Backtest: Buy SLB at the close when Brent crude closes above its 50-day simple moving a...
The pitch sounds reasonable: when crude is in a confirmed uptrend but an oil-services name like SLB hasn't caught up, buy the laggard and wait for the snap-back. The backtest says otherwise. The strategy returned -54.21% on $100,000, and it finished trailing SPY by 130.55 points.
That gap is not a rounding error. The setup sounds intuitive, but the edge does not appear to hold up in this execution. What looked like a laggard snap-back setup ended up giving back capital consistently. The full bar-by-bar results, trade-by-trade detail, and equity curve are below.
Buy SLB at the close when Brent crude closes above its 50-day simple moving average but SLB closes below its 20-day simple moving average; exit when SLB closes above its 20-day simple moving average or after 8 trading days, whichever comes first. Oil-services names lag confirmed crude uptrends because upstream spending takes longer to reprice, so this laggard snap-back tends to fire once the oil trend is firm.
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 SLB at the close when Brent crude closes above its 50-day simple moving average but SLB closes below its 20-day simple moving average; exit when SLB closes above its 20-day simple moving average or after 8 trading days, whichever comes first. Oil-services names lag confirmed crude uptrends because upstream spending takes longer to reprice, so this laggard snap-back tends to fire once the oil trend is firm.
The key numbers
The charts
The takeaway
The strategy returned -54.21% on $100,000 starting capital across 31 closed trades with a 26% win rate. Over the same window SPY buy-and-hold returned +76.34%, so the strategy finished trailing the benchmark by 130.55 points. Best single trade +4.66%, worst -12.74%.
The fine print
- Simulated results on historical data — fills, slippage and costs are idealized.
- Past performance does not predict future results.