Pass basic tests

qlib/backtest/exchange.py

@@ -18,7 +18,7 @@ from ..config import C, REG_CN
 from ..utils.resam import resam_ts_data, ts_data_last
 from ..log import get_module_logger
 from .order import Order, OrderDir, OrderHelper
-from .high_performance_ds import PandasQuote, CN1minNumpyQuote
+from .high_performance_ds import BaseQuote, PandasQuote, CN1minNumpyQuote
 
 
 class Exchange:
@@ -185,7 +185,7 @@ class Exchange:
 
         # init quote by quote_df
         self.quote_cls = quote_cls
-        self.quote = self.quote_cls(self.quote_df)
+        self.quote: BaseQuote = self.quote_cls(self.quote_df)
 
     def get_quote_from_qlib(self):
         # get stock data from qlib

qlib/backtest/high_performance_ds.py

@@ -40,7 +40,7 @@ class BaseQuote:
         end_time: Union[pd.Timestamp, str],
         field: Union[str],
         method: Union[str, Callable, None] = None,
-    ) -> Union[None, int, float, bool, "IndexData"]:
+    ) -> Union[None, int, float, bool, IndexData]:
         """get the specific field of stock data during start time and end_time,
            and apply method to the data.
 
@@ -154,7 +154,7 @@ class CN1minNumpyQuote(BaseQuote):
             # this is a very special case.
             # skip aggregating function to speed-up the query calculation
             try:
-                self.data[stock_id].loc[start_time, field]
+                return self.data[stock_id].loc[start_time, field]
             except KeyError:
                 return None
         else:
@@ -598,7 +598,7 @@ class NumpyOrderIndicator(BaseOrderIndicator):
         if isinstance(metrics, str):
             metrics = [metrics]
         for metric in metrics:
-            tmp_metric = IndexData.SingleData()
+            tmp_metric = idd.SingleData()
             for indicator in indicators:
                 tmp_metric = tmp_metric.add(indicator.data[metric], fill_value)
             order_indicator.data[metric] = tmp_metric

qlib/backtest/report.py

@@ -395,8 +395,9 @@ class Indicator:
         # NOTE: there are some zeros in the trading price. These cases are known meaningless
         # for aligning the previous logic, remove it.
         # remove zero and negative values.
-        price_s = price_s[~(price_s < 1e-08)]
+        price_s = price_s.loc[(price_s > 1e-08).data.astype(np.bool)]
         # NOTE ~(price_s < 1e-08) is different from price_s >= 1e-8
+        #   ~(np.NaN < 1e-8) -> ~(False)  -> True
 
         if agg == "vwap":
             volume_s = trade_exchange.get_volume(inst, trade_start_time, trade_end_time, method=None)