Signal methodology

AI stock picks built as conditional setups

We do not treat a popular ticker as a signal. A candidate has to pass the universe, catalyst, regime, trigger, and risk checks before it becomes actionable.

Research structure

Build the view layer by layer

Each section answers a different search or decision question, reducing the temptation to compare unlike companies with one blunt ranking.

01

1. Curated universe

Start with companies that have identifiable AI exposure and enough liquidity for clean price signals.

02

2. Catalyst and expectations

Define what could change the market’s view, then ask whether expectations already discount the good news.

03

3. TradingView confirmation

Use alert conditions for breakouts, reclaims, pullbacks, momentum, and relative strength instead of continuous screen watching.

04

4. Risk-first delivery

Every published setup should make the invalidation point, risk unit, and review condition as visible as the upside scenario.

Radar principles

Simple rules. Hard filters.

  1. 01No ticker is always a buy
  2. 02No trigger without invalidation
  3. 03No forecast without a scenario
  4. 04No performance claim without evidence
Frequently asked questions

The question, answered

Are AI stock picks generated entirely by AI?

No. Automation can help scan conditions and organise evidence, but the framework, data quality, risk rules, and final publication standards require explicit human-designed controls.

Do stock picks include entries and exits?

The intended format includes a trigger, invalidation, risk context, and scenario-based targets. Exact product features will be clearly labelled as they become available.

Are signals financial advice?

No. Signals and research are informational starting points. Users remain responsible for suitability, execution, and risk decisions.

This material is informational and educational. It is not personal financial advice, a recommendation, or a guarantee of future results.