use hasattr instead of version to check whether rolling.rank is implemented

Author: qianyun210603

qlib/data/ops.py

@@ -1148,25 +1148,15 @@ class Rank(Rolling):
 
     def __init__(self, feature, N):
         super(Rank, self).__init__(feature, N, "rank")
-        major_version, minor_version, *_ = pd.__version__.split(".")
-        self._load_internal = (
-            self._load_internal_pd14
-            if int(major_version) > 1 or int(major_version) == 1 and int(minor_version) > 3
-            else self._load_internal_pd_below_13
-        )
-
-    def _load_internal_pd14(self, instrument, start_index, end_index, *args):
-        series = self.feature.load(instrument, start_index, end_index, *args)
-        if self.N == 0:
-            series = series.expanding(min_periods=1).rank(pct=True)
-        else:
-            series = series.rolling(self.N, min_periods=1).rank(pct=True)
-        return series
 
     # for compatiblity of python 3.7, which doesn't support pandas 1.4.0+ which implements Rolling.rank
-    def _load_internal_pd_below_13(self, instrument, start_index, end_index, *args):
+    def _load_internal(self, instrument, start_index, end_index, *args):
         series = self.feature.load(instrument, start_index, end_index, *args)
 
+        rolling_or_expending = series.expanding(min_periods=1) if self.N == 0 else series.rolling(self.N, min_periods=1)
+        if hasattr(rolling_or_expending, "rank"):
+            return rolling_or_expending.rank(pct=True)
+
         def rank(x):
             if np.isnan(x[-1]):
                 return np.nan
@@ -1175,11 +1165,7 @@ def rank(x):
                 return np.nan
             return percentileofscore(x1, x1[-1]) / 100
 
-        if self.N == 0:
-            series = series.expanding(min_periods=1).apply(rank, raw=True)
-        else:
-            series = series.rolling(self.N, min_periods=1).apply(rank, raw=True)
-        return series
+        return rolling_or_expending.apply(rank, raw=True)
 
 
 class Count(Rolling):