Backtest: Buy XOP at the close when XOP closes in the bottom quintile of its 20-day hig...
A 63% win rate usually reads like a working strategy. This one lost money anyway: 38 trades, a -3.10% return on $100,000, and a finish 88 points behind simply holding SPY. The setup — buying XOP when it sits in the bottom quintile of its 20-day range while Brent trades below its 20-day average — is a bet that a crude-led flush in high-beta E&P is forced selling that snaps back.
The snap-back happens often enough to win most trades. It just doesn't happen big enough. The best trade made 4.39%; the worst gave back 7.21%, and that asymmetry is where the edge goes to die.
The full trade-by-trade evidence, equity curve, and drawdown detail are laid out in the analysis below.
Buy XOP at the close when XOP closes in the bottom quintile of its 20-day high-low range while Brent crude closes below its 20-day simple moving average; exit when XOP closes above its 5-day simple moving average or after 5 trading days, whichever comes first. A high-beta E&P flush with crude below trend is forced selling, and the range low tends to snap back once momentum exhausts.
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 XOP at the close when XOP closes in the bottom quintile of its 20-day high-low range while Brent crude closes below its 20-day simple moving average; exit when XOP closes above its 5-day simple moving average or after 5 trading days, whichever comes first. A high-beta E&P flush with crude below trend is forced selling, and the range low tends to snap back once momentum exhausts.
The key numbers
The charts
The takeaway
The strategy returned -3.10% on $100,000 starting capital across 38 closed trades with a 63% win rate. Over the same window SPY buy-and-hold returned +85.28%, so the strategy finished trailing the benchmark by 88.37 points. Best single trade +4.39%, worst -7.21%.
The fine print
- Simulated results on historical data — fills, slippage and costs are idealized.
- Past performance does not predict future results.