BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF (XHLF)
Seasonality Analysis
BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF Annual Seasonality Statistics
BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF Monthly Seasonality Performance
| Month | Avg Return | Win Rate | Strength |
|---|---|---|---|
| January | 0.30% | Moderate | |
| February | 0.27% | Moderate | |
| March | 0.36% | Moderate | |
| April | 0.27% | Moderate | |
| May | 0.32% | Moderate | |
| June | 0.35% | Moderate | |
| July | 0.37% | Moderate | |
| August BEST | 0.44% | Moderate | |
| September | 0.34% | Moderate | |
| October | 0.25% | Moderate | |
| November | 0.35% | Moderate | |
| December WORST | 0.03% | Moderate |
BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF 2026 vs Historical Pattern
BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF Interactive Seasonality Chart
BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF Pattern Scanner
BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF Seasonal Historical Performance
About BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF (XHLF) Seasonality
BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF (XHLF) has been analyzed using 4 years of historical data to identify seasonal patterns. Classified under ETFs, BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF shows distinct seasonal tendencies based on historical data.
The strongest month for BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF is historically August, with an average return of 0.44% and a win rate of 100%. Conversely, December tends to be the weakest month, averaging 0.03% return.
Looking at the full calendar year, BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF has an average annual return of 3.65% with an overall monthly win rate of 100.0%. Out of 12 months, 12 typically show positive average returns.
The seasonal pattern for BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF has a consistency score of 84.7 (Excellent), based on 5 years of data. Higher consistency means the seasonal pattern has been more reliable across different market conditions.
BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF Seasonality FAQ
What is the best month to buy BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF (XHLF)?
Historically, August has been the best month for BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF, with an average return of 0.44% and a win rate of 100%. However, past performance does not guarantee future results.
What is the worst month for BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF (XHLF)?
Based on historical data, December has been the weakest month for BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF, with an average return of 0.03%. This is a historical observation and does not guarantee future results.
How reliable is XHLF seasonality data?
The seasonality analysis for BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF is based on 4 years of historical price data. While seasonal patterns can provide useful insights, they should be combined with other forms of analysis. Past patterns do not guarantee future performance.
How can I use BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF seasonality in my trading?
Use BondBloxx ETF Trust BondBloxx Bloomberg Six Month Target Duration US Treasury ETF (XHLF) seasonality as one factor in your analysis. Identify historically strong and weak months, combine with other research methods. SeasOptima provides premium tools including interactive charts, pattern scanning, and historical performance data for deeper analysis.