Dataset for Measuring Cryptocurrency Maturity: A Network-Centric Framework Using Bitcoin and the Decker Comparative Maturity Equation (DCME)
Description
This dataset supports the research Measuring Cryptocurrency Maturity: A Network-Centric Framework Using Bitcoin and the Decker Comparative Maturity Equation (DCME)" by Nicolin Decker (2025). It provides real-time and historical data for analyzing cryptocurrency maturity, institutional liquidity, and systemic risk using the DCME framework. The dataset supports financial stability research, regulatory oversight, and institutional risk modeling, validated through Monte Carlo simulations, EGARCH/TGARCH volatility modeling, and institutional ownership analytics. Backtesting confirms DCME’s predictive accuracy in identifying liquidity risks preceding major market disruptions, including ETF-driven volatility spikes. Contents: - Supplementary Documentation. - DCME Collection & API Integration: API-based access to Glassnode, CoinGecko, Chainalysis, SEC Filings, and Bloomberg. - DCME Backtesting System: Python-based software for historical validation and institutional risk modeling using Monte Carlo simulations. - DCME Calculation Software: Python-based implementation for EGARCH/TGARCH volatility modeling and liquidity risk assessments. - Web-Based DCME Calculator: A tool for computing DCME maturity scores with API-driven data. Applications: - Market Maturity Analysis: Bitcoin, Ethereum, and traditional assets. - ETF Risk Assessment: Institutional ETF impact on volatility and liquidity. - Stress Testing: Simulating institutional exits and liquidity crises. - Regulatory Policy Development: Supporting Basel III, SEC, and IMF risk analysis. Primary Data Sources (API-Based Access): - Cryptocurrency Data: Glassnode, CoinGecko, Chainalysis, Binance - Institutional & ETF Data: SEC Filings (13F Reports), BlackRock, Grayscale, Fidelity - Macroeconomic Indicators: Federal Reserve (FRED), IMF, World Bank - Traditional Asset Benchmarks: S&P 500, Gold, Bonds, Treasury Yields (Bloomberg API) Citation & License: Users must cite the original thesis and developer: Decker, Nicolin (2025). Measuring Cryptocurrency Maturity: A Network-Centric Framework Using Bitcoin and the Decker Comparative Maturity Equation (DCME). Mendeley Data. Derivative works must embed: Decker Comparative Maturity Equation (DCME) Deployment System. Developed by Nicolin Decker. Based on the thesis: "Measuring Cryptocurrency Maturity," 2025. Licensed under MIT. Software Versioning & Terms: This dataset is linked to DCME Deployment System v1.0. Future updates will expand cryptocurrency adoption metrics and volatility-adjusted maturity models. The dataset relies on API-based data retrieval, requiring users to configure API keys and data requests. The DCME Backtesting System (Python-based) enables historical validation and stress testing, allowing researchers to test financial stability using Monte Carlo simulations and maturity scoring.
Files
Steps to reproduce
Steps to Reproduce the Decker Comparative Maturity Equation (DCME) Using the Software and APIs. 1. Set Up Environment - Install dependencies: Python (NumPy, Pandas, SciPy, Statsmodels), API clients (Requests, Web3), and financial modeling tools (TensorFlow, Scikit-learn). - Clone the DCME Deployment System v1.0 from the repository. 2. Configure API Access - Register API keys for Glassnode, CoinGecko, Chainalysis, Binance, Bloomberg, SEC, and FRED. - Store API keys in config.json and verify connectivity. 3. Collect Data - Fetch on-chain metrics (active addresses, transaction volume, network fees) from Glassnode API. - Retrieve institutional ownership data (ETF holdings, 13F filings) from SEC API. - Obtain historical price, liquidity depth, and volatility data from CoinGecko and Binance APIs. 4. Preprocess Data - Normalize transaction volumes, network growth, and market depth metrics. - Calculate volatility-adjusted asset returns using EGARCH/TGARCH models. - Apply macroeconomic scaling factors from FRED/IMF API data. 5. Compute DCME Components - Comparative Maturity Score (CM): CM = (ln(N_avg * A) * (1 - I_adj)) / V_nl Where N_avg is active network users, A is the transaction volume factor, I_adj is institutional influence, and V_nl is the nonlinear volatility adjustment. - Institutional Adjustment Factor (I_adj): I_adj = 1 - (IO * beta_ETF) / (1 + e^(-alpha * (theta - IO))) Where IO is institutional ownership, beta_ETF is the ETF impact coefficient. - Comparative Maturity Ratio (CMR): CMR = ((CM_crypto / CM_fiat) - 1) * 100% Comparing cryptocurrency maturity to fiat assets using GDP, M2, and inflation metrics. Example Calculation: If Bitcoin's active network users (N_avg) = 800,000 and transaction volume scaling factor (A) = 1.2, then: CM = (ln(800,000 * 1.2) * (1 - I_adj)) / V_nl For an I_adj of 0.35 and a volatility adjustment factor V_nl of 2.5, the CM would be computed as: CM = (ln(960,000) * (1 - 0.35)) / 2.5 CM = (13.77 * 0.65) / 2.5 CM = 3.58 This value represents the relative maturity of Bitcoin compared to traditional asset benchmarks, adjusting for network activity, institutional influence, and volatility. 6. Run Backtesting & Model Validation - Use DCME Backtesting System (Python-based) to validate scores against past crises (Lehman Brothers 2008, FTX 2022). - Perform Monte Carlo simulations to stress-test liquidity risks. 7. Interpret Results & Visualization - Generate volatility-adjusted maturity curves using Matplotlib. - Plot institutional influence vs. liquidity risks for ETF market impact. 8. Deploy Web-Based DCME Calculator - Host the DCME web app for real-time maturity score analysis. - Integrate API connections for automatic updates.