First-to-Last Day of Month on XLF
Financial Select Sector SPDR Fund — S&P 500 banks, insurers and asset managers. Backtest 2021-01-04 → 2026-07-17, $10,000 starting capital, computed by the same engine that runs live DeployQuant strategies.
Year by year
| Year | monthly cycle | buy & hold |
|---|---|---|
| 2021 | 33.9% | 35.8% |
| 2022 | −10.9% | −10.4% |
| 2023 | 8.9% | 11.9% |
| 2024 | 30.5% | 29.9% |
| 2025 | 14.5% | 15.5% |
| 2026 | 1.4% | 2.8% |
The rules
Buy the first session of each month, sell the last — a pure calendar-seasonality test.
- WHEN the first session of the month opens · IF not invested · THEN buy with 98% of the sleeve (once per month)
- WHEN the last session of the month opens · IF invested · THEN sell the whole position
No indicators at all: this template is a clean experiment on the turn-of-the-month effect, holding only from each month's first open to its last. Whatever it earns or loses is attributable to calendar seasonality alone, which makes it one of the most instructive templates to compare against buy-and-hold on the same page.
Good for: understanding how much of an asset's return accrues inside the month versus across month boundaries.
Watch out: this is a research template more than an edge: it holds ~95% of all sessions, so results usually shadow buy-and-hold minus the boundary days.
Build it from blocks (or type it in English), backtest it on 5.5 years of minute data in seconds, tweak any parameter, then paper trade it on live data. No card, no broker needed to start.
Frequently asked questions
Did monthly cycle beat buy-and-hold on XLF?
Over 2021-01-04–2026-07-17, monthly cycle on XLF returned 12.7% annualized vs 14.1% for buy-and-hold — it trailed buy-and-hold by 1.4% per year, with a maximum drawdown 2.1 points shallower than holding (23.4% vs 25.5%).
How many trades did it make?
67 completed round trips over 5.5 years (134 fills), with 58% of round trips closing profitably.
Is the turn-of-the-month effect real?
It has appeared in long historical studies, but it's regime-dependent and small. These pages let you check the recent five and a half years per ETF instead of trusting a decades-old average.
Related
Backtests are hypothetical, computed by DeployQuant's engine on minute-resolution consolidated US market data (2021-01-04 to 2026-07-17, $10,000 starting capital, no margin, fees and slippage not modeled) and do not guarantee future results. Nothing on this page is investment advice. Live trading involves risk of loss.