Expanded with more methods and examples

README.md

@@ -29,7 +29,7 @@ Example:
 ...    ('2021-06-01', 20)
 ...]
 
->>> ts = fc.TimeSeries(time_series_data)
+>>> ts = pft.TimeSeries(time_series_data)
 ```
 
 ### Sample usage
@@ -46,12 +46,169 @@ With PyFacts, you never have to go into the hassle of creating datetime objects
 
 ```
 >>> import pyfacts as pft
->>> fc.PyfactsOptions.date_format = '%d-%m-%Y'
+>>> pft.PyfactsOptions.date_format = '%d-%m-%Y'
 ```
 Now the library will automatically parse all dates as DD-MM-YYYY
 
 If you happen to have any one situation where you need to use a different format, all methods accept a date_format parameter to override the default.
 
+
+### Working with multiple time series
+While working with time series data, you will often need to perform calculations on the data. PyFacts supports all kinds of mathematical operations on time series.
+
+Example:
+```
+>>> import pyfacts as pft
+
+>>> time_series_data = [
+...    ('2021-01-01', 10),
+...    ('2021-02-01', 12),
+...    ('2021-03-01', 14),
+...    ('2021-04-01', 16),
+...    ('2021-05-01', 18),
+...    ('2021-06-01', 20)
+...]
+
+>>> ts = pft.TimeSeries(time_series_data)
+>>> print(ts/100)
+
+TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 0.1),
+	(datetime.datetime(2022, 1, 2, 0, 0), 0.12),
+	(datetime.datetime(2022, 1, 3, 0, 0), 0.14),
+	(datetime.datetime(2022, 1, 4, 0, 0), 0.16),
+	(datetime.datetime(2022, 1, 6, 0, 0), 0.18),
+	(datetime.datetime(2022, 1, 7, 0, 0), 0.2)], frequency='M')
+```
+
+Mathematical operations can also be done between time series as long as they have the same dates.
+
+Example:
+```
+>>> import pyfacts as pft
+
+>>> time_series_data = [
+...    ('2021-01-01', 10),
+...    ('2021-02-01', 12),
+...    ('2021-03-01', 14),
+...    ('2021-04-01', 16),
+...    ('2021-05-01', 18),
+...    ('2021-06-01', 20)
+...]
+
+>>> ts = pft.TimeSeries(time_series_data)
+>>> ts2 = pft.TimeSeries(time_series_data)
+>>> print(ts/ts2)
+
+TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 1.0),
+	(datetime.datetime(2022, 1, 2, 0, 0), 1.0),
+	(datetime.datetime(2022, 1, 3, 0, 0), 1.0),
+	(datetime.datetime(2022, 1, 4, 0, 0), 1.0),
+	(datetime.datetime(2022, 1, 6, 0, 0), 1.0),
+	(datetime.datetime(2022, 1, 7, 0, 0), 1.0)], frequency='M')
+```
+
+However, if the dates are not in sync, PyFacts provides convenience methods for syncronising dates.
+
+Example:
+```
+>>> import pyfacts as pft
+
+>>> data1 = [
+...    ('2021-01-01', 10),
+...    ('2021-02-01', 12),
+...    ('2021-03-01', 14),
+...    ('2021-04-01', 16),
+...    ('2021-05-01', 18),
+...    ('2021-06-01', 20)
+...]
+
+>>> data2 = [
+...    ("2022-15-01", 20),
+...    ("2022-15-02", 22),
+...    ("2022-15-03", 24),
+...    ("2022-15-04", 26),
+...    ("2022-15-06", 28),
+...    ("2022-15-07", 30)
+...]
+
+>>> ts = pft.TimeSeries(data, frequency='M', date_format='%Y-%d-%m')
+>>> ts2 = pft.TimeSeries(data2, frequency='M', date_format='%Y-%d-%m')
+>>> ts.sync(ts2, fill_method='bfill')  # Sync ts2 with ts1
+
+TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 20.0),
+	(datetime.datetime(2022, 2, 1, 0, 0), 22.0),
+	(datetime.datetime(2022, 3, 1, 0, 0), 24.0),
+	(datetime.datetime(2022, 4, 1, 0, 0), 26.0),
+	(datetime.datetime(2022, 6, 1, 0, 0), 28.0),
+	(datetime.datetime(2022, 7, 1, 0, 0), 30.0)], frequency='M')
+```
+
+Even if you need to perform calculations on data with different frequencies, PyFacts will let you easily handle this with the expand and shrink methods.
+
+Example:
+```
+>>> data = [
+...    ("2022-01-01", 10),
+...    ("2022-02-01", 12),
+...    ("2022-03-01", 14),
+...    ("2022-04-01", 16),
+...    ("2022-05-01", 18),
+...    ("2022-06-01", 20)
+...]
