Minor bug fixes

yfinance_cache/yfc_prices_manager.py

@@ -376,6 +376,8 @@ def _getCachedPrices(self):
         elif h is not None:
             h_modified = False
 
+            # h = yfcu.CustomNanCheckingDataFrame(h)
+
             if "Adj Close" in h.columns:
                 raise Exception("Adj Close in cached h")
 
@@ -817,14 +819,14 @@ def get(self, start=None, end=None, period=None, max_age=None, trigger_at_market
                         delete_range = (sched is not None) and dt_now < (sched["open"].iloc[0] + yf_lag)
                 if delete_range:
                     if debug_yfc:
-                        print("- deleting future range:", ranges_to_fetch[i])
+                        print("- deleting future range:", r[i])
                     del ranges_to_fetch[i]
                 else:
                     # Check if range ends in future, if yes then adjust to tomorrow max
                     y = r[1]
                     if isinstance(y, (datetime, pd.Timestamp)):
                         if y > dt_now:
-                            ranges_to_fetch[i][1] = min(dt_now.ceil(), y)
+                            ranges_to_fetch[i] = (r[0], min(dt_now.ceil('1D'), y))
                     elif y > d_now_exchange:
                         sched = yfct.GetExchangeSchedule(self.exchange, d_now_exchange, y + td_1d)
                         if sched is not None:
@@ -2440,6 +2442,8 @@ def _fetchYfHistory_dateRange(self, start, end, prepost, debug):
         elif debug_yfc:
             print(log_msg)
 
+        # df = yfcu.CustomNanCheckingDataFrame(df)
+
         return df
 
     def _reconstruct_intervals_batch(self, df, tag=-1):

yfinance_cache/yfc_utils.py

@@ -10,6 +10,34 @@
 from . import yfc_dat as yfcd
 
 
+class CustomNanCheckingDataFrame(pd.DataFrame):
+    def __init__(self, *args, **kwargs):
+        super(CustomNanCheckingDataFrame, self).__init__(*args, **kwargs)
+        self.check_nans()
+
+    def __setitem__(self, key, value):
+        super(CustomNanCheckingDataFrame, self).__setitem__(key, value)
+        self.check_nans()
+
+    @classmethod
+    def concat(cls, objs, *args, **kwargs):
+        result = super(CustomNanCheckingDataFrame, cls).concat(objs, *args, **kwargs)
+        result.check_nans()
+        return result
+
+    @classmethod
+    def merge(cls, *args, **kwargs):
+        result = super(CustomNanCheckingDataFrame, cls).merge(*args, **kwargs)
+        result.check_nans()
+        return result
+
+    def check_nans(self):
+        if 'Repaired?' not in self.columns:
+            return
+        if self['Repaired?'].isna().any():
+            raise Exception(f"NaNs detected in column 'Repaired?'!")
+
+
 def TypeCheckStr(var, varName):
     if not isinstance(var, str):
         raise TypeError(f"'{varName}' must be str not {type(var)}")