Core#

Attribution Dashboard — Main orchestrator class.

Coordinates all dashboard components, layouts, callbacks, and visualizations. Follows the same pattern as kaxanuk.backtest_engine.dashboard.backtest_dashboard.

class AttributionDashboard(attribution_results: AttributionResultDict, portfolio_name: str | None = None, benchmark_name: str | None = None, logo_path: str | None = None, days_of_smoothing: int = 45, date_column: str = 'date')[source]#

Bases: object

Interactive Dash-based dashboard for attribution analysis.

Metrics are pre-calculated by the engine and passed in rather than computed inside the dashboard.

Parameters:

  • attribution_results (AttributionResultDict) – Pre-calculated attribution results to display.

  • portfolio_name (str or None, default None) – Display name of the portfolio, shown in the dashboard header.

  • benchmark_name (str or None, default None) – Display name of the benchmark.

  • logo_path (str or None, default None) – Path to a logo image file to embed in the dashboard header.

  • days_of_smoothing (int, default 45) – Number of days used for smoothing in relevant chart components.

  • date_column (str, default "date") – Name of the date column shared across the attribution result tables.

results#

The raw attribution results passed to the constructor.

Type:

AttributionResultDict

date_column#

Name of the date column shared across the attribution result tables.

Type:

str

portfolio_returns#

Columns: [date, portfolio_returns].

Type:

pyarrow.Table

simulated_rets#

Columns: [date, factor1, factor2, ...].

Type:

pyarrow.Table

portfolio_attribution_ts#

Columns: [date, factor1, factor2, ...].

Type:

pyarrow.Table

pct_df_returns#

Columns: [date, factor1, factor2, ...].

Type:

pyarrow.Table

total_pct_decomposition#

Mapping of {factor_name: contribution_fraction}.

Type:

dict

summary_stats#

Mapping of {metric_name: value}.

Type:

dict

brinson_fachler_indexes#

Brinson-Fachler attribution index table, if computed.

Type:

pyarrow.Table or None

portfolio_name#

Display name of the portfolio.

Type:

str or None

benchmark_name#

Display name of the benchmark.

Type:

str or None

logo_path#

Path to the logo image file, if any.

Type:

str or None

days_of_smoothing#

Number of days used for smoothing in relevant chart components.

Type:

int

app#

The underlying Dash application instance.

Type:

dash.Dash

run_server(*, debug: bool = True, port: int = 8050) → None[source]#

Start the Dash development server.

Parameters:

  • debug (bool, default True) – Whether to run the Dash app in debug mode.

  • port (int, default 8050) – Local port on which to serve the dashboard.

class AttributionResultDict[source]#

Bases: TypedDict

Pre-calculated attribution results consumed by AttributionDashboard.

portfolio_returns#

Columns: [date, portfolio_returns].

Type:

pyarrow.Table

simulated_rets#

Columns: [date, factor1, factor2, ...].

Type:

pyarrow.Table

portfolio_attribution_ts#

Columns: [date, factor1, factor2, ...].

Type:

pyarrow.Table

pct_df_returns#

Columns: [date, factor1, factor2, ...].

Type:

pyarrow.Table

total_pct_decomposition#

Mapping of {factor_name: contribution_fraction}.

Type:

dict

summary_stats#

Mapping of {metric_name: value}.

Type:

dict

brinson_fachler_indexes#

Brinson-Fachler attribution index table, if computed.

Type:

pyarrow.Table or None

portfolio_name#

Display name of the portfolio.

Type:

str

benchmark_name#

Display name of the benchmark.

Type:

str

benchmark_name: str#
brinson_fachler_indexes: Table | None#
pct_df_returns: Table#
portfolio_attribution_ts: Table#
portfolio_name: str#
portfolio_returns: Table#
simulated_rets: Table#
summary_stats: dict#
total_pct_decomposition: dict#
create_dashboard_from_attribution(attribution_results: AttributionResultDict, portfolio_name: str | None = None, benchmark_name: str | None = None, logo_path: str | None = None, days_of_smoothing: int = 45, date_column: str = 'date') → AttributionDashboard[source]#

Create an AttributionDashboard from pre-calculated results.

Parameters:

  • attribution_results (AttributionResultDict) – Pre-calculated attribution results to display.

  • portfolio_name (str or None, default None) – Display name of the portfolio, shown in the dashboard header.

  • benchmark_name (str or None, default None) – Display name of the benchmark.

  • logo_path (str or None, default None) – Path to a logo image file to embed in the dashboard header.

  • days_of_smoothing (int, default 45) – Number of days used for smoothing in relevant chart components.

  • date_column (str, default "date") – Name of the date column shared across the attribution result tables.

Returns:

A dashboard instance configured with the given results and options, ready to be served with AttributionDashboard.run_server().

Return type:

AttributionDashboard