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https://github.com/wassname/catalyst.git
synced 2026-07-05 05:20:30 +08:00
MAINT: Remove outdated compat code.
This commit is contained in:
Scott Sanderson
@@ -80,12 +80,6 @@ from zipline.testing.fixtures import (
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)
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from zipline.utils.memoize import lazyval
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from zipline.utils.numpy_utils import bool_dtype, datetime64ns_dtype
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from zipline.utils.pandas_utils import (
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ewma,
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ewmstd,
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rolling_apply,
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rolling_mean,
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)
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class RollingSumDifference(CustomFactor):
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@@ -1010,16 +1004,16 @@ class SyntheticBcolzTestCase(WithAdjustmentReader,
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# Shift back the raw inputs by a trading day because we expect our
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# computed results to be computed using values anchored on the
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# **previous** day's data.
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expected_raw = rolling_mean(
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DataFrame(
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expected_bar_values_2d(
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dates - self.trading_calendar.day,
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self.equity_info,
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'close',
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),
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expected_raw = DataFrame(
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expected_bar_values_2d(
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dates - self.trading_calendar.day,
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self.equity_info,
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'close',
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),
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).rolling(
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window_length,
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min_periods=1,
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).mean(
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).values
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expected = DataFrame(
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@@ -1131,10 +1125,11 @@ class ParameterizedFactorTestCase(WithTradingEnvironment, ZiplineTestCase):
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# Don't use it outside of testing. We're using rolling-apply of an
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# ewma (which is itself a rolling-window function) because we only want
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# to look at ``window_length`` rows at a time.
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return rolling_apply(
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self.raw_data,
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window_length,
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lambda window: ewma(DataFrame(window), span=span).values[-1],
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return self.raw_data.rolling(window_length).apply(
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lambda subarray: (DataFrame(subarray)
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.ewm(span=span)
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.mean()
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.values[-1])
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)[window_length:]
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def expected_ewmstd(self, window_length, decay_rate):
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@@ -1145,10 +1140,11 @@ class ParameterizedFactorTestCase(WithTradingEnvironment, ZiplineTestCase):
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# EWMSTD. Don't use it outside of testing. We're using rolling-apply
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# of an ewma (which is itself a rolling-window function) because we
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# only want to look at ``window_length`` rows at a time.
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return rolling_apply(
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self.raw_data,
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window_length,
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lambda window: ewmstd(DataFrame(window), span=span).values[-1],
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return self.raw_data.rolling(window_length).apply(
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lambda subarray: (DataFrame(subarray)
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.ewm(span=span)
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.std()
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.values[-1])
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)[window_length:]
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@parameterized.expand([
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@@ -1273,7 +1269,7 @@ class ParameterizedFactorTestCase(WithTradingEnvironment, ZiplineTestCase):
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expected_1 = (self.raw_data[5:] ** 2) * 2
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assert_frame_equal(results['dv1'].unstack(), expected_1)
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expected_5 = rolling_mean((self.raw_data ** 2) * 2, window=5)[5:]
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expected_5 = ((self.raw_data ** 2) * 2).rolling(5).mean()[5:]
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assert_frame_equal(results['dv5'].unstack(), expected_5)
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# The following two use USEquityPricing.open and .volume as inputs.
