S&P 500
The S&P 500 index tracks 500 of the largest U.S. publicly traded companies, weighted by market capitalization. It is widely regarded as the best single gauge of the U.S. equity market.
The near-term seasonal setup for the S&P 500 flashes a clear bullish signal. The last 10 years show a 70% win rate over the next 30 days, projecting a move to 7,697 for a 3.93% gain. The 90-day window is even more striking, targeting 8,614 with a 16.31% projected return at a 60% win rate.
The midterm election year lens tells a more cautious story. Historically, midterm years produce negative 30-day and 60-day returns, with win rates dropping to 67% and 50% respectively. The divergence between the two frameworks is the key tension to watch. Bulls can point to recent momentum; bears can point to midterm headwinds. The 90-day window is where both frameworks finally agree on upside.
Seasonal Price Projections
Select a historical basis and projection horizon to see where seasonal patterns suggest S&P 500 may be headed.
Projection as of Jul 27, 2026 from closing price $7,411.98
How to Use This Data
Seasonal projection data reflects how the S&P 500 has historically performed during this specific calendar window. The consecutive pattern shows a 60.0% win rate, meaning the index closed higher in 60 out of every 100 comparable historical periods. The midterm election year pattern shows a lower 50.0% win rate, indicating a more mixed historical record during election cycles.
When the two bases point in opposite directions, it signals that the election cycle context may produce meaningfully different conditions than recent consecutive history alone would suggest. The median return is often more informative than the average in these cases, as extreme outlier years can pull the average away from what most historical periods actually looked like.
Seasonal patterns cannot account for breaking news, policy shifts, earnings surprises, or geopolitical developments that may unfold during the period. A 60.0% win rate still means the index declined in 40% of comparable years, and no statistical tendency guarantees an outcome in any individual year. The projection describes historical behavior, not a prediction.
Market participants often use seasonal data as one layer of context alongside fundamental analysis, technical indicators, and broader macroeconomic conditions. Understanding historical tendencies can help frame expectations and inform how other signals are interpreted, without substituting for a complete analytical picture.
This information is provided for educational purposes only and does not constitute financial advice, a recommendation, or a solicitation to buy or sell any security. Seasonal patterns are based on historical data and do not guarantee future performance. All investment decisions carry risk. Consult a qualified financial advisor before making investment decisions.
Understanding Seasonal Projections
Seasonal projections estimate future price movement based on how S&P 500 has historically performed during the same calendar period. These are statistical baselines derived from decades of market data, not predictions.
Consecutive Years (Last 10)
Uses the most recent 10 years of data regardless of market regime. This captures the broadest recent behavior, including all economic and political environments. Over the next 60 calendar days, this pattern has been positive 6 of 10 times with an average return of -0.1%.
Midterm Election Years (24 Available)
Uses only years that fall in the same position within the 4-year U.S. presidential election cycle. 2026 is a midterm election year. Markets often exhibit distinct patterns tied to fiscal and monetary policy shifts within this cycle. In 24 historical midterm election years, this 60-day window was positive 12 times with an average return of -1.3%.
Seasonal patterns reflect historical tendencies and do not guarantee future results. All projections are based on past performance and should be used as one input among many in your investment decision-making process. Data provided by TradeWave.ai.
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