AAPL Minimal Ω Dataset — Structural Concentration in High-Ω States

This dataset provides a minimal executable implementation of the Ω framework using AAPL daily market data. Core definition: Ω = I × G where: - I = rolling standard deviation of returns - G = absolute return The objective is to evaluate whether collapse-like events statistically concentrate in predefined high-Ω states under fixed ex-ante definitions. This is a structural concentration test, not a predictive model. Result summary: P(collapse | high Ω) = 0.80 P(collapse) = 0.052 Ratio ≈ 15.4× Files included: - minimal_results.csv - schema.json - README.md - LICENSE - aapl_omega_minimal_notebook.ipynb The notebook reproduces the complete pipeline: - data download - Ω computation - collapse definition - high-Ω extraction - concentration evaluation Output variables: - datetime - omega - collapse - high_omega Machine-readable structure and fixed definitions are intentionally prioritized for reproducibility and AI-assisted indexing. Canonical overview: https://zenodo.org/records/18296691本数据集基于苹果公司（AAPL）的每日市场行情数据，提供了Ω框架的最小可执行实现版本。 核心定义： Ω = I × G 其中： - I = 收益率的滚动标准差 - G = 绝对收益率 研究目标为：在事前既定的定义框架下，检验崩盘类事件是否在统计学意义上集中于预定义的高Ω状态中。 本研究属于结构性集中度检验，而非预测模型。 结果概述： P(collapse | high Ω) = 0.80 P(collapse) = 0.052 Ratio ≈ 15.4× 包含文件： - minimal_results.csv - schema.json - README.md - LICENSE - aapl_omega_minimal_notebook.ipynb 该Notebook可复现完整执行流程： - 数据下载 - Ω值计算 - 崩盘事件定义 - 高Ω状态提取 - 集中度评估 输出变量： - datetime - omega - collapse - high_omega 本数据集优先采用机器可读结构与固定定义，以保障可复现性并支持AI辅助索引。 规范概述： https://zenodo.org/records/18296691