abaquant.visualization.portfolio

Import path: abaquant.visualization.portfolio

Domain: Matplotlib and Plotly visualization helpers with shared themes.

Purpose

Theme-aware portfolio allocation visualizations.

When to use it

Use these functions to inspect model behavior, portfolio allocations, market surfaces, credit assessments, calibrations, and dashboard outputs.

Public objects

  • function: visualize_portfolio_allocator — Visualize weights, cumulative returns, or correlation using one theme.

  • function: visualize_portfolio_scenario — Visualize one portfolio shock scenario.

  • function: visualize_portfolio_backtest — Visualize deterministic portfolio-backtest diagnostics.

Detailed reference

Theme-aware portfolio allocation visualizations.

abaquant.visualization.portfolio.visualize_portfolio_allocator(allocator, *, weights=None, chart='cumulative_returns', backend=None, theme=None, save_path=None, filename=None)

Visualize weights, cumulative returns, or correlation using one theme.

Parameters:

  • allocator (object)

  • weights (Sequence[float] | Series | ndarray | None)

  • chart (str)

  • backend (Literal['matplotlib', 'plotly'] | None)

  • theme (VisualizationTheme | None)

  • save_path (str | Path | None)

  • filename (str | None)

abaquant.visualization.portfolio.visualize_portfolio_scenario(scenario, *, chart='contributions', backend=None, theme=None, save_path=None, filename=None)

Visualize one portfolio shock scenario.

Parameters:

  • scenario (object) – Scenario object exposing as_frame(), portfolio_return, base_value, and ending_value.

  • chart ({"contributions", "shocks", "waterfall"}, default="contributions") – Scenario diagnostic to plot.

  • backend ({"matplotlib", "plotly"}, optional) – Backend override for this figure.

  • theme (VisualizationTheme, optional) – Per-call style override.

  • save_path (str or pathlib.Path, optional) – Explicit export path.

  • filename (str, optional) – Filename relative to the active theme’s save directory.

Returns:

Backend-native figure object.

Return type:

matplotlib.figure.Figure or plotly.graph_objects.Figure

abaquant.visualization.portfolio.visualize_portfolio_backtest(backtest, *, chart='equity_curve', backend=None, theme=None, save_path=None, filename=None, rolling_window=63)

Visualize deterministic portfolio-backtest diagnostics.

Parameters:

  • backtest (object) – Backtest result exposing path, drawdown, weight, trade, and summary methods.

  • chart (str, default="equity_curve") – Supported values are "equity_curve", "benchmark", "drawdown", "weights", "turnover", "transaction_costs", "rolling_sharpe", "rolling_volatility", "return_heatmap", "contributions", and "trade_weights".

  • backend ({"matplotlib", "plotly"}, optional) – Backend override for this figure.

  • theme (VisualizationTheme, optional) – Per-call style override.

  • save_path (str or pathlib.Path, optional) – Explicit export path.

  • filename (str, optional) – Filename relative to the active theme’s save directory.

  • rolling_window (int, default=63) – Rolling window used for rolling-metric figures.

Returns:

Backend-native figure object.

Return type:

matplotlib.figure.Figure or plotly.graph_objects.Figure