A trading agent that reads the news first
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 See the decision 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
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?
Volledige (opgemaakte) versie: https://neuralex.nl/en/showcase/auto-trader