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HOW THE CYBERETIKA SCORE™ WORKS
Five independent AI signals, one actionable number.
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Historical Trend Analysis
Our proprietary algorithm processes up to 60 months of historical price action, applying time-weighted exponential decay functions to prioritize recent market behavior over legacy patterns. The system evaluates annualized returns across multiple timeframes — from short-term quarterly movements to long-term secular trends — constructing a composite directional bias that reflects the asset's structural trajectory within its broader market cycle.
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Natural Language Sentiment Extraction
Each article ingested by the platform undergoes deep NLP analysis through our Groq-accelerated inference pipeline. The system extracts entity-level sentiment polarity, distinguishing between macro-level market commentary and asset-specific catalysts. Sentiment scores are normalized across source credibility tiers and cross-referenced with historical sentiment-to-price correlation matrices to filter noise from actionable signal.
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Media Velocity & Attention Dynamics
Beyond individual article sentiment, our engine tracks the rate of change in media coverage density. Using rolling-window frequency analysis, the system detects abnormal spikes in publication volume — a statistically significant leading indicator of imminent price action. The acceleration vector between weekly and monthly coverage rates reveals institutional information asymmetry before it manifests in orderbook depth.
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Multi-Signal AI Consensus Engine
Every prediction generated by the platform is aggregated into a directional consensus matrix. The system computes the ratio of concordant versus discordant signals, measuring the degree of alignment across independent analytical passes. High inter-prediction agreement amplifies conviction, while divergent signals trigger automatic dampening — ensuring the final score reflects genuine market consensus rather than isolated outlier events.
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Bayesian Confidence Calibration
Not all signals carry equal epistemic weight. Our confidence layer applies Bayesian posterior estimation to assess the reliability of each analytical component based on data completeness, source diversity, and historical prediction accuracy. Assets with sparse coverage or conflicting indicators receive automatic confidence attenuation, ensuring the Cyberetika Score™ accurately reflects the degree of certainty behind each prediction.