fix-csi500

scripts/data_collector/cn_index/collector.py

@@ -345,7 +345,7 @@ class CSI100(CSIIndex):
 
 class CSI500(CSIIndex):
     @property
-    def index_code(self):
+    def index_code(self) -> str:
         return "000905"
 
     @property
@@ -353,22 +353,41 @@ class CSI500(CSIIndex):
         return pd.Timestamp("2007-01-15")
 
     @property
-    def html_table_index(self):
+    def html_table_index(self) -> int:
         return 0
 
-    def get_changes(self):
+    def get_changes(self) -> pd.DataFrame:
+        """get companies changes
+
+        Return
+        --------
+           pd.DataFrame:
+               symbol      date        type
+               SH600000  2019-11-11    add
+               SH600000  2020-11-10    remove
+           dtypes:
+               symbol: str
+               date: pd.Timestamp
+               type: str, value from ["add", "remove"]
+        """
         return self.get_changes_with_history_companies(self.get_history_companies())
 
-    def get_history_companies(self):
+    def get_history_companies(self) -> pd.DataFrame:
         """
-        Data source: http://baostock.com/baostock/index.php/%E4%B8%AD%E8%AF%81500%E6%88%90%E5%88%86%E8%82%A1
+
-                Avoid a large number of parallel data acquisition,
-                such as 1000 times of concurrent data acquisition, because IP will be blocked
         Returns
         -------
 
+            pd.DataFrame:
+                symbol      date        type
+                SH600000  2019-11-11    add
+                SH600000  2020-11-10    remove
+            dtypes:
+                symbol: str
+                date: pd.Timestamp
+                type: str, value from ["add", "remove"]
         """
-        lg = bs.login()
+        bs.login()
         today = pd.datetime.now()
         date_range = pd.DataFrame(pd.date_range(start="2007-01-15", end=today, freq="7D"))[0].dt.date
         ret_list = []
@@ -380,10 +399,64 @@ class CSI500(CSIIndex):
                 zz500_stocks.append(rs.get_row_data())
             result = pd.DataFrame(zz500_stocks, columns=col)
             result["symbol"] = result["symbol"].apply(lambda x: x.replace(".", "").upper())
+            result = self.get_data_from_baostock(date)
             ret_list.append(result[["date", "symbol"]])
         bs.logout()
         return pd.concat(ret_list, sort=False)
 
+    def get_data_from_baostock(self, date) -> pd.DataFrame:
+        """
+        Data source: http://baostock.com/baostock/index.php/%E4%B8%AD%E8%AF%81500%E6%88%90%E5%88%86%E8%82%A1
+        Avoid a large number of parallel data acquisition,
+        such as 1000 times of concurrent data acquisition, because IP will be blocked
+
+        Returns
+        -------
+            pd.DataFrame:
+                date      symbol        code_name
+                SH600039  2007-01-15    四川路桥
+                SH600051  2020-01-15    宁波联合
+            dtypes:
+                date: pd.Timestamp
+                symbol: str
+                code_name: str
+        """
+        col = ["date", "symbol", "code_name"]
+        rs = bs.query_zz500_stocks(date=str(date))
+        zz500_stocks = []
+        while (rs.error_code == "0") & rs.next():
+            zz500_stocks.append(rs.get_row_data())
+        result = pd.DataFrame(zz500_stocks, columns=col)
+        result["symbol"] = result["symbol"].apply(lambda x: x.replace(".", "").upper())
+        return result
+
+    def get_new_companies(self) -> pd.DataFrame:
+        """
+
+        Returns
+        -------
+            pd.DataFrame:
+
+                symbol     start_date    end_date
+                SH600000   2000-01-01    2099-12-31
+
+            dtypes:
+                symbol: str
+                start_date: pd.Timestamp
+                end_date: pd.Timestamp
+        """
+        logger.info("get new companies......")
+        today = datetime.date.today()
+        bs.login()
+        result = self.get_data_from_baostock(today)
+        bs.logout()
+        df = result[["date", "symbol"]]
+        df.columns = [self.END_DATE_FIELD, self.SYMBOL_FIELD_NAME]
+        df[self.END_DATE_FIELD] = pd.to_datetime(df[self.END_DATE_FIELD].astype(str))
+        df[self.START_DATE_FIELD] = self.bench_start_date
+        logger.info("end of get new companies.")
+        return df
+
 
 def get_instruments(
     qlib_dir: str,