fixing a few mypy ignores

openseries/common_model.py

@@ -7,7 +7,7 @@
 from json import dump
 from math import ceil
 from pathlib import Path
-from random import choices
+from secrets import choice
 from string import ascii_letters
 from typing import Any, Optional, Union, cast
 
@@ -35,7 +35,7 @@
 )
 
 
-class CommonModel(BaseModel):  # type: ignore[misc]
+class CommonModel(BaseModel):
 
     """Declare CommonModel."""
 
@@ -616,7 +616,7 @@ def plot_bars(
             dirpath = Path(stack()[1].filename).parent
 
         if not filename:
-            filename = "".join(choices(ascii_letters, k=6)) + ".html"  # noqa: S311
+            filename = "".join(choice(ascii_letters) for _ in range(6)) + ".html"
         plotfile = dirpath.joinpath(filename)
 
         fig, logo = load_plotly_dict()
@@ -717,7 +717,7 @@ def plot_series(
             dirpath = Path(stack()[1].filename).parent
 
         if not filename:
-            filename = "".join(choices(ascii_letters, k=6)) + ".html"  # noqa: S311
+            filename = "".join(choice(ascii_letters) for _ in range(6)) + ".html"
         plotfile = dirpath.joinpath(filename)
 
         fig, logo = load_plotly_dict()
@@ -1120,10 +1120,14 @@ def downside_deviation_func(
             .count(numeric_only=True)
         )
         if periods_in_a_year_fixed:
-            time_factor = periods_in_a_year_fixed
+            time_factor = Series(
+                data=[float(periods_in_a_year_fixed)] * how_many.shape[0],
+                index=self.tsdf.columns,
+                dtype="float64",
+            )
         else:
             fraction = (later - earlier).days / 365.25
-            time_factor = how_many / fraction  # type: ignore[assignment]
+            time_factor = how_many.div(fraction)
 
         dddf = (
             self.tsdf.loc[cast(int, earlier) : cast(int, later)]
@@ -1424,12 +1428,15 @@ def positive_share_func(
             .pct_change()[1:]
             .count()
         )
-        result = pos / tot
-        result.name = "Positive Share"
-        result = result.astype("float64")
+        share = pos / tot
         if self.tsdf.shape[1] == 1:
-            return float(result.iloc[0])
-        return result  # type: ignore[return-value]
+            return float(share.iloc[0])
+        return Series(
+            data=share,
+            index=self.tsdf.columns,
+            name="Positive Share",
+            dtype="float64",
+        )
 
     def ret_vol_ratio_func(
         self: CommonModel,
@@ -1469,26 +1476,29 @@ def ret_vol_ratio_func(
             Ratio of the annualized arithmetic mean of returns and annualized
             volatility or, if risk-free return provided, Sharpe ratio
         """
-        ratio = (
+        ratio = Series(
             self.arithmetic_ret_func(
                 months_from_last=months_from_last,
                 from_date=from_date,
                 to_date=to_date,
                 periods_in_a_year_fixed=periods_in_a_year_fixed,
             )
-            - riskfree_rate
+            - riskfree_rate,
         ) / self.vol_func(
             months_from_last=months_from_last,
             from_date=from_date,
             to_date=to_date,
             periods_in_a_year_fixed=periods_in_a_year_fixed,
         )
-        result = Series(ratio)
-        result = result.astype("float64")
-        result.name = "Return vol ratio"
+
         if self.tsdf.shape[1] == 1:
-            return float(result.iloc[0])
-        return result  # type: ignore[return-value]
+            return float(ratio.iloc[0])
+        return Series(
+            data=ratio,
+            index=self.tsdf.columns,
+            name="Return vol ratio",
+            dtype="float64",
+        )
 
     def sortino_ratio_func(
         self: CommonModel,
@@ -1531,27 +1541,30 @@ def sortino_ratio_func(
             Sortino ratio calculated as ( return - riskfree return ) /
             downside deviation (std dev of returns below MAR)
         """
-        ratio = (
+        ratio = Series(
             self.arithmetic_ret_func(
                 months_from_last=months_from_last,
                 from_date=from_date,
                 to_date=to_date,
                 periods_in_a_year_fixed=periods_in_a_year_fixed,
             )
-            - riskfree_rate
+            - riskfree_rate,
         ) / self.downside_deviation_func(
             min_accepted_return=min_accepted_return,
             months_from_last=months_from_last,
             from_date=from_date,
             to_date=to_date,
             periods_in_a_year_fixed=periods_in_a_year_fixed,
         )
-        result = Series(ratio)
-        result = result.astype("float64")
-        result.name = "Sortino ratio"
+
         if self.tsdf.shape[1] == 1:
-            return float(result.iloc[0])
-        return result  # type: ignore[return-value]
+            return float(ratio.iloc[0])
+        return Series(
+            data=ratio,
+            index=self.tsdf.columns,
+            name="Sortino ratio",
+            dtype="float64",
+        )
 
