algoplexity/computational-phase-transitions-data

Financial Structural Breaks & Regime Detection Benchmark

Maintainer: Algoplexity
Primary Repositories:

1. The Coherence Meter: GitHub Repo (Horizon 0)
2. The Computational Phase Transition: GitHub Repo (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_test.parquet / '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.

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:

@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}}
}