fix index data bug

qlib/backtest/high_performance_ds.py

@@ -203,7 +203,7 @@ class NumpyQuote(BaseQuote):
             elif method is None:
                 stock_data = self.data[stock_id][start_id:end_id, self.columns[fields]]
                 stock_dates = self.dates_list[stock_id][start_id:end_id].to_list()
-                return IndexData(stock_data, [stock_id], stock_dates)
+                return IndexData(stock_data, stock_dates)
             else:
                 agg_stock_data = self._agg_data(self.data[stock_id][start_id:end_id, self.columns[fields]], method)
 
@@ -721,7 +721,7 @@ class NumpyOrderIndicator(BaseOrderIndicator):
             self.data = np.zeros((len(NumpyOrderIndicator.ROW), len(metric)))
             self.column = list(metric.keys())
             self.column_map = dict(zip(self.column, range(len(self.column))))
-        
+
         metric_column = list(metric.keys())
         if self.column != metric_column:
             assert len(set(self.column) - set(metric_column)) == 0
@@ -753,9 +753,9 @@ class NumpyOrderIndicator(BaseOrderIndicator):
 
     def get_index_data(self, metric):
         if self._if_valid_metric(metric):
-            return IndexData(self.data[NumpyOrderIndicator.ROW_MAP[metric]], [metric], self.column)
+            return IndexData(self.data[NumpyOrderIndicator.ROW_MAP[metric]], self.column)
         else:
-            return IndexData([], [], [])
+            return IndexData([], [])
 
     def get_metric_series(self, metric: str) -> Union[pd.Series]:
         if self._if_valid_metric(metric):
@@ -819,52 +819,80 @@ class NumpyOrderIndicator(BaseOrderIndicator):
 
 
 class IndexData:
-    def __init__(self, data, row, column):
+    def __init__(self, data, column):
         if isinstance(data, list):
-            self.data = np.array([data])
+            self.data = np.array(data)
         elif isinstance(data, np.ndarray):
-            if data.ndim == 1:
+            self.data = data
-                self.data = data[np.newaxis, :]
-            elif data.ndim == 2:
-                self.data = data
-            else:
-                raise ValueError(f"the dimension of data must <= 2")
         else:
             raise ValueError(f"data must be list or np.ndarray")
-        self.data = data 
+        self.ndim = self.data.ndim
 
-        assert isinstance(row, list)
-        self.row = row 
-        self.row_map = dict(zip(self.row, range(len(self.row))))
         assert isinstance(column, list)
         self.col = column
         self.col_map = dict(zip(self.col, range(len(self.col))))
 
     def reindex(self, new_column):
-        tmp_data = self.data.copy()
+        assert self.ndim == 1
-        for row_id, row in enumerate(self.row):
+        tmp_data = np.full(len(new_column), np.NaN)
-            for col_id, col in new_column:
+        for col_id, col in enumerate(new_column):
-                if col in self.col:
+            if col in self.col:
-                    tmp_data[row_id, col_id] = self.data[row_id, self.row_map[col]]
+                tmp_data[col_id] = self.data[self.col_map[col]]
-                else:
+        return IndexData(tmp_data, list(new_column))
-                    tmp_data[row_id, col_id] = np.NaN
-        return IndexData(tmp_data, self.row, list(new_column))
 
     def to_dict(self):
-        assert len(self.row) == 1
+        assert self.ndim == 1
-        if self.data.size == 0:
+        return dict(zip(self.col, self.data.tolist()))
-            return {col: np.NaN for col in self.col}
+
+    def keep_positive(self, limit=1e-08):
+        assert self.ndim == 1
+        new_col = []
+        new_data = []
+        for col_id, col in enumerate(self.col):
+            if self.data[col_id] < 1e-08:
+                continue
+            else:
+                new_col.append(col)
+                new_data.append(self.data[col_id])
+        return IndexData(new_data, new_col)
+
+    def sum(self, axis=None):
+        if axis is None:
+            return np.nansum(self.data)
+        if axis == 0:
+            assert self.ndim == 2
+            tmp_data = np.nansum(self.data, axis=0)
+            return IndexData(tmp_data, self.col)
         else:
-            return dict(zip(self.col, self.data[0, :].tolist()))
+            raise NotImplementedError(f"axis must be 0 or None")
+
+    def __mul__(self, other):
+        if isinstance(other, IndexData):
+            assert self.ndim == other.ndim
+            assert self.col == other.col
+            assert len(self.data) == len(other.data)
+            return IndexData(self.data * other.data, self.col)
+        else:
+            return NotImplemented
+
+    def __truediv__(self, other):
+        if isinstance(other, IndexData):
+            assert self.ndim == other.ndim
+            assert self.col == other.col
+            assert len(self.data) == len(other.data)
+            return IndexData(self.data / other.data, self.col)
+        else:
+            return NotImplemented
+
+    def __len__(self):
+        return len(self.col)
 
