AI trading vs algorithmic trading: models, rules and execution authority

The reader question is usually framed as “Is AI trading better than algorithmic trading?” A more useful first question is which decision each component makes. A language model may explain a strategy; a predictive model may estimate a conditional return; an agent may choose a research tool; and execution logic may determine an order’s timing and quantity.

In the EU MiFID II definition, algorithmic trading includes a computer algorithm automatically determining individual order parameters with limited or no human intervention. The definition excludes systems used only for routing or certain processing without determining trading parameters. This is a jurisdiction-specific reference for precise terminology, not a finding about the legal status of a particular platform. [1]

“AI trading” is a broader technical and marketing description. It tells a buyer little until the provider identifies the model’s actual role. “Agentic” adds a process distinction: the model can direct a sequence of actions or tool choices. It does not replace the need to inspect the strategy.

The matrix is our analytical contribution. It makes procurement questions concrete. A forecasting model can feed a fully automated strategy without an LLM agent. An agent can organise research without producing a forecast. A familiar rule-based strategy can be operated through an agentic workspace. These arrangements overlap rather than form mutually exclusive product categories.

Take the illustrative idea: “Trade a breakout only during a specified session.” First write the rule precisely: session timezone, completed versus forming bars, entry condition, invalidation, sizing convention and exceptional conditions. This is a strategy specification, whether a person or model writes the first draft.

Next consider a learned filter that estimates whether conditions resemble the development sample. That introduces training data, fitted parameters and a second evaluation problem. Adding an agent to inspect test failures introduces orchestration. Finally, the execution service must map an allowed action to the correct account and broker conventions. Each addition creates something else to test.

A buyer can therefore ask for a trace of one decision through these boundaries. Where did the input come from? Which rule or model selected the action? Which component could refuse it? What record establishes the eventual outcome? A single attractive equity curve cannot answer all four questions.

A rule-based implementation can repeatedly make the same mistake. For example, a session high calculated after the supposed entry time can give a test access to information it would not have had. Stable code and identical outputs do not repair that design error.

A learned model creates different checks: preserve its training data boundaries, feature transformations and fitted artifact. Refitting on a later dataset produces a different model even when the source code is identical. Record model selection and unsuccessful candidates so that the final test is not presented as if it were the only experiment.

The same standard should apply to model-written code. Review a small set of expected trades and non-trades before running a large search. A generated program that executes successfully can still misunderstand the rule, especially around timestamps, units or exits. The walk-forward guide explains why chronological evaluation requires more than a train/test label.

Agentic orchestration is most plausible when the next useful action depends on an earlier result. A fixed test report might show a discrepancy that requires inspecting symbol mapping rather than trying another parameter. An agent can investigate the changed question. A predetermined pipeline remains attractive when the task and its exceptions are already well understood.

Anthropic’s engineering guidance distinguishes these architectures and recommends using the simplest sufficient approach. The relevant source documents design trade-offs; it does not demonstrate financial superiority. [2]

Our evaluation proposal is to compare the orchestration layer with a simpler baseline on the same task. Count verified completions, operator corrections, elapsed time and total resource use. If the extra reasoning contributes no useful change of direction, a simpler workflow may serve the reader better.

An AI system may use retrieved documents to supply context. The original retrieval-augmented generation work combines generated responses with external retrieved material and evaluates knowledge-intensive NLP tasks. It offers a mechanism for grounding an answer, rather than proof that retrieved information leads to profitable trades. [3]

For trading research, the crucial follow-up is whether the information was available at the relevant decision time. A model can retrieve an authentic document that is chronologically inadmissible. An algorithm can consume a revised statistic without noticing that the historical vintage was different. These failures cross the AI-versus-rules boundary; both architectures need point-in-time inputs.

See memory, provenance and temporal context for a practical evaluation. A storage architecture is useful only insofar as it answers the question with the right evidence.

The same model could operate in read-only research, prepare proposals or run within a deployment that permits automatic actions. Those permissions are an operating choice. Neither natural-language interaction nor a deterministic rule tells you where approval occurs.

Ask a provider to show the exact transition from a research artifact to an authorised operation. Is approval attached to one proposal, a specific strategy version, an account or a broad continuing mandate? What changes invalidate the approval? Which checks run again before submission? Write the answers as a compact authority map.

VC Trader AI’s documented early-access scope distinguishes agent-proposed orders requiring individual confirmation from customer-approved strategies that may execute automatically. This is a first-party product description checked in local copy, not evidence that every possible failure has been tested. The distinction should remain visible even when both paths share a workspace.

Evaluate research quality, forecast validity, operational correctness and authority as separate requirements. Evidence of one should not silently satisfy another. A fast automated strategy may be inappropriate for the intended account; a careful research agent may have no measured predictive edge; an informative model estimate may never be suitable for unattended execution.

The comparison can be revised if a provider supplies a controlled evaluation showing that a particular component improves a defined task under the intended conditions. Until then, category labels are descriptions of construction. Ask what changed, what was measured, what failed and what remains under human control. Those questions produce a more useful decision than choosing between “AI” and “algorithms” in the abstract.