diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f0b1714..c122664 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,7 +7,7 @@ repos: - id: trailing-whitespace repo: https://github.com/astral-sh/ruff-pre-commit # Ruff version. - rev: v0.13.0 + rev: v0.15.0 hooks: # Run the linter. - id: ruff-check diff --git a/.python-version b/.python-version index c8cfe39..24ee5b1 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.10 +3.13 diff --git a/README.md b/README.md index b4c8a74..1c5bd7b 100644 --- a/README.md +++ b/README.md @@ -271,6 +271,8 @@ The first beta version of `market_prices` was released May 2022. Whilst the test suite is pretty comprehensive, there will inevitably be bugs. Please do raise an [issue](https://github.com/maread99/market_prices/issues) with any that you come across. Even better, offer a PR! Contributions welcome. +The package is actively maintained to work with the latest versions of its dependencies, which include `numpy` and `pandas`. **Only limited consideration is given to backwards compatibility with earlier versions of dependencies** - no guarantees are offered in this regard. Of note, whilst the latest release works with pandas v3 and v2, support for pandas v1 was dropped some time ago. + Please use [discussions](https://github.com/maread99/market_prices/discussions) to make any suggestions and offer general feedback. ## Disclaimers diff --git a/pyproject.toml b/pyproject.toml index 43f69b1..2f7cce3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,7 +52,7 @@ classifiers = [ ] dependencies = [ - "exchange_calendars", + "exchange-calendars", "numpy", "pandas", "tzdata", @@ -64,6 +64,7 @@ dependencies = [ test = [ "attrs", "hypothesis", + "pytz", # required as stored hdf data includes pytz objects "pytest", "pytest-mock", "tables", diff --git a/requirements.txt b/requirements.txt index d960aab..a86bc31 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ # This file was autogenerated by uv via the following command: -# uv export --format requirements-txt --no-emit-project --no-dev --no-hashes -o requirements.txt +# uv export --format requirements-txt --no-emit-project --no-hashes --no-dev -o requirements.txt beautifulsoup4==4.14.3 # via yahooquery certifi==2026.1.4 @@ -14,7 +14,7 @@ colorama==0.4.6 ; sys_platform == 'win32' # via tqdm curl-cffi==0.14.0 # via yahooquery -exchange-calendars==4.12 +exchange-calendars==4.13.1 # via market-prices idna==3.11 # via requests @@ -27,23 +27,28 @@ numpy==2.2.6 ; python_full_version < '3.11' # exchange-calendars # market-prices # pandas -numpy==2.4.1 ; python_full_version >= '3.11' +numpy==2.4.2 ; python_full_version >= '3.11' # via # exchange-calendars # market-prices # pandas -pandas==2.3.3 +pandas==2.3.3 ; python_full_version < '3.11' # via # exchange-calendars # market-prices # yahooquery -pycparser==2.23 ; implementation_name != 'PyPy' +pandas==3.0.0 ; python_full_version >= '3.11' + # via + # exchange-calendars + # market-prices + # yahooquery +pycparser==3.0 ; implementation_name != 'PyPy' # via cffi pyluach==2.3.0 # via exchange-calendars python-dateutil==2.9.0.post0 # via pandas -pytz==2025.2 +pytz==2025.2 ; python_full_version < '3.11' # via pandas requests==2.32.5 # via requests-futures @@ -51,11 +56,11 @@ requests-futures==1.0.2 # via yahooquery six==1.17.0 # via python-dateutil -soupsieve==2.8.1 +soupsieve==2.8.3 # via beautifulsoup4 toolz==1.1.0 # via exchange-calendars -tqdm==4.67.1 +tqdm==4.67.3 # via yahooquery typing-extensions==4.15.0 # via beautifulsoup4 diff --git a/src/market_prices/helpers.py b/src/market_prices/helpers.py index a4d6936..8ef5bd1 100644 --- a/src/market_prices/helpers.py +++ b/src/market_prices/helpers.py @@ -211,7 +211,7 @@ def freq_format_error_msg() -> str: return ( f'interval/frequency received as "{freq}", although must be defined' " as one or more consecutive digits followed by one or more" - ' consecutive letters. Valid examples: "30min", "3h", "1d", "3m".' + ' consecutive letters. Valid examples: "30min", "3h", "1D", "3m".' ) unit = genutils.remove_digits(freq) @@ -375,6 +375,14 @@ def resample( else: agg_f = agg_funcs(data) + # convert days to hours as Day is not considered a Tick frequency since pandas 3.0 + if isinstance(rule, str): + n, unit = extract_freq_parts(rule) + if unit.upper() == "D": + rule = f"{24 * n}h" + if isinstance(rule, pd.offsets.Day): + rule = f"{24 * rule.n}h" + resampler = resample_me.resample(rule, closed="left", label="left", origin=origin) resampled = resampler.agg(agg_f) resampled.columns = columns_ diff --git a/src/market_prices/prices/base.py b/src/market_prices/prices/base.py index f84969b..67e1f68 100644 --- a/src/market_prices/prices/base.py +++ b/src/market_prices/prices/base.py @@ -3337,7 +3337,7 @@ def get( # noqa: C901, PLR0912 p.get('10T', weeks=2) last two calendar weeks at ten minute intervals. p.get(days=5) last five trading days at inferred interval. - p.get('1d', months=6) daily data for last six months. + p.get('1D', months=6) daily data for last six months. p.get('1h', months=3, end='2021-03-17 15:00') three months at hourly intervals to defined datetime (timezone of 'end' assumed as most common timezone of all symbols). @@ -3514,7 +3514,7 @@ def get( # noqa: C901, PLR0912 unit: "min", "MIN", "T" or "t" for minutes "h" or "H" for hours - "d" or "D' for days + "D' for days 'm' or "M" for months Examples: @@ -3529,7 +3529,7 @@ def get( # noqa: C901, PLR0912 timedelta(hours=3) one day: - "1d", "1D" + "1D" pd.Timedelta(1, "D"), pd.Timedelta(days=1) timedelta(days=1), timedelta(1) @@ -3538,8 +3538,8 @@ def get( # noqa: C901, PLR0912 others: "90min", "90MIN" - ninety minutes - "5D", "5d" - five days - "40D", "40d" - forty days + "5D" - five days + "40D" - forty days "1M", "1m" - one month pd.Timedelta(hours=3, minutes=30) - three and a half hours diff --git a/src/market_prices/prices/csv.py b/src/market_prices/prices/csv.py index 1213f38..b14ca7c 100644 --- a/src/market_prices/prices/csv.py +++ b/src/market_prices/prices/csv.py @@ -33,8 +33,9 @@ CSV_READ_DFLT_KWARGS: dict[str, Any] = { "header": 0, - "usecols": lambda x: x.lower() - in ["date", "open", "high", "low", "close", "volume"], + "usecols": lambda x: ( + x.lower() in ["date", "open", "high", "low", "close", "volume"] + ), "index_col": "date", "parse_dates": ["date"], } @@ -118,7 +119,7 @@ def _get_symbol_from_filename(name: str, symbols: list[str]) -> str | None: # at least two different symbols in filename return None symbol = part - return symbol if symbol else None + return symbol or None def _get_interval_from_filename(name: str) -> TDInterval | None: @@ -1095,7 +1096,7 @@ def __init__( root = path self.PM_SUBSESSION_ORIGIN = pm_subsession_origin # override class attr symbols_ = helpers.symbols_to_list(symbols) - delays = {symb: pd.Timedelta(0) for symb in symbols_} + delays = dict.fromkeys(symbols_, 0) csv_kwargs = _get_csv_read_kwargs(read_csv_kwargs) paths = self._csv_paths = get_csv_paths(root, symbols_) @@ -1167,7 +1168,7 @@ def _compile_tables( dfs.append(df_symb_) if not dfs: continue - df = pd.concat(dfs, axis=1) + df = pd.concat(dfs, axis=1, sort=True) df = df.sort_index() df.columns = df.columns.set_names("symbol", level=0) dfs_by_intrvl[interval] = df diff --git a/src/market_prices/prices/yahoo.py b/src/market_prices/prices/yahoo.py index 7720bcc..09e7695 100644 --- a/src/market_prices/prices/yahoo.py +++ b/src/market_prices/prices/yahoo.py @@ -814,7 +814,7 @@ def _tidy_yahoo( # noqa: C901, PLR0912 # for, at least, hong kong stocks include a single index after the close. params = dict(interval=interval, start=start, end=end) raise errors.PricesUnavailableFromSourceError(params, df) - df = pd.concat(sdfs, axis=1) + df = pd.concat(sdfs, axis=1, sort=True) if empty_sdfs: # add symbols for which prices unavailable over requested period diff --git a/src/market_prices/pt.py b/src/market_prices/pt.py index f437bfa..1c1d322 100644 --- a/src/market_prices/pt.py +++ b/src/market_prices/pt.py @@ -1244,7 +1244,7 @@ def _downsample_cbdays( "\nNB. Downsampling will downsample to a frequency defined in" " CustomBusinessDay when either `pdfreq` is passed as a" " CustomBusinessDay (or multiple of) or when the table has a" - ' CustomBusinessDay frequency and `pdfreq` is passed with unit "d".' + ' CustomBusinessDay frequency and `pdfreq` is passed with unit "D".' ) if not isinstance(calendar, xcals.ExchangeCalendar): @@ -1261,7 +1261,7 @@ def _downsample_cbdays( # Passing 'end' to the origin argument of DataFrame.resample has no # effect when passing rule as a CustomBusinessDay. - origin = "start" + origin = "start_day" # To ensure every indice, INCLUDING the last indice, comprises x # CustomBusinessDays it's necessary to curtail the start of the dataframe @@ -1286,8 +1286,8 @@ def _downsample_cbdays( resampled.index.right.freq = pdfreq return resampled - def _downsample_days(self, pdfreq: str | pd.offsets.BaseOffset) -> pd.DataFrame: - """Downsample to a frequency with unit "d".""" + def _downsample_days(self, pdfreq: str | pd.offsets.Day) -> pd.DataFrame: + """Downsample to a frequency with unit "D".""" df = self.prices pd_offset = pd.tseries.frequencies.to_offset(pdfreq) @@ -1329,7 +1329,9 @@ def _downsample_months( if not pre_table_sessions.empty: start_ds = pd_offset.rollforward(start_table) df = df[start_ds:] - resampled = helpers.resample(df, pdfreq, origin="start", nominal_start=start_ds) + resampled = helpers.resample( + df, pdfreq, origin="start_day", nominal_start=start_ds + ) resampled.index = pdutils.get_interval_index(resampled.index, pdfreq) if drop_incomplete_last_indice: @@ -1359,13 +1361,13 @@ def downsample( pdfreq Downsample frequency as CustomBusinessDay or any valid input to pd.tseries.frequencies.to_offset. Examples: - '2d', '3d', '5d', '10d', 'MS', 'QS' + '2D', '3D', '5D', '10D', 'MS', 'QS' pd.offsets.Day(5), pd.offsets.Day(15) 3 * calendar.day (where calendar is an instance of xcals.ExchangeCalendar and hence calendar.day is an instance of CustomBusinessDay). - If `pdfreq` has unit 'd', for example "5d", and table has a + If `pdfreq` has unit 'D', for example "5D", and table has a frequency in CustomBusinessDay then will assume required frequency is the table's CustomBusinessDay frequency. @@ -1440,7 +1442,7 @@ def downsample( f"Received `pdfreq` as {pdfreq} although must be either of" " type pd.offsets.CustomBusinessDay or acceptable input to" " pd.tseries.frequencies.to_offset that describes a" - ' frequency greater than one day. For example "2d", "5d"' + ' frequency greater than one day. For example "2D", "5D"' ' "QS" etc.' ) raise ValueError(msg) from None @@ -1449,7 +1451,7 @@ def downsample( freqstr: str = offset.freqstr value, unit = helpers.extract_freq_parts(freqstr) - if unit.lower() == "d": + if unit.upper() == "D": if isinstance(self.freq, pd.offsets.CustomBusinessDay): pdfreq = self.freq.base * value return self._downsample_cbdays(pdfreq, calendar) @@ -1459,7 +1461,7 @@ def downsample( ext = ["t", "T", "H", "S"] # for pandas pre 2.2 compatibility if unit in invalid_units + ext: raise ValueError( - "Cannot downsample to a `pdfreq` with a unit more precise than 'd'." + "Cannot downsample to a `pdfreq` with a unit more precise than 'D'." ) return self._downsample_months(freqstr, calendar, drop_incomplete_last_indice) diff --git a/src/market_prices/utils/pandas_utils.py