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

Executive Summary

Can a simple lower-band mean-reversion rule find useful dip-buying opportunities in defensive sector ETFs? 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 <span class="lf0-tooltip" tabindex="0" style="border-bottom: 1px dotted currentColor; cursor: help;" title="A deliberate check that makes assumptions harsher to see whether the result survives outside ideal conditions." data-tooltip="A deliberate check that makes assumptions harsher to see whether the result survives outside ideal conditions." aria-label="stress test: A deliberate check that makes assumptions harsher to see whether the result survives outside ideal conditions.">stress test</span>.

Research Card

The Setup

The anomaly I’m testing is simple but not obvious: when XLP gets pushed far enough below its recent trading range, does it tend to snap back before the selloff becomes something more durable? In plain English, this is a mean reversion bet on a defensive sector that often attracts capital when investors are nervous and then overshoots on the downside when the panic eases.

Results

I do not think the backtest supports the original mean-reversion hypothesis in a strong way. The strategy ended at $101,130.72 on $100,000 start capital, for net P&L of $1,130.72 and CAGR of 0.125%, which is economically tiny over nine years.

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 per-trade P&L distribution is tilted in the right direction, with 30 winners and 15 losers, but it is not a clean, symmetric edge. Most trades are small-to-moderate gains, while the losses are fewer but meaningfully larger: average win was $116.09 versus average loss of -$164.79, and the worst trade lost -$337.34.

Verdict

I set out to test whether an oversold XLP break below a PBands lower envelope reliably captures a short-lived mean-reversion bounce before the move turns into something more durable.

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_xlp')
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.