+
+>>> ts = pft.TimeSeries(data, 'M')
+>>> ts.expand(to_frequency='W', method='ffill')
+
+TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10.0),
+	    (datetime.datetime(2022, 1, 8, 0, 0), 10.0),
+	    (datetime.datetime(2022, 1, 15, 0, 0), 10.0)
+	    ...
+	    (datetime.datetime(2022, 5, 14, 0, 0), 18.0),
+	    (datetime.datetime(2022, 5, 21, 0, 0), 18.0),
+	    (datetime.datetime(2022, 5, 28, 0, 0), 18.0)], frequency='W')
+
+>>> ts.shrink(to_frequency='Q', method='ffill')
+
+TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10.0),
+	(datetime.datetime(2022, 4, 1, 0, 0), 16.0)], frequency='Q')
+```
+
+If you want to shorten the timeframe of the data with an aggregation function, the transform method will help you out. Currently it supports sum and mean.
+
+Example:
+```
+>>> data = [
+...    ("2022-01-01", 10),
+...    ("2022-02-01", 12),
+...    ("2022-03-01", 14),
+...    ("2022-04-01", 16),
+...    ("2022-05-01", 18),
+...    ("2022-06-01", 20),
+...    ("2022-07-01", 22),
+...    ("2022-08-01", 24),
+...    ("2022-09-01", 26),
+...    ("2022-10-01", 28),
+...    ("2022-11-01", 30),
+...    ("2022-12-01", 32)
+...]
+
+>>> ts = pft.TimeSeries(data, 'M')
+>>> ts.transform(to_frequency='Q', method='sum')
+
+TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 36.0),
+	(datetime.datetime(2022, 4, 1, 0, 0), 54.0),
+	(datetime.datetime(2022, 7, 1, 0, 0), 72.0),
+	(datetime.datetime(2022, 10, 1, 0, 0), 90.0)], frequency='Q')
+
+>>> ts.transform(to_frequency='Q', method='mean')
+
+TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 12.0),
+	(datetime.datetime(2022, 4, 1, 0, 0), 18.0),
+	(datetime.datetime(2022, 7, 1, 0, 0), 24.0),
+	(datetime.datetime(2022, 10, 1, 0, 0), 30.0)], frequency='Q')
+```
+
+
 ## To-do
 
 ### Core features

pyfacts/core.py

@@ -180,7 +180,7 @@ class Series(UserList):
             if len(self) != len(other):
                 raise ValueError("Length of Series must be same for comparison")
 
-        elif (self.dtype != float and isinstance(other, Number)) or not isinstance(other, self.dtype):
+        elif self.dtype != float and isinstance(other, Number):
             raise Exception(f"Cannot compare type {self.dtype.__name__} to {type(other).__name__}")
 
         return other
@@ -300,14 +300,16 @@ class Series(UserList):
 
 
 def _validate_frequency(
-    data: List[Tuple[datetime.datetime, float]], provided_frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None
+    data: List[Tuple[datetime.datetime, float]],
+    provided_frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
+    raise_error: bool = True,
 ):
     """Checks the data and returns the expected frequency."""
     if provided_frequency is not None:
         provided_frequency = getattr(AllFrequencies, provided_frequency)
     start_date = data[0][0]
     end_date = data[-1][0]
-    overall_gap = (end_date - start_date).days
+    overall_gap = (end_date - start_date).days + 1
     num_data_points = len(data)
     # days_per_data = num_data_points / overall_gap
 
@@ -325,7 +327,10 @@ def _validate_frequency(
             expected_frequency = frequency
             break
     else:
-        raise ValueError("Data does not match any known frequency. Perhaps you have too many missing data points.")
+        if raise_error:
+            raise ValueError("Data does not match any known frequency. Perhaps you have too many missing data points.")
