📄️ Conditional vs. Non-Conditional Studies
This page explains the distinction between conditional and non-conditional studies in quantitative research, highlighting how they affect the interpretation of statistical likelihoods for measurable features like price, ATR, and slope. It explores how these studies help in understanding global versus context-specific behaviors of phenomena.
📄️ Market Study Hierarchy
Defines the conceptual hierarchy between non-path-dependent characterization, path-dependent characterization, and backtesting layers within the QLIR research stack.
📄️ Distribution Pipeline Methodology
Explore the distribution pipeline methodology, a stepwise process for transforming and evaluating empirical distributions in financial studies, with insights into how each stage affects the statistical landscape.
📄️ Backward Analysis
How to perform backward (diagnostic) analysis in conditional trading studies — comparing feature distributions across success and failure cohorts, quantifying their separation, and turning those insights into context-aware refinements.
📄️ Forward Analysis
A methodology for forward (experimental) analysis in trading studies — using controlled conditioning to test hypotheses, interpret success-rate changes, and build resilient, context-aware rules. Complements the Distribution Pipeline and Backward Analysis articles.