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@@ -1283,9 +1279,11 @@ class ParameterizedFactorTestCase(WithTradingEnvironment, ZiplineTestCase):
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* self.raw_data[5:] * 2).fillna(0)
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assert_frame_equal(results['dv1_nan'].unstack(), expected_1_nan)
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expected_5_nan = rolling_mean((self.raw_data_with_nans
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* self.raw_data * 2).fillna(0),
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window=5)[5:]
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expected_5_nan = ((self.raw_data_with_nans * self.raw_data * 2)
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.fillna(0)
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.rolling(5).mean()
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[5:])
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assert_frame_equal(results['dv5_nan'].unstack(), expected_5_nan)
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@@ -187,7 +187,6 @@ from zipline.utils.input_validation import (
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optionally,
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)
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from zipline.utils.numpy_utils import bool_dtype, categorical_dtype
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from zipline.utils.pandas_utils import sort_values
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from zipline.utils.pool import SequentialPool
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from zipline.utils.preprocess import preprocess
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@@ -754,7 +753,7 @@ def overwrite_novel_deltas(baseline, deltas, dates):
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ignore_index=True,
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copy=False,
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)
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sort_values(cat, TS_FIELD_NAME, inplace=True)
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cat.sort_values(TS_FIELD_NAME, inplace=True)
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return cat, non_novel_deltas
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@@ -16,7 +16,6 @@ from pandas import (
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from zipline.lib.adjusted_array import AdjustedArray
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from zipline.lib.adjustment import make_adjustment_from_labels
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from zipline.utils.numpy_utils import as_column
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from zipline.utils.pandas_utils import sort_values
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from .base import PipelineLoader
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ADJUSTMENT_COLUMNS = Index([
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@@ -73,7 +72,7 @@ class DataFrameLoader(PipelineLoader):
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else:
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# Ensure that columns are in the correct order.
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adjustments = adjustments.reindex_axis(ADJUSTMENT_COLUMNS, axis=1)
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sort_values(adjustments, ['apply_date', 'sid'], inplace=True)
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adjustments.sort_values(['apply_date', 'sid'], inplace=True)
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self.adjustments = adjustments
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self.adjustment_apply_dates = DatetimeIndex(adjustments.apply_date)
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@@ -12,6 +12,10 @@ from distutils.version import StrictVersion
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pandas_version = StrictVersion(pd.__version__)
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def july_5th_holiday_observance(datetime_index):
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return datetime_index[datetime_index.year != 2013]
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def explode(df):
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"""
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Take a DataFrame and return a triple of
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@@ -21,19 +25,6 @@ def explode(df):
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return df.index, df.columns, df.values
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try:
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# This branch is hit in pandas 17
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sort_values = pd.DataFrame.sort_values
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except AttributeError:
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# This branch is hit in pandas 16
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sort_values = pd.DataFrame.sort
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if pandas_version >= StrictVersion('0.17.1'):
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july_5th_holiday_observance = lambda dtix: dtix[dtix.year != 2013]
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else:
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july_5th_holiday_observance = lambda dt: None if dt.year == 2013 else dt
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def _time_to_micros(time):
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"""Convert a time into microseconds since midnight.
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Parameters
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@@ -175,85 +166,3 @@ def ignore_pandas_nan_categorical_warning():
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category=FutureWarning,
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)
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yield
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# Remove when we drop support for 0.17
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if pandas_version >= StrictVersion('0.18'):
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def rolling_mean(arg,
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window,
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min_periods=None,
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freq=None,
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center=False,
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**kwargs):
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return arg.rolling(
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window,
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min_periods=min_periods,
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freq=freq,
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center=center,
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**kwargs
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).mean()
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def rolling_apply(arg,
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window,
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func,
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min_periods=None,
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freq=None,
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center=False,
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**kwargs):
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return arg.rolling(
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window,
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min_periods=min_periods,
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freq=freq,
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center=center,
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**kwargs
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).apply(func)
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def ewma(arg,
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com=None,
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span=None,
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halflife=None,
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alpha=None,
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min_periods=0,
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freq=None,
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adjust=True,
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how=None,
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ignore_na=False):
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return arg.ewm(
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com=com,
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span=span,
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halflife=halflife,
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alpha=alpha,
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min_periods=min_periods,
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freq=freq,
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adjust=adjust,
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ignore_na=ignore_na,
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).mean()
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def ewmstd(arg,
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com=None,
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span=None,
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halflife=None,
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alpha=None,
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min_periods=0,
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freq=None,
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adjust=True,
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how=None,
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ignore_na=False):
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return arg.ewm(
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com=com,
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span=span,
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halflife=halflife,
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alpha=alpha,
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min_periods=min_periods,
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freq=freq,
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adjust=adjust,
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ignore_na=ignore_na,
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).std()
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else:
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rolling_mean = pd.rolling_mean
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rolling_apply = pd.rolling_apply
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ewma = pd.ewma
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ewmstd = pd.ewmstd
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