Methodology & Data Standards
How AI StockScanner organizes data, model outputs, validation and risk controls—and where the limitations remain.
Data sources and freshness
AI StockScanner can use configured market-data providers for quotes, historical data, company information and related intelligence. Coverage, timing and symbol mapping vary by provider, exchange and plan. The displayed provider and timestamp should be checked before relying on any result.
Scoring and ranking
The platform combines measurable inputs such as price behaviour, trend, momentum, participation, liquidity, volatility, market regime, sector context and configured intelligence signals. Rankings are comparative decision-support outputs, not guarantees or personalized recommendations.
Explainability and conflicts
Where supported, AI outputs are paired with supporting evidence, conflicting evidence, confidence indicators and risk conditions. A high score can coexist with significant event, liquidity or portfolio risk. Conflicts are intended to remain visible rather than being compressed into a misleading single conclusion.
Validation
Backtesting, paper trading, prediction ledgers and post-trade review can be used to evaluate processes. Historical and simulated results are limited by sample size, data quality, assumptions, overfitting, commissions, slippage and changing market regimes.
Model and data limitations
- Quotes or fundamentals may be delayed, unavailable or mapped incorrectly.
- AI explanations can be incomplete, biased or wrong.
- News and events can invalidate a setup immediately.
- Simulated execution may differ from real fills and liquidity.
- No model can account for every user’s objectives or financial circumstances.
User responsibility
Users remain responsible for independent verification, suitability, position sizing, leverage, execution, tax and legal compliance. AI StockScanner is software for analysis and education; it is not a broker, investment adviser or profit-guarantee service.
Last reviewed: July 22, 2026