Solarpunk Bitcoin: Energy-Backed Cryptocurrency Research & Development

Academic research on renewable energy as a fundamental anchor for cryptocurrency value, with practical derivatives pricing framework for energy-backed assets.

📚 Research Papers

• CEIR-Trifecta.md – Core empirical work: "When Does Energy Cost Anchor Cryptocurrency Value?" Triple natural experiment design (China mining ban 2021, Ethereum merge 2022, Russia sanctions 2025)
• Quasi-SD-CEIR.md – Framework extension: Supply-demand dynamics with sentiment analysis and hidden Markov regimes
• Final-Iteration.md – SolarPunkCoin concept: Renewable-energy-backed stablecoin addressing 10 cryptocurrency failure modes
• Empirical-Milestone.md – Spring 2025 research proposal for Yuan Ze University

🔧 Energy Derivatives Framework

Production-ready Python package for pricing European-style options on renewable energy-backed assets.

Quick start:

cd energy_derivatives
pip install -r requirements.txt
jupyter notebook notebooks/main.ipynb

Core modules:

• binomial.py – Binomial tree pricing with convergence analysis
• monte_carlo.py – Monte Carlo simulation with confidence intervals
• sensitivities.py – Greeks computation (delta, gamma, vega, theta, rho)
• plots.py – Publication-quality visualizations
• data_loader.py – Energy data calibration

Details: ~2,300 lines of production code, full documentation, Jupyter notebook with 10-section walkthrough.

�� Empirical Data & Analysis

empirical/ contains CEIR computation pipeline:

• Bitcoin/Ethereum energy consumption (TWh/year from Digiconomist)
• Mining distribution (geographic concentration)
• Electricity prices (regional, time-varying)
• Macro controls (S&P 500, VIX, gold)
• Analysis scripts (gecko.py, CEIR.py, Regression.py)

📖 Project Structure

solarpunk-coin/
├── README.md                     # This file
├── CEIR-Trifecta.md              # Main research paper
├── Quasi-SD-CEIR.md              # Supply-demand extension
├── Final-Iteration.md            # SolarPunkCoin vision
├── Empirical-Milestone.md        # Research roadmap
│
├── energy_derivatives/           # Derivatives pricing package
│   ├── src/                      # Core modules
│   │   ├── binomial.py
│   │   ├── monte_carlo.py
│   │   ├── sensitivities.py
│   │   ├── plots.py
│   │   └── data_loader.py
│   ├── notebooks/
│   │   └── main.ipynb            # Full demonstration
│   └── requirements.txt
│
├── empirical/                    # CEIR data & scripts
│   ├── gecko.py                  # Data collection
│   ├── CEIR.py                   # CEIR calculations
│   ├── Regression.py             # Analysis
│   └── data/                     # CSV files
│
└── examples/
    └── presentation_colab.ipynb  # Solar energy demo

🎯 Key Features

✅ Rigorous Theory: Risk-neutral valuation, geometric Brownian motion, arbitrage-free pricing
✅ Two Methods: Binomial tree (exact) + Monte Carlo (distribution analysis)
✅ Complete Greeks: All 5 sensitivities via finite differences
✅ Real Data: Calibrated to Bitcoin CEIR (2018–2025)
✅ Multi-Location: Taiwan, Arizona, Spain solar data comparison
✅ Production Code: Type hints, comprehensive docstrings, error handling

🚀 Usage

Python API:

from energy_derivatives.binomial import BinomialTree
from energy_derivatives.data_loader import load_parameters

params = load_parameters(data_dir='empirical')
price = BinomialTree(**params, N=400).price()

Jupyter Notebook:

cd energy_derivatives
jupyter notebook notebooks/main.ipynb

See notebooks/main.ipynb for complete 10-section demo with explanations.

📝 Author

Spectating101 (s1133958@mail.yzu.edu.tw)
Yuan Ze University

📄 License

MIT

Status: Research papers completed (peer review in progress). Derivatives framework complete and submission-ready.