Calendar metrics#

sigtech.framework.analytics.performance.performance_calendars.get_presentation_performance_table(ts, use_ir_calc=False, method: str = 'compounding', scaling_units=1)

Calculate the combined performance calendar and annual statistics DataFrame.

Parameters

  • ts – Input timeseries.

  • use_ir_calc – Use risk Sharpe calculation (default is False).

  • method – 'arithmetic' or 'compounding' , aggregation method (default is 'compounding').

  • scaling_units – value to divide timeseries by (default is 1).

Returns

Combined performance calendar and annual statistics DataFrame.

sigtech.framework.analytics.performance.performance_calendars.performance_calendar(rets: pandas.core.series.Series, geometric: bool = True, method: str = 'compounding') → pandas.core.frame.DataFrame

Calculate the performance calendar.

Parameters

  • rets – Daily returns.

  • geometric – If True, compute the geometric returns (default is True).

  • method – 'arithmetic' or 'compounding' , aggregation method (default is 'compounding').

Returns

DataFrame of monthly returns.

sigtech.framework.analytics.performance.performance_calendars.rolling_risk_sharpe(ts, period=252)

Calculate the rolling risk Sharpe using the risk day count factors.

Parameters

  • ts – Input timeseries.

  • period – Input period (default is 252).

Returns

Rolling risk Sharpe.

sigtech.framework.analytics.performance.performance_calendars.rolling_risk_volatility(ts, period=252)

Calculate the rolling risk volatility annualised using the risk business day count.

Parameters

  • ts – Input timeseries.

  • period – Input period (default is 252).

Returns

Rolling risk volatility.