+        else:
+            expected_frequency = provided_frequency.symbol
 
     expected_data_points = expected_data_points[expected_frequency]
     if provided_frequency is None:
@@ -387,7 +392,7 @@ class TimeSeriesCore:
 
         ts_data = _preprocess_timeseries(ts_data, date_format=date_format)
 
-        validation = _validate_frequency(data=ts_data, provided_frequency=frequency)
+        validation = _validate_frequency(data=ts_data, provided_frequency=frequency, raise_error=validate_frequency)
         if frequency is None:
             frequency = validation["expected_frequency"]
 
@@ -508,7 +513,7 @@ class TimeSeriesCore:
         """Helper function to retrieve items using a list"""
 
         data_to_return = [self._get_item_from_key(key) for key in date_list]
-        return self.__class__(data_to_return, frequency=self.frequency.symbol)
+        return self.__class__(data_to_return, frequency=self.frequency.symbol, validate_frequency=False)
 
     def _get_item_from_series(self, series: Series):
         """Helper function to retrieve item using a Series object

pyfacts/pyfacts.py

@@ -344,8 +344,8 @@ class TimeSeries(TimeSeriesCore):
     @date_parser(1, 2)
     def calculate_rolling_returns(
         self,
-        from_date: datetime.date | str,
+        from_date: datetime.date | str = None,
-        to_date: datetime.date | str,
+        to_date: datetime.date | str = None,
         frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
         as_on_match: str = "closest",
         prior_match: str = "closest",
@@ -429,6 +429,13 @@ class TimeSeries(TimeSeriesCore):
                 frequency = getattr(AllFrequencies, frequency)
             except AttributeError:
                 raise ValueError(f"Invalid argument for frequency {frequency}")
+        if from_date is None:
+            from_date = self.start_date + relativedelta(
+                days=int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1)
+            )
+
+        if to_date is None:
+            to_date = self.end_date
 
         dates = create_date_series(from_date, to_date, frequency.symbol)
         if frequency == AllFrequencies.D:

pyfacts/statistics.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import datetime
+import math
 import statistics
 from typing import Literal
 
@@ -472,13 +473,14 @@ def sortino_ratio(
     closest: Literal["previous", "next"] = "previous",
     date_format: str = None,
 ) -> float:
-    """Calculate the Sharpe ratio of any time series
+    """Calculate the Sortino ratio of any time series
 
-    Sharpe ratio is a measure of returns per unit of risk,
+    Sortino ratio is a variation of the Sharpe ratio,
-    where risk is measured by the standard deviation of the returns.
+    where risk is measured as standard deviation of negative returns only.
+    Since deviation on the positive side is not undesirable, hence sortino ratio excludes positive deviations.
 
-    The formula for Sharpe ratio is:
+    The formula for Sortino ratio is:
-        (average asset return - risk free rate)/volatility of asset returns
+        (average asset return - risk free rate)/volatility of negative asset returns
 
     Parameters
     ----------
@@ -528,7 +530,7 @@ def sortino_ratio(
 
     Returns
     -------
-        Value of Sharpe ratio as a float.
+        Value of Sortino ratio as a float.
 
     Raises
     ------
@@ -559,11 +561,13 @@ def sortino_ratio(
         "closest": closest,
         "date_format": date_format,
     }
-    average_rr_ts = time_series_data.calculate_rolling_returns(**common_params, annual_compounded_returns=True)
+    average_rr_ts = time_series_data.calculate_rolling_returns(**common_params, annual_compounded_returns=False)
     average_rr = statistics.mean(average_rr_ts.values)
+    annualized_average_rr = (1 + average_rr) ** (365 / interval_days) - 1
 
-    excess_returns = average_rr - risk_free_rate
+    excess_returns = annualized_average_rr - risk_free_rate
     sd = statistics.stdev([i for i in average_rr_ts.values if i < 0])
+    sd *= math.sqrt(365 / interval_days)
 
     sortino_ratio_value = excess_returns / sd
     return sortino_ratio_value

test_series.py

@@ -1,6 +1,6 @@
 import datetime
 
