Added function to generate test data and tests for volatility

Added documentation to volatility function

fincal/fincal.py

@@ -200,8 +200,8 @@ class TimeSeries(TimeSeriesCore):
         closest_max_days: int = -1,
         if_not_found: Literal["fail", "nan"] = "fail",
         annual_compounded_returns: bool = True,
-        interval_type: Literal["years", "months", "days"] = "years",
-        interval_value: int = 1,
+        return_period_unit: Literal["years", "months", "days"] = "years",
+        return_period_value: int = 1,
         date_format: str = None,
     ) -> float:
         """Method to calculate returns for a certain time-period as on a particular date
@@ -239,10 +239,10 @@ class TimeSeries(TimeSeriesCore):
         compounding : bool, optional
             Whether the return should be compounded annually.
 
-        interval_type : 'years', 'months', 'days'
+        return_period_unit : 'years', 'months', 'days'
             The type of time period to use for return calculation.
 
-        interval_value : int
+        return_period_value : int
             The value of the specified interval type over which returns needs to be calculated.
 
         date_format: str
@@ -268,7 +268,7 @@ class TimeSeries(TimeSeriesCore):
 
         as_on_delta, prior_delta = _preprocess_match_options(as_on_match, prior_match, closest)
 
-        prev_date = as_on - relativedelta(**{interval_type: interval_value})
+        prev_date = as_on - relativedelta(**{return_period_unit: return_period_value})
         current = _find_closest_date(self.data, as_on, closest_max_days, as_on_delta, if_not_found)
         if current[1] != str("nan"):
             previous = _find_closest_date(self.data, prev_date, closest_max_days, prior_delta, if_not_found)
@@ -278,7 +278,7 @@ class TimeSeries(TimeSeriesCore):
 
         returns = current[1] / previous[1]
         if annual_compounded_returns:
-            years = _interval_to_years(interval_type, interval_value)
+            years = _interval_to_years(return_period_unit, return_period_value)
             returns = returns ** (1 / years)
         return (current[0] if return_actual_date else as_on), returns - 1
 
@@ -293,8 +293,8 @@ class TimeSeries(TimeSeriesCore):
         closest: Literal["previous", "next", "exact"] = "previous",
         if_not_found: Literal["fail", "nan"] = "fail",
         annual_compounded_returns: bool = True,
-        interval_type: Literal["years", "months", "days"] = "years",
-        interval_value: int = 1,
+        return_period_unit: Literal["years", "months", "days"] = "years",
+        return_period_value: int = 1,
         date_format: str = None,
     ) -> TimeSeries:
         """Calculate the returns on a rolling basis.
@@ -339,10 +339,10 @@ class TimeSeries(TimeSeriesCore):
         compounding : bool, optional
             Should the returns be compounded annually.
 
-        interval_type : years | month | days
+        return_period_unit : years | month | days
             The interval for the return calculation.
 
-        interval_value : int, optional
+        return_period_value : int, optional
             The value of the interval for return calculation.
 
         date_format : str, optional
@@ -380,8 +380,8 @@ class TimeSeries(TimeSeriesCore):
             returns = self.calculate_returns(
                 as_on=i,
                 annual_compounded_returns=annual_compounded_returns,
-                interval_type=interval_type,
-                interval_value=interval_value,
+                return_period_unit=return_period_unit,
+                return_period_value=return_period_value,
                 as_on_match=as_on_match,
                 prior_match=prior_match,
                 closest=closest,
@@ -396,22 +396,41 @@ class TimeSeries(TimeSeriesCore):
         self,
         from_date: Union[datetime.date, str] = None,
         to_date: Union[datetime.date, str] = None,
+        annualize_volatility: bool = True,
+        traded_days: int = None,
         frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
+        return_period_unit: Literal["years", "months", "days"] = "days",
+        return_period_value: int = 1,
         as_on_match: str = "closest",
         prior_match: str = "closest",
         closest: Literal["previous", "next", "exact"] = "previous",
         if_not_found: Literal["fail", "nan"] = "fail",
         annual_compounded_returns: bool = None,
-        interval_type: Literal["years", "months", "days"] = "days",
-        interval_value: int = 1,
         date_format: str = None,
-        annualize_volatility: bool = True,
-        traded_days: int = None,
     ):
         """Calculates the volatility of the time series.add()
 
