def projection(x: np.ndarray, groups: np.ndarray=None, axis=1) -> np.ndarray:
if groups is not None and axis == 0:
groups = group_mapping(groups)
projected = transform(groups, x, 'project')
return projected
else:
return x / simple_sqrsum(x, axis=axis).reshape((-1, 1))
def fit(self, x: np.ndarray, groups: np.ndarray = None):
if groups is not None:
group_index = group_mapping(groups)
self.mean = aggregate(group_index, x, 'mean')
self.std = aggregate(group_index, x, 'std', self.ddof)
self.labels = np.unique(groups)
else:
self.mean = simple_mean(x, axis=0)
self.std = simple_std(x, axis=0, ddof=self.ddof)
def standardize(x: np.ndarray, groups: np.ndarray = None, ddof=1) -> np.ndarray:
if groups is not None:
groups = group_mapping(groups)
mean_values = transform(groups, x, 'mean')
std_values = transform(groups, x, 'std', ddof)
return (x - mean_values) / np.maximum(std_values, 1e-8)
else:
return (x - simple_mean(x, axis=0)) / np.maximum(simple_std(x, axis=0, ddof=ddof), 1e-8)
def long_short_build(er: np.ndarray,
leverage: float=1.,
groups: np.ndarray=None) -> np.ndarray:
if er.ndim == 1:
er = er.reshape((-1, 1))
if groups is None:
return er / simple_abssum(er, axis=0) * leverage
else:
groups = group_mapping(groups)
return transform(groups, er, 'scale', scale=leverage)
def winsorize_normal(x: np.ndarray,
num_stds: int = 3,
groups: np.ndarray = None) -> np.ndarray:
if groups is not None:
groups = group_mapping(groups)
mean_values = transform(groups, x, 'mean')
std_values = transform(groups, x, 'std')
res = mask_values_2d(x, mean_values, std_values, num_stds)
else:
std_values = simple_std(x, axis=0)
mean_values = simple_mean(x, axis=0)
res = mask_values_1d(x, mean_values, std_values, num_stds)
return res
def simple_settle(weights: np.ndarray,
ret_series: np.ndarray,
groups: np.ndarray = None) -> np.ndarray:
if ret_series.ndim == 1:
ret_series = ret_series.reshape((-1, 1))
ret_mat = weights * ret_series
if groups is not None:
groups = group_mapping(groups)
return aggregate(groups, ret_mat, 'sum')
else:
if ret_mat.ndim == 1:
ret_mat = ret_mat.reshape((-1, 1))
return simple_sum(ret_mat, axis=0)
def long_short_builder(er: np.ndarray,
leverage: float = 1.,
groups: np.ndarray = None,
masks: np.ndarray = None) -> np.ndarray:
er = er.copy()
if masks is not None:
er[masks] = 0.
er[~masks] = er[~masks] - er[~masks].mean()
if er.ndim == 1:
er = er.reshape((-1, 1))
if groups is None:
return er / simple_abssum(er, axis=0) * leverage
else:
groups = group_mapping(groups)
return transform(groups, er, 'scale', scale=leverage)
def winsorize_normal(x: np.ndarray,
num_stds: int = 3,
ddof=1,
groups: np.ndarray = None,
method: str = 'flat',
interval: float = 0.5) -> np.ndarray:
if groups is not None:
groups = group_mapping(groups)
mean_values = transform(groups, x, 'mean')
std_values = transform(groups, x, 'std', ddof)
if method == 'flat':
res = mask_values_2d(x, mean_values, std_values, num_stds)
else:
res = interp_values_2d(x, groups, mean_values, std_values,
num_stds, interval)
else:
std_values = simple_std(x, axis=0, ddof=ddof)
mean_values = simple_mean(x, axis=0)
if method == 'flat':
res = mask_values_1d(x, mean_values, std_values, num_stds)
else:
res = interp_values_1d(x, mean_values, std_values, num_stds,
interval)
return res
def fit(self, x: np.ndarray):
raw_groups = x[:, 0].astype(int)
groups = group_mapping(raw_groups)
self.mean_ = aggregate(groups, x[:, 1:], 'mean')
self.std_ = aggregate(groups, x[:, 1:], 'std', self.ddof_)
self.labels_ = np.unique(raw_groups)
