def future_max_apply_nb(close: tp.Array2d,
window: int,
wait: int = 1) -> tp.Array2d:
"""Get the maximum of the next period."""
out = generic_nb.rolling_max_nb(close[::-1], window, minp=window)[::-1]
if wait > 0:
return generic_nb.bshift_nb(out, wait)
return out
def stoch_caching_nb(high_ts, low_ts, close_ts, k_windows, d_windows, d_ewms):
"""Caching function for `vectorbt.indicators.basic.Stochastic`."""
cache_dict = dict()
for i in range(k_windows.shape[0]):
h = hash(k_windows[i])
if h not in cache_dict:
roll_min = generic_nb.rolling_min_nb(low_ts, k_windows[i])
roll_max = generic_nb.rolling_max_nb(high_ts, k_windows[i])
cache_dict[h] = roll_min, roll_max
return cache_dict
def stoch_cache_nb(high, low, close, k_windows, d_windows, d_ewms, adjust):
"""Caching function for `vectorbt.indicators.basic.STOCH`."""
cache_dict = dict()
for i in range(len(k_windows)):
h = hash(k_windows[i])
if h not in cache_dict:
roll_min = generic_nb.rolling_min_nb(low, k_windows[i])
roll_max = generic_nb.rolling_max_nb(high, k_windows[i])
cache_dict[h] = roll_min, roll_max
return cache_dict
def stoch_cache_nb(high: tp.Array2d, low: tp.Array2d, close: tp.Array2d,
k_windows: tp.List[int], d_windows: tp.List[int], d_ewms: tp.List[bool],
adjust: bool) -> tp.Dict[int, tp.Tuple[tp.Array2d, tp.Array2d]]:
"""Caching function for `vectorbt.indicators.basic.STOCH`."""
cache_dict = dict()
for i in range(len(k_windows)):
h = hash(k_windows[i])
if h not in cache_dict:
roll_min = generic_nb.rolling_min_nb(low, k_windows[i])
roll_max = generic_nb.rolling_max_nb(high, k_windows[i])
cache_dict[h] = roll_min, roll_max
return cache_dict