tests.TestDataPortal.test_adjust_forward_fill_minute() - Code Metrics - Inspection of "Q 2.0" - quantopian/zipline - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Pull Request — master (#858)

by Eddie

created 2015-12-14 16:28 UTC

test_adjust_forward_fill_minute() C

↳ Parent: tests.TestDataPortal

Complexity

Conditions

Size

Total Lines

150

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	7
dl	0
loc	150
rs	5.3043

How to fix Long Method

import os
from datetime import timedelta
import bcolz
import numpy as np
import pandas as pd

from unittest import TestCase
from pandas.tslib import normalize_date
from testfixtures import TempDirectory
from zipline.data.data_portal import DataPortal
from zipline.data.us_equity_pricing import SQLiteAdjustmentWriter, \
    SQLiteAdjustmentReader
from zipline.finance.trading import TradingEnvironment, SimulationParameters
from zipline.data.us_equity_minutes import (
    MinuteBarWriterFromDataFrames,
    BcolzMinuteBarReader
)
from .utils.daily_bar_writer import DailyBarWriterFromDataFrames


class TestDataPortal(TestCase):
    def test_forward_fill_minute(self):
        tempdir = TempDirectory()
        try:
            env = TradingEnvironment()
            env.write_data(
                equities_data={
                    0: {
                        'start_date': pd.Timestamp("2015-09-28", tz='UTC'),
                        'end_date': pd.Timestamp("2015-09-29", tz='UTC')
                        + timedelta(days=1)
                    }
                }
            )

            minutes = env.minutes_for_days_in_range(
                start=pd.Timestamp("2015-09-28", tz='UTC'),
                end=pd.Timestamp("2015-09-29", tz='UTC')
            )

            df = pd.DataFrame({
                # one missing bar, then 200 bars of real data,
                # then 1.5 days of missing data
                "open": np.array([0] + list(range(0, 200)) + [0] * 579)
                * 1000,
                "high": np.array([0] + list(range(1000, 1200)) + [0] * 579)
                * 1000,
                "low": np.array([0] + list(range(2000, 2200)) + [0] * 579)
                * 1000,
                "close": np.array([0] + list(range(3000, 3200)) + [0] * 579)
                * 1000,
                "volume": [0] + list(range(4000, 4200)) + [0] * 579,
                "minute": minutes
            })

            MinuteBarWriterFromDataFrames(
                pd.Timestamp('2002-01-02', tz='UTC')).write(
                    tempdir.path, {0: df})

            sim_params = SimulationParameters(
                period_start=minutes[0],
                period_end=minutes[-1],
                data_frequency="minute",
                env=env,
            )

            equity_minute_reader = BcolzMinuteBarReader(tempdir.path)

            dp = DataPortal(
                env,
                equity_minute_reader=equity_minute_reader,
                sim_params=sim_params
            )

            for minute_idx, minute in enumerate(minutes):
                for field_idx, field in enumerate(
                        ["open", "high", "low", "close", "volume"]):
                    val = dp.get_spot_value(
                        0, field,
                        dt=minute,
                        data_frequency=sim_params.data_frequency)
                    if minute_idx == 0:
                        self.assertEqual(0, val)
                    elif minute_idx < 200:
                        self.assertEqual((minute_idx - 1) +
                                         (field_idx * 1000), val)
                    else:
                        self.assertEqual(199 + (field_idx * 1000), val)
        finally:
            tempdir.cleanup()

    def test_forward_fill_daily(self):
        tempdir = TempDirectory()
        try:
            # 17 trading days
            start_day = pd.Timestamp("2015-09-07", tz='UTC')
            end_day = pd.Timestamp("2015-09-30", tz='UTC')

            env = TradingEnvironment()
            env.write_data(
                equities_data={
                    0: {
                        'start_date': start_day,
                        'end_date': end_day
                    }
                }
            )

            days = env.days_in_range(start_day, end_day)

            # first bar is missing.  then 8 real bars.  then 8 more missing
            # bars.
            df = pd.DataFrame({
                "open": [0] + list(range(0, 8)) + [0] * 8,
                "high": [0] + list(range(10, 18)) + [0] * 8,
                "low": [0] + list(range(20, 28)) + [0] * 8,
                "close": [0] + list(range(30, 38)) + [0] * 8,
                "volume": [0] + list(range(40, 48)) + [0] * 8,
                "day": [day.value for day in days]
            }, index=days)

            assets = {0: df}
            path = os.path.join(tempdir.path, "testdaily.bcolz")