         The volatility is calculated as the standard deviaion of periodic returns.
         The periodicity of returns is based on the periodicity of underlying data.
+
+        Parameters:
+        ----------
+        from_date: datetime.datetime | str, optional
+            Starting date for the volatility calculation.
+            Default is the first date on which volatility can be calculated based on the interval type.
+
+        to_date: datetime.datetime | str, optional
+            Ending date for the volatility calculation.
+            Default is the last date in the TimeSeries.
+
+        annualize_volatility: bool, default True
+            Whether the volatility number should be annualized.
+            Multiplies the standard deviation with the square root of the number of periods in a year
+
+        traded_days: bool, optional
+            Number of traded days per year to be considered for annualizing volatility.
+            Only used when annualizing volatility for a time series with daily frequency.
+            If not provided, will use the value in FincalOptions.traded_days.
         """
 
         if frequency is None:
@@ -423,7 +442,7 @@ class TimeSeries(TimeSeriesCore):
                 raise ValueError(f"Invalid argument for frequency {frequency}")
 
         if from_date is None:
-            from_date = self.start_date + relativedelta(**{interval_type: interval_value})
+            from_date = self.start_date + relativedelta(**{return_period_unit: return_period_value})
         if to_date is None:
             to_date = self.end_date
 
@@ -439,17 +458,17 @@ class TimeSeries(TimeSeriesCore):
             closest=closest,
             if_not_found=if_not_found,
             annual_compounded_returns=annual_compounded_returns,
-            interval_type=interval_type,
-            interval_value=interval_value,
+            return_period_unit=return_period_unit,
+            return_period_value=return_period_value,
         )
         sd = statistics.stdev(rolling_returns.values)
         if annualize_volatility:
             if traded_days is None:
                 traded_days = FincalOptions.traded_days
 
-            if interval_type == "months":
+            if return_period_unit == "months":
                 sd *= math.sqrt(12)
-            elif interval_type == "days":
+            elif return_period_unit == "days":
                 sd *= math.sqrt(traded_days)
 
         return sd

tests/test_fincal.py

@@ -200,7 +200,7 @@ class TestFincalBasic:
         assert time_series.iloc[:3] is not None
         assert time_series.iloc[5:7] is not None
         assert isinstance(time_series.iloc[0], tuple)
-        assert isinstance(time_series.iloc[10:20], list)
+        assert isinstance(time_series.iloc[10:20], TimeSeries)
         assert len(time_series.iloc[10:20]) == 10
 
     def test_key_slicing(self):
@@ -236,57 +236,65 @@ class TestReturns:
     def test_returns_calc(self):
         ts = TimeSeries(self.data, frequency="M")
         returns = ts.calculate_returns(
-            "2021-01-01", annual_compounded_returns=False, interval_type="years", interval_value=1
+            "2021-01-01", annual_compounded_returns=False, return_period_unit="years", return_period_value=1
         )
         assert returns[1] == 2.4
         returns = ts.calculate_returns(
-            "2020-04-01", annual_compounded_returns=False, interval_type="months", interval_value=3
+            "2020-04-01", annual_compounded_returns=False, return_period_unit="months", return_period_value=3
         )
         assert round(returns[1], 4) == 0.6
         returns = ts.calculate_returns(
-            "2020-04-01", annual_compounded_returns=True, interval_type="months", interval_value=3
+            "2020-04-01", annual_compounded_returns=True, return_period_unit="months", return_period_value=3
         )
         assert round(returns[1], 4) == 5.5536
         returns = ts.calculate_returns(
-            "2020-04-01", annual_compounded_returns=False, interval_type="days", interval_value=90
+            "2020-04-01", annual_compounded_returns=False, return_period_unit="days", return_period_value=90
         )
         assert round(returns[1], 4) == 0.6
         returns = ts.calculate_returns(
-            "2020-04-01", annual_compounded_returns=True, interval_type="days", interval_value=90
+            "2020-04-01", annual_compounded_returns=True, return_period_unit="days", return_period_value=90
         )
         assert round(returns[1], 4) == 5.727
         returns = ts.calculate_returns(
-            "2020-04-10", annual_compounded_returns=True, interval_type="days", interval_value=90
+            "2020-04-10", annual_compounded_returns=True, return_period_unit="days", return_period_value=90
         )
         assert round(returns[1], 4) == 5.727
         with pytest.raises(DateNotFoundError):
-            ts.calculate_returns("2020-04-10", interval_type="days", interval_value=90, as_on_match="exact")
+            ts.calculate_returns("2020-04-10", return_period_unit="days", return_period_value=90, as_on_match="exact")
         with pytest.raises(DateNotFoundError):
-            ts.calculate_returns("2020-04-10", interval_type="days", interval_value=90, prior_match="exact")
+            ts.calculate_returns("2020-04-10", return_period_unit="days", return_period_value=90, prior_match="exact")
 
