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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