tightened OpenFrame.make_portfolio() method to not allow mixed series…

… types. Also updated dependencies

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
 
 repos:
  - repo: https://github.com/charliermarsh/ruff-pre-commit
- rev: v0.6.6
+ rev: v0.6.7
  hooks:
  - id: ruff
  args: [ --fix, --exit-non-zero-on-fix ]

openseries/frame.py

@@ -1407,27 +1407,30 @@ def make_portfolio(
  "to run the make_portfolio method."
  )
  raise ValueError(msg)
- dframe = self.tsdf.copy()
- if not any(
- x == ValueType.RTRN
- for x in self.tsdf.columns.get_level_values(1).to_numpy()
- ):
- dframe = dframe.pct_change()
- dframe.iloc[0] = 0
+
+ vtypes = [x == ValueType.RTRN for x in self.tsdf.columns.get_level_values(1)]
+ if not any(vtypes):
+ returns = self.tsdf.ffill().pct_change()
+ returns.iloc[0] = 0
+ elif all(vtypes):
+ returns = self.tsdf.copy()
+ else:
+ msg = "Mix of series types will give inconsistent results"
+ raise ValueError(msg)
 
  msg = "Weight strategy not implemented"
  if weight_strat:
  if weight_strat == "eq_weights":
  self.weights = [1.0 / self.item_count] * self.item_count
  elif weight_strat == "inv_vol":
- vol = divide(1.0, std(dframe, axis=0, ddof=1))
+ vol = divide(1.0, std(returns, axis=0, ddof=1))
  vol[isinf(vol)] = nan
  self.weights = list(divide(vol, vol.sum()))
  else:
  raise NotImplementedError(msg)
 
  return DataFrame(
- data=(dframe @ self.weights).add(1.0).cumprod(),
+ data=(returns @ self.weights).add(1.0).cumprod(),
  index=self.tsdf.index,
  columns=[[name], [ValueType.PRICE]],
  dtype="float64",

pyproject.toml

@@ -53,7 +53,7 @@ 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"
-ruff = "^0.6.6"
+ruff = "^0.6.7"
 types-openpyxl = ">=3.1.2,<4.0.0"
 types-python-dateutil = ">=2.8.2,<3.0.0"
 types-requests = ">=2.20.0,<3.0.0"

tests/test_frame.py

@@ -618,6 +618,17 @@ def test_make_portfolio_weight_strat(self: TestOpenFrame) -> None:
  )
  raise ValueError(msg)
 
+ series = self.randomseries.from_deepcopy()
+ returns = self.randomseries.from_deepcopy()
+ returns.set_new_label(lvl_zero="returns")
+ returns.value_to_ret()
+ mixframe = OpenFrame(constituents=[series, returns])
+ with pytest.raises(
+ expected_exception=ValueError,
+ match="Mix of series types will give inconsistent results",
+ ):
+ _ = mixframe.make_portfolio(name=name, weight_strat="eq_weights")
+
  with pytest.raises(
  expected_exception=NotImplementedError,
  match="Weight strategy not implemented",