Portfolio Performance Measurement › Rolling Performance Analysis
Michael BrenndoerferDecember 16, 20254 min read
Quantitative FinanceAll chapters Part 9 of 15Portfolio Theory and Investment Analysis
Portfolio Performance Measurement › Rolling Performance AnalysisOne section · Sections in this chapter7 / 11 ↓
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Static performance metrics over the full period can mask time-varying behavior. A manager might have excellent performance in one regime but poor performance in another. Rolling analysis captures this dynamic by computing metrics over moving windows.
In[29]:
Codedef rolling_sharpe(returns, risk_free_rate, window, periods_per_year=252):
"""Calculate rolling Sharpe ratio."""
rolling_mean = pd.Series(returns).rolling(window).mean()
rolling_std = pd.Series(returns).rolling(window).std()
rolling_sr = (
(rolling_mean - risk_free_rate)
/ rolling_std
* np.sqrt(periods_per_year)
)
return rolling_sr.values
def rolling_max_drawdown(returns, window):
"""Calculate rolling maximum drawdown."""
cumulative = (1 + pd.Series(returns)).cumprod()
rolling_max = cumulative.rolling(window, min_periods=1).max()
drawdown = (rolling_max - cumulative) / rolling_max
rolling_max_dd = drawdown.rolling(window).max()
return rolling_max_dd.valuesIn[30]:
Code# Calculate rolling metrics with 252-day (1 year) window
window = 252
df["rolling_sharpe_portfolio"] = rolling_sharpe(
df["portfolio"].values, daily_rf, window
)
df["rolling_sharpe_benchmark"] = rolling_sharpe(
df["benchmark"].values, daily_rf, window
)
df["rolling_maxdd_portfolio"] = rolling_max_drawdown(
df["portfolio"].values, window
)
df["rolling_maxdd_benchmark"] = rolling_max_drawdown(
df["benchmark"].values, window
)Out[31]:
Visualization
Rolling analysis reveals that performance metrics are not constants; they fluctuate with market conditions. A manager with an impressive full-period Sharpe ratio might have experienced extended periods of negative Sharpe, which rolling analysis would reveal.
Out[32]:
Visualization