     def test_date_formats(self):
         ts = TimeSeries(self.data, frequency="M")
         FincalOptions.date_format = "%d-%m-%Y"
         with pytest.raises(ValueError):
-            ts.calculate_returns("2020-04-10", annual_compounded_returns=True, interval_type="days", interval_value=90)
+            ts.calculate_returns(
+                "2020-04-10", annual_compounded_returns=True, return_period_unit="days", return_period_value=90
+            )
 
-        returns1 = ts.calculate_returns("2020-04-10", interval_type="days", interval_value=90, date_format="%Y-%m-%d")
-        returns2 = ts.calculate_returns("10-04-2020", interval_type="days", interval_value=90)
+        returns1 = ts.calculate_returns(
+            "2020-04-10", return_period_unit="days", return_period_value=90, date_format="%Y-%m-%d"
+        )
+        returns2 = ts.calculate_returns("10-04-2020", return_period_unit="days", return_period_value=90)
         assert round(returns1[1], 4) == round(returns2[1], 4) == 5.727
 
         FincalOptions.date_format = "%m-%d-%Y"
         with pytest.raises(ValueError):
-            ts.calculate_returns("2020-04-10", annual_compounded_returns=True, interval_type="days", interval_value=90)
+            ts.calculate_returns(
+                "2020-04-10", annual_compounded_returns=True, return_period_unit="days", return_period_value=90
+            )
 
-        returns1 = ts.calculate_returns("2020-04-10", interval_type="days", interval_value=90, date_format="%Y-%m-%d")
-        returns2 = ts.calculate_returns("04-10-2020", interval_type="days", interval_value=90)
+        returns1 = ts.calculate_returns(
+            "2020-04-10", return_period_unit="days", return_period_value=90, date_format="%Y-%m-%d"
+        )
+        returns2 = ts.calculate_returns("04-10-2020", return_period_unit="days", return_period_value=90)
         assert round(returns1[1], 4) == round(returns2[1], 4) == 5.727
 
     def test_limits(self):
         ts = TimeSeries(self.data, frequency="M")
         FincalOptions.date_format = "%Y-%m-%d"
         with pytest.raises(DateNotFoundError):
-            ts.calculate_returns("2020-04-25", interval_type="days", interval_value=90, closest_max_days=10)
+            ts.calculate_returns("2020-04-25", return_period_unit="days", return_period_value=90, closest_max_days=10)
 
 
 class TestVolatility:

tests/test_fincal2.py

@@ -3,18 +3,37 @@ import math
 import random
 
 import pytest
+from dateutil.relativedelta import relativedelta
+from fincal.core import AllFrequencies, Frequency
 from fincal.exceptions import DateNotFoundError
 from fincal.fincal import TimeSeries, create_date_series
 from fincal.utils import FincalOptions
 
