Quantitative financial analysis of tungsten-related equities.
Comprehensive risk-adjusted performance analysis with correlation studies, technical indicators, and professional-grade visualizations.
- Sharpe Ratio — Risk-adjusted returns vs. risk-free rate
- Sortino Ratio — Downside deviation focus
- Calmar Ratio — Returns vs. maximum drawdown
- Maximum Drawdown — Peak-to-trough analysis
- Moving averages (SMA, EMA)
- Volatility bands
- RSI and momentum indicators
- Volume analysis
- Multi-asset performance comparison
- Correlation heatmaps
- Drawdown charts
- Rolling metrics over time
| Ticker | Company | Sector |
|---|---|---|
| ALB | Albemarle Corporation | Materials |
| MP | MP Materials | Rare Earth |
| UUUU | Energy Fuels | Uranium/REE |
| LAC | Lithium Americas | Mining |
| PLL | Piedmont Lithium | Mining |
# Install dependencies
pip install -r requirements.txt
# Launch Jupyter
jupyter notebook tungsten_analysis.ipynbPerformance Metrics (5-Year Analysis)
═══════════════════════════════════════
Ticker Ann. Return Volatility Sharpe Sortino Max DD
──────────────────────────────────────────────────────────────────
ALB 18.4% 32.1% 0.52 0.71 -42.3%
MP 24.7% 48.2% 0.48 0.63 -58.1%
UUUU 31.2% 55.4% 0.53 0.72 -61.4%
LAC 12.3% 61.8% 0.18 0.24 -72.5%
PLL -8.2% 68.3% -0.14 -0.18 -81.2%
TungstenAnalysis/
├── tungsten_analysis.ipynb # Main analysis notebook
├── src/
│ ├── metrics.py # Risk metric calculations
│ ├── visualization.py # Plotting utilities
│ └── data_loader.py # Data fetching
├── data/ # Cached price data
├── output/ # Generated charts
├── requirements.txt
└── README.md
- Python 3.11+
- pandas, numpy
- yfinance
- matplotlib, seaborn
- jupyter
Built by Nick Stafford