Backtest: Buy XLE at the close when Brent crude's trailing 5-day return exceeds XLE's t...
Sixty trades, a 67% win rate, and still a wide gap to the S&P 500. That is the tension in this backtest of a simple energy catch-up trade: buy XLE when Brent's five-day return outpaces XLE's by at least three percentage points and crude sits above its 20-day moving average, then exit on XLE's close above its own five-day average or after five sessions.
The pattern did produce frequent wins — 60 closed trades, two-thirds profitable — but the strategy returned +8.71% on $100,000 while SPY buy-and-hold returned +85.28%, leaving it 76.57 points behind.
The full bar-by-bar evidence and trade details are in the analysis below.
Buy XLE at the close when Brent crude's trailing 5-day return exceeds XLE's trailing 5-day return by at least 3 percentage points and Brent closes above its 20-day simple moving average; exit when XLE closes above its 5-day simple moving average or after 5 trading days, whichever comes first. Energy equities tend to catch up after spot crude outruns the sector while the oil trend is intact.
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's trailing 5-day return exceeds XLE's trailing 5-day return by at least 3 percentage points and Brent closes above its 20-day simple moving average; exit when XLE closes above its 5-day simple moving average or after 5 trading days, whichever comes first. Energy equities tend to catch up after spot crude outruns the sector while the oil trend is intact.
The key numbers
The charts
The takeaway
The strategy returned +8.71% on $100,000 starting capital across 60 closed trades with a 67% win rate. Over the same window SPY buy-and-hold returned +85.28%, so the strategy finished trailing the benchmark by 76.57 points. Best single trade +3.41%, worst -7.95%.
The fine print
- Simulated results on historical data — fills, slippage and costs are idealized.
- Past performance does not predict future results.