XLE PBands Backtest: Profitable, But Risk-Adjusted Evidence Is Weak

Executive Summary

Energy ETFs trend and gap more aggressively, which makes them a useful stress test for <span class="lf0-tooltip" tabindex="0" style="border-bottom: 1px dotted currentColor; cursor: help;" title="A tendency for prices that moved unusually far in one direction to snap back toward a more normal level." data-tooltip="A tendency for prices that moved unusually far in one direction to snap back toward a more normal level." aria-label="mean reversion: A tendency for prices that moved unusually far in one direction to snap back toward a more normal level.">mean reversion</span>. This run covers 2016-01-01 to 2025-01-01 using quantstrat in R, with initial equity of $100,000. I compare it with buy-and-hold, apply base friction, and run a first sequence stress test.

Research Card

The Setup

I’m testing a simple but non-obvious version of mean reversion in XLE: when energy names get pushed below a short-term lower bound, do they tend to snap back rather than keep trending lower? The idea is that sector ETF flows, commodity-linked overreaction, and periodic macro shocks can create temporary dislocations that price eventually digests.

Results

I do not think the backtest supports the original mean-reversion hypothesis in a strong way. Final equity was 101,469.67 from 100,000, for net P&L of 1,469.67 and CAGR of 0.162%. That is economically small, and the statistical limit matters here: the closed-trade bootstrap puts the 95% CI for mean P&L at -1.03 to 61.08, the Wilcoxon p-value is 0.2548, and the win-rate binomial test is 60.8% with p…

Statistical Limits And Regime Dependence

The key question is whether the evidence survives sampling uncertainty, clustered trades, and regime shifts. P-values are diagnostics, not trading signals.

Regime dependency: this setup can degrade when volatility expands, gaps dominate, or the sector enters a persistent trend. The forward-degradation rows test stability, not parameter-optimization skill.

Costs And Stress Test

I now run a simple friction pass before drafting the article. For this strategy test, the base case assumes 2.00 bps of <span class="lf0-tooltip" tabindex="0" style="border-bottom: 1px dotted currentColor; cursor: help;" title="The difference between the expected execution price and the actual fill. It usually gets worse in fast or illiquid markets." data-tooltip="The difference between the expected execution price and the actual fill. It usually gets worse in fast or illiquid markets." aria-label="slippage: The difference between the expected execution price and the actual fill. It usually gets worse in fast or illiquid markets.">slippage</span> per side and 1.00 bps of spread cost per side, or 6.00 bps per round trip. That is still a model, but it is a better starting point than pretending fills are free.

Watch the break-even round-trip cost: above that level, the historical edge is gone. Monte Carlo separates edge from path luck; the in/out row is a closed-trade split, not walk-forward optimization.

What Breaks It

The trade P&L distribution is right-skewed: there are a lot of small-to-moderate gains, a meaningful cluster of losses below zero, and a few larger winners that pull the average up. That matters because the edge is not coming from a clean, symmetric payoff; it depends on occasional outsized snapbacks to offset many mediocre trades.

Verdict

I set out to test whether a simple lower-band PBands mean-reversion rule in XLE could capture temporary overshoots and turn them into a repeatable long-only edge.

What would change my mind: the same rule needs to survive costs while closing the gap versus buy-and-hold, or prove useful as a timing overlay rather than a standalone strategy.

R Notes

The reproducibility contract is the saved artifact set plus the R scripts that created it. The key JSON/RDS files are recorded in the frontmatter.

# Rebuild the evidence layer for one saved strategy output
source('backtesting_blog_posts/statistical_diagnostics.R')
source('backtesting_blog_posts/walk_forward_diagnostics.R')
source('backtesting_blog_posts/analysis_test_selector.R')

out_dir <- file.path('backtesting_blog_posts', 'outputs', 'strat_pbands_mean_reversion_long_only_xle')
per_trade <- readRDS(file.path(out_dir, 'per_trade_stats.rds'))
portfolio <- readRDS(file.path(out_dir, 'portfolio_summary.rds'))

diagnostics <- build_statistical_diagnostics(per_trade, portfolio)
walk_forward <- build_walk_forward_summary(per_trade)
test_plan <- build_analysis_test_plan(out_dir)

For the current PBands series, parameter_sensitivity uses a deterministic next-bar rerun helper because native quantstrat parameter clones can fail to propagate osMaxPos limits safely. Future strategy families should call register_parameter_sensitivity_builder() with their own sensitivity builder.

Important: Backtests are simulations. Past performance of a simulated strategy does not guarantee future results. Results do not yet fully account for real-world factors including <span class="lf0-tooltip" tabindex="0" style="border-bottom: 1px dotted currentColor; cursor: help;" title="The gap between the price buyers bid and sellers ask. Crossing that gap is a real trading cost." data-tooltip="The gap between the price buyers bid and sellers ask. Crossing that gap is a real trading cost." aria-label="bid-ask spread: The gap between the price buyers bid and sellers ask. Crossing that gap is a real trading cost.">bid-ask spread</span>, market impact, borrow costs, or taxes. This is for educational purposes only and is not investment advice.