serejaris Fund Risk Compare — Multi-Dimensional ETF Comparison Tool
Performs multi-dimensional risk-return analysis on multiple ETFs based on user-provided NAV (Net Asset Value) data. Automatically calculates annualized returns, max drawdown, Sharpe ratio, and generates a correlation matrix.
Quick Start
Basic Comparison
python scripts/etf_screener.py --input nav_data.csv
Custom Risk-Free Rate + CSV Export
python scripts/etf_screener.py --input nav_data.csv --risk-free 0.03 --output report.csv
JSON Output (for programmatic processing)
python scripts/etf_screener.py --input nav_data.csv --json
Input Data Format
CSV file with dates in the first column and NAV values for each ETF in subsequent columns:
date,SP500_ETF,NASDAQ_ETF,BOND_ETF
2023-01-03,1.0000,1.0000,1.0000
2023-01-04,1.0050,0.9980,1.0020
2023-01-05,1.0120,1.0010,1.0080
...
- The date column name and format are flexible (used only to label the time range)
- ETF column names are used as labels in the comparison report
- Missing values can be left blank or marked as
NaN— they are automatically skipped
Calculation Details
Annualized Return
Computed from the first and last NAV values, then annualized by the number of trading days:
Ann. Return = (NAV_end / NAV_start) ^ (trading_days / n_days) - 1
Max Drawdown
The largest peak-to-trough decline in the NAV series:
MDD = max( (peak - trough) / peak )
Sharpe Ratio
A risk-adjusted return metric:
Sharpe = (Annualized Return - Risk-Free Rate) / Annualized Volatility
Annualized volatility is derived from the standard deviation of daily returns multiplied by √(trading_days).
Correlation Matrix
Pearson correlation coefficients computed from daily returns, measuring the co-movement between ETFs. A coefficient near 1 indicates strong positive correlation, near 0 indicates no correlation, and near -1 indicates negative correlation.
Parameters
| Parameter | Required | Default | Description |
|---|---|---|---|
--input / -i |
Yes | - | Path to the NAV CSV file |
--risk-free / -rf |
No | 0.02 | Annual risk-free rate (e.g., 0.03 for 3%) |
--trading-days |
No | 252 | Trading days per year (typically 252 for US/China markets) |
--output / -o |
No | - | Output file path (.csv or .json) |
--json |
No | false | Output results as JSON to stdout |
Use Cases
- Compare risk-return profiles across multiple ETFs to support asset allocation decisions
- Analyze correlations between ETFs to build diversified, low-correlation portfolios
- Evaluate fund manager performance (higher Sharpe ratio = better risk-adjusted returns)
- Backtest the performance of different assets over a specific time period
Notes
- This tool uses only Python standard libraries — no additional dependencies required
- NAV data should span a sufficient time range (at least 60 trading days recommended) for meaningful statistical metrics
- The Sharpe ratio is sensitive to the risk-free rate assumption — adjust the
--risk-freeparameter to match current market conditions - The correlation matrix requires at least 2 ETFs to generate