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Risk Analytics & Research

TODO: Complete Guide for Advanced Data Analysis​

How to use EigenWatch data for research and custom analytics.

Required Sections​

  • Use Cases for Raw Data

    • Building custom risk models
    • Correlating operators with AVSs
    • Predictive slashing models
    • Market microstructure research
    • Operator clustering & classification

  • Data Export Options

    • Bulk data exports
    • CSV/Parquet formats
    • Time period selection
    • Sampling strategies

  • Time Series Analysis

    • Historical risk score queries
    • Trend analysis
    • Seasonality detection
    • Anomaly detection

  • Analytical Tools Integration

    • Pandas/Python integration
    • R language support
    • Jupyter notebooks
    • SQL queries (if applicable)

  • Data Schema & Definitions

    • Available fields
    • Data types & formats
    • Null value handling
    • Updates & revisions

  • Example Analysis

    import pandas as pd
    from eigenwatch import EigenWatchAPI

    client = EigenWatchAPI(api_key="...")

    # Analyze operator clustering by risk
    operators = client.query_operators(limit=1000)
    df = pd.DataFrame(operators)

    # Risk distribution
    print(df['risk_score'].describe())

    # Correlation with AVS count
    print(df[['risk_score', 'avs_count']].corr())

    # Historical analysis
    history = client.get_operator_history(operator_addr, days=180)
    hist_df = pd.DataFrame(history)
    hist_df['date'] = pd.to_datetime(hist_df['timestamp'])
    hist_df.plot(x='date', y='risk_score')

  • Statistical Methods

    • Slashing prediction (logistic regression, ML)
    • Operator clustering (K-means, hierarchical)
    • Risk factor analysis (PCA, factor models)
    • Correlation & causation analysis

  • Data Limitations & Caveats

    • Survivorship bias in operator data
    • Correlation vs causation in risk factors
    • Limits of historical extrapolation
    • External regime changes

  • Publication & Sharing

    • Publishing research using EigenWatch data
    • Attribution requirements
    • Peer review considerations
    • Community feedback

Use Cases​

  • Academic research on restaking ecosystem
  • Insurance company building predictive underwriting models
  • Hedge fund developing operator selection strategy
  • Protocol governance research

Status: NOT STARTED — Requires research team input & use case development