| name | fund-risk-compare |
| description | Compare multiple ETFs using NAV CSV data, generating key risk-return metrics like annualized return, max drawdown, Sharpe ratio, and a correlation matrix. Triggered when users ask to compare ETFs or funds, calculate performance metrics, analyze NAV data, or mention terms like Sharpe ratio, correlation analysis, or max drawdown. |
| license | MIT |
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-free parameter to match current market conditions
- The correlation matrix requires at least 2 ETFs to generate
