Understand what it truly means to use LLMs in trading — not to predict prices, but to bring reasoning, context, and conviction into algorithmic systems.
Published: Sunday, Oct 19th 2025
Large Language Models (LLMs) have fundamentally changed how we approach complex problems. They are no longer just tools for writing emails or summarizing text; they are powerful reasoning engines that enable us to brainstorm through intricate challenges, connect disparate pieces of information, and dig deeper into issues than ever before. If a technology can so dramatically enhance our critical thinking, it makes perfect sense to ask: How can we leverage this superpower to make money in the financial markets?
This is where the narrative usually stalls. The hottest buzzword in finance right now is "LLM-powered trading," a term that conjures a spectacular, yet largely fictional, image: a chatbot like Gemini or ChatGPT magically predicting the next stock rally and trading your entire portfolio by itself.
The reality is that the gulf between this hype and the actual engineering is massive. You hear stories about AI making perfect market calls, but the details—how the model is used, where it plugs into the system, what specific data it reads—are always vague. This lack of practical focus is where most aspiring AI traders get into trouble.
The main misunderstanding is believing that LLMs are built to predict the future. They simply aren't. Models like Gemini or GPT-5 aren't designed to calculate complex statistical curves or analyze price movements over time. They aren't statistical models; they are context models. Their real power is their ability to understand and interpret narratives, connect scattered information, and provide a clear, human-like summary.
This is an incredible technological tool, but it only belongs at a specific checkpoint in the trading system. Think of a successful trade operation having three parts:
Historically, automated systems fail because they eliminate Layer 2 (human judgment), merging the signal straight into the execution. That's why an algorithm might walk blindly into a trap; it can't read a breaking headline about the CEO or a regulatory change.
This is precisely where the LLM is deployed. It replaces that missing human reasoning—not by guessing the next price movement, but by delivering a contextual understanding of why a particular trade should be taken, or, more importantly, why it must be skipped.
To move past theory, let's look at a real-world example with TSLA (Tesla Inc.). This is how a working LLM integration operates in practice.
Every trading morning, our Neptune platform screens U.S. stocks. Let's say our momentum algo flags TSLA. The logic is purely mathematical: Volume is high, the stock price has crossed above its 50-day moving average, and the Relative Strength Index (RSI) is strong. The technical signal is a clear "BUY."
But before Neptune commits any money, it pauses. The strategy contains a mandatory checkpoint: ask the AI Decision Layer for confirmation.
The LLM doesn't look at a price chart. It receives a precise, structured snapshot. In the code, the information is carefully organized:
$symbolTechnicalData = $this->prepareTechnicalData("TSLA");
// ...gets the last 20 price movements...
$newsTitlesString = $this->marketNewsModel()
->getNewsBySymbol("TSLA", 'json', 'string')
->getData(true)['titles'] ?? "No recent news articles available.";The model is fed technical metrics, recent price microstructure (the last 20 price movements), and compact news headlines about the company. This contextual snapshot is then paired with a strict, strategy-specific instruction ("momentum_buy_algo").
This is context engineering: giving the LLM clear direction, not just a pile of data. The model is forced to reason within a structured framework, which prevents the "hallucinations" that make less-disciplined AI applications unreliable.
The LLM processes this context and returns a JSON object. This is not a guess; it's a justification.
{
"symbol": "TSLA",
"decision": "entry_true",
"justification": "TSLA has regained its 50-day EMA after consolidating above $235. Volume is 2.2x the 20-day average, suggesting strong institutional participation. RSI (63) shows momentum without overextension, and recent news on the Cybertruck production ramp is positive. Conditions favor continuation toward $250 short term."
}If the verdict is entry_true, Neptune proceeds to buy. If the LLM returns
entry_false because it noticed a major CEO announcement is scheduled for 10 minutes
from now, the trade is instantly skipped. The LLM provides the judgment; the algo
provides the
discipline.
If you've worked with generative AI, you know the rule: garbage in, garbage out. In trading, this means information must be super-relevant and timely. We don't flood the LLM with unnecessary noise:
Instead, we feed it only the data a successful short-term human trader would focus on:
This intense filtering allows the model to reason like a trader—focusing only on inputs that truly affect the decision—and prevents it from guessing like an overzealous journalist. This is the difference between a functional trading tool and a mere novelty.
The second major mistake new traders make is overusing the LLM. Calling it on every single price movement is expensive, slow, and degrades the quality of its reasoning. LLMs are built to interpret moments, not to chase milliseconds.
At RuggedX, we define explicit LLM checkpoint points—times when the model’s reasoning provides genuine value:
This separation of duties is essential. Here is what the LLM never touches:
The next step in LLM trading isn't just a single verdict. It's the development of Agentic LLMs that can dynamically manage the entire context-gathering process.
Imagine a smart agent that:
This is where disciplined trading is heading: toward AI orchestration, where the LLM is an intelligent, autonomous partner in decision-making, not a rogue operator.
But even in that future, the fundamental principle will remain: LLMs decide if; algorithms decide how. That boundary is the difference between a professional system and reckless speculation.
Using LLMs for trading is not a license to give your portfolio to a digital fortune-teller. It's a commitment to giving your fixed systems a sense of judgment—a way to reason about the complex, narrative forces of the market that simple code can never capture.
At RuggedX, our systems use LLMs not as fortune-tellers, but as context interpreters that analyze, justify, and advise. The deterministic code then executes, manages risk, and protects the capital. Every decision—justified or rejected—is recorded, creating a continuous learning loop that shows us whether the AI layer is adding genuine value or just noise.
That is what it actually means to use LLMs for trading. Not to guess. Not to gamble. But to think—precisely, consistently, and within unbreakable constraints.