新增基金的数据联动

app/service/folio_comment_service.py

+from app.service.portfolio_diagnose import PortfolioDiagnose
+from app.service.result_service_v2 import UserCustomerResultAdaptor
+import datetime
+
+from app.utils.draw import draw_combination_chart
+
+
+def recalculation_fund_comment(ifa_id, customer_id, folio, suggest_result):
+    suggest_fund_dict = {}
+    suggest_fund_list = []
+    suggest_amount_list = []
+    for key, value_list in suggest_result.items():
+        for value in value_list:
+            if value["after_optimization"] <= 0:
+                continue
+            suggest_fund_dict[value["fund_id"]] = value["fund_type"]
+            suggest_fund_list.append(value["fund_id"])
+            suggest_amount_list.append(value["after_optimization"])
+    sum_amount = sum(suggest_amount_list)
+    suggest_weight_list = [round(amount / sum_amount, 4) for amount in suggest_amount_list]
+
+    user_customer = UserCustomerResultAdaptor(ifa_id, customer_id, end_date=datetime.datetime.now().strftime("%Y-%m-%d"))
+    group_result = user_customer.calculate_group_result_data()
+    cur_folio_result = group_result[folio]
+
+    portfolio_diagnose = PortfolioDiagnose(client_type=1, portfolio=user_customer.all_fund_type_dict,
+                                           invest_amount=float(cur_folio_result["total_cost"]),
+                                           start_date=user_customer.start_date)
+
+    portfolio_diagnose.proposal_customize(suggest_fund_list, suggest_weight_list, suggest_fund_dict)
+
+    abandon_fund = list(set(cur_folio_result["fund_id_list"]) - set(suggest_fund_list))
+    portfolio_diagnose.abandon_fund_score = abandon_fund
+
+    finall_reslut = {}
+    finall_reslut["suggestions_result"], finall_reslut["suggestions_result_asset"], \
+    finall_reslut["return_compare_data"], \
+    finall_reslut["indicator_compare"], finall_reslut["new_group_evaluation"] = \
+        portfolio_diagnose.new_evaluation(folio, group_result, user_customer, suggest_fund_dict)
+
+    finall_reslut["return_compare_pic"] = draw_combination_chart(
+        finall_reslut["return_compare_data"]["xlabels"],
+        finall_reslut["return_compare_data"]["new_combination"],
+        finall_reslut["return_compare_data"]["origin_combination"],
+        finall_reslut["return_compare_data"]["index"])
+
+    return finall_reslut
\ No newline at end of file

app/service/portfolio_diagnose.py

@@ -737,6 +737,10 @@ class PortfolioDiagnose(object):
         prod.ffill(inplace=True)
         prod = prod[prod.index >= self.start_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)
+        if 'end_date' in prod.columns:
+            prod.drop(labels='end_date', inplace=True, axis=1)
         self.new_correlation = cal_correlation(prod)
 
         prod.dropna(how='all', inplace=True)
@@ -1092,7 +1096,6 @@ class PortfolioDiagnose(object):
                     product_df = pd.DataFrame(list(data) + list(data2),
                                               columns=['fund_id', 'fund_name', 'freq', 'substrategy'])
                 propose_fund_df = product_df[product_df["fund_id"].isin(propose_fund_id_list)]
-
                 for index, row in propose_fund_df.iterrows():
                     if math.isnan(row["freq"]):
                         propose_fund_df.loc[index, "freq"] = 2
@@ -1100,6 +1103,7 @@ class PortfolioDiagnose(object):
                         propose_fund_df.loc[index, "substrategy"] = 5010
 
