XLE — XLE | S&P 500 Energy Sector ETF 13F holdings and portfolio analysis

ReadyXLE | S&P 500 Energy Sector ETF · Michael Riley

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Baseline

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Baselinetested
Strategy Q&A reference3 reference Q&A

Pre-generated Q&A about this fund. Use as reference context for your own analysis.

baseline13FChat AI
What does directly following the disclosed baseline portfolio expose an investor to?

Directly following the baseline means accepting a top-heavy energy portfolio with weak benchmark-relative quality. strategyViews.baseline.topHoldings shows XOM at 23.72%, CVX at 17.61%, and COP at 7.07%, while strategyViews.baseline.concentration puts top5 at 57.07%, top10 at 75.72%, and top20 at 98.21. strategyViews.baseline.sectorWeights is 100.02% Energy, so there is essentially no sector diversification. The backtest summary reinforces the risk: annualizedReturn 5.49%, alpha -4.63, sharpe 0.33, and maxDrawdown -68.39. An investor is therefore exposed mainly to oil-price, commodity-cycle, and mega-cap energy concentration risk rather than a balanced equity portfolio.

Show me how baseline concentration would change if I capped any single name at 15%.
baseline13FChat AI
Which recent baseline periods best explain the risk-return trade-off?

The recent baseline periods show that returns come with unstable, episodic payoffs rather than steady compounding. strategyViews.baseline.periodPerformance highlights strong relative months like 2025-02-28, when optimizedReturn was 2.19% versus benchmarkReturn -6.48% for +8.68% excess, and 2025-01-31, with +1.51% versus -2.79% for +4.29% excess. But that upside was offset by weak stretches such as 2025-03-31, when optimizedReturn was -11.59% versus -0.91% for -10.68% excess, and 2025-09-30, when optimizedReturn was -2.12% versus +2.05% for -4.17% excess. The turnover burden also spiked in some difficult periods, such as 3.44 on 2025-03-31 and 3.64 on 2025-07-31, which helps explain why the long-run Sharpe stayed low at 0.33 despite some good rebound months.

Rank the last 12 baseline periods by excess return and turnover so I can see the cleanest wins and worst whipsaws.
baseline13FChat AI
What should a user inspect next before deciding whether the baseline is acceptable?

A user should inspect three things next: concentration, drawdown path, and turnover stress. concentration.top5 is 57.09% and concentration.top10 is 75.74%, so first confirm whether that level of name concentration is tolerable. Second, inspect the drawdown path in strategyViews.baseline.drawdownSeries and navSeries, because the baseline reached -68.39% max drawdown and needed 495 recoveryDays. Third, review strategyViews.baseline.turnoverSeries and periodPerformance: turnover hit 3.64 in 2025-07-31 and 3.44 in 2025-03-31, while the baseline also logged -10.68% excess return in 2025-03-31. If those implementation and downside characteristics are unacceptable, the baseline probably is too cyclical even before considering valuation or fundamentals.

Summarize the worst baseline drawdown windows and the turnover levels that occurred around them.
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