b/src/market_prices/utils/pandas_utils.py index fe9ccd5..9178d13 100644 --- a/src/market_prices/utils/pandas_utils.py +++ b/src/market_prices/utils/pandas_utils.py @@ -94,12 +94,11 @@ def timestamps_in_interval_of_intervals( ... pd.Timestamp('2021-03-16 13:00'), ... ] ... ) - >>> start, end = pd.Timestamp('2021-03-02'), pd.Timestamp('2021-03-31') + >>> start, end = pd.Timestamp('2021-03-02').as_unit("ns"), pd.Timestamp('2021-03-31').as_unit("ns") >>> closed = "both" >>> intervals = pd.interval_range(start, end, freq="3D", closed=closed) >>> timestamps_in_interval_of_intervals(timestamps, intervals) False - >>> intervals = pd.interval_range(start, end, freq="10D", closed=closed) >>> intervals IntervalIndex([[2021-03-02 00:00:00, 2021-03-12 00:00:00], [2021-03-12 00:00:00, 2021-03-22 00:00:00]], dtype='interval[datetime64[ns], both]') @@ -594,9 +593,11 @@ def indexes_union(indexes: list[pd.Index]) -> pd.Index: Examples -------- - >>> index1 = pd.date_range('2021-05-01 12:20', periods=2, freq='1h') - >>> index2 = pd.date_range('2021-05-02 17:10', periods=2, freq='22min') - >>> index3 = pd.date_range('2021-05-03', periods=2, freq='1D') + >>> index1 = pd.date_range('2021-05-01 12:20', periods=2, freq='1h').as_unit("ns") + >>> index2 = pd.date_range( + ... '2021-05-02 17:10', periods=2, freq='22min' + ... ).as_unit("ns") + >>> index3 = pd.date_range('2021-05-03', periods=2, freq='1D').as_unit("ns") >>> indexes_union([index1, index2, index3]) DatetimeIndex(['2021-05-01 12:20:00', '2021-05-01 13:20:00', '2021-05-02 17:10:00', '2021-05-02 17:32:00', @@ -621,9 +622,11 @@ def index_union(indexes: list[pd.Index | pd.Series | pd.DataFrame]) -> pd.Index: Examples -------- - >>> index1 = pd.date_range('2021-05-01 12:20', periods=2, freq='1h') - >>> index2 = pd.date_range('2021-05-02 17:10', periods=2, freq='22min') - >>> index3 = pd.date_range('2021-05-03', periods=2, freq='1D') + >>> index1 = pd.date_range('2021-05-01 12:20', periods=2, freq='1h').as_unit("ns") + >>> index2 = pd.date_range( + ... '2021-05-02 17:10', periods=2, freq='22min' + ... ).as_unit("ns") + >>> index3 = pd.date_range('2021-05-03', periods=2, freq='1D').as_unit("ns") >>> ser = pd.Series(range(2), index=index2) >>> df = pd.DataFrame({'col_int': range(2)}, index=index3) >>> index_union([index1, ser, df]) diff --git a/tests/resources/store.h5 b/tests/resources/store.h5 index f805220..c30dba9 100644 Binary files a/tests/resources/store.h5 and b/tests/resources/store.h5 differ diff --git a/tests/resources/store_pbt.h5 b/tests/resources/store_pbt.h5 index 0c63cff..e7032b2 100644 Binary files a/tests/resources/store_pbt.h5 and b/tests/resources/store_pbt.h5 differ diff --git a/tests/test_base.py b/tests/test_base.py index ff5af85..f40e0be 100644 --- a/tests/test_base.py +++ b/tests/test_base.py @@ -2182,7 +2182,7 @@ def assertions(prices: m.PricesBase, H1_aligned: pd.DatetimeIndex): "2021-12-28", # xlon not open, xhkg and xnys do not overlap "2021-12-31", # all open but don't conflict due to early closes ] - ) + ).as_unit("ns") assertions(prices, H1_expected) bi = prices.bis.H1 # limit of range over which indices aligned at 1H @@ -2204,7 +2204,7 @@ def assertions(prices: m.PricesBase, H1_aligned: pd.DatetimeIndex): "2021-09-06", # xhkg pm session and xlon session overlap but do not conflict # noqa: E501 "2021-11-25", # xhkg pm session close contiguous with start xlon session # noqa: E501 ] - ) + ).as_unit("ns") H1_expected = H1_expected.union(additional_expected) assertions(prices, H1_expected) @@ -2212,7 +2212,7 @@ def assertions(prices: m.PricesBase, H1_aligned: pd.DatetimeIndex): calendars = [xnys, xnys, x247] prices = PricesMock(symbols, calendars) xnys_sessions = xnys.sessions_in_range("2021", "2021-12-31") - all_dates = pd.date_range("2021", "2021-12-31") + all_dates = pd.date_range("2021", "2021-12-31").as_unit("ns") H1_expected = all_dates.difference(xnys_sessions) assertions(prices, H1_expected) diff --git a/tests/test_base_prices.py b/tests/test_base_prices.py index 024d013..0dfc9c5 100644 --- a/tests/test_base_prices.py +++ b/tests/test_base_prices.py @@ -1197,11 +1197,13 @@ def test__get_table_daily(prices_us, one_day, monkeypatch): ds_interval = intervals.DOInterval.M1 prices = set_get_prices_params(prices, pp, ds_interval) table = f(force_ds_daily=False) + expected_start_left = pd.Timestamp("2021-01-01").as_unit("ns") + expected_start_right = pd.Timestamp("2021-02-01").as_unit("ns") assert_index_equal( - table.index.left, pd.date_range("2021-01-01", freq="MS", periods=12) + table.index.left, pd.date_range(expected_start_left, freq="MS", periods=12) ) assert_index_equal( - table.index.right, pd.date_range("2021-02-01", freq="MS", periods=12) + table.index.right, pd.date_range(expected_start_right, freq="MS", periods=12) ) # set up as a year where last indice is not complete start = pd.Timestamp("2021") @@ -1211,10 +1213,10 @@ def test__get_table_daily(prices_us, one_day, monkeypatch): prices = set_get_prices_params(prices, pp, ds_interval) table = f(force_ds_daily=False) assert_index_equal( - table.index.left, pd.date_range("2021-01-01", freq="MS", periods=11) + table.index.left, pd.date_range(expected_start_left, freq="MS", periods=11) ) assert_index_equal( - table.index.right, pd.date_range("2021-02-01", freq="MS", periods=11) + table.index.right, pd.date_range(expected_start_right, freq="MS", periods=11) ) # verify that last 'incomplete' indice included if pp to now. @@ -1224,10 +1226,11 @@ def test__get_table_daily(prices_us, one_day, monkeypatch): prices = set_get_prices_params(prices, pp, ds_interval) table = f(force_ds_daily=False) assert_index_equal( - table.index.left, pd.date_range("2021-01-01", freq="MS", periods=12) + table.index.left, pd.date_range(expected_start_left, freq="MS", periods=12) ) assert_index_equal( - table.index.right, pd.date_range("2021-02-01", freq="MS", periods=12) + table.index.right, + pd.date_range(expected_start_right, freq="MS", periods=12), ) # verify force_ds_daily=True returns daily table @@ -2653,14 +2656,14 @@ def test_interval_invalid(self, prices_us, session_length_xnys, one_min): " minutes when interval is daily or higher." ) with pytest.raises(errors.PricesUnvailableDurationConflict, match=match): - prices.get("1d", hours=3) + prices.get("1D", hours=3) with pytest.raises(errors.PricesUnvailableDurationConflict, match=match): - prices.get("3d", minutes=40) + prices.get("3D", minutes=40) with pytest.raises(errors.PricesUnvailableDurationConflict, match=match): prices.get("1m", minutes=40, hours=3) # Verify raises PricesUnavailableIntervalPeriodError - interval, days_limit = "3d", 3 + interval, days_limit = "3D", 3 f(interval, days=days_limit) with pytest.raises(errors.PricesUnavailableIntervalPeriodError): f(interval, days=days_limit - 1) @@ -3827,8 +3830,8 @@ def assertions_test_monthly(self, prices, monkeypatch): # Verify with `start` df = prices.get("3M", start="2021-03-01", months=7) assert len(df) == 2 - start = pd.Timestamp("2021-03-01") - end = pd.Timestamp("2021-09-01") + start = pd.Timestamp("2021-03-01").as_unit("ns") + end = pd.Timestamp("2021-09-01").as_unit("ns") index = self.create_single_index(start, end, DOInterval.M3) assert_index_equal(df.index, index) @@ -3841,8 +3844,8 @@ def assertions_test_monthly(self, prices, monkeypatch): # Verify with `end` df = prices.get("2M", end="2021-12-31", months=7) assert len(df) == 3 - start = pd.Timestamp("2021-07-01") - end = pd.Timestamp("2022-01-01") + start = pd.Timestamp("2021-07-01").as_unit("ns") + end = pd.Timestamp("2022-01-01").as_unit("ns") index = self.create_single_index(start, end, DOInterval.M2) assert_index_equal(df.index, index) @@ -6167,7 +6170,7 @@ def test_prices_for_symbols(prices_us_lon): elif cal.name == "XLON": symb_lon = s - _ = prices.get("1d", start="2021-12-31", end="2022-01-05") + _ = prices.get("1D", start="2021-12-31", end="2022-01-05") # set up inraday data as period within a single session during which # us and lon calendars overlap (from inspection of calendar schedules). diff --git a/tests/test_calendar_utils.py b/tests/test_calendar_utils.py index 0fc7b06..3754f3e 100644 --- a/tests/test_calendar_utils.py +++ b/tests/test_calendar_utils.py @@ -286,7 +286,7 @@ def __init__( self._side = side self._start = start self._end = end - self._dates = pd.date_range(start, end) + self._dates = pd.date_range(start, end).as_unit("ns") self._session_idx = len(self.sessions) // 2 assert self._session_idx != 0 @@ -305,8 +305,12 @@ def _get_csv(self, name: str) -> pd.DataFrame: # Necessary for csv saved prior to xcals v4.0 if df.index.tz is not None: df.index = df.index.tz_convert(None) + + df.index = df.index.as_unit("ns") + # Necessary for csv saved prior to xcals v4.0 for col in df: + df[col] = df[col].dt.as_unit("ns") if df[col].dt.tz is None: df[col] = df[col].dt.tz_localize(UTC) return df @@ -973,8 +977,8 @@ def test_non_trading_index1(self, composite_calendars): def intrvl(left: str, right: str) -> pd.Interval: return pd.Interval( - pd.Timestamp("2021-12-" + left), - pd.Timestamp("2021-12-" + right), + pd.Timestamp("2021-12-" + left).as_unit("ns"), + pd.Timestamp("2021-12-" + right).as_unit("ns"), "left", ) @@ -1021,7 +1025,7 @@ def test_non_trading_index2(self): cc = m.CompositeCalendar([x245]) rtrn = cc.non_trading_index(utc=False) - dates = pd.date_range(start, end) + dates = pd.date_range(start, end).as_unit("ns") left = dates[dates.weekday == 5] right = dates[dates.weekday == 0] expected = pd.IntervalIndex.from_arrays(left, right, "left") @@ -1040,8 +1044,8 @@ def test_non_trading_index2(self): def intrvl(left: str, right: str) -> pd.Interval: return pd.Interval( - pd.Timestamp("2021-12-" + left), - pd.Timestamp("2021-12-" + right), + pd.Timestamp("2021-12-" + left).as_unit("ns"), + pd.Timestamp("2021-12-" + right).as_unit("ns"), "left", ) @@ -1151,8 +1155,14 @@ def test_xlon(self, xlon, intervals): f = cc.trading_index # from inspection of schedule - opens = [T("2021-12-24 08:00"), T("2021-12-29 08:00")] - closes = [T("2021-12-24 12:30"), T("2021-12-29 16:30")] + opens = [ + T("2021-12-24 08:00").as_unit("ns"), + T("2021-12-29 08:00").as_unit("ns"), + ] + closes = [ + T("2021-12-24 12:30").as_unit("ns"), + T("2021-12-29 16:30").as_unit("ns"), + ] last_session_duration = closes[-1] - opens[-1] for interval in intervals: @@ -1206,8 +1216,16 @@ def test_xhkg(self, xhkg, intervals, match_xhkg): f = cc.trading_index # if ignoring breaks - opens = [T("2021-12-23 01:30"), T("2021-12-24 01:30"), T("2021-12-28 01:30")] - closes = [T("2021-12-23 08:00"), T("2021-12-24 04:00"), T("2021-12-28 08:00")] + opens = [ + T("2021-12-23 01:30").as_unit("ns"), + T("2021-12-24 01:30").as_unit("ns"), + T("2021-12-28 01:30").as_unit("ns"), + ] + closes = [ + T("2021-12-23 08:00").as_unit("ns"), + T("2021-12-24 04:00").as_unit("ns"), + T("2021-12-28 08:00").as_unit("ns"), + ] for interval in intervals: start, end = T("2021-12-23"), T("2021-12-28") @@ -1217,19 +1235,19 @@ def test_xhkg(self, xhkg, intervals, match_xhkg): # if including breaks opens = [ - T("2021-12-23 01:30"), - T("2021-12-23 05:00"), - T("2021-12-24 01:30"), - T("2021-12-28 01:30"), - T("2021-12-28 05:00"), + T("2021-12-23 01:30").as_unit("ns"), + T("2021-12-23 05:00").as_unit("ns"), + T("2021-12-24 01:30").as_unit("ns"), + T("2021-12-28 01:30").as_unit("ns"), + T("2021-12-28 05:00").as_unit("ns"), ] closes = [ - T("2021-12-23 04:00"), - T("2021-12-23 08:00"), - T("2021-12-24 04:00"), - T("2021-12-28 04:00"), - T("2021-12-28 08:00"), + T("2021-12-23 04:00").as_unit("ns"), + T("2021-12-23 08:00").as_unit("ns"), + T("2021-12-24 04:00").as_unit("ns"), + T("2021-12-28 04:00").as_unit("ns"), + T("2021-12-28 08:00").as_unit("ns"), ] start, end = T("2021-12-23"), T("2021-12-28") @@ -1258,8 +1276,16 @@ def test_overlapping(self, xnys, xlon): f = cc.trading_index start, end = T("2021-12-23"), T("2021-12-27") - opens = [T("2021-12-23 08:00"), T("2021-12-24 08:00"), T("2021-12-27 14:30")] - closes = [T("2021-12-23 21:00"), T("2021-12-24 12:30"), T("2021-12-27 21:00")] + opens = [ + T("2021-12-23 08:00").as_unit("ns"), + T("2021-12-24 08:00").as_unit("ns"), + T("2021-12-27 14:30").as_unit("ns"), + ] + closes = [ + T("2021-12-23 21:00").as_unit("ns"), + T("2021-12-24 12:30").as_unit("ns"), + T("2021-12-27 21:00").as_unit("ns"), + ] for interval in [TDInterval.T1, TDInterval.T5, TDInterval.T30]: rtrn = f(interval, start, end, curtail_calendar_overlaps=False) @@ -1299,21 +1325,21 @@ def test_detached(self, xhkg, bvmf, intervals, match_xhkg): # if ignoring breaks opens = [ - T("2021-12-23 01:30"), - T("2021-12-23 13:00"), - T("2021-12-24 01:30"), - T("2021-12-27 13:00"), - T("2021-12-28 01:30"), - T("2021-12-28 13:00"), + T("2021-12-23 01:30").as_unit("ns"), + T("2021-12-23 13:00").as_unit("ns"), + T("2021-12-24 01:30").as_unit("ns"), + T("2021-12-27 13:00").as_unit("ns"), + T("2021-12-28 01:30").as_unit("ns"), + T("2021-12-28 13:00").as_unit("ns"), ] closes = [ - T("2021-12-23 08:00"), - T("2021-12-23 21:00"), - T("2021-12-24 04:00"), - T("2021-12-27 21:00"), - T("2021-12-28 08:00"), - T("2021-12-28 21:00"), + T("2021-12-23 08:00").as_unit("ns"), + T("2021-12-23 21:00").as_unit("ns"), + T("2021-12-24 04:00").as_unit("ns"), + T("2021-12-27 21:00").as_unit("ns"), + T("2021-12-28 08:00").as_unit("ns"), + T("2021-12-28 21:00").as_unit("ns"), ] for interval in intervals: @@ -1324,25 +1350,25 @@ def test_detached(self, xhkg, bvmf, intervals, match_xhkg): # if including breaks opens = [ - T("2021-12-23 01:30"), - T("2021-12-23 05:00"), - T("2021-12-23 13:00"), - T("2021-12-24 01:30"), - T("2021-12-27 13:00"), - T("2021-12-28 01:30"), - T("2021-12-28 05:00"), - T("2021-12-28 13:00"), + T("2021-12-23 01:30").as_unit("ns"), + T("2021-12-23 05:00").as_unit("ns"), + T("2021-12-23 13:00").as_unit("ns"), + T("2021-12-24 01:30").as_unit("ns"), + T("2021-12-27 13:00").as_unit("ns"), + T("2021-12-28 01:30").as_unit("ns"), + T("2021-12-28 05:00").as_unit("ns"), + T("2021-12-28 13:00").as_unit("ns"), ] closes = [ - T("2021-12-23 04:00"), - T("2021-12-23 08:00"), - T("2021-12-23 21:00"), - T("2021-12-24 04:00"), - T("2021-12-27 21:00"), - T("2021-12-28 04:00"), - T("2021-12-28 08:00"), - T("2021-12-28 21:00"), + T("2021-12-23 04:00").as_unit("ns"), + T("2021-12-23 08:00").as_unit("ns"), + T("2021-12-23 21:00").as_unit("ns"), + T("2021-12-24 04:00").as_unit("ns"), + T("2021-12-27 21:00").as_unit("ns"), + T("2021-12-28 04:00").as_unit("ns"), + T("2021-12-28 08:00").as_unit("ns"), + T("2021-12-28 21:00").as_unit("ns"), ] start, end = T("2021-12-23"), T("2021-12-28") diff --git a/tests/test_csv.py b/tests/test_csv.py index 930eb99..061fb05 100644 --- a/tests/test_csv.py +++ b/tests/test_csv.py @@ -101,8 +101,8 @@ def test__get_csv_interval(): ("t", TDInterval.T1), ("min", TDInterval.T1), ("h", TDInterval.H1), - ("d", TDInterval.D1), - ("1d", TDInterval.D1), + ("D", TDInterval.D1), + ("1D", TDInterval.D1), ("mIN", TDInterval.T1), ("T2", TDInterval.T2), ("2T", TDInterval.T2), @@ -133,7 +133,7 @@ def test__get_csv_interval(): ("1D1", ERROR_MALFORMED_INTRVL), ("1daily1", None), ("D5", ERROR_DAILY_INTRVL), - ("5d", ERROR_DAILY_INTRVL), + ("5D", ERROR_DAILY_INTRVL), ] for inp, out in inputs_outputs: assert m._get_csv_interval(inp) is out @@ -682,9 +682,10 @@ def test_raises_csv_no_data_error(csv_dir, symbols, calendars): # assign non-overlapping calendar so that all indices are rejected when compiling table calendars["MSFTEXTRA"] = "XHKG" + res = "us" if pd.__version__ >= "3.0.0" else "ns" match = ( re.escape( - f"For symbols '['MSFT', 'AZN.L', '9988.HK', 'MSFTEXTRA']' it was not possible to create a price table for any interval from csv files. The following errors and warnings occurred during parsing:\n\n0) Prices are not available at base interval {TDInterval.T20} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\n1) Prices are not available at base interval {TDInterval.T1} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\n2) Prices are not available at base interval {TDInterval.D1} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\n3) Prices are not available at base interval {TDInterval.H1} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\n4) Prices are not available at base interval {TDInterval.T2} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\n5) For symbol 'MSFT' at base interval {TDInterval.T5} the csv file included the following indices that are not aligned with the evaluated index and: have therefore been ignored:\nDatetimeIndex(['2022-04-18 16:02:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)\n\n6) For symbol 'MSFTEXTRA with interval '{TDInterval.T5}' no indice aligned with index evaluated from calendar 'XHKG'.