 
 def create_prices(s0: float, mu: float, sigma: float, num_prices: int) -> list:
-    """Generates a price following a geometric brownian motion process based on the input of the arguments:
-    - s0: Asset inital price.
-    - mu: Interest rate expressed annual terms.
-    - sigma: Volatility expressed annual terms.
-    - seed: seed for the random number generator
-    - num_prices: number of prices to generate
+    """Generates a price following a geometric brownian motion process based on the input of the arguments.
+
+        Since this function is used only to generate data for tests, the seed is fixed as 1234.
+        Many of the tests rely on exact values generated using this seed.
+        If the seed is changed, those tests will fail.
+
+    Parameters:
+    ------------
+    s0: float
+        Asset inital price.
+
+    mu: float
+        Interest rate expressed annual terms.
+
+    sigma: float
+        Volatility expressed annual terms.
+
+    num_prices: int
+        number of prices to generate
+
+    Returns:
+    --------
+        Returns a list of values generated using GBM algorithm
     """
 
     random.seed(1234)  # WARNING! Changing the seed will cause most tests to fail
@@ -28,68 +47,124 @@ def create_prices(s0: float, mu: float, sigma: float, num_prices: int) -> list:
     return all_values
 
 
-def create_data():
-    """Creates TimeSeries data"""
+def create_test_timeseries(
+    frequency: Frequency, num: int = 1000, skip_weekends: bool = False, mu: float = 0.1, sigma: float = 0.05
+) -> TimeSeries:
+    """Creates TimeSeries data
+
+    Parameters:
+    -----------
+    frequency: Frequency
+        The frequency of the time series data to be generated.
+
+    num: int
+        Number of date: value pairs to be generated.
 
-    dates = create_date_series("2017-01-01", "2020-10-31", "D", skip_weekends=True)
-    values = create_prices(1000, 0.1, 0.05, 1000)
-    ts = TimeSeries(dict(zip(dates, values)), frequency="D")
+    skip_weekends: bool
+        Whether weekends (saturday, sunday) should be skipped.
+        Gets used only if the frequency is daily.
+
+    mu: float
+        Mean return for the values.
+
+    sigma: float
+        standard deviation of the values.
+
+    Returns:
+    --------
+        Returns a TimeSeries object
+    """
+
+    start_date = datetime.datetime(2017, 1, 1)
+    timedelta_dict = {
+        frequency.freq_type: int(frequency.value * num * (7 / 5 if frequency == "D" and skip_weekends else 1))
+    }
+    end_date = start_date + relativedelta(**timedelta_dict)
+    dates = create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends)
+    values = create_prices(1000, mu, sigma, num)
+    ts = TimeSeries(dict(zip(dates, values)), frequency=frequency.symbol)
     return ts
 
 
 class TestReturns:
     def test_returns_calc(self):
-        ts = create_data()
+        ts = create_test_timeseries()
         returns = ts.calculate_returns(
-            "2020-01-01", annual_compounded_returns=False, interval_type="years", interval_value=1
+            "2020-01-01", annual_compounded_returns=False, return_period_unit="years", return_period_value=1
         )
         assert round(returns[1], 6) == 0.112913
 
         returns = ts.calculate_returns(
-            "2020-04-01", annual_compounded_returns=False, interval_type="months", interval_value=3
+            "2020-04-01", annual_compounded_returns=False, return_period_unit="months", return_period_value=3
         )
         assert round(returns[1], 6) == 0.015908
 
         returns = ts.calculate_returns(
-            "2020-04-01", annual_compounded_returns=True, interval_type="months", interval_value=3
+            "2020-04-01", annual_compounded_returns=True, return_period_unit="months", return_period_value=3
         )
         assert round(returns[1], 6) == 0.065167
 
         returns = ts.calculate_returns(
-            "2020-04-01", annual_compounded_returns=False, interval_type="days", interval_value=90
+            "2020-04-01", annual_compounded_returns=False, return_period_unit="days", return_period_value=90
         )
         assert round(returns[1], 6) == 0.017673
 