     def value_ret_func(
         self: CommonModel,
@@ -1632,12 +1645,15 @@ def value_ret_calendar_period(
         vrdf.index = DatetimeIndex(vrdf.index)
         resultdf = DataFrame(vrdf.ffill().pct_change())
         result = resultdf.loc[period] + 1
-        result = result.cumprod(axis="index").iloc[-1] - 1
-        result.name = period
-        result = result.astype("float64")
+        cal_period = result.cumprod(axis="index").iloc[-1] - 1
         if self.tsdf.shape[1] == 1:
-            return float(result.iloc[0])
-        return result  # type: ignore[return-value]
+            return float(cal_period.iloc[0])
+        return Series(
+            data=cal_period,
+            index=self.tsdf.columns,
+            name=period,
+            dtype="float64",
+        )
 
     def var_down_func(
         self: CommonModel,

openseries/datefixer.py

@@ -10,7 +10,15 @@
     list_supported_countries,
 )
 from numpy import array, busdaycalendar, datetime64, is_busday, where
-from pandas import DataFrame, DatetimeIndex, Series, Timestamp, concat, date_range
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Series,
+    Timestamp,
+    concat,
+    date_range,
+)
 from pandas.tseries.offsets import CustomBusinessDay
 from pydantic import PositiveInt
 
@@ -435,9 +443,7 @@ def do_resample_to_business_period_ends(
     data.index = DatetimeIndex(data.index)
     data = data.resample(rule=freq, convention=convention).last()
     data = data.drop(index=data.index[-1])
-    data.index = [  # type: ignore[assignment,unused-ignore]
-        d.date() for d in DatetimeIndex(data.index)
-    ]
+    data.index = Index(d.date() for d in DatetimeIndex(data.index))
 
     if newhead.index[0] not in data.index:
         data = concat([data, newhead])

openseries/frame.py

@@ -62,7 +62,7 @@
 )
 
 
-class OpenFrame(CommonModel):  # type: ignore[misc]
+class OpenFrame(CommonModel):
 
     """
     OpenFrame objects hold OpenTimeSeries in the list constituents.

openseries/series.py

@@ -19,6 +19,7 @@
 from pandas import (
     DataFrame,
     DatetimeIndex,
+    Index,
     MultiIndex,
     Series,
     date_range,
@@ -56,7 +57,7 @@
 TypeOpenTimeSeries = TypeVar("TypeOpenTimeSeries", bound="OpenTimeSeries")
 
 
-class OpenTimeSeries(CommonModel):  # type: ignore[misc]
+class OpenTimeSeries(CommonModel):
 
     """
     OpenTimeSeries objects are at the core of the openseries package.
@@ -535,10 +536,12 @@ def value_to_diff(
         self.tsdf = self.tsdf.diff(periods=periods)
         self.tsdf.iloc[0] = 0
         self.valuetype = ValueType.RTRN
-        self.tsdf.columns = [  # type: ignore[assignment]
-            [self.label],
-            [self.valuetype],
-        ]
+        self.tsdf.columns = MultiIndex.from_arrays(
+            [
+                [self.label],
+                [self.valuetype],
+            ],
+        )
         return self
 
     def to_cumret(self: OpenTimeSeries) -> OpenTimeSeries:
@@ -559,10 +562,12 @@ def to_cumret(self: OpenTimeSeries) -> OpenTimeSeries:
         self.tsdf = self.tsdf.add(1.0)
         self.tsdf = self.tsdf.cumprod(axis=0) / self.tsdf.iloc[0]
         self.valuetype = ValueType.PRICE
-        self.tsdf.columns = [  # type: ignore[assignment]
-            [self.label],
-            [self.valuetype],
-        ]
+        self.tsdf.columns = MultiIndex.from_arrays(
+            [
+                [self.label],
+                [self.valuetype],
+            ],
+        )
         return self
 
     def from_1d_rate_to_cumret(
@@ -622,9 +627,7 @@ def resample(
         """
         self.tsdf.index = DatetimeIndex(self.tsdf.index)
         self.tsdf = self.tsdf.resample(freq).last()
-        self.tsdf.index = [  # type: ignore[assignment]
-            d.date() for d in DatetimeIndex(self.tsdf.index)
-        ]
+        self.tsdf.index = Index(d.date() for d in DatetimeIndex(self.tsdf.index))
         return self
 
     def resample_to_business_period_ends(
@@ -806,13 +809,13 @@ def running_adjustment(
             prev = idx
         self.tsdf = DataFrame(data=values, index=dates)
         self.valuetype = ValueType.PRICE
-        self.tsdf.columns = [  # type: ignore[assignment]
-            [self.label],
-            [self.valuetype],
-        ]
-        self.tsdf.index = [  # type: ignore[assignment]
-            d.date() for d in DatetimeIndex(self.tsdf.index)
-        ]
+        self.tsdf.columns = MultiIndex.from_arrays(
+            [
+                [self.label],
+                [self.valuetype],
+            ],
+        )
+        self.tsdf.index = Index(d.date() for d in DatetimeIndex(self.tsdf.index))
         if returns_input:
             self.value_to_ret()
         return self
@@ -904,7 +907,7 @@ def timeseries_chain(
     values = values.mul(
         new.tsdf.iloc[:, 0].loc[first] / old.tsdf.iloc[:, 0].loc[first],
     )
-    values = append(values, new.tsdf.iloc[:, 0])  # type: ignore[assignment]
+    values = Series(append(values, new.tsdf.iloc[:, 0]))
 
     dates.extend([x.strftime("%Y-%m-%d") for x in new.tsdf.index])