fund-risk-analyzer

You can calculate annualized return and max drawdown for multiple ETFs by processing NAV CSV data through a Python standard library script. The analysis computes annualized volatility and Sharpe ratio for each fund, handling missing data robustly to generate a comparative risk-return report.

Generating a correlation matrix for daily returns requires computing Pearson correlation coefficients across multiple ETF NAV time series. The script calculates daily returns from your NAV CSV data and produces a matrix assessing inter-asset relationships for portfolio risk assessment.

Yes, you can compute ETF risk-adjusted metrics like the Sharpe ratio using only Python's standard library. The analysis requires no external dependencies, using a configurable risk-free rate and trading days to calculate annualized volatility and returns.

The best way to compare fund performance using NAV data is to compute key risk-return metrics including annualized return, max drawdown, and Sharpe ratio. This approach generates human-readable reports or exports data to CSV and JSON for portfolio benchmarking.

Yes, this fund risk analysis approach works with missing data in NAV CSV files by applying robust handling of missing values. The script processes incomplete NAV time series without failing, ensuring reliable calculation of annualized volatility and correlation matrices.

You can configure the risk-free rate for ETF Sharpe ratio calculations as a script parameter. The analysis uses this configurable rate alongside a defined number of trading days to compute annualized returns and volatility for accurate risk-adjusted performance benchmarking.