def repeat(df, s, on: str, repeat: int) -> ReturnType:
result = df.exec(on)[s]
if repeat == 1:
return result, repeat
return rolling_calc(result, repeat, np.all, False, 1), repeat
def increase(df, s, on: str, repeat: int, direction: int) -> ReturnType:
period = repeat + 1
current = NEGATIVE_INFINITY if direction == 1 else POSITIVE_INFINITY
compare = partial(check_increase, direction, current)
return rolling_calc(df.exec(on)[s], period, compare, False), period
def calc_ma(
array: np.ndarray,
period: int
) -> np.ndarray:
"""Calculates N-period Simple Moving Average
"""
return rolling_calc(array, period, np.mean)
def boll_band(upper: bool, df, s, period, times, column) -> ReturnType:
"""Gets the upper band or the lower band of bolinger bands
Args:
upper (bool): Get the upper band if True else the lower band
"""
prices = df[column][s].to_numpy()
ma = df.exec(f'ma:{period},{column}')[s]
mstd = rolling_calc(prices, period, np.std)
if upper:
return np.add(ma, np.multiply(times, mstd)), period
else:
return np.subtract(ma, np.multiply(times, mstd)), period
def hhv(df, s, period, column) -> ReturnType:
"""Gets HHV (Highest of High Value)
"""
return rolling_calc(df.get_column(column)[s].to_numpy(), period,
max), period
def llv(df, s, period, column) -> ReturnType:
"""Gets LLV (Lowest of Low Value)
"""
return rolling_calc(df.get_column(column)[s].to_numpy(), period,
min), period