gs_quant.timeseries.econometrics.sharpe_ratio

sharpe_ratio(series, currency=RiskFreeRateCurrency.USD, w=None, curve_type=CurveType.PRICES, method=Interpolate.NAN)[source]

Calculate Sharpe ratio

Parameters:

  • series (Series) – series of prices or excess returns for an asset

  • currency (RiskFreeRateCurrency) – currency for risk-free rate, defaults to USD

  • w (Union[Window, int, str, None]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size and 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc. Window size defaults to length of series.

  • curve_type (CurveType) – whether input series is of prices or excess returns, defaults to prices

  • method (Interpolate) – interpolation method (default: nan). Used to calculate returns on dates without data (i.e. weekends) when window is a relative date. Defaults to no interpolation.

Return type:

Series

Returns:

Sharpe ratio

Usage

Given a price series P, risk-free rate R, and window of size w returns the rolling Sharpe ratio S:

\(S_t = \frac{(E_t / E_{t-w+1})^{365.25 / (D_t - D_{t-w})}-1}{volatility(E, w)_t}\)

Excess returns E are defined as:

\(E_t = E_{t-1} + P_t - P_{t-1} * (1 + R * (D_t - D_{t-1}) / 360)\)

where D is the date for a data point. The Actual/360 day count convention is used.

Examples

Get rolling sharpe ratio of a price series (with window of 22).

>>> sr = sharpe_ratio(generate_series(365, END_TODAY), USD, 22, PRICES)

See also

volatility()