plot_seasonality

---

function backtide.analysis.seasonality.plot_seasonality(data, price_col="close", title=None, legend=None, figsize=(900, 600), filename=None, display=True)[source]

Create a seasonality heatmap.

For daily or longer intervals, aggregates returns into calendar months and displays a year x month grid. For intraday intervals, displays a day-of-week x hour grid of average returns. Useful for spotting recurring seasonal or intraday patterns.

Parameters

data : pd.DataFrame | pl.DataFrame Input data containing columns symbol, the column specified by price_col, and dt with the datetime. price_col : str, default="close" Column name used to compute returns. title : str | dict | None, default=None Title for the plot. If None, no title is shown. If str, text for the title. If dict, title configuration. legend : str | dict | None, default=None Legend for the plot. Defaults to None since a colorbar is shown instead. figsize : tuple[int, int], default=(900, 600) Figure's size in pixels, format as (x, y). filename : str | Path | None, default=None Save the plot using this name. The type of the file depends on the provided name (.html, .png, .pdf, etc...). If filename has no file type, the plot is saved as .html. If None, the plot isn't saved. display : bool | None, default=True Whether to render the plot. If None, it returns the figure.

Returns

Figure | None The Plotly figure object. Only returned if display=None.

See Also

plot_correlation

Create a correlation heatmap.

plot_drawdown

Create a drawdown chart.

plot_returns

Create a returns distribution histogram.

Example

>>> from backtide.storage import query_bars
>>> from backtide.analysis import plot_seasonality

>>> # Daily interval
>>> df = query_bars("AAPL", "1d")
>>> plot_seasonality(df.iloc[-5 * 250:, :])  # Just show the last 5 years

>>> # Intraday interval
>>> df = query_bars("AAPL", "1h")
>>> plot_seasonality(df)