Every stock carries a unique seasonal profile. Some rally in July, others strengthen in Q4, and some consistently weaken in September. These aren't random — they're driven by earnings cycles, institutional flows, and sector dynamics.
Seasonality360 lets you screen, chart, and backtest seasonal patterns for hundreds of individual stocks — from mega-caps to sector leaders.
20-Year Seasonality Profile
Watch the workflow before exploring the use case: discovery, validation, and execution planning in one clear process.
500+
US Stocks Covered
S&P 500 + major large caps
11
Sectors
All GICS sectors
20Y
Data Depth
Per stock, per window
100%
Backtest Ready
Any pattern, instant validation
Each stock has a unique seasonal fingerprint. Compare tech giants to see how their seasonal rhythms differ dramatically.
Seasonality Profile
Individual stock seasonality captures dynamics that index-level analysis misses entirely.
Stocks with predictable earnings calendars often show consistent pre-earnings rally or post-earnings drift patterns during the same weeks each year.
Energy stocks tend to rally in fall, retailers before holidays, tech in January. Sector-specific seasonality lets you ride these rotations with data.
Quarterly rebalancing, window dressing, and year-end tax positioning create recurring flows that push specific stocks at predictable times.
A stock's seasonal pattern can be very different from the broad market. AAPL's summer rally doesn't appear in SPY's aggregate seasonality.
Index patterns are well-known. Stock-level patterns are less crowded, potentially more actionable, and can offer higher win rates on specific names.
Every pattern you discover can be instantly backtested — equity curve, drawdown, trade list. No guessing, no faith-based trading.
Tax-loss harvesting resets, Santa Claus rally, and January inflows create the strongest seasonal window for most equities.
Stocks with strong seasonal earnings patterns often exhibit pre-earnings drift and post-earnings momentum during predictable calendar windows.
Statistically weaker period for many equities. Lower institutional activity and summer liquidity gaps reduce momentum.
Energy tends to rally in fall/winter, tech in January, financials around year-end — sector-specific seasonality adds a layer of edge.
Different sectors have different seasonal drivers. Understanding these helps you go beyond generic market-level analysis.
New product cycles, CES momentum, budget resets
Winter demand expectations, refinery maintenance cycles
Retail spending expectations, holiday catalog releases
Year-end rebalancing, rate outlook positioning, tax planning
JPM Healthcare Conference catalyst, new-year allocation flows
Some of the most reliable stock seasonal patterns are tied to earnings calendars. Here's how this relationship works and why it matters.
Many stocks consistently rally in the 2–4 weeks before their quarterly earnings date. This effect is well-documented in academic research and can be isolated using precise seasonal windows.
Stocks that beat expectations in the same quarter repeatedly often show seasonal momentum after earnings. This creates a tradable window that combines fundamental and seasonal edge.
Companies report in predictable quarterly cycles. This creates sector-wide seasonal effects — tech earnings cluster in late January and late July, creating sector-level seasonal surges.
The backtest engine lets you test whether a stock's seasonal window holds when you exclude earnings months or only include them — isolating the true driver of the pattern.
Filter by ticker, sector, calendar window, win rate, and more. Every row is one click away from a full backtest.
| Asset | Window | Dir | Win Rate | Avg Ret | Max DD | PF |
|---|---|---|---|---|---|---|
| AAPL | Jul 1 – Aug 15 | Long | 78% | +5.1% | -6.1% | 1.9 |
| MSFT | Oct 15 – Dec 20 | Long | 80% | +4.9% | -5.0% | 2.2 |
| NVDA | Jan 5 – Mar 10 | Long | 72% | +8.2% | -9.3% | 1.6 |
| JPM | Nov 1 – Jan 15 | Long | 76% | +4.4% | -4.8% | 2.0 |
| XOM | Sep 15 – Nov 30 | Long | 74% | +5.8% | -7.1% | 1.8 |
| TSLA | Mar 15 – May 1 | Short | 65% | +4.2% | -8.5% | 1.4 |
| META | Oct 20 – Dec 15 | Long | 73% | +6.8% | -7.2% | 1.7 |
| AMZN | Oct 1 – Nov 15 | Long | 77% | +6.3% | -5.9% | 2.1 |
78%
Win Rate
+5.1%
Avg Return
20Y
History
80%
Win Rate
+4.9%
Avg Return
20Y
History
76%
Win Rate
+4.4%
Avg Return
20Y
History
72%
Win Rate
+8.2%
Avg Return
15Y
History
74%
Win Rate
+5.8%
Avg Return
20Y
History
77%
Win Rate
+6.3%
Avg Return
18Y
History
Seasonality360 takes you from a raw seasonal observation to a backtested, portfolio-ready edge — in one integrated workflow.
Step 1
Scan thousands of seasonal patterns across markets, timeframes, and calendar windows.
Step 2
Read seasonality charts, monthly returns, and historical consistency at a glance.
Step 3
Backtest any pattern with real historical data — equity curves, drawdowns, trade-by-trade.
Step 4
Combine validated patterns into diversified portfolios with aggregate risk metrics.
Composite performance over 20 years, showing the typical annual rhythm of Apple stock.
20-Year Composite
Screen, chart, and backtest seasonal patterns for 500+ stocks — with one integrated platform.