-from fincal.core import Series
+from pyfacts.core import Series
 
 s1 = Series([2.5, 6.2, 5.6, 8.4, 7.4, 1.5, 9.6, 5])
 
@@ -19,7 +19,7 @@ dt_lst = [
     datetime.datetime(2020, 6, 19, 0, 0),
     datetime.datetime(2016, 3, 16, 0, 0),
     datetime.datetime(2017, 4, 25, 0, 0),
-    datetime.datetime(2016, 7, 10, 0, 0)
+    datetime.datetime(2016, 7, 10, 0, 0),
 ]
 
 s2 = Series(dt_lst)

tests/conftest.py

@@ -62,6 +62,7 @@ def sample_data_generator(
     mu: float = 0.1,
     sigma: float = 0.05,
     eomonth: bool = False,
+    dates_as_string: bool = False,
 ) -> List[tuple]:
     """Creates TimeSeries data
 
@@ -95,6 +96,8 @@ def sample_data_generator(
     }
     end_date = start_date + relativedelta(**timedelta_dict)
     dates = pft.create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends, eomonth=eomonth)
+    if dates_as_string:
+        dates = [dt.strftime("%Y-%m-%d") for dt in dates]
     values = create_prices(1000, mu, sigma, num)
     ts = list(zip(dates, values))
     return ts

tests/test_core.py

@@ -1,16 +1,15 @@
 import datetime
 import random
-from typing import Literal, Mapping, Sequence
+from typing import Mapping
 
+import pyfacts as pft
 import pytest
-from pyfacts.core import AllFrequencies, Frequency, Series, TimeSeriesCore
-from pyfacts.pyfacts import create_date_series
 from pyfacts.utils import PyfactsOptions
 
 
 class TestFrequency:
     def test_creation(self):
-        D = Frequency("daily", "days", 1, 1, "D")
+        D = pft.Frequency("daily", "days", 1, 1, "D")
         assert D.days == 1
         assert D.symbol == "D"
         assert D.name == "daily"
@@ -18,106 +17,103 @@ class TestFrequency:
         assert D.freq_type == "days"
 
 
-def create_test_data(
-    frequency: str,
-    eomonth: bool,
-    n: int,
-    gaps: float,
-    month_position: Literal["start", "middle", "end"],
-    date_as_str: bool,
-    as_outer_type: Literal["dict", "list"] = "list",
-    as_inner_type: Literal["dict", "list", "tuple"] = "tuple",
-) -> Sequence[tuple]:
-    start_dates = {
-        "start": datetime.datetime(2016, 1, 1),
-        "middle": datetime.datetime(2016, 1, 15),
-        "end": datetime.datetime(2016, 1, 31),
-    }
-    end_date = datetime.datetime(2021, 12, 31)
-    dates = create_date_series(start_dates[month_position], end_date, frequency=frequency, eomonth=eomonth)
-    dates = dates[:n]
-    if gaps:
-        num_gaps = int(len(dates) * gaps)
-        to_remove = random.sample(dates, num_gaps)
-        for i in to_remove:
-            dates.remove(i)
-    if date_as_str:
-        dates = [i.strftime("%Y-%m-%d") for i in dates]
-
-    values = [random.randint(8000, 90000) / 100 for _ in dates]
-
-    data = list(zip(dates, values))
-    if as_outer_type == "list":
-        if as_inner_type == "list":
-            data = [list(i) for i in data]
-        elif as_inner_type == "dict[1]":
-            data = [dict((i,)) for i in data]
-        elif as_inner_type == "dict[2]":
-            data = [dict(date=i, value=j) for i, j in data]
-    elif as_outer_type == "dict":
-        data = dict(data)
-
-    return data
-
-
 class TestAllFrequencies:
     def test_attributes(self):
-        assert hasattr(AllFrequencies, "D")
+        assert hasattr(pft.AllFrequencies, "D")
-        assert hasattr(AllFrequencies, "M")
+        assert hasattr(pft.AllFrequencies, "M")
-        assert hasattr(AllFrequencies, "Q")
+        assert hasattr(pft.AllFrequencies, "Q")
 
     def test_days(self):
-        assert AllFrequencies.D.days == 1
+        assert pft.AllFrequencies.D.days == 1
-        assert AllFrequencies.M.days == 30
+        assert pft.AllFrequencies.M.days == 30
-        assert AllFrequencies.Q.days == 91
+        assert pft.AllFrequencies.Q.days == 91
 
     def test_symbol(self):
-        assert AllFrequencies.H.symbol == "H"
+        assert pft.AllFrequencies.H.symbol == "H"
-        assert AllFrequencies.W.symbol == "W"
+        assert pft.AllFrequencies.W.symbol == "W"
 
     def test_values(self):
-        assert AllFrequencies.H.value == 6
+        assert pft.AllFrequencies.H.value == 6
-        assert AllFrequencies.Y.value == 1
+        assert pft.AllFrequencies.Y.value == 1
 
     def test_type(self):