\n\n7) Prices are not available at base interval {TDInterval.T5} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\nSee the 'path' parameter and 'Notes' sections of help(PricesCsv) for advices on how csv files should be named and formatted and for use of the `read_csv_kwargs` parameter." + f"For symbols '['MSFT', 'AZN.L', '9988.HK', 'MSFTEXTRA']' it was not possible to create a price table for any interval from csv files. The following errors and warnings occurred during parsing:\n\n0) Prices are not available at base interval {TDInterval.T20} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\n1) Prices are not available at base interval {TDInterval.T1} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\n2) Prices are not available at base interval {TDInterval.D1} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\n3) Prices are not available at base interval {TDInterval.H1} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\n4) Prices are not available at base interval {TDInterval.T2} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\n5) For symbol 'MSFT' at base interval {TDInterval.T5} the csv file included the following indices that are not aligned with the evaluated index and: have therefore been ignored:\nDatetimeIndex(['2022-04-18 16:02:00+00:00'], dtype='datetime64[{res}, UTC]', freq=None)\n\n6) For symbol 'MSFTEXTRA with interval '{TDInterval.T5}' no indice aligned with index evaluated from calendar 'XHKG'.\n\n7) Prices are not available at base interval {TDInterval.T5} as (aligned) data was not found at this interval for symbols '['MSFTEXTRA']'.\n\nSee the 'path' parameter and 'Notes' sections of help(PricesCsv) for advices on how csv files should be named and formatted and for use of the `read_csv_kwargs` parameter." ) if WINDOWS else None @@ -694,9 +695,10 @@ def test_raises_csv_no_data_error(csv_dir, symbols, calendars): def test_consolidated_warning(csv_dir, symbols, calendars): + res = "us" if pd.__version__ >= "3.0.0" else "ns" match = ( re.escape( - f"Price data has been found for all symbols at a least one interval, however, you may find that not all the expected price data is available. See the `limits` property for available base intervals and the limits between which price data is available at each of these intervals. See the `csv_paths` property for paths to all csv files that were found for the requested symbols. See the 'path' parameter and 'Notes' sections of help(PricesCsv) for advices on how csv files should be named and formatted and for use of the `read_csv_kwargs` parameter.\n\nThe following errors and/or warnings occurred during parsing:\n\n0) Unable to create dataframe from csv file at 'f_9988.HK_T20_fails_on_ohlc_data.csv' due to the following error:\n\t Date indices do not reflect the expected interval.\n\n1) Unable to create dataframe from csv file at 'f_AZN.L_H1_fails_on_vol_dtype.csv' due to the following error:\n\t 'volume' column will not convert to 'float64' dtype.\nThe source error's message was:\n\t: could not convert string to float: 'not a volume'\n\n2) Unable to create dataframe from csv file at 'f_AZN.L_H1_fails_on_vol_dtype.csv' due to the following error:\n\t Date indices do not reflect the expected interval.\n\n3) Unable to create dataframe from csv file at 'f_AZN.L_T20_fails_on_read_csv.csv' due to the following error:\n\t `pd.read_csv` raises error.\nThe source error's message was:\n\t: could not convert string to float: 'not_digits'\n\n4) Unable to create dataframe from csv file at 'f_MSFT_H1_fails_on_no_data.csv' due to the following error:\n\t No price data parsed from csv file.\n\n5) Unable to create dataframe from csv file at 'f_MSFT_H1_fails_on_no_data.csv' due to the following error:\n\t Date indices do not reflect the expected interval.\n\n6) Unable to create dataframe from csv file at 'f_MSFT_T20_fails_on_high_low.csv' due to the following error:\n\t At least one row has a high value that is lower than the corresponding low value.\n\n7) Unable to create dataframe from csv file at 'f_MSFT_T20_fails_on_high_low.csv' due to the following error:\n\t Date indices do not reflect the expected interval.\n\n8) Prices are not available at base interval {TDInterval.H1} as (aligned) data was not found at this interval for symbols '['AZN.L', 'MSFT']'.\n\n9) For symbol 'MSFT' at base interval {TDInterval.T5} the csv file included the following indices that are not aligned with the evaluated index and: have therefore been ignored:\nDatetimeIndex(['2022-04-18 16:02:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)" + f"Price data has been found for all symbols at a least one interval, however, you may find that not all the expected price data is available. See the `limits` property for available base intervals and the limits between which price data is available at each of these intervals. See the `csv_paths` property for paths to all csv files that were found for the requested symbols. See the 'path' parameter and 'Notes' sections of help(PricesCsv) for advices on how csv files should be named and formatted and for use of the `read_csv_kwargs` parameter.\n\nThe following errors and/or warnings occurred during parsing:\n\n0) Unable to create dataframe from csv file at 'f_9988.HK_T20_fails_on_ohlc_data.csv' due to the following error:\n\t Date indices do not reflect the expected interval.\n\n1) Unable to create dataframe from csv file at 'f_AZN.L_H1_fails_on_vol_dtype.csv' due to the following error:\n\t 'volume' column will not convert to 'float64' dtype.\nThe source error's message was:\n\t: could not convert string to float: 'not a volume'\n\n2) Unable to create dataframe from csv file at 'f_AZN.L_H1_fails_on_vol_dtype.csv' due to the following error:\n\t Date indices do not reflect the expected interval.\n\n3) Unable to create dataframe from csv file at 'f_AZN.L_T20_fails_on_read_csv.csv' due to the following error:\n\t `pd.read_csv` raises error.