diff --git a/pyfacts/pyfacts.py b/pyfacts/pyfacts.py
index 2a8e3af..2177067 100644
--- a/pyfacts/pyfacts.py
+++ b/pyfacts/pyfacts.py
@@ -262,7 +262,7 @@ class TimeSeries(TimeSeriesCore):
         return_period_unit: Literal["years", "months", "days"] = "years",
         return_period_value: int = 1,
         date_format: str = None,
-    ) -> float:
+    ) -> Tuple[datetime.datetime, float]:
         """Method to calculate returns for a certain time-period as on a particular date
 
         Parameters
@@ -295,7 +295,7 @@ class TimeSeries(TimeSeriesCore):
             * fail: Raise a ValueError
             * nan: Return nan as the value
 
-        compounding : bool, optional
+        annual_compounded_returns : bool, optional
             Whether the return should be compounded annually.
 
         return_period_unit : 'years', 'months', 'days'
@@ -321,14 +321,14 @@ class TimeSeries(TimeSeriesCore):
 
         Example
         --------
-        >>> calculate_returns(datetime.date(2020, 1, 1), years=1)
+        >>> ts.calculate_returns(datetime.date(2020, 1, 1), years=1)
         (datetime.datetime(2020, 1, 1, 0, 0), .0567)
         """
 
         as_on_delta, prior_delta = _preprocess_match_options(as_on_match, prior_match, closest)
 
-        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)
+        prev_date = as_on - relativedelta(**{return_period_unit: return_period_value})
         if current[1] != str("nan"):
             previous = _find_closest_date(self.data, prev_date, closest_max_days, prior_delta, if_not_found)
 
@@ -368,16 +368,16 @@ class TimeSeries(TimeSeriesCore):
             End date for the returns calculation.
 
         frequency : str, optional
-            Frequency at which the returns should be calcualated.
+            Frequency at which the returns should be calculated.
             Valid values are {D, W, M, Q, H, Y}
 
         as_on_match : str, optional
             The match mode to be used for the as on date.
-            If not specified, the value for the closes parameter will be used.
+            If not specified, the value for the closest parameter will be used.
 
         prior_match : str, optional
             The match mode to be used for the prior date, i.e., the date against which the return will be calculated.
-            If not specified, the value for the closes parameter will be used.
+            If not specified, the value for the closest parameter will be used.
 
         closest : previous | next | exact
             The default match mode for dates.
@@ -395,7 +395,7 @@ class TimeSeries(TimeSeriesCore):
             For instance, if the input date is before the starting of the first date of the time series,
             but match mode is set to previous. A DateOutOfRangeError will be raised in such cases.
 
-        compounding : bool, optional
+        annual_compounded_returns : bool, optional
             Should the returns be compounded annually.
 
         return_period_unit : years | month | days
@@ -410,7 +410,7 @@ class TimeSeries(TimeSeriesCore):
 
         Returns
         -------
-            Returs the rolling returns as a TimeSeries object.
+            Returns the rolling returns as a TimeSeries object.
 
         Raises
         ------
@@ -431,7 +431,7 @@ class TimeSeries(TimeSeriesCore):
                 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)
+                days=math.ceil(_interval_to_years(return_period_unit, return_period_value) * 365)
             )
 
         if to_date is None:
@@ -476,7 +476,7 @@ class TimeSeries(TimeSeriesCore):
     ) -> float:
         """Calculates the volatility of the time series.add()
 
-        The volatility is calculated as the standard deviaion of periodic returns.
+        The volatility is calculated as the standard deviation of periodic returns.
         The periodicity of returns is based on the periodicity of underlying data.
 
         Parameters:
@@ -761,10 +761,10 @@ class TimeSeries(TimeSeriesCore):
         Parameters:
         -----------
         other: TimeSeries
-            Another object of TimeSeries class whose dates need to be syncronized
+            Another object of TimeSeries class whose dates need to be synchronized
 
         fill_method: ffill | bfill, default ffill
-            Method to use to fill missing values in time series when syncronizing
+            Method to use to fill missing values in time series when synchronizing
 
         Returns:
         --------
@@ -903,7 +903,7 @@ def read_csv(
         header = data[read_start_row]
         print(header)
         # fmt: off
-        # Black and pylance disagree on the foratting of the following line, hence formatting is disabled
+        # Black and pylance disagree on the formatting of the following line, hence formatting is disabled
         data = data[(read_start_row + 1):read_end_row]
         # fmt: on