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Theme plots, save artefacts, and export data

How to style figures, save them, serialise pipeline outputs, and switch to the interactive Plotly backend.

Themes and saving

There is no global theme to set. Pass a PlotTheme to plot() and it applies only to that call (matplotlib backend only):

from ob_analytics.visualization import plot, save_figure, prepare, PlotTheme

theme = PlotTheme(
    style="whitegrid",
    context="talk",
    font_scale=1.2,
    rc={"axes.facecolor": "#f8f9fa", "figure.facecolor": "#ffffff"},
)

fig = plot("trade_tape", level="L2", theme=theme, **prepare.trades(result.trades))
save_figure(fig, "trades_hires.png", dpi=300)

Serialisation

Pipeline outputs are dict-of-DataFrames; save_data writes one Parquet file per key, load_data reads them back.

from ob_analytics import save_data, load_data

save_data(
    {
        "events": result.events,
        "trades": result.trades,
        "depth": result.depth,
        "depth_summary": result.depth_summary,
    },
    "output/my_analysis",
)

data = load_data("output/my_analysis")

For LOBSTER round-trip output (back to message + orderbook CSVs), pass fmt="lobster" and a RunContext so the registered writer factory can pick up trading_date:

from ob_analytics import save_data
from ob_analytics.protocols import RunContext

save_data(
    data, "round_trip/", fmt="lobster",
    config=config, ctx=RunContext(trading_date="2012-06-21"),
)

Plotly (interactive)

plot() accepts backend="plotly" for interactive figures with zoom, pan, and hover tooltips. Plotly is an optional dependency:

pip install "ob-analytics[interactive]"

from ob_analytics import Pipeline, sample_csv_path

result = Pipeline().run(sample_csv_path())

# col_bias is a power-law gamma: 1.0 (default) is linear so high-volume walls
# stand out; 0.1 brightens thin levels to expose near-touch structure in
# heavy-tailed books; <= 0 selects a log scale.
fig = result.plot("depth_heatmap", backend="plotly", col_bias=0.1)
fig.show()
fig.write_html("depth.html")

Whole new backends can be registered by module path:

from ob_analytics.visualization import register_plot_backend
register_plot_backend("bokeh", "my_package._bokeh_backend")