Independent Methodology & Citations

Quantura Adaptive Forecasting

Quantura Adaptive Forecasting combines structural time-series trend decomposition, rolling volatility regime detection, and quantile calibration to generate probabilistic forward ranges rather than single-point targets.

The process dynamically shifts confidence weighting by market regime and prioritizes stability of lower-bound outcomes when candidate selection is used for portfolio generation.

Investment Thesis Formula

The thesis score blends directional conviction, seasonality persistence, and valuation support while penalizing unstable downside confidence.

thesis_score = 0.45 * trend_strength
             + 0.25 * catalyst_signal
             + 0.20 * valuation_support
             + 0.10 * analyst_revision_momentum
             - downside_confidence_penalty

Research Library

Citations are formatted for auditability and reproducibility. Use Copy BibTeX to export references directly into notebooks or institutional reports.

1. Taylor, S. J., & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45. [Software: Meta Prophet Library]
   Copy BibTeX
2. OpenAI. (2025). OpenAI Agents SDK documentation. [Software Documentation]
   Copy BibTeX
3. Massive.com. (2026). Massive MCP server and market data interfaces. [Data Interface]
   Copy BibTeX