            DailyBarWriterFromDataFrames(assets).write(
                path,
                days,
                assets
            )

            sim_params = SimulationParameters(
                period_start=days[0],
                period_end=days[-1],
                data_frequency="daily"
            )

            dp = DataPortal(
                env,
                daily_equities_path=path,
                sim_params=sim_params
            )

            for day_idx, day in enumerate(days):
                for field_idx, field in enumerate(
                        ["open", "high", "low", "close", "volume"]):
                    val = dp.get_spot_value(
                        0, field,
                        dt=day,
                        data_frequency=sim_params.data_frequency)
                    if day_idx == 0:
                        self.assertEqual(0, val)
                    elif day_idx < 9:
                        self.assertEqual((day_idx - 1) + (field_idx * 10), val)
                    else:
                        self.assertEqual(7 + (field_idx * 10), val)
        finally:
            tempdir.cleanup()

    def test_adjust_forward_fill_minute(self):
        tempdir = TempDirectory()
        try:
            start_day = pd.Timestamp("2013-06-21", tz='UTC')
            end_day = pd.Timestamp("2013-06-24", tz='UTC')

            env = TradingEnvironment()
            env.write_data(
                equities_data={
                    0: {
                        'start_date': start_day,
                        'end_date': env.next_trading_day(end_day)
                    }
                }
            )

            minutes = env.minutes_for_days_in_range(
                start=start_day,
                end=end_day
            )

            df = pd.DataFrame({
                # 390 bars of real data, then 100 missing bars, then 290
                # bars of data again
                "open": np.array(list(range(0, 390)) + [0] * 100 +
                                 list(range(390, 680))) * 1000,
                "high": np.array(list(range(1000, 1390)) + [0] * 100 +
                                 list(range(1390, 1680))) * 1000,
                "low": np.array(list(range(2000, 2390)) + [0] * 100 +
                                list(range(2390, 2680))) * 1000,
                "close": np.array(list(range(3000, 3390)) + [0] * 100 +
                                  list(range(3390, 3680))) * 1000,
                "volume": np.array(list(range(4000, 4390)) + [0] * 100 +
                                   list(range(4390, 4680))),
                "minute": minutes
            })

            MinuteBarWriterFromDataFrames(
                pd.Timestamp('2002-01-02', tz='UTC')).write(
                    tempdir.path, {0: df})

            sim_params = SimulationParameters(
                period_start=minutes[0],
                period_end=minutes[-1],
                data_frequency="minute",
                env=env
            )

            # create a split for 6/24
            adjustments_path = os.path.join(tempdir.path, "adjustments.db")
            writer = SQLiteAdjustmentWriter(adjustments_path,
                                            pd.date_range(start=start_day,
                                                          end=end_day),
                                            None)

            splits = pd.DataFrame([{
                'effective_date': int(end_day.value / 1e9),
                'ratio': 0.5,
                'sid': 0
            }])

            dividend_data = {
                # Hackery to make the dtypes correct on an empty frame.
                'ex_date': np.array([], dtype='datetime64[ns]'),
                'pay_date': np.array([], dtype='datetime64[ns]'),
                'record_date': np.array([], dtype='datetime64[ns]'),
                'declared_date': np.array([], dtype='datetime64[ns]'),
                'amount': np.array([], dtype=float),
                'sid': np.array([], dtype=int),
            }
            dividends = pd.DataFrame(
                dividend_data,
                index=pd.DatetimeIndex([], tz='UTC'),
                columns=['ex_date',
                         'pay_date',
                         'record_date',
                         'declared_date',
                         'amount',
                         'sid']
            )

            merger_data = {
                # Hackery to make the dtypes correct on an empty frame.
                'effective_date': np.array([], dtype=int),
                'ratio': np.array([], dtype=float),
                'sid': np.array([], dtype=int),
            }
            mergers = pd.DataFrame(
                merger_data,
                index=pd.DatetimeIndex([], tz='UTC')
            )

            writer.write(splits, mergers, dividends)

            equity_minute_reader = BcolzMinuteBarReader(tempdir.path)

            dp = DataPortal(
                env,
                equity_minute_reader=equity_minute_reader,
                sim_params=sim_params,
                adjustment_reader=SQLiteAdjustmentReader(adjustments_path)
            )

