SPY Seasonality

SPY Seasonality

Analysis for February 2026

This page shows SPY seasonality for February 2026, including a calendar view of historical seasonal windows and monthly patterns. Seasonality is a historical tendency – not a prediction – and should be combined with risk controls and your own filters.

SPY Top Trades for February 2026

SPY seasonality calendar for February 2026

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Best short-term trade for SPY this month

“Entering SPY on Feb 13 and closing on Feb 15 has won 11 years in a row for an average return of 0.93%.”

* We define a short-term trade as having a holding period of 3 days or less.

Best long-term swing trade for SPY this month

“Entering SPY on Feb 12 and closing on Feb 15 has won 13 years in a row for an average return of 1.34%.”

* We define a long-term swing as any trade with a holding period of 4 or more days.

Historically stronger days for SPY in February

Historical win percentage heatmap for SPY in February

SPY chances of closing the month higher

SPY probability of closing February higher

SPY average return by month

How traders use seasonality for SPY

As a timing lens, not a standalone signal

Seasonality highlights dates when SPY has historically shown consistent strength or weakness. Traders often combine it with a simple trend or volatility filter to avoid fighting the tape.

To plan entries/exits in advance

Instead of reacting to headlines, traders use known seasonal windows to pre-plan risk, size smaller when conditions are noisy, and focus attention only when a window is active.

To build a watchlist efficiently

Seasonality can narrow “what to watch” to a short list for February, then technicals/fundamentals decide “whether to act.”

For ETFs like SPY, traders often use seasonality alongside broad market regime filters (risk-on/risk-off).

Risk notes (please read)

  • Seasonality is descriptive, not predictive. These are historical tendencies based on past price behavior – there is no guarantee they repeat.
  • Outliers happen. Even strong historical windows can fail due to news, macro shocks, earnings, or regime changes.
  • Backtests can mislead. Small sample sizes and changing market structure can inflate historical results.
  • Use risk controls. Consider position sizing, stop logic, and diversification.
  • Educational content only. This page is for informational purposes and is not financial advice.

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