Backtest: Buy XLE at the close when Brent crude is above its 100-day moving average and...
Twenty-eight trades, a positive P&L, and a 61-point shortfall against simply holding SPY. That is the scoreboard for a strategy built on a clean-sounding idea: buy XLE when it has dropped more than 2% over five days while Brent sits above its 100-day moving average, then exit in five days or on a 2% stop.
The mean-reversion logic is easy to respect — pullbacks inside an uptrend are often buyable. But a 46.4% win rate and a benchmark gap that wide raise a blunt question about whether the edge survives the drag of whipsaws and stop-outs.
The full analysis below walks through all 28 trades, the conditions that made the strategy work or fail, and where it bled performance — so you can judge the setup on evidence rather than narrative.
Buy XLE at the close when Brent crude is above its 100-day moving average and XLE's 5-day total return is below -2%; exit after 5 trading days or on a 2% stop-loss, whichever comes first. These are short-term energy-sector pullbacks inside a crude uptrend, and headline-driven dips tend to get bought back before the supply story reasserts.
How this was measured
This is a simulated backtest generated from the plain-English strategy below, executed bar-by-bar on historical market data using the price + news data mode with $100,000 starting capital. Strategy: Buy XLE at the close when Brent crude is above its 100-day moving average and XLE's 5-day total return is below -2%; exit after 5 trading days or on a 2% stop-loss, whichever comes first. These are short-term energy-sector pullbacks inside a crude uptrend, and headline-driven dips tend to get bought back before the supply story reasserts.
The key numbers
The charts
The takeaway
The strategy returned +7.29% on $100,000 starting capital across 28 closed trades with a 46% win rate. Over the same window SPY buy-and-hold returned +68.30%, so the strategy finished trailing the benchmark by 61.01 points. Best single trade +6.27%, worst -2.74%.
The fine print
- Simulated results on historical data — fills, slippage and costs are idealized.
- Past performance does not predict future results.