            # phew, finally ready to start testing.
            for idx, minute in enumerate(minutes[:390]):
                for field_idx, field in enumerate(["open", "high", "low",
                                                   "close", "volume"]):
                    self.assertEqual(
                        dp.get_spot_value(
                            0, field,
                            dt=minute,
                            data_frequency=sim_params.data_frequency),
                        idx + (1000 * field_idx)
                    )

            for idx, minute in enumerate(minutes[390:490]):
                # no actual data for this part, so we'll forward-fill.
                # make sure the forward-filled values are adjusted.
                for field_idx, field in enumerate(["open", "high", "low",
                                                   "close"]):
                    self.assertEqual(
                        dp.get_spot_value(
                            0, field,
                            dt=minute,
                            data_frequency=sim_params.data_frequency),
                        (389 + (1000 * field_idx)) / 2.0
                    )

                self.assertEqual(
                    dp.get_spot_value(
                        0, "volume",
                        dt=minute,
                        data_frequency=sim_params.data_frequency),
                    8778  # 4389 * 2
                )

            for idx, minute in enumerate(minutes[490:]):
                # back to real data
                for field_idx, field in enumerate(["open", "high", "low",
                                                   "close", "volume"]):
                    self.assertEqual(
                        dp.get_spot_value(
                            0, field,
                            dt=minute,
                            data_frequency=sim_params.data_frequency
                        ),
                        (390 + idx + (1000 * field_idx))
                    )
        finally:
            tempdir.cleanup()

    def test_spot_value_futures(self):
        tempdir = TempDirectory()
        try:
            start_dt = pd.Timestamp("2015-11-20 20:11", tz='UTC')
            end_dt = pd.Timestamp(start_dt + timedelta(minutes=10000))

            zeroes_buffer = \
                [0] * int((start_dt -
                           normalize_date(start_dt)).total_seconds() / 60)

            df = pd.DataFrame({
                "open": np.array(zeroes_buffer + list(range(0, 10000))) * 1000,
                "high": np.array(
                    zeroes_buffer + list(range(10000, 20000))) * 1000,
                "low": np.array(
                    zeroes_buffer + list(range(20000, 30000))) * 1000,
                "close": np.array(
                    zeroes_buffer + list(range(30000, 40000))) * 1000,
                "volume": np.array(zeroes_buffer + list(range(40000, 50000)))
            })

            path = os.path.join(tempdir.path, "123.bcolz")
            ctable = bcolz.ctable.fromdataframe(df, rootdir=path)
            ctable.attrs["start_dt"] = start_dt.value / 1e9
            ctable.attrs["last_dt"] = end_dt.value / 1e9

            env = TradingEnvironment()
            env.write_data(futures_data={
                123: {
                    "start_date": normalize_date(start_dt),
                    "end_date": env.next_trading_day(normalize_date(end_dt)),
                    'symbol': 'TEST_FUTURE',
                    'asset_type': 'future',
                }
            })

            dp = DataPortal(
                env,
                minutes_futures_path=tempdir.path
            )

            future123 = env.asset_finder.retrieve_asset(123)

            data_frequency = 'minute'

            for i in range(0, 10000):
                dt = pd.Timestamp(start_dt + timedelta(minutes=i))

                self.assertEqual(i,
                                 dp.get_spot_value(
                                     future123, "open", dt, data_frequency))
                self.assertEqual(i + 10000,
                                 dp.get_spot_value(
                                     future123, "high", dt, data_frequency))
                self.assertEqual(i + 20000,
                                 dp.get_spot_value(
                                     future123, "low", dt, data_frequency))
                self.assertEqual(i + 30000,
                                 dp.get_spot_value(
                                     future123, "close", dt, data_frequency))
                self.assertEqual(i + 40000,
                                 dp.get_spot_value(
                                     future123, "volume", dt, data_frequency))

        finally:
            tempdir.cleanup()