\nThe source error's message was:\n\t: could not convert string to float: 'not_digits'\n\n4) Unable to create dataframe from csv file at 'f_MSFT_H1_fails_on_no_data.csv' due to the following error:\n\t No price data parsed from csv file.\n\n5) Unable to create dataframe from csv file at 'f_MSFT_H1_fails_on_no_data.csv' due to the following error:\n\t Date indices do not reflect the expected interval.\n\n6) Unable to create dataframe from csv file at 'f_MSFT_T20_fails_on_high_low.csv' due to the following error:\n\t At least one row has a high value that is lower than the corresponding low value.\n\n7) Unable to create dataframe from csv file at 'f_MSFT_T20_fails_on_high_low.csv' due to the following error:\n\t Date indices do not reflect the expected interval.\n\n8) Prices are not available at base interval {TDInterval.H1} as (aligned) data was not found at this interval for symbols '['AZN.L', 'MSFT']'.\n\n9) For symbol 'MSFT' at base interval {TDInterval.T5} the csv file included the following indices that are not aligned with the evaluated index and: have therefore been ignored:\nDatetimeIndex(['2022-04-18 16:02:00+00:00'], dtype='datetime64[{res}, UTC]', freq=None)" ) if WINDOWS else None diff --git a/tests/test_daterange.py b/tests/test_daterange.py index 0a1752e..c2baa6e 100644 --- a/tests/test_daterange.py +++ b/tests/test_daterange.py @@ -13,6 +13,7 @@ import itertools import re from collections import abc +from datetime import timedelta import exchange_calendars as xcals import hypothesis as hyp @@ -2207,7 +2208,7 @@ def match( end: pd.Timestamp, pp: dict, anchor: Anchor, - duration: int | None = None, + duration: int | timedelta | None = None, ) -> str: """Match message for errors.PricesUnavailableIntervalPeriodError. @@ -2217,7 +2218,10 @@ def match( duration_insert = "" if anchor is Anchor.WORKBACK: assert duration is not None - duration_ = pd.Timedelta(duration, "min") + if isinstance(duration, timedelta): + duration_ = pd.Timedelta(duration) + else: + duration_ = pd.Timedelta(duration, "min") duration_insert = f"\nPeriod duration evaluated as {duration_}." return re.escape( f"Period does not span a full indice of length {final_interval}." diff --git a/tests/test_helpers.py b/tests/test_helpers.py index ddbeb26..16f9a02 100644 --- a/tests/test_helpers.py +++ b/tests/test_helpers.py @@ -294,12 +294,6 @@ def test_volume_to_na(intraday_pt, intraday_pt_ss): assert_frame_equal(rtrn, df) -# TODO: Remove xfail when pandas >3 or fix otherwise released -# NB pd bug manifests in test, not package (manifests where evaluate -# `expected_ooo.index = df[::12]`) -# pandas issue ref is https://github.com/pandas-dev/pandas/issues/58604 -# https://github.com/pandas-dev/pandas/pull/58043, related PR, ya merged to dev -@pytest.mark.xfail(reason="Known pd issue with py3.12, should resolve with pd>3.0") def test_resample(intraday_pt): """Test `resample`. diff --git a/tests/test_intervals.py b/tests/test_intervals.py index 4fcaf0c..a544b01 100644 --- a/tests/test_intervals.py +++ b/tests/test_intervals.py @@ -307,7 +307,7 @@ def test_unit(unit: str, limit: int, Cls: m.PTInterval): f(str(i) + unit_) test_unit("h", 22, m.TDInterval) - test_unit("d", 250, m.TDInterval) + test_unit("D", 250, m.TDInterval) test_unit("m", 36, m.DOInterval) assert f("60min") == f("60T") == f("1H") == f("1h") == m.TDInterval.H1 diff --git a/tests/test_pandas_utils.py b/tests/test_pandas_utils.py index 3a4bd2a..5f94e48 100644 --- a/tests/test_pandas_utils.py +++ b/tests/test_pandas_utils.py @@ -156,7 +156,8 @@ def mock_func( match = re.escape( "'arg' can only take a pd.IntervalIndex that has each side" " as type pd.DatetimeIndex, although received with left side" - " as type ''." + " as type ''.") diff --git a/tests/test_pt.py b/tests/test_pt.py index a29aded..96a4c2d 100644 --- a/tests/test_pt.py +++ b/tests/test_pt.py @@ -27,7 +27,7 @@ def daily_pt() -> abc.Iterator[pd.DataFrame]: Recreate table with: > symbols = ["MSFT", "AZN.L", "ES=F"] - > df = prices.get("1d", start="2021", end="2021-12-31") + > df = prices.get("1D", start="2021", end="2021-12-31") """ yield get_resource("daily_pt") @@ -82,7 +82,7 @@ def multiple_sessions_pt() -> abc.Iterator[pd.DataFrame]: Recreate table with: > symbols = ["MSFT", "AZN.L", "ES=F"] - > df = prices.get("3d", start="2021", end="2021-12-31") + > df = prices.get("3D", start="2021", end="2021-12-31") """ yield get_resource("multiple_sessions_pt") @@ -93,7 +93,7 @@ def multiple_sessions_alldays_pt() -> abc.Iterator[pd.DataFrame]: Recreate table with: > symbols = ["MSFT", "AZN.L", "BTC-USD"] - > df = prices.get("3d", start="2021", end="2021-12-31", lead_symbol="BTC-USD") + > df = prices.get("3D", start="2021", end="2021-12-31", lead_symbol="BTC-USD") """ yield get_resource("multiple_sessions_alldays_pt") @@ -300,17 +300,20 @@ def test_new_constructor_errors( match = ( "To use PT accessor index must be of type pd.DatetimeIndex or" " pd.IntervalIndex with left and right sides as pd.DatetimeIndex," - " although index is of type ."): + + with pytest.raises(TypeError, match=match) as excinfo: _ = daily_df.pt + assert str(excinfo.value).endswith("RangeIndex'>.") intraday_df = intraday_pt.copy() intraday_df.index = pd.interval_range(start=0, periods=len(intraday_pt), freq=1) - with pytest.raises(TypeError, match=match + "base.Index'>."): + with pytest.raises(TypeError, match=match) as excinfo: _ = intraday_df.pt + assert str(excinfo.value).endswith(".Index'>.") daily_df = daily_pt.copy() daily_df.index = daily_df.index + pd.Timedelta(1, "min") @@ -1946,7 +1949,7 @@ def test_errors(self, daily_pt, daily_pt_ss, calendars, xnys, xlon): f = df.pt.downsample # test raises expected errors - match = "Cannot downsample to a `pdfreq` with a unit more precise than 'd'." + match = "Cannot downsample