Seasonality Analysis

6 Data Sources

We aggregate daily OHLCV data for each symbol, going back up to 10+ years where available. Data is grouped by calendar month, quarter, and day-of-week to reveal recurring patterns across multiple timeframes.

Overlapping Year Lines

The seasonal pattern chart overlays each year's cumulative YTD return as a separate line. Where lines cluster together, the pattern is strong. Diverging lines reveal years that broke the seasonal norm — often tied to macro events.

Win Rate + Magnitude

Win rate shows how often a month closed positive. Average return shows magnitude. A 75% win rate with +2% avg is more reliable than 50% with +15% avg — the latter is driven by a single outlier year.

Seasonal Pattern (Overlay)

Each colored line represents one calendar year's cumulative return. The bold white line is the average across all years in the selected timeframe. When individual year lines cluster tightly around the average, the seasonal pattern is reliable. Wide dispersion means the pattern is noisy — driven by one or two outlier years.

The vertical dashed line marks today's position in the calendar. Lines to the right of it show what seasonality predicts for the remainder of the year.

Monthly Returns Bar Chart

Green bars indicate months that historically close positive on average; red bars are historically negative months. The purple win-rate line on the right axis shows the percentage of years that month was positive — this is often more useful than the average, since a single 100%+ month can skew the average dramatically.

Look for months where both the bar is green and the win rate is above 60%. These are the most consistently profitable seasonal windows.

Quarterly & Day-of-Week

Quarterly data aggregates three months into a single performance number — useful for identifying broad seasonal regimes (e.g., "Q4 rally" or "summer slump"). Day-of-week patterns capture intraweek microstructure: institutional rebalancing on Mondays, retail activity on weekends, and options expiry effects on Fridays.

Multi-Asset Heatmap

The heatmap compares seasonal patterns across the top 10+ crypto assets simultaneously. Look for vertical green columns (months where the entire market tends to rise) and red columns (market-wide weakness). Individual cells that diverge from their column reveal asset-specific seasonal edges — for example, an altcoin with a strong February when the market is typically weak.

Past != Future

Past performance does not guarantee future results. Seasonal patterns are statistical tendencies, not certainties. A month with 80% historical win rate will still have losing years. Always use seasonality as one input alongside price action, fundamentals, and risk management.

Limited History

Crypto has far less history than traditional assets. BTC has ~12 years of reliable data; most altcoins have 3-5 years. Small sample sizes mean averages can shift dramatically when a single outlier year is added or removed. Prefer 5Y+ windows for more stable patterns.

Regime Changes

Macro events (Fed policy shifts, regulation, black swan events) can override seasonal patterns entirely. Bitcoin halving cycles create their own 4-year rhythm that can amplify or cancel calendar seasonality. Always check if the current macro regime matches the historical period the data covers.