def annualized_volatility_1d_nb(returns, ann_factor, levy_alpha=2.0):
"""See `empyrical.annual_volatility`."""
if returns.shape[0] < 2:
return np.nan
return generic_nb.nanstd_1d_nb(returns,
ddof=1) * ann_factor**(1.0 / levy_alpha)
def information_ratio_1d_nb(returns, benchmark_rets):
"""See `empyrical.excess_sharpe`."""
if returns.shape[0] < 2:
return np.nan
active_return = returns - benchmark_rets
return np.nanmean(active_return) / generic_nb.nanstd_1d_nb(active_return,
ddof=1)
def annualized_volatility_1d_nb(returns: tp.Array1d,
ann_factor: float,
levy_alpha: float = 2.0,
ddof: int = 1) -> float:
"""Annualized volatility of a strategy."""
if returns.shape[0] < 2:
return np.nan
return generic_nb.nanstd_1d_nb(returns, ddof) * ann_factor ** (1.0 / levy_alpha)
def sharpe_ratio_1d_nb(returns, ann_factor, risk_free=0.):
"""See `empyrical.sharpe_ratio`."""
if returns.shape[0] < 2:
return np.nan
returns_risk_adj = returns - risk_free
mean = np.nanmean(returns_risk_adj)
std = generic_nb.nanstd_1d_nb(returns_risk_adj, ddof=1)
if std == 0.:
return np.inf
return mean / std * np.sqrt(ann_factor)
def information_ratio_1d_nb(returns, benchmark_rets, ddof=1):
"""See `empyrical.excess_sharpe`."""
if returns.shape[0] < 2:
return np.nan
active_return = returns - benchmark_rets
mean = np.nanmean(active_return)
std = generic_nb.nanstd_1d_nb(active_return, ddof)
if std == 0.:
return np.inf
return mean / std
def information_ratio_1d_nb(returns: tp.Array1d, benchmark_rets: tp.Array1d, ddof: int = 1) -> float:
"""Information ratio of a strategy."""
if returns.shape[0] < 2:
return np.nan
active_return = returns - benchmark_rets
mean = np.nanmean(active_return)
std = generic_nb.nanstd_1d_nb(active_return, ddof)
if std == 0.:
return np.inf
return mean / std
def sharpe_ratio_1d_nb(returns: tp.Array1d,
ann_factor: float,
risk_free: float = 0.,
ddof: int = 1) -> float:
"""See `empyrical.sharpe_ratio`."""
if returns.shape[0] < 2:
return np.nan
returns_risk_adj = returns - risk_free
mean = np.nanmean(returns_risk_adj)
std = generic_nb.nanstd_1d_nb(returns_risk_adj, ddof)
if std == 0.:
return np.inf
return mean / std * np.sqrt(ann_factor)