to a `pdfreq` with a unit more precise than 'D'." invalid_freqs = ("5min", "1ms", "3h", "120s", "1000ns", "26h") for freq in invalid_freqs: with pytest.raises(ValueError, match=match): @@ -1957,7 +1960,7 @@ def match_f(freq) -> str: f"Received `pdfreq` as {freq} although must be either of type" " pd.offsets.CustomBusinessDay or acceptable input to" " pd.tseries.frequencies.to_offset that describes a frequency greater" - ' than one day. For example "2d", "5d" "QS" etc.' + ' than one day. For example "2D", "5D" "QS" etc.' ) invalid_freqs = ("D2", "astring", "3E", "3") @@ -1971,7 +1974,7 @@ def match_f(freq) -> str: "\nNB. Downsampling will downsample to a frequency defined in" " CustomBusinessDay when either `pdfreq` is passed as a CustomBusinessDay" " (or multiple of) or when the table has a CustomBusinessDay frequency and" - ' `pdfreq` is passed with unit "d".' + ' `pdfreq` is passed with unit "D".' ) # Verify raises error when calendar not passed although required @@ -1989,7 +1992,7 @@ def match_f(freq) -> str: assert isinstance(df_cbday_freq.pt.freq, pd.offsets.CustomBusinessDay) with pytest.raises(TypeError, match=match): - df_cbday_freq.pt.downsample("3d", None) + df_cbday_freq.pt.downsample("3D", None) # Verify raises error when calendar.day does not match frequency (passed or # imferred) base. @@ -2004,7 +2007,7 @@ def match_f(freq) -> str: daily_pt.pt.downsample(xnys.day * 3, xlon) with pytest.raises(ValueError, match=match): - df_cbday_freq.pt.downsample("3d", xlon) + df_cbday_freq.pt.downsample("3D", xlon) def test_cbdays_freq(self, daily_pt, calendars, symbols, one_day): """Verify daily price table with frequency as multiple of CustomBusinessDay.""" @@ -2078,10 +2081,10 @@ def test_d_freq(self, daily_pt, xnys, symbols, one_day): table_start = df.pt.first_ts for days in range(1, 31): - freq = str(days) + "d" + freq = str(days) + "D" offset = pd.tseries.frequencies.to_offset(freq) rtrn = f(freq, xnys) - assert rtrn.pt.last_ts == expected_end + assert rtrn.pt.last_ts == expected_end, f"{days=}, {freq=}" # Assert every indice of rtrn has length i. values = rtrn.index.length.value_counts().index @@ -2118,15 +2121,9 @@ def test_cbday_freq(self, daily_pt_ss, xnys): assert_frame_equal( df.pt.downsample(xnys.day * 3, xnys), - df.pt.downsample("3d", xnys), + df.pt.downsample("3D", xnys), ) - # TODO: Remove xfail when pandas >3 or fix otherwise released - # NB pd bug manifests in test, not package (manifests where takes a slice to - # evaluate 'subset'). - # pandas issue ref is https://github.com/pandas-dev/pandas/issues/58604 - # https://github.com/pandas-dev/pandas/pull/58043, related PR, ya merged to dev - @pytest.mark.xfail(reason="Known pd issue with py3.12, should resolve with pd>3.0") def test_monthly_freq(self, daily_pt, xnys, x247, one_day, symbols): """Verify "MS" and "QS" frequencies.""" df = daily_pt @@ -2313,7 +2310,7 @@ def match_f(pdfreq) -> str: f" received `pdfreq` as {pdfreq}." ) - invalid_pdfreqs = ["1d", "1s", "1ns", "1ms"] + invalid_pdfreqs = ["1D", "1s", "1ns", "1ms"] ext = ["1M", "1Y"] if pandas_pre_22 else ["1ME", "1YE"] invalid_pdfreqs += ext for pdfreq in invalid_pdfreqs: diff --git a/tests/test_tutorial_helpers.py b/tests/test_tutorial_helpers.py index 4691e95..27db437 100644 --- a/tests/test_tutorial_helpers.py +++ b/tests/test_tutorial_helpers.py @@ -264,7 +264,7 @@ def match( no_session = pd.Timedelta(0) length = full_session_xlon - expected = pd.DatetimeIndex([start]) + expected = pd.DatetimeIndex([start]).as_unit("ns") num = 1 assert_index_equal(f(cals, [length], start, end, num), expected) assert_index_equal(f_cc(cc, length, start, end, num), expected) @@ -299,7 +299,7 @@ def match( length = half_session_xlon lengths = [[length]] num = 1 - expected = pd.DatetimeIndex([T("2021-12-24")]) + expected = pd.DatetimeIndex([T("2021-12-24")]).as_unit("ns") assert_index_equal(f(cals, [length], start, end, num), expected) assert_index_equal(f_cc(cc, length, start, end, num), expected) assert_index_equal(f_var(cals, lengths, start, end), expected) @@ -339,7 +339,7 @@ def match( # test var for variatons full_expected = pd.DatetimeIndex( [T("2021-12-23"), T("2021-12-24"), T("2021-12-29")] - ) + ).as_unit("ns") cc_lengths = [full_session_xlon, half_session_xlon] lengths = [cc_lengths] assert_index_equal(f_var(cals, lengths, start, end), full_expected[:2]) @@ -358,7 +358,7 @@ def match( length_by_cal = [full_session_xlon, full_session_xnys] length_cc = full_session_cc num = 1 - expected = pd.DatetimeIndex([start]) + expected = pd.DatetimeIndex([start]).as_unit("ns") assert_index_equal(f(cals, length_by_cal, start, end, num), expected) assert_index_equal(f_cc(cc, length_cc, start, end, num), expected) @@ -396,7 +396,7 @@ def match( lengths = [half_session_xlon, no_session] length_cc = half_session_xlon num = 1 - expected = pd.DatetimeIndex([T("2021-12-24")]) + expected = pd.DatetimeIndex([T("2021-12-24")]).as_unit("ns") assert_index_equal(f(cals, lengths, start, end, num), expected) assert_index_equal(f_cc(cc, length_cc, start, end, num), expected) lengths_by_cal = [[half_session_xlon], [no_session]] @@ -422,7 +422,7 @@ def match( # test var for variatons full_expected = pd.DatetimeIndex( [T("2021-12-" + str(day)) for day in (23, 24, 27, 28, 29)] - ) + ).as_unit("ns") lengths = [ [full_session_xlon, half_session_xlon] + [no_session] * 2 + [full_session_xlon], [full_session_xnys, no_session] + [full_session_xnys] * 3, @@ -460,7 +460,7 @@ def match( length_by_cal = [full_session_xlon, full_session_xnys, full_session_xhkg] length_cc = full_session_cc num = 1 - expected = pd.DatetimeIndex([start]) + 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