def mstd_nb(a, window, ewm, adjust=False, ddof=0):
"""Compute simple or exponential moving STD (`ewm=True`)."""
if ewm:
return generic_nb.ewm_std_nb(a,
window,
minp=window,
adjust=adjust,
ddof=ddof)
return generic_nb.rolling_std_nb(a, window, minp=window, ddof=ddof)
def future_std_apply_nb(close, window, ewm, wait=1, adjust=False, ddof=0):
"""Get the standard deviation of the next period."""
if ewm:
out = generic_nb.ewm_std_nb(close[::-1], window, minp=window, adjust=adjust, ddof=ddof)[::-1]
else:
out = generic_nb.rolling_std_nb(close[::-1], window, minp=window, ddof=ddof)[::-1]
if wait > 0:
return generic_nb.bshift_nb(out, wait)
return out
def mstd_caching_nb(ts, windows, ewms):
"""Caching function for `vectorbt.indicators.basic.MSTD`."""
cache_dict = dict()
for i in range(windows.shape[0]):
h = hash((windows[i], ewms[i]))
if h not in cache_dict:
if ewms[i]:
mstd = generic_nb.ewm_std_nb(ts, windows[i])
else:
mstd = generic_nb.rolling_std_nb(ts, windows[i])
cache_dict[h] = mstd
return cache_dict
def mstd_nb(a: tp.Array2d,
window: int,
ewm: int,
adjust: bool = False,
ddof: int = 0) -> tp.Array2d:
"""Compute simple or exponential moving STD (`ewm=True`)."""
if ewm:
return generic_nb.ewm_std_nb(a,
window,
minp=window,
adjust=adjust,
ddof=ddof)
return generic_nb.rolling_std_nb(a, window, minp=window, ddof=ddof)
def future_std_apply_nb(close: tp.Array2d,
window: int,
ewm: bool,
wait: int = 1,
adjust: bool = False,
ddof: int = 0) -> tp.Array2d:
"""Get the standard deviation of the next period."""
if ewm:
out = generic_nb.ewm_std_nb(close[::-1],
window,
minp=window,
adjust=adjust,
ddof=ddof)[::-1]
else:
out = generic_nb.rolling_std_nb(close[::-1],
window,
minp=window,
ddof=ddof)[::-1]
if wait > 0:
return generic_nb.bshift_nb(out, wait)
return out
def rolling_std(self, window, minp=1, ddof=1): # pragma: no cover
return self.wrap(
nb.rolling_std_nb(self.to_2d_array(), window, minp=minp,
ddof=ddof))
def rolling_std(self, window, minp=1, ddof=1): # pragma: no cover
"""See `vectorbt.generic.nb.rolling_std_nb`."""
return self.wrap(
nb.rolling_std_nb(self.to_2d_array(), window, minp=minp,
ddof=ddof))