                 # 基金名称，策略分级
+                propose_fund_type_list = [2 for fund_id in propose_fund_id_list]
                 propose_fund_id_name_list = [
                     propose_fund_df[propose_fund_df["fund_id"] == fund_id]["fund_name"].values[0]
                     for
@@ -1112,6 +1116,7 @@ class PortfolioDiagnose(object):
                 propose_fund_asset = [round(self.new_weights[i] * total_asset, 2) for i in range(len(propose_fund_id_name_list))]
 
             else:
+                propose_fund_type_list = [fund_tye_dict[fund_id] for fund_id in propose_fund_id_list]
                 propose_fund_id_name_list = [get_fund_name(fund_id, fund_tye_dict[fund_id]).values[0][0] for fund_id in
                                              propose_fund_id_list]
                 propose_fund_id_strategy_name_list = [
@@ -1123,7 +1128,10 @@ class PortfolioDiagnose(object):
             propose_info = {propose_fund_id_strategy_name_list[i]:
                                 {"fund_name": propose_fund_id_name_list[i],
                                  "substrategy": propose_fund_id_strategy_name_list[i],
-                                 "asset": propose_fund_asset[i]}
+                                 "asset": propose_fund_asset[i],
+                                 "fund_id": propose_fund_id_list[i],
+                                 "fund_type": propose_fund_type_list[i]
+                                 }
                             for i in range(len(propose_fund_id_list))}
             # 调仓建议
             suggestions_result = {}
@@ -1136,6 +1144,8 @@ class PortfolioDiagnose(object):
                 suggestions["fund_name"] = hold["fund_name"]
                 suggestions["before_optimization"] = hold["market_values"]
                 suggestions["after_optimization"] = 0
+                suggestions["fund_id"] = hold["fund_id"]
+                suggestions["fund_type"] = hold["fund_type"]
                 if suggestions["fund_strategy_name"] in propose_fund_id_strategy_name_list:
                     suggestions["after_optimization"] = 0
                 suggestions_result[hold["fund_strategy_name"]][suggestions["fund_name"]] = suggestions
@@ -1149,6 +1159,8 @@ class PortfolioDiagnose(object):
                     suggestions["fund_name"] = value["fund_name"]
                     suggestions["before_optimization"] = 0
                     suggestions["after_optimization"] = value["asset"]
+                    suggestions["fund_id"] = value["fund_id"]
+                    suggestions["fund_type"] = value["fund_type"]
                     suggestions_result[key][suggestions["fund_name"]] = suggestions
                 else:
                     suggestions_result[key][value["fund_name"]]["after_optimization"] = value["asset"]
@@ -1235,7 +1247,8 @@ class PortfolioDiagnose(object):
             hold_fund_name = [get_fund_name(x, self.portfolio_dict[x]).values[0][0] for x in hold_fund]
             abandon_fund = (self.abandon_fund_score + self.abandon_fund_corr)
             abandon_fund_name = [get_fund_name(x, self.portfolio_dict[x]).values[0][0] for x in abandon_fund]
-            proposal_fund = self.proposal_fund
+            # proposal_fund = self.proposal_fund
+            proposal_fund = [item for item in self.proposal_fund if item not in list(fund_tye_dict.keys())]
             proposal_fund_name = [get_fund_name(x, fund_tye_dict[x]).values[0][0] for x in proposal_fund]
 
             sentence = []

app/service/result_service_v2.py

@@ -503,6 +503,8 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             fund_hoding_info["cum_profit"] = "%.2f" % round(cum_profit / 10000, 2)
             # 累积收益率
             fund_hoding_info["cum_profit_ratio"] = "%.2f" % round(cum_profit / temp_cost *100, 2)
+            fund_hoding_info["fund_id"] = cur_fund_id
+            fund_hoding_info["fund_type"] = row["type"]
             cum_profit_ratio_temp = cum_profit / temp_cost
             # 累积年化收益率
             cur_resample_df = resample_df[[row["fund_id"]]]

app/utils/jinjia2html_v2.py

@@ -87,7 +87,7 @@ class DataIntegrate:
 
             self.all_folio_result[group_name] = cur_group_portfolio_result
 
-    def get_portfolio_diagnose(self, portfolio, client_type=1, invest_amount=10000000, weight_dict={}):
+    def get_portfolio_diagnose(self, portfolio, client_type=1, invest_amount=10000000, weight_dict=None):
         if invest_amount < 10000000:
             invest_amount = 10000000
         folio_fund_dict = {}