adjustments to align with new pandas-stubs version

openseries/_common_model.py

@@ -351,9 +351,9 @@ def worst_month(self: Self) -> float | Series[float]:
  """
  wmdf = self.tsdf.copy()
  wmdf.index = DatetimeIndex(wmdf.index)
- result = (
- wmdf.resample("BME").last().pct_change(fill_method=cast(str, None)).min()
- )
+ result = wmdf.resample("BME").last().pct_change(
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
+ ).min()
 
  if self.tsdf.shape[1] == 1:
  return float(result.iloc[0])
@@ -1023,7 +1023,7 @@ def arithmetic_ret_func(
 
  result = (
  self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .mean()
  * time_factor
  )
@@ -1082,9 +1082,9 @@ def vol_func(
  time_factor = how_many / fraction
 
  data = self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- result = (
- data.pct_change(fill_method=cast(str, None)).std().mul(sqrt(time_factor))
- )
+ result = data.pct_change(
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
+ ).std().mul(sqrt(time_factor))
 
  if self.tsdf.shape[1] == 1:
  return float(cast(SupportsFloat, result.iloc[0]))
@@ -1280,22 +1280,22 @@ def _var_implied_vol_and_target_func(
  if drift_adjust:
  imp_vol = (-sqrt(time_factor) / norm.ppf(level)) * (
  self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .quantile(1 - level, interpolation=interpolation)
  - self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .sum()
  / len(
  self.tsdf.loc[cast(int, earlier) : cast(int, later)].pct_change(
- fill_method=cast(str, None),
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
  ),
  )
  )
  else:
  imp_vol = (
  -sqrt(time_factor)
  * self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .quantile(1 - level, interpolation=interpolation)
  / norm.ppf(level)
  )
@@ -1358,14 +1358,16 @@ def cvar_down_func(
  cvar_df = self.tsdf.loc[cast(int, earlier) : cast(int, later)].copy(deep=True)
  result = [
  cvar_df.loc[:, x] # type: ignore[call-overload,index]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .sort_values()
  .iloc[
  : int(
  ceil(
  (1 - level)
  * cvar_df.loc[:, x] # type: ignore[index]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
+ )
  .count(),
  ),
  )
@@ -1425,7 +1427,7 @@ def downside_deviation_func(
  )
  how_many = (
  self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .count(numeric_only=True)
  )
  if periods_in_a_year_fixed:
@@ -1440,7 +1442,7 @@ def downside_deviation_func(
 
  dddf = (
  self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .sub(min_accepted_return / time_factor)
  )
 
@@ -1543,7 +1545,7 @@ def skew_func(
  )
  result: NDArray[float64] = skew(
  a=self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .to_numpy(),
  bias=True,
  nan_policy="omit",
@@ -1591,7 +1593,7 @@ def kurtosis_func(
  )
  result: NDArray[float64] = kurtosis(
  self.tsdf.loc[cast(int, earlier) : cast(int, later)].pct_change(
- fill_method=cast(str, None),
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
  ),
  fisher=True,
  bias=True,
@@ -1688,17 +1690,17 @@ def positive_share_func(
  )
  pos = (
  self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))[1:][
+ .pct_change(fill_method=None)[1:][ # type: ignore[arg-type,unused-ignore]
  self.tsdf.loc[cast(int, earlier) : cast(int, later)].pct_change(
- fill_method=cast(str, None),
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
  )[1:]
  > zero
  ]
  .count()
  )
  tot = (
  self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))[1:]
+ .pct_change(fill_method=None)[1:] # type: ignore[arg-type,unused-ignore]
  .count()
  )
  share = pos / tot
@@ -1877,7 +1879,7 @@ def omega_ratio_func(
  to_dt=to_date,
  )
  retdf = self.tsdf.loc[cast(int, earlier) : cast(int, later)].pct_change(
- fill_method=cast(str, None),
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
  )
  pos = retdf[retdf > min_accepted_return].sub(min_accepted_return).sum()
  neg = retdf[retdf < min_accepted_return].sub(min_accepted_return).sum()
@@ -1966,7 +1968,9 @@ def value_ret_calendar_period(
  period = "-".join([str(year), str(month).zfill(2)])
  vrdf = self.tsdf.copy()
  vrdf.index = DatetimeIndex(vrdf.index)
- resultdf = DataFrame(vrdf.pct_change(fill_method=None)) # type: ignore[arg-type]
+ resultdf = DataFrame(vrdf.pct_change(
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
+ ))
  result = resultdf.loc[period] + 1
  cal_period = result.cumprod(axis="index").iloc[-1] - 1
  if self.tsdf.shape[1] == 1:
@@ -2018,7 +2022,7 @@ def var_down_func(
  )
  result = (
  self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .quantile(1 - level, interpolation=interpolation)
  )
 
