def smooth_xy(x, y):
x = np.squeeze(x)
y = np.squeeze(y)
#v = lowess(y, x, frac=.05)
kernel_reg = KernelReg(y, x, var_type='c', reg_type='lc')
kernel_reg.bw = np.asarray([.01])
y = kernel_reg.fit(x)[0]
return x, y
def calc_smooth(prices: pd.Series, *, bw: Union[np.ndarray, str] = 'cv_ls', a: float = None, use_array: bool = True) -> Union[pd.Series, np.ndarray]:
"""计算Nadaraya-Watson核估计后的价格数据
Args:
prices (pd.Series): 价格数据
bw (Union[np.ndarray,str]): Either a user-specified bandwidth or the method for bandwidth selection. Defaults to cv_ls.
a (float, optional): 论文中所说的比例数据. Defaults to None.
use_array (bool, optional): 为True返回ndarray,False返回为pd.Series. Defaults to True.
Returns:
Union[pd.Series,np.ndarry]
"""
if not isinstance(prices, pd.Series):
raise ValueError('prices必须为pd.Series')
idx = np.arange(len(prices))
kr = KernelReg(prices.values, idx,
var_type='c', reg_type='ll', bw=bw)
if a is None:
f = kr.fit(idx)[0]
else:
kr.bw = a * kr.bw # 论文用的0.3 * h
f = kr.fit(idx)[0]
if use_array:
return f
else:
return pd.Series(data=f, index=prices.index)