-        assert AllFrequencies.Q.freq_type == "months"
+        assert pft.AllFrequencies.Q.freq_type == "months"
-        assert AllFrequencies.W.freq_type == "days"
+        assert pft.AllFrequencies.W.freq_type == "days"
 
 
 class TestSeries:
     def test_creation(self):
-        series = Series([1, 2, 3, 4, 5, 6, 7], dtype="number")
+        series = pft.Series([1, 2, 3, 4, 5, 6, 7], dtype="number")
         assert series.dtype == float
         assert series[2] == 3
 
-        dates = create_date_series("2021-01-01", "2021-01-31", frequency="D")
+        dates = pft.create_date_series("2021-01-01", "2021-01-31", frequency="D")
-        series = Series(dates, dtype="date")
+        series = pft.Series(dates, dtype="date")
         assert series.dtype == datetime.datetime
 
 
 class TestTimeSeriesCore:
     data = [("2021-01-01", 220), ("2021-02-01", 230), ("2021-03-01", 240)]
 
-    def test_repr_str(self):
+    def test_repr_str(self, create_test_data):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         assert str(ts) in repr(ts).replace("\t", " ")
 
-        data = create_test_data(frequency="D", eomonth=False, n=50, gaps=0, month_position="start", date_as_str=True)
+        data = create_test_data(frequency=pft.AllFrequencies.D, eomonth=False, num=50, dates_as_string=True)
-        ts = TimeSeriesCore(data, frequency="D")
+        ts = pft.TimeSeriesCore(data, frequency="D")
         assert "..." in str(ts)
         assert "..." in repr(ts)
 
     def test_creation(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
-        assert isinstance(ts, TimeSeriesCore)
+        assert isinstance(ts, pft.TimeSeriesCore)
         assert isinstance(ts, Mapping)
 
+    def test_creation_no_freq(self, create_test_data):
+        data = create_test_data(num=300, frequency=pft.AllFrequencies.D)
+        ts = pft.TimeSeriesCore(data)
+        assert ts.frequency == pft.AllFrequencies.D
+
+        data = create_test_data(num=300, frequency=pft.AllFrequencies.M)
+        ts = pft.TimeSeriesCore(data)
+        assert ts.frequency == pft.AllFrequencies.M
+
+    def test_creation_no_freq_missing_data(self, create_test_data):
+        data = create_test_data(num=300, frequency=pft.AllFrequencies.D)
+        data = random.sample(data, 182)
+        ts = pft.TimeSeriesCore(data)
+        assert ts.frequency == pft.AllFrequencies.D
+
+        data = create_test_data(num=300, frequency=pft.AllFrequencies.D)
+        data = random.sample(data, 175)
+        with pytest.raises(ValueError):
+            ts = pft.TimeSeriesCore(data)
+
+        data = create_test_data(num=100, frequency=pft.AllFrequencies.W)
+        data = random.sample(data, 70)
+        ts = pft.TimeSeriesCore(data)
+        assert ts.frequency == pft.AllFrequencies.W
+
+        data = create_test_data(num=100, frequency=pft.AllFrequencies.W)
+        data = random.sample(data, 68)
+        with pytest.raises(ValueError):
+            pft.TimeSeriesCore(data)
+
+    def test_creation_wrong_freq(self, create_test_data):
+        data = create_test_data(num=100, frequency=pft.AllFrequencies.W)
+        with pytest.raises(ValueError):
+            pft.TimeSeriesCore(data, frequency="D")
+
+        data = create_test_data(num=100, frequency=pft.AllFrequencies.D)
+        with pytest.raises(ValueError):
+            pft.TimeSeriesCore(data, frequency="W")
+
 
 class TestSlicing:
     data = [("2021-01-01", 220), ("2021-02-01", 230), ("2021-03-01", 240)]
 
     def test_getitem(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         assert ts.dates[0] == datetime.datetime(2021, 1, 1, 0, 0)
         assert ts.values[0] == 220
         assert ts["2021-01-01"][1] == 220
@@ -129,11 +125,11 @@ class TestSlicing:
             ts["2021-02-03"]
         subset_ts = ts[["2021-01-01", "2021-03-01"]]
         assert len(subset_ts) == 2
-        assert isinstance(subset_ts, TimeSeriesCore)
+        assert isinstance(subset_ts, pft.TimeSeriesCore)
         assert subset_ts.iloc[1][1] == 240
 
     def test_get(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         assert ts.dates[0] == datetime.datetime(2021, 1, 1, 0, 0)
         assert ts.values[0] == 220
         assert ts.get("2021-01-01")[1] == 220