1			import os
2			from datetime import timedelta
3			import bcolz
4			import numpy as np
5			import pandas as pd
6
7			from unittest import TestCase
8			from pandas.tslib import normalize_date
9			from testfixtures import TempDirectory
10			from zipline.data.data_portal import DataPortal
11			from zipline.data.us_equity_pricing import SQLiteAdjustmentWriter, \
12			SQLiteAdjustmentReader
13			from zipline.finance.trading import TradingEnvironment, SimulationParameters
14			from zipline.data.us_equity_minutes import (
15			MinuteBarWriterFromDataFrames,
16			BcolzMinuteBarReader
17			)
18			from .utils.daily_bar_writer import DailyBarWriterFromDataFrames
19
20
21			class TestDataPortal(TestCase):
22			def test_forward_fill_minute(self):
23			tempdir = TempDirectory()
24			try:
25			env = TradingEnvironment()
26			env.write_data(
27			equities_data={
28			0: {
29			'start_date': pd.Timestamp("2015-09-28", tz='UTC'),
30			'end_date': pd.Timestamp("2015-09-29", tz='UTC')
31			+ timedelta(days=1)
32			}
33			}
34			)
35
36			minutes = env.minutes_for_days_in_range(
37			start=pd.Timestamp("2015-09-28", tz='UTC'),
38			end=pd.Timestamp("2015-09-29", tz='UTC')
39			)
40
41			df = pd.DataFrame({
42			# one missing bar, then 200 bars of real data,
43			# then 1.5 days of missing data
44			"open": np.array([0] + list(range(0, 200)) + [0] * 579)
45			* 1000,
46			"high": np.array([0] + list(range(1000, 1200)) + [0] * 579)
47			* 1000,
48			"low": np.array([0] + list(range(2000, 2200)) + [0] * 579)
49			* 1000,
50			"close": np.array([0] + list(range(3000, 3200)) + [0] * 579)
51			* 1000,
52			"volume": [0] + list(range(4000, 4200)) + [0] * 579,
53			"minute": minutes
54			})
55
56			MinuteBarWriterFromDataFrames(
57			pd.Timestamp('2002-01-02', tz='UTC')).write(
58			tempdir.path, {0: df})
59
60			sim_params = SimulationParameters(
61			period_start=minutes[0],
62			period_end=minutes[-1],
63			data_frequency="minute",
64			env=env,
65			)
66
67			equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
68
69			dp = DataPortal(
70			env,
71			equity_minute_reader=equity_minute_reader,
72			sim_params=sim_params
73			)
74
75			for minute_idx, minute in enumerate(minutes):
76			for field_idx, field in enumerate(
77			["open", "high", "low", "close", "volume"]):
78			val = dp.get_spot_value(
79			0, field,
80			dt=minute,
81			data_frequency=sim_params.data_frequency)
82			if minute_idx == 0:
83			self.assertEqual(0, val)
84			elif minute_idx < 200:
85			self.assertEqual((minute_idx - 1) +
86			(field_idx * 1000), val)
87			else:
88			self.assertEqual(199 + (field_idx * 1000), val)
89			finally:
90			tempdir.cleanup()
91
92			def test_forward_fill_daily(self):
93			tempdir = TempDirectory()
94			try:
95			# 17 trading days
96			start_day = pd.Timestamp("2015-09-07", tz='UTC')
97			end_day = pd.Timestamp("2015-09-30", tz='UTC')
98
99			env = TradingEnvironment()
100			env.write_data(
101			equities_data={
102			0: {
103			'start_date': start_day,
104			'end_date': end_day
105			}
106			}
107			)
108
109			days = env.days_in_range(start_day, end_day)
110
111			# first bar is missing. then 8 real bars. then 8 more missing
112			# bars.
113			df = pd.DataFrame({
114			"open": [0] + list(range(0, 8)) + [0] * 8,
115			"high": [0] + list(range(10, 18)) + [0] * 8,
116			"low": [0] + list(range(20, 28)) + [0] * 8,
117			"close": [0] + list(range(30, 38)) + [0] * 8,
118			"volume": [0] + list(range(40, 48)) + [0] * 8,
119			"day": [day.value for day in days]
120			}, index=days)
121
122			assets = {0: df}
123			path = os.path.join(tempdir.path, "testdaily.bcolz")
124
125			DailyBarWriterFromDataFrames(assets).write(
126			path,
127			days,
