Short answer: they're not really either/or — the strongest trading systems use both, in layers. But if you have to pick a starting point, the honest technical answer is that rule-based logic is the foundation, and an LLM is a layer on top — not the other way around. Here's why.
What rule-based logic is good at
- Deterministic and auditable. Every decision has a traceable cause: "if RSI < 30 and volume > X, enter." You can backtest it against years of data and know exactly what you're testing.
- Fast and cheap. A rule engine executes in microseconds and costs fractions of a cent per decision.
- Predictable failure. When it loses, you can see why and fix the specific rule. No mystery.
- Regulatory and risk-control friendly. Exchanges and regulators want to understand your risk limits — a rule set is transparent.
What LLMs are good at
- Unstructured information. Earnings calls, Fed statements, Twitter/X sentiment, news headlines, regulatory filings — an LLM can extract signal from text that a rule engine can't even read.
- Generating hypotheses. It can propose new strategies, new parameter ranges, or summarize research far faster than a human.
- Natural-language risk reporting — explaining why a position was taken in plain English.
Where LLMs are genuinely bad for execution
- Hallucination. It will confidently "reason" about a price that doesn't exist or invent a causal story that fits the recent move but isn't real. For a system placing real orders, that's disqualifying as the sole decision-maker.
- Latency and cost. Token generation takes hundreds of milliseconds and costs real money per call — a high-frequency rule engine would be far too slow and expensive.
- No built-in probability calibration. LLMs are not trained to output accurate odds; they're trained to be plausible. Plausible ≠ profitable.
- No native safety rails. Left to its own devices it can escalate risk in ways you can't easily predict or audit.
The architecture most serious systems actually use
- Rule-based / deterministic core does execution, position sizing, risk limits, and stop-losses. This is non-negotiable — it's your safety net.
- LLM layer (read-only) ingests news, filings, and sentiment, and feeds structured signals into the rule engine. It suggests, it doesn't execute.
- Backtesting and kill-switch on top. Any rule the LLM proposes gets validated historically before it touches real capital, and manual overrides always win.
The clearest framing: rules are the operating system, the LLM is a sensor. A pure rule system is limited but safe; a pure LLM system is adaptive but dangerously unreliable. Both, layered with the LLM as a signal source rather than the decision-maker, is where you get adaptivity without losing auditability.
One practical note regardless of approach: backtest honestly (avoid lookahead bias and overfitting to the period you trained on), and treat any backtested win rate as optimistic until it survives live, out-of-sample conditions.
Are you exploring this for your own use, or just generally curious? If you're considering deploying real capital into crypto trading, I can pull current market data on a few coins or exchanges that offer API trading if that's useful — just say the word.