Scenario framing
Define bullish, bearish, sideways or volatility scenarios as neutral market studies.
Probability Methodology
How Loventy structures neutral probability studies.
Every study starts with scenario framing, catalyst mapping, contradiction checks, evidence quality scoring, confidence bands and forecast review windows.
Methodology prototype only. No guaranteed accuracy, no guaranteed prediction, no live API activation and no financial advice.
Catalyst mapping
Map macro, earnings, policy, commodities, crypto and sector catalysts to affected assets.
Contradiction checking
Separate support signals from contradiction signals, missing data and invalidation triggers.
Evidence quality scoring
Track source density, freshness, agreement, contradiction load and missing-data warnings.
Confidence bands
Show AI confidence as a study label, not a certainty claim or instruction.
Forecast windows
Attach review timing so scenario labels do not appear permanent.
Volatility sensitivity
Flag leverage, liquidity, concentration and catalyst clustering when pressure can change quickly.
Review cycle
Queue stale, contradicted or low-evidence studies for future review before publication depth expands.
Future AI/provider routing
Future providers must be backend-only, reviewed and compliant before any production use.
No guaranteed accuracy
Loventy methodology explains structure and review discipline. It does not claim guaranteed accuracy, does not personalize outputs and does not tell users what to buy, sell or hold.