Every market carries recurring patterns driven by institutional flows, economic cycles, and human behavior. Seasonality charts make these hidden rhythms visible — so you can spot opportunities before they unfold.
Seasonality360 gives you interactive, multi-layer seasonality charts across stocks, indices, commodities, and forex — backed by 20+ years of data.
20-Year Composite Average
Watch the workflow before exploring the use case: discovery, validation, and execution planning in one clear process.
1,200+
Assets Covered
Stocks, ETFs, Indices, FX, Commodities
20+
Years of Data
Per asset, per pattern
4
Seasonal Layers
Annual · Monthly · Weekly · Intraday
∞
Backtested Patterns
Any window, any asset
A seasonality chart shows how an asset has performed, on average, during specific time periods over many years. Instead of looking at a single year's price action, it stacks multiple years on top of each other to reveal recurring patterns.
Think of it as a statistical fingerprint. Markets don't repeat exactly, but many assets exhibit remarkably consistent tendencies — certain months tend to be bullish, certain weeks tend to weaken, and some calendar windows show patterns that have held for decades.
These patterns are driven by real forces: earnings seasons, fund rebalancing, commodity harvest cycles, fiscal year-end flows, and behavioral effects. A good seasonality chart makes all of this legible at a glance.
Visualize 20+ years of calendar behavior in one view
Layer annual, monthly, weekly, and intraday patterns
Identify the strongest and weakest windows for any asset
Compare seasonal profiles across markets and sectors
20-Year Average Performance
Not all seasonality charts are the same. Different visualizations reveal different layers of market behavior. Here's what each type tells you.
A composite line that shows cumulative average performance from January to December, stacking 15–20 years of data. This is the most common seasonality chart and reveals the overall seasonal shape — where the market tends to rally, plateau, or weaken during the calendar year. Best for spotting broad windows and trend-like seasonal movements.
Shows average returns for each month as individual bars — green for positive, red for negative. Unlike the annual curve, this view isolates each month independently. It's ideal for quick scanning: which months have historically been strongest? Which are the most volatile? Which consistently underperform?
A color-coded grid showing year-by-year returns for each month. Instead of an average, you see the full distribution — including outlier years. This is critical for assessing consistency: a +3% average might hide years of +15% and -9%. The heatmap tells you how reliable the average really is.
Combines annual, monthly, weekly, and even intraday seasonality in one view. When multiple layers align (e.g., November is strong annually AND the third week of November is historically the best weekly window), the statistical edge becomes more robust. This is where professional-grade analysis begins.
Seasonality doesn't predict the future — but it reveals the statistical tendencies that make markets less random than they appear.
Instead of guessing where to look, seasonality points you toward historically favorable time windows. It's systematic opportunity scanning.
Know which months historically carry higher volatility or drawdown risk. Avoid entering positions during statistically weak periods.
Seasonality gives you a timing edge. Not a crystal ball, but a data-backed framework for when to act and when to wait.
Markets are noisy. Seasonality strips away the daily randomness and reveals the underlying calendar structure.
Professional traders use seasonality as a starting point for deeper analysis. It's the first layer in a structured research workflow.
Seasonal patterns vary by asset class, sector, and geography. Comparing them reveals opportunities hidden in single-market analysis.
You could build a basic seasonal average in Excel. But real seasonal analysis requires much more.
A +4% average could be +20% one year and -12% the next. Proper seasonal tools show you the distribution, not just the mean — so you know whether the pattern is reliable or just noise.
Spreadsheets give you monthly averages. But true seasonal edge comes from layering annual, monthly, weekly, and intraday data. When multiple layers align, the edge compounds.
Seeing a seasonal tendency is step one. Testing whether it would have been profitable — with entries, exits, drawdowns, and equity curves — requires a backtesting engine, not a pivot table.
With 1,200+ assets and infinite calendar windows, finding the best seasonal patterns manually is impossible. A screener lets you scan, rank, and filter in seconds.
While seasonality analysis goes far deeper than famous calendar effects, understanding the major seasonal windows gives you context for what drives market rhythm.
Historically the strongest seasonal window for US equities. Institutional rebalancing, tax positioning, and holiday sentiment tend to push prices higher.
Lower volumes, vacation-driven liquidity gaps, and historically weaker returns create a cautious seasonal backdrop.
Gold and silver tend to rally on seasonal demand cycles, Chinese New Year buying, and portfolio rebalancing flows.
October has historically seen more sharp corrections and volatility clusters, though it often marks bottoms for subsequent rallies.
A +3% average return means less if it was +20% one year and -14% the next. Look for patterns with high win rates across many years — that's where the real edge lives.
Commodity seasonality is often driven by supply cycles (harvest, demand peaks). Equity seasonality is driven by earnings, rebalancing, and sentiment. Forex seasonality connects to macro flows and fiscal calendar effects.
A seasonal tendency is a starting point, not a signal. The best traders use it to know where to look — then validate with backtesting, risk analysis, and portfolio context.
Annual, monthly, weekly, and intraday seasonality can overlap. When they align, the statistical edge strengthens. Seasonality360 lets you visualize all four layers for any asset.
The Seasonality360 screener lets you scan thousands of patterns by asset, calendar window, win rate, and more — then drill into any pattern with a single click.
| Asset | Window | Dir | Win Rate | Avg Ret | Max DD | PF |
|---|---|---|---|---|---|---|
| SPY | Nov 1 – Dec 31 | Long | 82% | +3.8% | -4.2% | 2.4 |
| AAPL | Jul 1 – Aug 15 | Long | 78% | +5.1% | -6.1% | 1.9 |
| Gold | Jan 15 – Mar 10 | Long | 75% | +4.2% | -3.5% | 2.1 |
| EUR/USD | Apr 1 – May 15 | Short | 70% | +2.1% | -2.8% | 1.7 |
| Crude Oil | Feb 1 – Apr 30 | Long | 73% | +6.5% | -8.2% | 1.8 |
| MSFT | Oct 15 – Dec 20 | Long | 80% | +4.9% | -5.0% | 2.2 |
82%
Win Rate
+3.8%
Avg Return
20Y
History
75%
Win Rate
+4.2%
Avg Return
20Y
History
70%
Win Rate
+2.1%
Avg Return
20Y
History
73%
Win Rate
+6.5%
Avg Return
20Y
History
80%
Win Rate
+4.9%
Avg Return
20Y
History
68%
Win Rate
+3.6%
Avg Return
20Y
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.
Find high-probability entry windows based on decades of historical behavior. Focus your attention on the calendar periods that statistically favor your strategy.
Build seasonal overlays into allocation decisions. Know when to increase exposure, hedge risk, or rotate sectors based on recurring calendar patterns.
Use seasonality as a factor in strategy design. Combine calendar effects with momentum, value, or volatility factors to build more robust systematic models.
See the pattern. Understand the driver. Validate it with data. Build it into a strategy. All in one platform.