@@ -147,43 +143,63 @@ class TestSlicing:
         assert ts.get("2021-02-10")[1] == 240
 
     def test_contains(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         assert datetime.datetime(2021, 1, 1) in ts
         assert "2021-01-01" in ts
         assert "2021-01-14" not in ts
 
     def test_items(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         for i, j in ts.items():
             assert j == self.data[0][1]
             break
 
     def test_special_keys(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         dates = ts["dates"]
         values = ts["values"]
-        assert isinstance(dates, Series)
+        assert isinstance(dates, pft.Series)
-        assert isinstance(values, Series)
+        assert isinstance(values, pft.Series)
         assert len(dates) == 3
         assert len(values) == 3
         assert dates[0] == datetime.datetime(2021, 1, 1, 0, 0)
         assert values[0] == 220
 
     def test_iloc_slicing(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         assert ts.iloc[0] == (datetime.datetime(2021, 1, 1), 220)
         assert ts.iloc[-1] == (datetime.datetime(2021, 3, 1), 240)
 
         ts_slice = ts.iloc[0:2]
-        assert isinstance(ts_slice, TimeSeriesCore)
+        assert isinstance(ts_slice, pft.TimeSeriesCore)
         assert len(ts_slice) == 2
 
 
+class TestComparativeSlicing:
+    def test_date_gt_daily(self, create_test_data):
+        data = create_test_data(num=300, frequency=pft.AllFrequencies.D)
+        ts = pft.TimeSeries(data, "D")
+        ts_rr = ts.calculate_rolling_returns(return_period_unit="months")
+        assert len(ts_rr) == 269
+        subset = ts_rr[ts_rr.values < 0.1]
+        assert isinstance(subset, pft.TimeSeriesCore)
+        assert subset.frequency == pft.AllFrequencies.D
+
+    def test_date_gt_monthly(self, create_test_data):
+        data = create_test_data(num=60, frequency=pft.AllFrequencies.M)
+        ts = pft.TimeSeries(data, "M")
+        ts_rr = ts.calculate_rolling_returns(return_period_unit="months")
+        assert len(ts_rr) == 59
+        subset = ts_rr[ts_rr.values < 0.1]
+        assert isinstance(subset, pft.TimeSeriesCore)
+        assert subset.frequency == pft.AllFrequencies.M
+
+
 class TestSetitem:
     data = [("2021-01-01", 220), ("2021-01-04", 230), ("2021-03-07", 240)]
 
     def test_setitem(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         assert len(ts) == 3
 
         ts["2021-01-02"] = 225
@@ -195,7 +211,7 @@ class TestSetitem:
         assert ts["2021-01-02"][1] == 227.6
 
     def test_errors(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         with pytest.raises(TypeError):
             ts["2021-01-03"] = "abc"
 
@@ -223,25 +239,25 @@ class TestTimeSeriesCoreHeadTail:
     ]
 
     def test_head(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         assert len(ts.head()) == 6
         assert len(ts.head(3)) == 3
-        assert isinstance(ts.head(), TimeSeriesCore)
+        assert isinstance(ts.head(), pft.TimeSeriesCore)
         head_ts = ts.head(6)
         assert head_ts.iloc[-1][1] == 270
 
     def test_tail(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         assert len(ts.tail()) == 6
         assert len(ts.tail(8)) == 8
-        assert isinstance(ts.tail(), TimeSeriesCore)
+        assert isinstance(ts.tail(), pft.TimeSeriesCore)
         tail_ts = ts.tail(6)
         assert tail_ts.iloc[0][1] == 280
 
     def test_head_tail(self):
-        ts = TimeSeriesCore(self.data, frequency="M")
+        ts = pft.TimeSeriesCore(self.data, frequency="M")
         head_tail_ts = ts.head(8).tail(2)
-        assert isinstance(head_tail_ts, TimeSeriesCore)
+        assert isinstance(head_tail_ts, pft.TimeSeriesCore)
         assert "2021-07-01" in head_tail_ts
         assert head_tail_ts.iloc[1][1] == 290
 
@@ -255,7 +271,7 @@ class TestDelitem:
     ]
 
     def test_deletion(self):
-        ts = TimeSeriesCore(self.data, "M")
+        ts = pft.TimeSeriesCore(self.data, "M")
         assert len(ts) == 4
         del ts["2021-03-01"]
         assert len(ts) == 3
@@ -281,42 +297,42 @@ class TestTimeSeriesComparisons:
     ]
 
     def test_number_comparison(self):
