新版本

app/service/fund_service.py

@@ -172,7 +172,7 @@ def single_evaluation(fund_id, invest_type=2, index_id='000905.SH'):
     return evaluation_dict
 
 
-def fund_index_compare(fund_id, invest_type=2, index_id='000905.SH'):
+def fund_index_compare(fund_id, invest_type=2, index_id='4000233'):
     start_date = datetime.datetime(2000, 1, 1)
     index_daily = get_index_daily(index_id, start_date)
     fund_nav = get_tamp_nav(fund_id, start_date, invest_type=invest_type)

app/service/portfolio_diagnose.py

@@ -203,14 +203,12 @@ def get_index_daily(index_id, start_date):
     Returns:与组合净值形式相同的表
 
     """
-    with TAMP_SQL(tamp_fund_engine) as tamp_product:
-        tamp_product_session = tamp_product.session
-        sql = "SELECT ts_code, trade_date, close FROM index_daily " \
-              "WHERE ts_code='{}' AND trade_date>'{}'".format(index_id, start_date)
-        # df = pd.read_sql(sql, con).dropna(how='any')
-        cur = tamp_product_session.execute(sql)
+    with TAMP_SQL(tamp_fund_engine) as tamp_fund:
+        tamp_fund_session = tamp_fund.session
+        sql = "select DISTINCT `index_id`, `price_date`, `close` from tx_fund_market_indexes where index_id='{}'  order by price_date ASC".format(
+            index_id)
+        cur = tamp_fund_session.execute(sql)
         data = cur.fetchall()
-
         df = pd.DataFrame(list(data), columns=['ts_code', 'trade_date', 'close'])
         df.rename({'ts_code': 'fund_id', 'trade_date': 'end_date', 'close': 'adj_nav'}, axis=1, inplace=True)
         df['end_date'] = pd.to_datetime(df['end_date'])
@@ -490,7 +488,7 @@ tamp_fund = get_tamp_fund()
 
 class PortfolioDiagnose(object):
     def __init__(self, client_type, portfolio, invest_amount, expect_return=0.1,
-                 expect_drawdown=0.15, index_id='000905.SH', invest_type='private', start_date=None, end_date=None):
+                 expect_drawdown=0.15, index_id='4000233', invest_type='private', start_date=None, end_date=None):
         """基金诊断
 
         Args:
@@ -519,7 +517,8 @@ class PortfolioDiagnose(object):
 
         if self.end_date is None:
             self.end_date = datetime.datetime(datetime.date.today().year,
-                                              datetime.date.today().month, 1) - datetime.timedelta(1)
+                                              datetime.date.today().month,
+                                              datetime.date.today().day)
         if self.start_date is None:
             self.start_date = cal_date(self.end_date, 'Y', 1)
         else:
@@ -536,6 +535,7 @@ class PortfolioDiagnose(object):
         self.origin_portfolio = None
         self.abandoned_portfolio = None
         self.propose_portfolio = None
+        self.ori_prod = None
 
     def get_portfolio(self, ):
         """获取组合净值表
@@ -734,8 +734,7 @@ class PortfolioDiagnose(object):
             prod[proposal] = proposal_nav[proposal]
             self.proposal_fund.append(proposal)
 
-        prod.ffill(inplace=True)
-        prod = prod[prod.index >= self.start_date]
+        prod = prod[(prod.index >= self.start_date) & (prod.index <= self.end_date)]
         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)
@@ -744,6 +743,8 @@ class PortfolioDiagnose(object):
         self.new_correlation = cal_correlation(prod)
 
         prod.dropna(how='all', inplace=True)
+        prod.ffill(inplace=True)
+        self.ori_prod = prod.copy()
         prod.fillna(method="bfill", inplace=True)
         self.new_correlation = self.new_correlation.fillna(1).round(2)
         self.new_correlation.columns = self.new_correlation.columns.map(lambda x: get_fund_name(x, suggest_fund_type[x]).values[0][0])
@@ -863,12 +864,20 @@ class PortfolioDiagnose(object):
         print("模型计算一次时间：", end4 - start)
 
     def return_compare(self):
+        weight_prod = self.ori_prod.copy()
+        for col in weight_prod.columns:
+            weight_prod[col] = weight_prod[col].apply(lambda x: 0 if math.isnan(x) else 1)
+        weight_prod = weight_prod * self.new_weights
+        weight_prod = weight_prod.apply(lambda x: x/x.sum(), axis=1)
+
         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_return = index_data.iloc[:, :] / index_data.iloc[0, :] - 1
         # origin_fund_return = origin_portfolio.iloc[:, :] / origin_portfolio.iloc[0, :] - 1
         propose_fund_return = self.propose_portfolio.iloc[:, :] / self.propose_portfolio.iloc[0, :] - 1
-        propose_fund_return['return'] = propose_fund_return.T.iloc[:, :].apply(lambda x: np.dot(self.new_weights, x))
+        pct_change = self.propose_portfolio.pct_change().fillna(0) * weight_prod
+        propose_fund_return['return'] = (pct_change.sum(axis=1)+1).cumprod() - 1
+        # propose_fund_return['return'] = propose_fund_return.T.iloc[:, :].apply(lambda x: np.dot(self.new_weights, x))
         return index_return, propose_fund_return
 
     def old_evaluation(self, group_name, group_result, data_adaptor):