bug修复

app/service/portfolio_diagnose.py

@@ -73,6 +73,13 @@ def search_rank(fund_rank, fund, metric):
     Returns: 基金指标的值
 
     """
+    if len(fund_rank[fund_rank['fund_id'] == fund]) == 0:
+        now_fund = {'index': np.nan, 'fund_id': fund, 'range_return': 0.5, 'annual_return': 0.5,
+                    'max_drawdown': 0.5, 'sharp_ratio': 1, 'volatility': 0.4, 'sortino_ratio': 0,
+                    'downside_risk': 0, 'substrategy': 1010, 'manager': ['PL000000F5'], 'annual_return_rank': 0.5,
+                    'downside_risk_rank': 0.5, 'max_drawdown_rank': 0.5, 'sharp_ratio_rank': 0.5, 'z_score': 50}
+        fund_rank = fund_rank.append(now_fund, ignore_index=True)
+
     return fund_rank[fund_rank['fund_id'] == fund][metric].values[0]
 
 
@@ -325,6 +332,15 @@ def get_fund_name(fund):
         cur = tamp_fund_session.execute(sql)
         data = cur.fetchall()
         df = pd.DataFrame(list(data), columns=['fund_short_name'])
+        if len(df) == 0:
+            with TAMP_SQL(tamp_product_engine) as tamp_product:
+                tamp_product_session = tamp_product.session
+                sql = "SELECT fund_short_name FROM fund_info WHERE id='{}'".format(fund)
+                # df = pd.read_sql(sql, con)
+                cur = tamp_product_session.execute(sql)
+                data = cur.fetchall()
+                df = pd.DataFrame(list(data), columns=['fund_short_name'])
+                return df
         return df
 
 
@@ -448,6 +464,10 @@ class PortfolioDiagnose(object):
         prod.sort_index(inplace=True)
         prod.ffill(inplace=True)
         prod = resample(prod, get_trade_cal(), min(self.freq_list))
+        if 'cal_date' in prod.columns:
+            prod.drop(labels='cal_date', inplace=True, axis=1)
+        if 'end_date' in prod.columns:
+            prod.drop(labels='end_date', inplace=True, axis=1)
         prod.dropna(how='any', inplace=True)
         return prod
 
@@ -662,8 +682,8 @@ class PortfolioDiagnose(object):
         z_score = fund_rank_data["z_score"].mean()
         drawdown_rank = fund_rank_data["max_drawdown_rank"].mean()
         return_rank_df = fund_rank_data["annual_return_rank"]
-        z_score_level = np.select([z_score >= 70,
-                                   50 <= z_score < 70,
+        z_score_level = np.select([z_score >= 80,
+                                   50 <= z_score < 80,
                                    z_score < 50], [0, 1, 2]).item()
         drawdown_level = np.select([drawdown_rank >= 0.8,
                                     0.7 <= drawdown_rank < 0.8,
@@ -726,6 +746,9 @@ class PortfolioDiagnose(object):
         evaluate_enum = [["优秀", "良好", "一般"],
                          ["优秀", "良好", "合格", "较差"]]
 
+        if data_adaptor.group_result_data["cumulative_profit"] < 0 and z_score_level == 0:
+            z_score_level = 1
+
         z_score_evaluate = evaluate_enum[0][z_score_level]
         drawdown_evaluate = evaluate_enum[1][drawdown_level]
         if z_score_evaluate in ["优秀", "良好"]:
@@ -776,14 +799,17 @@ class PortfolioDiagnose(object):
         old_sharpe = group_result_data["sharpe"]
 
         # 指数收益
-        index_data = get_index_daily(self.index_id, self.start_date)
-        index_data = pd.merge(index_data, self.propose_portfolio, how='inner', left_index=True, right_index=True)
+        # index_data = get_index_daily(self.index_id, self.start_date)
+        # index_data = pd.merge(index_data, self.propose_portfolio, how='inner', left_index=True, right_index=True)
+        index_data = data_adaptor.fund_cnav_total[["index"]]
+        index_data = index_data[index_data.index >= pd.to_datetime(data_adaptor.start_date)]
         index_return = index_data.iloc[:, :] / index_data.iloc[0, :] - 1
 
         # 指数收益
         index_return = index_return[index_return.index >= group_order_start_date]
-        start_index_return = index_return[" close"].values[0]
-        index_return["new_index_return"] = (index_return[" close"] - start_index_return) / (1 + start_index_return)
+        index_return["index"] = index_return["index"].astype('float')
+        start_index_return = index_return["index"].values[0]
+        index_return["new_index_return"] = (index_return["index"] - start_index_return) / (1 + start_index_return)
         index_return_ratio = index_return["new_index_return"].values[-1]
         index_return_ratio_year = annual_return(index_return["new_index_return"].values[-1],
                                                 index_return["new_index_return"], n_freq)