     @staticmethod
     def concat_by_col(index_data_list):
         # get all col and row
         all_col = set()
-        all_row = []
         for index_data in index_data_list:
             all_col = all_col | set(index_data.col)
-            all_row.append(index_data.row[0])
         all_col = list(all_col)
         all_col.sort()
         all_col_map = dict(zip(all_col, range(len(all_col))))
@@ -874,52 +902,4 @@ class IndexData:
         for data_id, index_data in enumerate(index_data_list):
             now_data_map = [all_col_map[col] for col in index_data.col]
             tmp_data[data_id, now_data_map] = index_data.data
-        return IndexData(tmp_data, all_row, all_col)
+        return IndexData(tmp_data, all_col)
-
-    def sum(self, axis = None):
-        if axis is None:
-            return np.nansum(self.data)
-        if axis == 0:
-            tmp_data = np.nansum(self.data, axis=0)
-            return IndexData(tmp_data, [self.row[0]], self.col)
-        else:
-            raise NotImplementedError(f"axis must be 0 or None")
-
-    def keep_positive(self, limit = 1e-08):
-        assert len(self.row) == 1
-        new_col = []
-        new_data = []
-        for col_id, col in enumerate(self.col):
-            if self.data[0: col_id] < 1e-08:
-                continue
-            else:
-                new_col.append(col)
-                new_data.append(self.data[0: col_id])
-        return IndexData(new_data, self.row, new_col)
-
-    def __mul__(self, other):
-        if isinstance(other, IndexData):
-            assert len(self.row) == len(other.row)
-            assert self.col == other.col
-            return IndexData(self.data * other.data, ["mul"], self.col)
-        else:
-            return NotImplemented
-
-    def __truediv__(self, other):
-        if isinstance(other, IndexData):
-            assert len(self.row) == len(other.row)
-            assert self.col == other.col
-            return IndexData(self.data / other.data, ["div"], self.col)
-        else:
-            return NotImplemented
-
-    def __len__(self):
-        return len(self.col)
-
-    
-        
-
-    
-
-    
-        

qlib/backtest/report.py

@@ -391,7 +391,7 @@ class Indicator:
             return None, None
 
         if isinstance(price_s, (int, float)):
-            price_s = IndexData([price_s], [inst], [trade_start_time])
+            price_s = IndexData([price_s], [trade_start_time])
 
         # NOTE: there are some zeros in the trading price. These cases are known meaningless
         # for aligning the previous logic, remove it.
@@ -402,16 +402,15 @@ class Indicator:
         if agg == "vwap":
             volume_s = trade_exchange.get_volume(inst, trade_start_time, trade_end_time, method=None)
             if isinstance(volume_s, (int, float)):
-                volume_s = IndexData([volume_s], [inst], [trade_start_time])
+                volume_s = IndexData([volume_s], [trade_start_time])
             volume_s = volume_s.reindex(price_s.col)
         elif agg == "twap":
-            volume_s = IndexData([1 for i in range(price_s.col)], [inst], price_s.col)
+            volume_s = IndexData([1 for i in range(len(price_s.col))], price_s.col)
         else:
             raise NotImplementedError(f"This type of input is not supported")
 
         base_volume = volume_s.sum()
         base_price = (price_s * volume_s).sum() / base_volume
-
         return base_price, base_volume
 
     def _agg_base_price(
@@ -451,6 +450,7 @@ class Indicator:
             for oi, (dec, start, end) in zip(inner_order_indicators, decision_list):
                 bp_s = oi.get_index_data("base_price").reindex(trade_dir.col)
                 bv_s = oi.get_index_data("base_volume").reindex(trade_dir.col)
+
                 bp_new, bv_new = {}, {}
                 for pr, v, (inst, direction) in zip(bp_s.data, bv_s.data, zip(trade_dir.col, trade_dir.data)):
                     if np.isnan(pr):
@@ -468,16 +468,16 @@ class Indicator:
                     else:
                         bp_new[inst], bv_new[inst] = pr, v
 
-                bp_new = IndexData(list(bp_new.values()), ["base_price"], list(bp_new.keys()))
+                bp_new = IndexData(list(bp_new.values()), list(bp_new.keys()))
-                bv_new = IndexData(list(bv_new.values()), ["base_volume"], list(bv_new.keys()))
+                bv_new = IndexData(list(bv_new.values()), list(bv_new.keys()))
                 bp_all.append(bp_new)
                 bv_all.append(bv_new)
             bp_all = IndexData.concat_by_col(bp_all)
             bv_all = IndexData.concat_by_col(bv_all)
 
-            base_volume = bv_all.sum(axis = 0)
+            base_volume = bv_all.sum(axis=0)
             self.order_indicator.assign("base_volume", base_volume.to_dict())
-            self.order_indicator.assign("base_price", ((bp_all * bv_all).sum(axis = 0) / base_volume).to_dict())
+            self.order_indicator.assign("base_price", ((bp_all * bv_all).sum(axis=0) / base_volume).to_dict())
 
     def _agg_order_price_advantage(self):
         def if_empty_func(trade_price):