def minmax(candles: np.ndarray, order: int = 3, sequential: bool = False) -> EXTREMA:
"""
minmax - Get extrema
:param candles: np.ndarray
:param order: int - default = 3
:param sequential: bool - default: False
:return: EXTREMA(min, max, last_min, last_max)
"""
candles = slice_candles(candles, sequential)
low = candles[:, 4]
high = candles[:, 3]
minimaIdxs = argrelextrema(low, np.less, order=order, axis=0)
maximaIdxs = argrelextrema(high, np.greater, order=order, axis=0)
is_min = np.full_like(low, np.nan)
is_max = np.full_like(high, np.nan)
# set the extremas with the matching price
is_min[minimaIdxs] = low[minimaIdxs]
is_max[maximaIdxs] = high[maximaIdxs]
# forward fill Nan values to get the last extrema
last_min = np_ffill(is_min)
last_max = np_ffill(is_max)
if sequential:
return EXTREMA(is_min, is_max, last_min, last_max)
else:
return EXTREMA(is_min[-(order+1)], is_max[-(order+1)], last_min[-1], last_max[-1])
def minmax(candles: np.ndarray,
order: int = 3,
sequential: bool = False) -> EXTREMA:
"""
minmax - Get extrema
:param candles: np.ndarray
:param order: int - default = 3
:param sequential: bool - default=False
:return: EXTREMA(min, max, last_min, last_max)
"""
warmup_candles_num = get_config('env.data.warmup_candles_num', 240)
if not sequential and len(candles) > warmup_candles_num:
candles = candles[-warmup_candles_num:]
low = candles[:, 4]
high = candles[:, 3]
minimaIdxs = argrelextrema(low, np.less, order=order, axis=0)
maximaIdxs = argrelextrema(high, np.greater, order=order, axis=0)
min = np.full_like(low, np.nan)
max = np.full_like(high, np.nan)
# set the extremas with the matching price
min[minimaIdxs] = low[minimaIdxs]
max[maximaIdxs] = high[maximaIdxs]
# forward fill Nan values to get the last extrema
last_min = np_ffill(min)
last_max = np_ffill(max)
if sequential:
return EXTREMA(min, max, last_min, last_max)
else:
return EXTREMA(min[-1], max[-1], last_min[-1], last_max[-1])
def test_np_ffill():
arr = np.array([0, 1, np.nan, np.nan])
res = jh.np_ffill(arr)
expected = np.array([0, 1, 1, 1])
np.equal(res, expected)