fix for #409

chainladder/__init__.py

@@ -42,4 +42,4 @@ def auto_sparse(auto_sparse=True):
 from chainladder.methods import *  # noqa (API Import)
 from chainladder.workflow import *  # noqa (API Import)
 
-__version__ = "0.8.15"
+__version__ = "0.8.16"

chainladder/core/tests/test_grain.py

@@ -77,7 +77,7 @@ def test_different_forms_of_grain(prism_dense, grain, trailing, alt, atol):
     assert abs(a - b).sum().sum() < atol
 
 
-def test_assymetrric_origin_grain(prism_dense):
+def test_asymmetric_origin_grain(prism_dense):
     x = prism_dense.iloc[..., 8:, :].incr_to_cum()
     x = x[x.valuation < x.valuation_date]
     assert x.grain("OYDM").development[0] == 1
@@ -90,6 +90,14 @@ def test_vector_triangle_grain_mismatch(prism):
     assert (tri / exposure).development_grain == "Q"
 
 
+def test_annual_trailing(prism):
+    tri = prism["Paid"].sum().incr_to_cum()
+    # (limit data to November)
+    tri = tri[tri.valuation < tri.valuation_date].incr_to_cum()
+    tri = tri.grain("OQDQ", trailing=True).grain("OYDY")
+    assert np.all(tri.ddims[:4] == np.array([3, 6, 9, 12]))
+
+
 def test_development_age():
     raa_tri = cl.load_sample("raa")
     assert (raa_tri.ddims == [12, 24, 36, 48, 60, 72, 84, 96, 108, 120]).all()

chainladder/core/triangle.py

@@ -670,22 +670,27 @@ def grain(self, grain="", trailing=False, inplace=False):
         obj = self.dev_to_val()
         if ograin_new != ograin_old:
             freq = {"Y": "A", "S": "2Q"}.get(ograin_new, ograin_new)
-            mn = self.origin[-1].strftime("%b").upper() if trailing else "DEC"
+            if trailing or obj.origin.freqstr[-3:] != "DEC":
+                origin_period_end = self.origin[-1].strftime("%b").upper()  
+            else:
+                origin_period_end = "DEC"
             indices = (
                 pd.Series(range(len(self.origin)), index=self.origin)
-                .resample("-".join([freq, mn]))
+                .resample("-".join([freq, origin_period_end]))
                 .indices
             )
             groups = pd.concat(
                 [pd.Series([k] * len(v), index=v) for k, v in indices.items()], axis=0
             ).values
             obj = obj.groupby(groups, axis=2).sum()
-            obj.origin_close = mn
-            if len(obj.ddims) > 1 and pd.Timestamp(obj.odims[0]).strftime(
-                "%Y%m"
-            ) != obj.valuation[0].strftime("%Y%m"):
+            obj.origin_close = origin_period_end
+            d_start = pd.Period(
+                obj.valuation[0], 
+                freq=dgrain_old if dgrain_old == 'M' else dgrain_old + obj.origin.freqstr[-4:]
+            ).to_timestamp(how='s')
+            if (len(obj.ddims) > 1 and obj.origin.to_timestamp(how='s')[0] != d_start):
                 addl_ts = (
-                    pd.period_range(obj.odims[0], obj.valuation[0], freq="M")[:-1]
+                    pd.period_range(obj.odims[0], obj.valuation[0], freq=dgrain_old)[:-1]
                     .to_timestamp()
                     .values
                 )

setup.py

@@ -14,7 +14,7 @@
 descr = "Chainladder Package - P&C Loss Reserving package "
 name = 'chainladder'
 url = 'https://github.com/casact/chainladder-python'
-version='0.8.15' # Put this in __init__.py
+version='0.8.16' # Put this in __init__.py
 
 data_path = ''
 setup(