def __new__(cls,
target,
regression_length,
allowed_missing_percentage=0.25):
daily_returns = Returns(
window_length=2,
mask=(AssetExists() | SingleAsset(asset=target)),
)
allowed_missing_count = int(allowed_missing_percentage *
regression_length)
return super().__new__(
cls,
inputs=[daily_returns, daily_returns[target]],
window_length=regression_length,
allowed_missing_count=allowed_missing_count,
)
def __new__(cls,
target,
returns_length,
regression_length,
mask=NotSpecified):
# Use the `SingleAsset` filter here because it protects against
# inputting a non-existent target asset.
returns = Returns(
window_length=returns_length,
mask=(AssetExists() | SingleAsset(asset=target)),
)
return super().__new__(
cls,
dependent=returns,
independent=returns[target],
regression_length=regression_length,
mask=mask,
)
def __new__(cls,
target,
returns_length,
correlation_length,
mask=NotSpecified):
# Use the `SingleAsset` filter here because it protects against
# inputting a non-existent target asset.
returns = Returns(
window_length=returns_length,
mask=(AssetExists() | SingleAsset(asset=target)),
)
return super(RollingSpearmanOfReturns, cls).__new__(
cls,
base_factor=returns,
target=returns[target],
correlation_length=correlation_length,
mask=mask,
)
def __new__(cls,
target,
returns_length,
regression_length,
mask=NotSpecified,
**kwargs):
if mask is NotSpecified:
mask = AssetExists()
# Make sure we do not filter out the asset of interest.
mask = mask | SingleAsset(asset=target)
return super(RollingLinearRegressionOfReturns, cls).__new__(
cls,
target=target,
inputs=[Returns(window_length=returns_length)],
window_length=regression_length,
mask=mask,
**kwargs)