-        ts1 = TimeSeriesCore(self.data1, "M")
+        ts1 = pft.TimeSeriesCore(self.data1, "M")
-        assert isinstance(ts1 > 23, TimeSeriesCore)
+        assert isinstance(ts1 > 23, pft.TimeSeriesCore)
-        assert (ts1 > 230).values == Series([0.0, 0.0, 1.0, 1.0], "float")
+        assert (ts1 > 230).values == pft.Series([0.0, 0.0, 1.0, 1.0], "float")
-        assert (ts1 >= 230).values == Series([0.0, 1.0, 1.0, 1.0], "float")
+        assert (ts1 >= 230).values == pft.Series([0.0, 1.0, 1.0, 1.0], "float")
-        assert (ts1 < 240).values == Series([1.0, 1.0, 0.0, 0.0], "float")
+        assert (ts1 < 240).values == pft.Series([1.0, 1.0, 0.0, 0.0], "float")
-        assert (ts1 <= 240).values == Series([1.0, 1.0, 1.0, 0.0], "float")
+        assert (ts1 <= 240).values == pft.Series([1.0, 1.0, 1.0, 0.0], "float")
-        assert (ts1 == 240).values == Series([0.0, 0.0, 1.0, 0.0], "float")
+        assert (ts1 == 240).values == pft.Series([0.0, 0.0, 1.0, 0.0], "float")
-        assert (ts1 != 240).values == Series([1.0, 1.0, 0.0, 1.0], "float")
+        assert (ts1 != 240).values == pft.Series([1.0, 1.0, 0.0, 1.0], "float")
 
     def test_series_comparison(self):
-        ts1 = TimeSeriesCore(self.data1, "M")
+        ts1 = pft.TimeSeriesCore(self.data1, "M")
-        ser = Series([240, 210, 240, 270], dtype="int")
+        ser = pft.Series([240, 210, 240, 270], dtype="int")
 
-        assert (ts1 > ser).values == Series([0.0, 1.0, 0.0, 0.0], "float")
+        assert (ts1 > ser).values == pft.Series([0.0, 1.0, 0.0, 0.0], "float")
-        assert (ts1 >= ser).values == Series([0.0, 1.0, 1.0, 0.0], "float")
+        assert (ts1 >= ser).values == pft.Series([0.0, 1.0, 1.0, 0.0], "float")
-        assert (ts1 < ser).values == Series([1.0, 0.0, 0.0, 1.0], "float")
+        assert (ts1 < ser).values == pft.Series([1.0, 0.0, 0.0, 1.0], "float")
-        assert (ts1 <= ser).values == Series([1.0, 0.0, 1.0, 1.0], "float")
+        assert (ts1 <= ser).values == pft.Series([1.0, 0.0, 1.0, 1.0], "float")
-        assert (ts1 == ser).values == Series([0.0, 0.0, 1.0, 0.0], "float")
+        assert (ts1 == ser).values == pft.Series([0.0, 0.0, 1.0, 0.0], "float")
-        assert (ts1 != ser).values == Series([1.0, 1.0, 0.0, 1.0], "float")
+        assert (ts1 != ser).values == pft.Series([1.0, 1.0, 0.0, 1.0], "float")
 
     def test_tsc_comparison(self):
-        ts1 = TimeSeriesCore(self.data1, "M")
+        ts1 = pft.TimeSeriesCore(self.data1, "M")
-        ts2 = TimeSeriesCore(self.data2, "M")
+        ts2 = pft.TimeSeriesCore(self.data2, "M")
 
-        assert (ts1 > ts2).values == Series([0.0, 1.0, 0.0, 0.0], "float")
+        assert (ts1 > ts2).values == pft.Series([0.0, 1.0, 0.0, 0.0], "float")
-        assert (ts1 >= ts2).values == Series([0.0, 1.0, 1.0, 0.0], "float")
+        assert (ts1 >= ts2).values == pft.Series([0.0, 1.0, 1.0, 0.0], "float")
-        assert (ts1 < ts2).values == Series([1.0, 0.0, 0.0, 1.0], "float")
+        assert (ts1 < ts2).values == pft.Series([1.0, 0.0, 0.0, 1.0], "float")
-        assert (ts1 <= ts2).values == Series([1.0, 0.0, 1.0, 1.0], "float")
+        assert (ts1 <= ts2).values == pft.Series([1.0, 0.0, 1.0, 1.0], "float")
-        assert (ts1 == ts2).values == Series([0.0, 0.0, 1.0, 0.0], "float")
+        assert (ts1 == ts2).values == pft.Series([0.0, 0.0, 1.0, 0.0], "float")
-        assert (ts1 != ts2).values == Series([1.0, 1.0, 0.0, 1.0], "float")
+        assert (ts1 != ts2).values == pft.Series([1.0, 1.0, 0.0, 1.0], "float")
 
     def test_errors(self):
-        ts1 = TimeSeriesCore(self.data1, "M")
+        ts1 = pft.TimeSeriesCore(self.data1, "M")
-        ts2 = TimeSeriesCore(self.data2, "M")
+        ts2 = pft.TimeSeriesCore(self.data2, "M")
-        ser = Series([240, 210, 240], dtype="int")
+        ser = pft.Series([240, 210, 240], dtype="int")
-        ser2 = Series(["2021-01-01", "2021-02-01", "2021-03-01", "2021-04-01"], dtype="date")
+        ser2 = pft.Series(["2021-01-01", "2021-02-01", "2021-03-01", "2021-04-01"], dtype="date")
 
         del ts2["2021-04-01"]
 
@@ -345,7 +361,7 @@ class TestTimeSeriesArithmatic:
     ]
 
     def test_add(self):
-        ts = TimeSeriesCore(self.data, "M")
