PerformanceReport#

class sigtech.framework.analytics.performance.performance_report.PerformanceReport

Class providing customised statistics, rolling graphs and performance calendar output for strategies, instruments and time series.

Basic usage:

from sigtech.framework.default_strategy_objects.rolling_futures import es_index_front

few_views = [sig.View.MONTHLY_STATS, sig.View.PRESENTATION_PERF_TABLE]

# Define a performance report for a strategy
report = sig.PerformanceReport(es_index_front(), name='My Strategy', views=few_views)

# Define a performance report for an instrument
bond = sig.obj.get('US 2 2025/08/15 GOVT')
report = sig.PerformanceReport(bond, name='My bond', views=few_views)

# Once a performance report has been defined, it can be generated via `report.report()`

The parameter cash is used to compute specific metrics, e.g. Annualised Sortino Ratio (vs Cash). Accepted types are pandas Series and Instrument. If cash is set to 'automatic', the cash index object returned by the currency ticker of the strategy is used (when applicable).

The parameter legend controls the location of the legend when plotting graphs. The graphs currently available are:

historical performance with View.HISTORY, and

rolling excess return, rolling sharpe, rolling drawdown with View.ROLLING_PLOTS.

See View for a full list of available views. To display all views, specify views='ALL'.

Using the legend constants available for pyplot, legend=(1, 2, 3, 4) will position the historical performance legend in the top right corner of the graph, the rolling excess return legend in the top left corner, the rolling sharpe legend in the bottom left corner, and the rolling drawdown legend in the bottom right corner.

Sharpe ratios can be tailored to use compounding returns by setting compound_metrics=True or simple, arithmetic returns with compound_metrics=False (default).

If passing a strategy with zero initial cash you may wish to pass a representative AUM via the aum keyword. This is expected in units of the strategy currency and if passed, the strategy history series is considered relative to this constant AUM level. E.g. Returns = (history[t+1] - history[t]) / aum.

Use adjust_benchmark_by_aum=True to consider the benchmark history series (if provided) relative to the constant AUM level (default is False).

Use profit_metrics_only to ignore metrics that are calculated using the percentage returns. If left as None this is automatically set by looking for negative values in the valuation history.

property stats_df

DataFrame of metrics.

add_view(view) deprecated

Add a view to the customised report.

Parameters:: view – Input view.

add_views(views)

Add a list of views to the customised report.

Parameters:: views – List of views.

arrange_views(views)

Set the views for the customised report.

Parameters:: views – List of views.

default_views()

Return the default views available.

interactive_report()

Generate the full suite of plots and display in ipywidgets to allow user browsing and parameter choices.

Note

The method must be called from a Jupyter Lab or Notebook session in order to display the widgets.

report()

Generate a performance report.

rolling_stat(stat_nameuse_returns=True)

Retrieve a rolling statistic.

Parameters:: stat_name – Statistic identifier.
Returns:: pandas DataFrame.

stats_display()

Display the formatted summary statistics.

stats_display_ccy()

Display the formatted summary statistics.

views()

Return the list of views for the customised report.