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– Deep Dive into AI, LLMs, Automation, and
Engineering
Published: Sunday, Oct 19th 2025
The conversation around AI trading today is loud, confident, and mostly wrong. Everyone claims to have “AI-powered signals,” “autonomous trading agents,” or “LLM-driven hedge funds,” yet almost none of these explanations survive basic engineering scrutiny. The truth is simple: LLMs are powerful, but they don’t work the way the hype suggests. If you want to use AI in real markets—not simulations, not marketing decks—you have to understand what LLMs are actually good at, what they fail at, and where they belong in the life cycle of a trade.
At RuggedX, our platforms—Neptune for stocks, Triton for forex, Virgil for crypto, and Orion for options—run in live environments where latency, risk limits, broker rules, and human psychology all collide. We’ve wired LLMs into these systems in dozens of places: from strategy reviews and risk coaching to natural-language ops, GitHub automation, and Alexa voice interfaces. Along the way, one principle became obvious: LLMs are not price oracles; they are reasoning engines.
So the real question isn’t “Can an LLM predict the market?” The real question is: Where in the trading lifecycle does reasoning create edge? To answer that, we need to zoom out and look at the entire cycle a trade travels through—from the first macro scan to the last post-trade debrief.
A trade does not start when you press Buy or end when you press Sell. It lives inside a continuous loop: markets shift, strategies adapt, prompts evolve, risk rules tighten, and insights feed back into future trades. To reason about where LLMs belong, we built a full circular diagram of the trade lifecycle with twenty distinct stages.
Here is the same lifecycle in text form:
This is what it actually means to “use LLMs in trading.” Not a chatbot pressing buy and sell, but a reasoning layer injected into specific points of the loop.
The first misconception we have to kill is the idea that LLMs are built to forecast price. They’re not. These models don’t compute probability distributions or optimize over historical time series the way a statistical model does. Instead, they excel at something different: understanding and transforming context.
That means they can:
In other words, LLMs don’t replace your signal engine—they surround it with intelligence. The math proposes. The LLM evaluates, explains, and sometimes vetoes.
Let’s take a simplified version of how this works inside Neptune.
Our momentum strategy scans U.S. stocks and flags TSLA. The conditions are clean: price pushes above the 50-day EMA, volume is 2x its 20-day average, volatility is expanding, and the broader market is supportive. From a purely technical perspective, it’s a textbook long.
Before the system commits capital, we route a compact, structured snapshot into the LLM layer:
The model isn’t browsing the web or guessing from thin air. It’s reading a curated slice of exactly what a good human discretionary trader would look at—no more, no less.
{
"symbol": "TSLA",
"decision": "entry_false",
"justification": "Technical momentum is strong, but TSLA reports earnings in 90 minutes. Entering now exposes the position to binary gap risk that conflicts with the intraday momentum playbook."
}The verdict isn’t a mystical prediction. It’s a reasoned veto grounded in context the raw strategy does not see. Neptune’s trading engine respects that response and simply does not open the position. The math was right about the momentum; the LLM was right about the risk.
If you’ve played with generative AI, you already know the basic rule: garbage in, garbage out. In trading, “garbage” usually means one of two things: irrelevant data or unbounded instructions.
Our systems do the opposite:
This is what gives LLM decisions weight. They aren’t hallucinating stories; they are constrained consultants reviewing a curated context window with a very specific job: “Does this trade make sense right now?”
There’s one non-negotiable rule if you want to use LLMs in live markets without getting wrecked:
The LLM is allowed to influence if a trade happens. It is never allowed to decide how it happens.
That means:
This separation of concerns is what turns LLMs from a toy into an actual trading edge. The model thinks; the engine executes; the risk layer enforces discipline.
Yes—but only if you stop asking them to be fortune-tellers and start using them as what they truly are: high-bandwidth reasoning partners embedded at the right stages of the trade lifecycle.
In our systems, LLMs:
What they never do is place an order, move a stop, or gamble with capital on their own.
That is the real answer to the question, “Can you actually use LLMs in trading?” Not by handing them your account and hoping they’re right—but by embedding them into a disciplined, circular lifecycle where every decision, explanation, and veto is logged, auditable, and constrained by hard rules.
In that environment, LLMs stop being a buzzword and become what they should have been all along: a durable edge in how you think, not a shortcut in what you gamble on.