金融分析技能 (Financial Analysis Skill)

To run a risk parity backtest without future data leakage, use expanding-window volatility estimation to derive inverse-volatility weights, applying previous-day weights to current daily returns for realistic evaluation.

Yes, you can backtest a multi-asset portfolio from a CSV file by ingesting market return data for assets like bonds and commodities to calculate dynamic risk parity weights and portfolio returns.

Expanding-window volatility estimation in portfolio analysis computes annualized volatility using all historical data up to the current point, ensuring inverse-volatility weights adapt dynamically without incorporating future data.

Yes, this portfolio backtesting approach works with monthly rebalancing by recalculating dynamic risk parity weights at monthly intervals across bond and commodity asset sets for long-horizon evaluation.

You can visualize rolling volatility and portfolio allocation changes by generating charts such as returns curves, correlation heatmaps, asset cumulative comparisons, and rolling weight changes from the backtest output.

A limitation of using inverse-volatility weights for risk parity is that it relies entirely on historical expanding-window volatility estimates, which may not capture sudden market regime shifts or asset correlation breakdowns.