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
Major financial institutions and leading quantitative hedge funds have aggressively pursued the siren song of technology—from "quant trading" to "machine learning"—but nothing has generated confusion quite like the term "LLM-powered trading." It suggests an exciting, almost terrifying possibility: that a chatbot could simply outthink the market and handle your portfolio with autonomy.
The popular narrative is pure fiction. It features ChatGPT or Gemini issuing prescient calls on Tesla's next rally. Yet, ask any purported expert to specify the mechanics—how the model is integrated into the trading stack, where it is called in real time, what data it actually consumes—and the answers dissolve into vague generalities. The gap between the hype and the engineering is vast, and it is here that traders lose money.
At RuggedX, where we build serious AI-driven algorithmic platforms like Neptune (stocks) and Virgil (Options), we rejected that fantasy from the start. We do not use Large Language Models to replace the foundational mathematics of our algorithms. We use them to perform the most critical and often-missing step in automated trading: reasoning.
This is the necessary distinction: Our LLMs do not predict prices. They evaluate conviction—interpreting the confusing confluence of technical data, real-time context, and market sentiment—and then issue a clear, actionable verdict: enter or don't enter.
Every other component—execution, risk, and portfolio limits—remains immutable, governed by deterministic code. When capital is at stake, AI must advise, not improvise.
The core misunderstanding is the belief that LLMs function as prediction engines. They simply are not built for this task. LLMs, whether we speak of Gemini or GPT-5, are not designed to calculate regression curves or optimize feature vectors against time-series data. They are not statistical models; they are context models. Their power lies in their ability to interpret narratives, connect disparate pieces of information, and deliver a reasoned, human-like summary.
This is a profound technological tool, but its utility is confined to a specific layer of the trading system. Consider the standard, three-part anatomy of a successful trading operation:
The historical flaw of automated systems is that they surgically remove Layer 2, merging the signal with the execution. This is why a powerful algorithm might sail blindly into a trap; it lacks the capacity to interpret nuance, unable to read a breaking headline.
This is precisely where the LLM is deployed. It replaces that missing human reasoning layer—not by guessing the next candle, but by delivering a contextual understanding of why a particular trade should be taken, or, more critically, why it must be avoided.
To move past theoretical hype, let us examine a concrete scenario involving TSLA (Tesla Inc.). This is how a real LLM integration operates.
Every morning, our Neptune platform screens U.S. equities. Say the momentum algo flags TSLA. The logic is purely mathematical: Volume has surged over 200%, the stock has cleared its 50-day EMA, and the Relative Strength Index (RSI) is strong but not yet overbought. The signal is "BUY."
But before Neptune commits capital, it pauses. The strategy contains a mandatory checkpoint: invoke the AI Decision Layer.
The LLM does not receive an image of a chart. It receives a structured, contextual snapshot. In code, the information is meticulously curated:
$symbolTechnicalData = $this->prepareTechnicalData("TSLA");
$stockInfoRecentData = json_encode(
$this->getStockInfo("TSLA")->stocksRecentRealtimeJsonData(20),
JSON_PRETTY_PRINT
);
$newsTitlesString = $this->marketNewsModel()
->getNewsBySymbol("TSLA", 'json', 'string')
->getData(true)['titles'] ?? "No recent news articles available.";The model is fed technical metrics, recent microstructure data (the last 20 price movements), and compact headlines about the company. This contextual snapshot is then paired with a rigorous, strategy-specific prompt ("momentum_buy_algo").
This is context engineering: giving the LLM direction, not merely data. The model is forced to reason within a structured framework, preventing the "hallucinations" that plague less disciplined applications.
The LLM processes this information and returns a JSON object. This is not a guess; it is 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 with the deterministic buy. If the
LLM returns entry_false because it identified a major CEO announcement 30 minutes
away, the trade is skipped entirely. The LLM provides the judgment; the algo provides the
discipline.
For those familiar with generative AI, the adage is true: garbage in, garbage out. In trading, this means information must be signal-rich and time-relevant. We do not overwhelm the LLM with unnecessary noise:
Instead, we feed it data that a successful short-term human trader would focus on:
This rigorous curation allows the model to reason like a trader—focusing only on inputs that affect conviction—and prevents it from hallucinating like an overzealous journalist. This is the difference between a functional trading tool and a mere novelty.
The second fundamental mistake of new traders is overusing the LLM. Calling it on every single price tick is wasteful, inefficient, and degrades the quality of its reasoning. LLMs are built to interpret moments, not to chase milliseconds.
At RuggedX, we define explicit LLM injection points—times when the model’s reasoning provides genuine alpha:
This separation of duties is non-negotiable. Here is what the LLM never touches:
The next frontier is not simply using LLMs for a single verdict. It is the development of Agentic LLMs that can dynamically orchestrate the entire context-gathering process.
Imagine an agent that:
This is where disciplined trading is heading: toward AI orchestration, not AI control. The LLM is evolving from a mere function call into an intelligent, autonomous partner in the decision-making process.
But even in that future, the fundamental principle will remain: LLMs decide if; algorithms decide how. That boundary separates professional systems from reckless speculation.
Using LLMs for trading is not a license to hand your portfolio to a digital oracle. It is a commitment to providing your deterministic systems with a sense of judgment—a way to reason about the swirling, narrative forces of the market that static code can never capture.
At RuggedX, our systems use LLMs not as fortune-tellers, but as context interpreters that analyze, justify, and decide. The deterministic code then executes, manages risk, and protects the capital. Every single decision—justified or rejected—is logged, creating a continuous feedback loop that tells us whether the AI layer is adding genuine alpha or just noise.
That is what it actually means to use LLMs for trading. Not to predict. Not to gamble. But to think—precisely, consistently, and within unbreakable constraints.