> Bron: https://neuralex.nl/en/showcase/auto-trader
> An autonomous, learning trading agent that turns news and market data into substantiated positions — paper-only, with strict risk guardrails and every trade traceable back to its source.

Showcase · Auto-Trader

# A trading agent that reads the news first

Most trading bots react to a chart. Auto-Trader **reads the cause**: news and market data become signals, a brain weighs them, and a risk layer decides whether a position is allowed at all. Fully autonomous, **paper-only**, and every trade traceable back to the source that triggered it.

[Request a demo](/en/contact) [See the decision flow](#flow)

paper

only — no real money

24/7

news & data harvester

100%

trades traceable to source

0

positions outside the risk guardrails

From news item to position

## Six steps, one log

Every position goes through the same chain. The brain may propose; the risk layer has the final word. No step happens without being traceable afterwards.

1

### News & market data

A harvester continuously collects prices, news and sentiment — the raw input, with source.

2

### Signal extraction

Noise stripped out: only events that matter for a position, with a weight and a source reference.

3

### Reasoning brain

Model-agnostic. Weighs signal against existing positions and context, and proposes a trade with reasoning.

4

### Risk guardrails

Position limits, exposure caps and stop-loss review every trade. Outside the limits? Rejected and logged.

5

### Paper execution

The approved trade is executed in a paper portfolio — realistic, but without real money.

6

### Log & learner

Every trade, piece of reasoning and outcome is logged and evaluated. The next decision is better informed.

Technology in detail

## Discipline over conviction

### Source-first, like everything else

No trade without a traceable cause. Every signal links back to the news item or data point that produced it — the same citation discipline as the rechtspraak RAG.

### Model-agnostic brain

The reasoning layer is decoupled from the model: plug in an API, an EU-hosted model, or one running fully on-prem. The brain proposes — the risk layer decides whether it's allowed.

### Risk is a guardrail, not advice

Position size, exposure per market and stop-loss are hard-enforced in code. A convincing prediction still doesn't open a position outside the limits.

### Paper-only by design

The agent trades in a paper portfolio. This proves the strategy on real data without financial risk — and without Neuralex giving investment advice.

### Learning loop

An evaluation after every session: what worked, what didn't, and why. That feedback sharpens the next decisions.

### Fully traceable

Signal, reasoning, (rejected) trade and outcome sit as a single thread in the log. Every move can be explained after the fact.

Built with News/data harvesterSentiment extractionModel-agnostic LLM layerRisk enginePaper brokerEvaluation log

[View the Trader cockpit](https://trader.neuralex.nl/)

In development · paper-only

## Same blueprint, on market data

Auto-Trader proves the Neuralex approach on one of the hardest domains: source-first, autonomous where possible, guardrails where necessary. Paper-only — no investment advice. Curious how this would work on your data?

[View the Lab](/en/lab) [All showcases](/en/showcase)

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Volledige (opgemaakte) versie: https://neuralex.nl/en/showcase/auto-trader