@@ -2066,7 +2070,7 @@ def worst_func(
  )
  result = (
  self.tsdf.loc[cast(int, earlier) : cast(int, later)]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .rolling(observations, min_periods=observations)
  .sum()
  .min()
@@ -2113,7 +2117,7 @@ def z_score_func(
  to_dt=to_date,
  )
  zscframe = self.tsdf.loc[cast(int, earlier) : cast(int, later)].pct_change(
- fill_method=cast(str, None),
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
  )
  result = (zscframe.iloc[-1] - zscframe.mean()) / zscframe.std()
 
@@ -2183,7 +2187,7 @@ def rolling_return(
  ret_label = cast(tuple[str], self.tsdf.iloc[:, column].name)[0]
  retseries = (
  self.tsdf.iloc[:, column]
- .pct_change(fill_method=cast(str, None))
+ .pct_change(fill_method=None) # type: ignore[arg-type,unused-ignore]
  .rolling(observations, min_periods=observations)
  .sum()
  )
@@ -2260,7 +2264,9 @@ def rolling_vol(
  else:
  time_factor = self.periods_in_a_year
  vol_label = cast(tuple[str, ValueType], self.tsdf.iloc[:, column].name)[0]
- dframe = self.tsdf.iloc[:, column].pct_change(fill_method=cast(str, None))
+ dframe = self.tsdf.iloc[:, column].pct_change(
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
+ )
  volseries = dframe.rolling(
  observations,
  min_periods=observations,

openseries/series.py

@@ -436,7 +436,9 @@ def value_to_ret(self: Self) -> Self:
  The returns of the values in the series
 
  """
- self.tsdf = self.tsdf.pct_change(fill_method=None) # type: ignore[arg-type]
+ self.tsdf = self.tsdf.pct_change(
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
+ )
  self.tsdf.iloc[0] = 0
  self.valuetype = ValueType.RTRN
  self.tsdf.columns = MultiIndex.from_arrays(
@@ -696,7 +698,9 @@ def running_adjustment(
  returns_input = True
  else:
  values = [cast(float, self.tsdf.iloc[0, 0])]
- ra_df = self.tsdf.pct_change(fill_method=None) # type: ignore[arg-type]
+ ra_df = self.tsdf.pct_change(
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
+ )
  returns_input = False
  ra_df = ra_df.dropna()
 

openseries/simulation.py

@@ -123,7 +123,9 @@ def realized_mean_return(self: Self) -> float:
  return cast(
  float,
  (
- self.results.pct_change(fill_method=cast(str, None)).mean()
+ self.results.pct_change(
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
+ ).mean()
  * self.trading_days_in_year
  ).iloc[0],
  )
@@ -141,7 +143,9 @@ def realized_vol(self: Self) -> float:
  return cast(
  float,
  (
- self.results.pct_change(fill_method=cast(str, None)).std()
+ self.results.pct_change(
+ fill_method=None, # type: ignore[arg-type,unused-ignore]
+ ).std()
  * sqrt(self.trading_days_in_year)
  ).iloc[0],
  )

pyproject.toml

@@ -52,7 +52,7 @@ genbadge = {version = ">=1.1.1,<2.0.0", extras = ["coverage"]}
 mypy = "^1.11.2"
 pandas-stubs = ">=2.1.2,<3.0.0"
 pre-commit = ">=3.7.1,<4.0.0"
-pytest = ">=8.2.2,<9.0.0"
+pytest = "^8.3.3"
 ruff = "^0.6.4"
 types-openpyxl = ">=3.1.2,<4.0.0"
 types-python-dateutil = ">=2.8.2,<3.0.0"