In the fast-paced world of cryptocurrency trading, having the right insights at the right time can make all the difference. Large Language Models (LLMs), like the ones behind ElsaAI, is used to analyze the market and predict trends. But how do these AI models actually work? They rely on various types of data.
The core of Data Engine lies in its ability to process and analyze four main types of data: Numerical, Textual, Visual, and Simulated. Each of these data types plays a crucial role in painting a complete picture of the market, enabling ElsaAI to predict trends, identify opportunities, find best paths and mitigate risks. Let’s break down the four main types of data and explore how they work.
Numerical Data: The Core of Market Analysis
Numerical data forms the backbone of most quantitative trading models. Numerical data is all about numbers and statistics. This category includes metrics such as cryptocurrency prices, trading volumes, market capitalization, and other statistical data points.
How It Works: LLMs aren't naturally built to handle numbers directly. Instead, these numerical values are often converted into text formats so the models can process them. For instance, an LLM might be given a series of price changes over three days, converted into a simple narrative: "Bitcoin price increased by 5% over the last three days." The AI can then use this information to predict whether the price might go up or down next.
Example: If trading volumes are increasing rapidly, the LLM might interpret this as growing interest in a cryptocurrency. Coupled with rising prices, the model might signal a potential bullish (upward) trend, suggesting it might be a good time to buy.
Textual Data: Understanding Market Sentiment
Textual data is information that comes in text form, such as news articles, tweets, and financial reports. This data is crucial because it gives insight into what’s happening in the market beyond just the numbers.
Types of Textual Data:
- Fundamental Data: This includes project updates, whitepapers, or financial disclosures. For example, if a project releases a new update or a significant bug is discovered, the LLM can analyze this text to understand how it might impact the cryptocurrency’s value.
- Alternative Data: This is data from non-traditional sources like social media, forums, and influencer opinions. Tweets from influential crypto traders or discussions on Reddit can sway market sentiment. LLMs can analyze thousands of these posts to gauge the market’s mood—whether it’s bullish (positive) or bearish (negative).
How It Works: LLMs use Natural Language Processing (NLP) to analyze and interpret the sentiment behind these texts. If the model sees a surge in positive mentions and strong sentiment around a particular coin, it might predict a rise in that coin’s price.
Example: An LLM might analyze news of a major partnership between a blockchain project and a big tech company. Positive news like this could drive up the price, and the LLM would recognize this as a buy signal.
Visual Data: Decoding Market Patterns
Visual data includes charts, graphs, and other visual representations of data, which are essential for technical analysis in trading. While LLMs are traditionally text-based, newer models are capable of interpreting visual data to some extent ( able to interpret visual data is in ElsaAI's roadmap).
How It Works: For example, candlestick charts show the price movement of a token over time and are a staple in trading. An LLM trained to recognize patterns in these charts could identify common signals like "head and shoulders" or "double bottom," which are patterns that traders use to predict price movements.
Simulated Data: Preparing for the Unexpected
Simulated data involves creating artificial scenarios that mimic real-world conditions, allowing AI models to learn how to handle different market situations. This data type is particularly useful for stress-testing trading strategies.
How It Works: Developers create a simulated trading environment where the LLM can interact with other AI agents. Simulating a sudden market crash, regulatory news, or a large sell-off event. The LLM learns how to react to these events in a controlled setting, helping refine its trading strategy (In roadmap)
Example: An LLM might be tested in a simulation where Bitcoin suddenly drops by 20% in response to new regulations. The model’s reaction—whether to buy, sell, or hold—can provide insights into its robustness and decision-making under pressure.
Conclusion
Incorporating diverse data types—numerical, textual, visual, and simulated—allows LLM-powered copilot like ElsaAI to suggest more informed and nuanced trading decisions. By leveraging these data sources, LLMs can capture a comprehensive picture of market conditions, predict price movements, and adapt to rapidly changing environments.
