your risk settings, and ensure your trading bot is robust enough to handle changing market conditions.
Regularly evaluating your trading bot’s performance using historical data is essential for ongoing improvement. It ensures that your strategy remains relevant and effective, giving you the confidence to move forward with live trading.
ChatGPT for Options Trading: Can AI Predict Market Trends?
The last part of this article is concerned with the derivatives market. It has been growing steadily since 2018. Back in 2020, during the DeFi Summer, many decentralized protocols started offering different types of derivatives as well. Liquidity pools with fixed expiration dates, futures contracts, and options became the new norm for the decentralized ecosystem. However, it was even more prevalent on centralized exchanges.
The problem with derivatives is that risks are amplified. You can expect to earn more by perfectly timing the market, but even a single mistake can cost you everything. It is essential to exercise caution when engaging with such dangerous financial instruments.
On the other hand, many people who are entering the crypto market are specifically here to make money right here and right now. If you are one such risk-tolerant investor, options and futures could be the only thing that attracts your attention.
So, you might be wondering if it is possible to use large language models to enhance your chances of making correct trading decisions in the rapidly changing futures and options market. The short answer is no. The expanded answer is that you can rely on LLMs to gather insights and interesting investment ideas, but you should never make moves in a leveraged market based on what a machine tells you.
Here are some advantages of using a ChatGPT trading algorithm for options:
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You can use any strategy you like. Options are highly speculative instruments with very short expiration periods. It means that a retail trader must make decisions quickly and analyze the market without wasting any time. An LLM can help you find the right analytical approach and point you in the general direction of great tools that can be used effectively.
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Build custom applications. While the quality of code produced by these LLMs is still nowhere near what the best humans can do, it is still a great way for newcomers to create unique ATS designs. Yes, they will be clunky and somewhat difficult to use, but you can make a system that will utilize very specific technical indicators and algorithms.
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Gathering data. When it comes to making decisions, it is crucial to have relevant information about target assets readily available. ChatGPT, Claude, Deepseek, and other LLMs can collect and structure data to provide you with valuable summaries about the market, social sentiment, and public opinions about certain digital assets.
These are the benefits of using these tools. Unfortunately, they are also associated with significant risks when it comes to using them for volatile and rapidly changing markets:
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The quality of data is everything. You must customize your chats quite well and make sure that it does not produce any hallucinations. Citing trusted sources that can be used for data gathering and ensuring that the quality of outputs is good are both hugely important.
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Outputs can be factually incorrect. For high-level investment decisions, you must base your thinking on solid facts and actionable insights. If you are reading some gibberish sloppily put together from unreliable news sources, the portfolio will suffer.
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Poor personalization. Even if you provide the chat with some information about your portfolio, it will still lack the larger picture of your investment profile. Information about your debts, leverage size, risk tolerance, and even taxation can be crucial when trying to find an optimal strategy.
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Inaccurate outputs. The problem with LLMs is that they can sound quite plausible and realistic without generating valuable information based on true facts. They can also use outdated information about price action, regulations, and other crucial aspects of the derivatives market.
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Biased interpretation of data. Since the information used for training comes from the internet, it can carry inherent biases due to the inclusion of trendy social media posts, popular memes, overbought assets, and more.
An important thing about using these models is that your behavior can be affected. You may relax and do less research when it comes to searching for valuable investment opportunities. You may stop relying on advice from experienced professionals. It is hugely important to keep rational decision-making separate from any thought processes assisted by large language models.
Mitigate risks by verifying information, regularly consulting professionals, and attentively reading about model limitations and restrictions.
The main takeaway
It is undeniable that you can use the latest models to build automated trading systems. However, the bigger and more important question is whether you should. The problem with relying on artificial intelligence in delicate matters like finance is that many humans simply lack the necessary experience and knowledge to discern high-quality outputs from bad ones.
In order to use these models correctly and effectively, you must be an expert yourself. The code, written by a machine, can work as intended, while having critical flaws that an inexperienced user simply won’t notice.
We strongly recommend using LLMs only for educational and informational purposes. They should not dramatically affect your judgment or substitute for excellent third-party services like WunderTrading, one of the leading trading automation providers in the crypto market.