+        ts = pft.TimeSeriesCore(self.data, "M")
         ser = ts.values
 
         num_add_ts = ts + 40
@@ -365,8 +381,8 @@ class TestTimeSeriesArithmatic:
         assert ts_add_ts["2021-04-01"][1] == 540
 
     def test_sub(self):
-        ts = TimeSeriesCore(self.data, "M")
+        ts = pft.TimeSeriesCore(self.data, "M")
-        ser = Series([20, 30, 40, 50], "number")
+        ser = pft.Series([20, 30, 40, 50], "number")
 
         num_sub_ts = ts - 40
         assert num_sub_ts["2021-01-01"][1] == 180
@@ -385,8 +401,8 @@ class TestTimeSeriesArithmatic:
         assert ts_sub_ts["2021-04-01"][1] == 40
 
     def test_truediv(self):
-        ts = TimeSeriesCore(self.data, "M")
+        ts = pft.TimeSeriesCore(self.data, "M")
-        ser = Series([22, 23, 24, 25], "number")
+        ser = pft.Series([22, 23, 24, 25], "number")
 
         num_div_ts = ts / 10
         assert num_div_ts["2021-01-01"][1] == 22
@@ -404,8 +420,8 @@ class TestTimeSeriesArithmatic:
         assert ts_div_ts["2021-04-01"][1] == 10
 
     def test_floordiv(self):
-        ts = TimeSeriesCore(self.data, "M")
+        ts = pft.TimeSeriesCore(self.data, "M")
-        ser = Series([22, 23, 24, 25], "number")
+        ser = pft.Series([22, 23, 24, 25], "number")
 
         num_div_ts = ts // 11
         assert num_div_ts["2021-02-01"][1] == 20

tests/test_stats.py

@@ -84,6 +84,84 @@ class TestSharpe:
         assert round(sharpe_ratio, 4) == 0.3199
 
 
+class TestSortino:
+    def test_sortino_daily_freq(self, create_test_data):
+        data = create_test_data(num=1305, frequency=pft.AllFrequencies.D, skip_weekends=True)
+        ts = pft.TimeSeries(data, "D")
+        sortino_ratio = pft.sortino_ratio(
+            ts,
+            risk_free_rate=0.06,
+            from_date="2017-02-02",
+            to_date="2021-12-31",
+            return_period_unit="months",
+            return_period_value=1,
+        )
+        assert round(sortino_ratio, 4) == 2.5377
+
+    #     sharpe_ratio = pft.sharpe_ratio(
+    #         ts,
+    #         risk_free_rate=0.06,
+    #         from_date="2017-01-09",
+    #         to_date="2021-12-31",
+    #         return_period_unit="days",
+    #         return_period_value=7,
+    #     )
+    #     assert round(sharpe_ratio, 4) == 1.0701
+
+    #     sharpe_ratio = pft.sharpe_ratio(
+    #         ts,
+    #         risk_free_rate=0.06,
+    #         from_date="2018-01-02",
+    #         to_date="2021-12-31",
+    #         return_period_unit="years",
+    #         return_period_value=1,
+    #     )
+    #     assert round(sharpe_ratio, 4) == 1.4374
+
+    #     sharpe_ratio = pft.sharpe_ratio(
+    #         ts,
+    #         risk_free_rate=0.06,
+    #         from_date="2017-07-03",
+    #         to_date="2021-12-31",
+    #         return_period_unit="months",
+    #         return_period_value=6,
+    #     )
+    #     assert round(sharpe_ratio, 4) == 0.8401
+
+    # def test_sharpe_weekly_freq(self, create_test_data):
+    #     data = create_test_data(num=261, frequency=pft.AllFrequencies.W, mu=0.6, sigma=0.7)
+    #     ts = pft.TimeSeries(data, "W")
+    #     sharpe_ratio = pft.sharpe_ratio(
+    #         ts,
+    #         risk_free_rate=0.052,
+    #         from_date="2017-01-08",
+    #         to_date="2021-12-31",
+    #         return_period_unit="days",
+    #         return_period_value=7,
+    #     )
+    #     assert round(sharpe_ratio, 4) == 0.4533
+
+    #     sharpe_ratio = pft.sharpe_ratio(
+    #         ts,
+    #         risk_free_rate=0.052,
+    #         from_date="2017-02-05",
+    #         to_date="2021-12-31",
+    #         return_period_unit="months",
+    #         return_period_value=1,
+    #     )
+    #     assert round(sharpe_ratio, 4) == 0.4898
+
+    #     sharpe_ratio = pft.sharpe_ratio(
+    #         ts,
+    #         risk_free_rate=0.052,
+    #         from_date="2018-01-01",
+    #         to_date="2021-12-31",
+    #         return_period_unit="months",
+    #         return_period_value=12,
+    #     )
+    #     assert round(sharpe_ratio, 4) == 0.3199
+
+
 class TestBeta:
     def test_beta_daily_freq(self, create_test_data):
         market_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D)