Update README.md

README.md

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----
-license: mit
----
+
+---
+license: mit
+task_categories:
+- time-series-forecasting
+- tabular-classification
+- other
+pretty_name: Financial Structural Breaks & Regime Detection Benchmark
+tags:
+- finance
+- econophysics
+- algorithmic-information-theory
+- structural-breaks
+- time-series
+- anomaly-detection
+size_categories:
+- 1M<n<10M
+---
+
+# Financial Structural Breaks & Regime Detection Benchmark
+
+**Maintainer:** [Algoplexity](https://github.com/algoplexity)  
+**Primary Repositories:**
+1.  **The Coherence Meter:** [GitHub Repo](https://github.com/algoplexity/Coherence-Meter) (Horizon 0)
+2.  **The Computational Phase Transition:** [GitHub Repo](https://github.com/algoplexity/computational-phase-transitions) (Horizon 1)
+
+## 1. Overview
+This repository serves as the **immutable data artifact** for the Algoplexity research program into **Algorithmic Information Dynamics (AID)** in financial markets. 
+
+It contains a large-scale collection of non-stationary, continuous financial time series, specifically curated to benchmark methods for **Structural Break Detection** and **Market Regime Diagnosis**. This data underpins the validation of two distinct methodologies:
+*   **The Coherence Meter:** A statistical, falsification-driven framework comparing "Stethoscope" (univariate) vs. "Microscope" (multivariate) approaches.
+*   **The AIT Physicist:** A transformer-based diagnostic tool that maps market dynamics to **Wolfram Complexity Classes** (e.g., Rule 54 vs. Rule 60) to detect "Computational Phase Transitions."
+
+## 2. Dataset Utility
+This dataset allows researchers to reproduce key findings from the associated papers, including:
+*   The **"Cost of Complexity"** curve (MDL analysis).
+*   The **-27.07% Early Warning** signal in algorithmic entropy.
+*   The distinct topological signatures of **Systemic** vs. **Exogenous** crashes.
+
+## 3. Dataset Structure
+The data is stored in highly compressed **Parquet** format, optimized for scientific computing and cloud-based ingestion.
+
+### Files
+*   **`X_train.parquet`**: The primary feature set containing thousands of continuous financial time series.
+*   **`y_train.parquet`**: The ground-truth labels indicating the precise timestamp of structural breaks.
+*   **`X/Y_test.parquet`**: Out-of-sample series (derived from the Falcon forecasting challenge) used for generalization testing.
+
+### Schema
+**Features (`X_train.parquet`)**:
+*   `id` (string): Unique identifier for the time series.
+*   `period` (int): Sequential time step.
+*   `value` (float): The continuous signal (price/return).
+
+**Labels (`y_train.parquet`)**:
+*   `id` (string): Unique identifier.
+*   `structural_breakpoint` (int): The time step where the regime shift formally occurs.
+*   `label` (int): Class identifier (0 = No Break, 1 = Break).
+
+## 4. Provenance
+*   **Source:** Derived from the **CrunchDAO** research competitions (Structural Break & Falcon).
+*   **Preprocessing:** Data has been anonymized, standardized, and formatted for both statistical analysis (rolling variance) and algorithmic encoding (quantile binning).
+
+## 5. Universal Loading (Python)
+This dataset is designed to be ingested directly from the cloud, removing dependencies on local storage or Google Drive.
+
+```python
+from huggingface_hub import hf_hub_download
+import pandas as pd
+
+def load_benchmark_data(filename):
+    """
+    Fetches data from the Algoplexity Benchmark Repository.
+    Uses local caching for offline capability.
+    """
+    repo_id = "algoplexity/computational-phase-transitions-data"
+    print(f"--- Fetching {filename} from Scientific Repository ---")
+    
+    local_path = hf_hub_download(
+        repo_id=repo_id, 
+        filename=filename, 
+        repo_type="dataset"
+    )
+    return pd.read_parquet(local_path)
+
+# Usage
+df_features = load_benchmark_data("X_train.parquet")
+df_labels = load_benchmark_data("y_train.parquet")
+```
+
+## 6. Citation
+If you use this data in your research, please cite the associated Algoplexity repositories:
+
+```bibtex
+@misc{ait_physicist_2025,
+  author = {Mak, Yeu Wen},
+  title = {The Computational Phase Transition: Quantifying the Algorithmic Information Dynamics of Financial Crises},
+  year = {2025},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/algoplexity/computational-phase-transitions}}
+}
+
+@misc{coherence_meter_2025,
+  author = {Mak, Yeu Wen},
+  title = {The Coherence Meter: A Hybrid AIT-MDL Framework for Early-Warning Structural Break Detection},
+  year = {2025},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/algoplexity/Coherence-Meter}}
+}
+```