128			assets
129			)
130
131			sim_params = SimulationParameters(
132			period_start=days[0],
133			period_end=days[-1],
134			data_frequency="daily"
135			)
136
137			dp = DataPortal(
138			env,
139			daily_equities_path=path,
140			sim_params=sim_params
141			)
142
143			for day_idx, day in enumerate(days):
144			for field_idx, field in enumerate(
145			["open", "high", "low", "close", "volume"]):
146			val = dp.get_spot_value(
147			0, field,
148			dt=day,
149			data_frequency=sim_params.data_frequency)
150			if day_idx == 0:
151			self.assertEqual(0, val)
152			elif day_idx < 9:
153			self.assertEqual((day_idx - 1) + (field_idx * 10), val)
154			else:
155			self.assertEqual(7 + (field_idx * 10), val)
156			finally:
157			tempdir.cleanup()
158
159			def test_adjust_forward_fill_minute(self):
160			tempdir = TempDirectory()
161			try:
162			start_day = pd.Timestamp("2013-06-21", tz='UTC')
163			end_day = pd.Timestamp("2013-06-24", tz='UTC')
164
165			env = TradingEnvironment()
166			env.write_data(
167			equities_data={
168			0: {
169			'start_date': start_day,
170			'end_date': env.next_trading_day(end_day)
171			}
172			}
173			)
174
175			minutes = env.minutes_for_days_in_range(
176			start=start_day,
177			end=end_day
178			)
179
180			df = pd.DataFrame({
181			# 390 bars of real data, then 100 missing bars, then 290
182			# bars of data again
183			"open": np.array(list(range(0, 390)) + [0] * 100 +
184			list(range(390, 680))) * 1000,
185			"high": np.array(list(range(1000, 1390)) + [0] * 100 +
186			list(range(1390, 1680))) * 1000,
187			"low": np.array(list(range(2000, 2390)) + [0] * 100 +
188			list(range(2390, 2680))) * 1000,
189			"close": np.array(list(range(3000, 3390)) + [0] * 100 +
190			list(range(3390, 3680))) * 1000,
191			"volume": np.array(list(range(4000, 4390)) + [0] * 100 +
192			list(range(4390, 4680))),
193			"minute": minutes
194			})
195
196			MinuteBarWriterFromDataFrames(
197			pd.Timestamp('2002-01-02', tz='UTC')).write(
198			tempdir.path, {0: df})
199
200			sim_params = SimulationParameters(
201			period_start=minutes[0],
202			period_end=minutes[-1],
203			data_frequency="minute",
204			env=env
205			)
206
207			# create a split for 6/24
208			adjustments_path = os.path.join(tempdir.path, "adjustments.db")
209			writer = SQLiteAdjustmentWriter(adjustments_path,
210			pd.date_range(start=start_day,
211			end=end_day),
212			None)
213
214			splits = pd.DataFrame([{
215			'effective_date': int(end_day.value / 1e9),
216			'ratio': 0.5,
217			'sid': 0
218			}])
219
220			dividend_data = {
221			# Hackery to make the dtypes correct on an empty frame.
222			'ex_date': np.array([], dtype='datetime64[ns]'),
223			'pay_date': np.array([], dtype='datetime64[ns]'),
224			'record_date': np.array([], dtype='datetime64[ns]'),
225			'declared_date': np.array([], dtype='datetime64[ns]'),
226			'amount': np.array([], dtype=float),
227			'sid': np.array([], dtype=int),
228			}
229			dividends = pd.DataFrame(
230			dividend_data,
231			index=pd.DatetimeIndex([], tz='UTC'),
232			columns=['ex_date',
233			'pay_date',
234			'record_date',
235			'declared_date',
236			'amount',
237			'sid']
238			)
239
240			merger_data = {
241			# Hackery to make the dtypes correct on an empty frame.
242			'effective_date': np.array([], dtype=int),
243			'ratio': np.array([], dtype=float),
244			'sid': np.array([], dtype=int),
245			}
246			mergers = pd.DataFrame(
247			merger_data,
248			index=pd.DatetimeIndex([], tz='UTC')
249			)
250
251			writer.write(splits, mergers, dividends)
252
253			equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
254
255			dp = DataPortal(
256			env,
257			equity_minute_reader=equity_minute_reader,
