finish joinquant stock valuation recorder

init_env.sh

@@ -24,7 +24,7 @@ if ! which python > /dev/null; then
 fi
 
 pip_opt=''
-#pip_opt='-i http://pypi.douban.com/simple --trusted-host pypi.douban.com'
+pip_opt='-i http://pypi.douban.com/simple --trusted-host pypi.douban.com'
 
 if ! which virtualenv > /dev/null; then
    echo -e "virtualenv not found! Install? (y/n) \c"

requirements.txt

@@ -7,7 +7,7 @@ tzlocal == 1.5.1
 xlrd == 1.1.0
 apscheduler == 3.4.0
 # TA-Lib
-jqdatasdk
+jqdatasdk == 1.7.8
 demjson == 2.2.4
 marshmallow-sqlalchemy == 0.19.0
 marshmallow == 3.2.2

zvt/domain/valuation.py

@@ -14,7 +14,10 @@ class Valuation(ValuationBase, Mixin):
 
     code = Column(String(length=32))
     name = Column(String(length=32))
-
+    # 总股本(股)
+    capitalization = Column(Float)
+    # 公司已发行的普通股股份总数(包含A股，B股和H股的总股本)
+    circulating_cap = Column(Float)
     # 市值
     market_cap = Column(Float)
     # 流通市值

zvt/recorders/joinquant/fundamental/stock_valuation_recorder.py

@@ -41,11 +41,24 @@ def record(self, entity, start, end, size, timestamps):
             valuation.code == to_jq_entity_id(entity)
         )
         count: pd.Timedelta = now_pd_timestamp() - start
-        panel = get_fundamentals_continuously(q, end_date=now_time_str(), count=count.days + 1)
-        if not panel.empty:
-            print(panel)
-            df = panel.to_frame()
-            df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)
+        df = get_fundamentals_continuously(q, end_date=now_time_str(), count=count.days + 1, panel=False)
+        df['entity_id'] = entity.id
+        df['timestamp'] = pd.to_datetime(df['day'])
+        df['code'] = entity.code
+        df['name'] = entity.name
+        df['id'] = df[['timestamp']].apply(lambda x: "{}_{}".format(entity.id, x), axis=1)
+        df = df.rename({'pe_ratio_lyr': 'pe',
+                        'pe_ratio': 'pe_ttm',
+                        'pb_ratio': 'pb',
+                        'ps_ratio': 'ps',
+                        'pcf_ratio': 'pcf'},
+                       axis='columns')
+
+        df['market_cap'] = df['market_cap'] * 100000000
+        df['circulating_cap'] = df['circulating_cap'] * 100000000
+        df['capitalization'] = df['capitalization'] * 10000
+        df['circulating_cap'] = df['circulating_cap'] * 10000
+        df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)
 
         return None