         returns = ts.calculate_returns(
-            "2020-04-01", annual_compounded_returns=True, interval_type="days", interval_value=90
+            "2020-04-01", annual_compounded_returns=True, return_period_unit="days", return_period_value=90
         )
         assert round(returns[1], 6) == 0.073632
 
         with pytest.raises(DateNotFoundError):
-            ts.calculate_returns("2020-04-04", interval_type="days", interval_value=90, as_on_match="exact")
+            ts.calculate_returns("2020-04-04", return_period_unit="days", return_period_value=90, as_on_match="exact")
         with pytest.raises(DateNotFoundError):
-            ts.calculate_returns("2020-04-04", interval_type="months", interval_value=3, prior_match="exact")
+            ts.calculate_returns("2020-04-04", return_period_unit="months", return_period_value=3, prior_match="exact")
 
     def test_date_formats(self):
-        ts = create_data()
+        ts = create_test_timeseries()
         FincalOptions.date_format = "%d-%m-%Y"
         with pytest.raises(ValueError):
-            ts.calculate_returns("2020-04-10", annual_compounded_returns=True, interval_type="days", interval_value=90)
+            ts.calculate_returns(
+                "2020-04-10", annual_compounded_returns=True, return_period_unit="days", return_period_value=90
+            )
 
-        returns1 = ts.calculate_returns("2020-04-01", interval_type="days", interval_value=90, date_format="%Y-%m-%d")
-        returns2 = ts.calculate_returns("01-04-2020", interval_type="days", interval_value=90)
+        returns1 = ts.calculate_returns(
+            "2020-04-01", return_period_unit="days", return_period_value=90, date_format="%Y-%m-%d"
+        )
+        returns2 = ts.calculate_returns("01-04-2020", return_period_unit="days", return_period_value=90)
         assert round(returns1[1], 6) == round(returns2[1], 6) == 0.073632
 
         FincalOptions.date_format = "%m-%d-%Y"
         with pytest.raises(ValueError):
-            ts.calculate_returns("2020-04-01", annual_compounded_returns=True, interval_type="days", interval_value=90)
+            ts.calculate_returns(
+                "2020-04-01", annual_compounded_returns=True, return_period_unit="days", return_period_value=90
+            )
 
-        returns1 = ts.calculate_returns("2020-04-01", interval_type="days", interval_value=90, date_format="%Y-%m-%d")
-        returns2 = ts.calculate_returns("04-01-2020", interval_type="days", interval_value=90)
+        returns1 = ts.calculate_returns(
+            "2020-04-01", return_period_unit="days", return_period_value=90, date_format="%Y-%m-%d"
+        )
+        returns2 = ts.calculate_returns("04-01-2020", return_period_unit="days", return_period_value=90)
         assert round(returns1[1], 6) == round(returns2[1], 6) == 0.073632
 
     def test_limits(self):
-        ts = create_data()
         FincalOptions.date_format = "%Y-%m-%d"
+        ts = create_test_timeseries()
         with pytest.raises(DateNotFoundError):
-            ts.calculate_returns("2020-11-25", interval_type="days", interval_value=90, closest_max_days=10)
+            ts.calculate_returns("2020-11-25", return_period_unit="days", return_period_value=90, closest_max_days=10)
+
+
+class TestVolatility:
+    def test_daily_ts(self):
+        ts = create_test_timeseries(AllFrequencies.D)
+        assert len(ts) == 1000
+        sd = ts.volatility(annualize_volatility=False)
+        assert round(sd, 6) == 0.002622
+        sd = ts.volatility()
+        assert round(sd, 6) == 0.050098
+        sd = ts.volatility(annual_compounded_returns=True)
+        assert round(sd, 4) == 37.9329
+        sd = ts.volatility(return_period_unit="months", annual_compounded_returns=True)
+        assert round(sd, 4) == 0.6778
+        sd = ts.volatility(return_period_unit="years")
+        assert round(sd, 6) == 0.023164
+        sd = ts.volatility(from_date="2017-10-01", to_date="2019-08-31", annualize_volatility=True)
+        assert round(sd, 6) == 0.050559