Image credit: Shubham Dhage on Unsplash
It's no secret that large language models (LLMs) like ChatGPT have transformed how we work today. The Crypto trading landscape is no different.
With the introduction of these powerful and new technologies, Crypto traders have thought of various ways they can use these technologies to revolutionize the way traders approach market analysis, generate trading signals, automate trading processes, and even democratize decision-making.
In this article, I will mention some ways LLMs can be used to change the way we trade and potentially reshape the trading landscape:
Sentiment analysis based trading Strategy
LLM models, which are deep learning algorithms, can perform various tasks. One of these tasks is natural language processing (NLP) tasks. This means they can process large volumes of textual data from news articles, social media, and other sources to provide a comprehensive market analysis that can be used for sentiment analysis-based trading. This can save time for traders as they no longer need to sift through large volumes of news sources.
So, how does this work? A sentiment analysis-based trading strategy allows traders to gauge the market sentiment of some cryptocurrencies and identify market-moving events. Traders can use the result from this sentiment for market trends analysis, allowing traders to swiftly react to news and decide when to buy and sell. Zanyang talked a bit more about this in hisUse of NLP-Powered sentiment analysis in trading strategy paper, where he used algorithmic trading platforms like QuantConnect.
Customer support and communication
Besides Traders, LLMs can benefit everyone in the crypto space, including cryptocurrency founders, project owners, community leaders, and trading platforms. For community and trading platforms, LLMs can be used for knowledge-based databases, Q&A bots, and customer support. This way, they can seamlessly help users navigate the platform, improve customer experience, answer project-related questions, resolve issues, and assist in technical support. The best thing about this system is that, unlike traditional conversational AI, LLM-powered bots can understand human language and respond better 24/7.
Some AI Chatbots you can explore for this are AwesomeQA and UltimateGPT.
Generate trading signals
A trading signal is a trigger telling a trader when to buy or sell orders. Triggers are generated by simply analyzing indicators, patterns, or data points. These common technical indicators include MACD, RSI, KD, and Bollinger Bands. This article by BYDFi talks a lot more about these indicators.
With LLMs, you can generate trading signals in various ways:
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Building trading algorithms that depend on pre-established rules.
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Identify trends through sentiment.
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Interprets news events that can cause market movements.
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Recognize patterns and interpret news events.
With these signals, traders can use the insights to capitalize on anticipated price reactions and make more informed decision-making. The best part is that this signal generation can be automated, thus reducing any latency in trade execution.
This exciting discussion on the Numerai forum discusses using LLMs to create trading signals.
Build, Build, and Build with LLMs
You’ve probably used LLMs in code generation or to assist you in generating codes; If not, I have got an [Analytics Vidhya’s article on How to Build LLMs for Code?](https://www.analyticsvidhya.com/blog/2023/09/llms-for-code/#:~:text=A%20Large%20Language%20Model%20(LLM,understand%20and%20generate%20computer%20code.) for you.
Based on your prompt, LLM can be used to write code faster and much more efficiently. You can also use it to generate a whole project.
Bringing this idea to the crypto space, you can use LLM models like WizardCoder, StarCoder, and Codeium to generate codes, write smart contracts, and test. These models can also read, analyze and modify your codes or even improve your codebase.
All this can be done using simple prompts and knowing what you want.
Democratizing decision-making and knowledge
One of my favorite things about LLM models is that they make information accessible. You can get information about just about anything by writing a simple prompt.
Imagine a model trained on real-world financial data that can understand financial concepts, formulas, and market data. This means you can ask this model about anything in this domain, and it will provide you with information and help you make better decisions. Such a custom LLM would benefit not just market leaders but novice traders and beginners in the crypto space.
One of my favorite Crypto knowledge bases is MinMax, an AI-powered search engine.
Wrap-up
You can use LLMs in various ways, from generating codes to providing 24/7 customer support. While many of these models are still under development, they are becoming increasingly powerful and more accurate as the day passes. As this happens, you will likely use them for even more tasks in the crypto industry in the future.