markowizard.visualization¶

visualization ¶

Plotly-based visualization functions for portfolio analysis.

Returns standalone Plotly Figure objects (not tied to Dash).

efficiency_frontier_plot(
    portfolios: DataFrame, highlight_portfolio: int = 0
) -> Figure

Plot the efficient frontier as a scatter plot of expected return vs risk.

Parameters:

Name	Type	Description	Default
`portfolios`	`DataFrame`	DataFrame with 'Expected Return', 'Risk', and 'Sharpe' columns (as produced by MarkowitzOptimizer).	required
`highlight_portfolio`	`int`	Index of the portfolio to highlight (default 0).	`0`

Returns:

Type	Description
`Figure`

allocation_pie(portfolio: Series) -> Figure

Pie chart showing asset allocation for a single portfolio.

Parameters:

Name	Type	Description	Default
`portfolio`	`Series`	A single portfolio row from the efficient frontier DataFrame. Non-zero asset weights are displayed; meta columns like 'Expected Return', 'Risk', 'Sharpe' are excluded.	required

Returns:

Type	Description
`Figure`

capital_allocation_line_plot(
    cal_points: list[dict], highlight_point: int = 0
) -> Figure

Plot the Capital Allocation Line (CAL) showing risk-return trade-offs for different mixes of risky portfolio and risk-free asset.

Parameters:

Name	Type	Description	Default
`cal_points`	`list of dict`	Output from CapitalAllocator.capital_allocation_line().	required
`highlight_point`	`int`	Index of the point to highlight (default 0).	`0`

Returns:

Type	Description
`Figure`

correlation_timeline(
    prices: DataFrame,
    ticker_a: str,
    ticker_b: str | None = None,
) -> Figure

Plot the price history of one or two assets, normalizing when comparing two different assets.

Parameters:

Name	Type	Description	Default
`prices`	`DataFrame`	DataFrame of historical prices with DatetimeIndex and tickers as columns.	required
`ticker_a`	`str`	Primary ticker.	required
`ticker_b`	`str or None`	Secondary ticker. If None or equal to ticker_a, plots a single line.	`None`

Returns:

Type	Description
`Figure`

correlation_heatmap(corr_matrix: DataFrame) -> Figure

Plot a correlation matrix heatmap.

Parameters:

Name	Type	Description	Default
`corr_matrix`	`DataFrame`	Square correlation matrix with ticker names as index and columns.	required

Returns:

Type	Description
`Figure`