258			sim_params=sim_params,
259			adjustment_reader=SQLiteAdjustmentReader(adjustments_path)
260			)
261
262			# phew, finally ready to start testing.
263			for idx, minute in enumerate(minutes[:390]):
264			for field_idx, field in enumerate(["open", "high", "low",
265			"close", "volume"]):
266			self.assertEqual(
267			dp.get_spot_value(
268			0, field,
269			dt=minute,
270			data_frequency=sim_params.data_frequency),
271			idx + (1000 * field_idx)
272			)
273
274			for idx, minute in enumerate(minutes[390:490]):
275			# no actual data for this part, so we'll forward-fill.
276			# make sure the forward-filled values are adjusted.
277			for field_idx, field in enumerate(["open", "high", "low",
278			"close"]):
279			self.assertEqual(
280			dp.get_spot_value(
281			0, field,
282			dt=minute,
283			data_frequency=sim_params.data_frequency),
284			(389 + (1000 * field_idx)) / 2.0
285			)
286
287			self.assertEqual(
288			dp.get_spot_value(
289			0, "volume",
290			dt=minute,
291			data_frequency=sim_params.data_frequency),
292			8778 # 4389 * 2
293			)
294
295			for idx, minute in enumerate(minutes[490:]):
296			# back to real data
297			for field_idx, field in enumerate(["open", "high", "low",
298			"close", "volume"]):
299			self.assertEqual(
300			dp.get_spot_value(
301			0, field,
302			dt=minute,
303			data_frequency=sim_params.data_frequency
304			),
305			(390 + idx + (1000 * field_idx))
306			)
307			finally:
308			tempdir.cleanup()
309
310			def test_spot_value_futures(self):
311			tempdir = TempDirectory()
312			try:
313			start_dt = pd.Timestamp("2015-11-20 20:11", tz='UTC')
314			end_dt = pd.Timestamp(start_dt + timedelta(minutes=10000))
315
316			zeroes_buffer = \
317			[0] * int((start_dt -
318			normalize_date(start_dt)).total_seconds() / 60)
319
320			df = pd.DataFrame({
321			"open": np.array(zeroes_buffer + list(range(0, 10000))) * 1000,
322			"high": np.array(
323			zeroes_buffer + list(range(10000, 20000))) * 1000,
324			"low": np.array(
325			zeroes_buffer + list(range(20000, 30000))) * 1000,
326			"close": np.array(
327			zeroes_buffer + list(range(30000, 40000))) * 1000,
328			"volume": np.array(zeroes_buffer + list(range(40000, 50000)))
329			})
330
331			path = os.path.join(tempdir.path, "123.bcolz")
332			ctable = bcolz.ctable.fromdataframe(df, rootdir=path)
333			ctable.attrs["start_dt"] = start_dt.value / 1e9
334			ctable.attrs["last_dt"] = end_dt.value / 1e9
335
336			env = TradingEnvironment()
337			env.write_data(futures_data={
338			123: {
339			"start_date": normalize_date(start_dt),
340			"end_date": env.next_trading_day(normalize_date(end_dt)),
341			'symbol': 'TEST_FUTURE',
342			'asset_type': 'future',
343			}
344			})
345
346			dp = DataPortal(
347			env,
348			minutes_futures_path=tempdir.path
349			)
350
351			future123 = env.asset_finder.retrieve_asset(123)
352
353			data_frequency = 'minute'
354
355			for i in range(0, 10000):
356			dt = pd.Timestamp(start_dt + timedelta(minutes=i))
357
358			self.assertEqual(i,
359			dp.get_spot_value(
360			future123, "open", dt, data_frequency))
361			self.assertEqual(i + 10000,
362			dp.get_spot_value(
363			future123, "high", dt, data_frequency))
364			self.assertEqual(i + 20000,
365			dp.get_spot_value(
366			future123, "low", dt, data_frequency))
367			self.assertEqual(i + 30000,
368			dp.get_spot_value(
369			future123, "close", dt, data_frequency))
370			self.assertEqual(i + 40000,
371			dp.get_spot_value(
372			future123, "volume", dt, data_frequency))
373
374			finally:
375			tempdir.cleanup()
376

quantopian / zipline

Pull Request — master (#858)

test_adjust_forward_fill_minute() C

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