新组合指标计算初始化数据以及数据库操作

app/service/portfolio_diagnose.py

@@ -3,6 +3,7 @@ from app.utils.risk_parity import *
 from app.pypfopt import risk_models
 from app.pypfopt import expected_returns
 from app.pypfopt import EfficientFrontier
+from app.api.engine import tamp_user_engine, tamp_product_engine, TAMP_SQL
 
 
 def cal_correlation(prod):
@@ -598,12 +599,40 @@ class PortfolioDiagnose(object):
             ret.append(sentence[k].format(*data[k]).replace("，；", "；"))
         return ret
 
-    def new_evaluation(self):
-        hold_fund = set(self.portfolio) - set(self.abandon_fund)
-        abandon_fund = self.abandon_fund
-        proposal_fund = self.proposal_fund
-        data = [hold_fund, abandon_fund, proposal_fund]
-        return data
+    def new_evaluation(self, group_name, group_result, data_adaptor):
+
+        group_result_data = group_result[group_name]
+        hold_info = group_result_data["group_hoding_info"]
+
+        # 原组合总市值, 区间收益, 年化收益,	波动率,	最大回撤,	夏普比率
+        total_asset = round(pd.DataFrame(hold_info)["market_values"].sum(),2)
+        old_return = group_result_data["cumulative_return"]
+        old_return_ratio_year = group_result_data["return_ratio_year"]
+        old_volatility = group_result_data["volatility"]
+        old_max_drawdown = group_result_data["max_drawdown"]
+        old_sharpe = group_result_data["sharpe"]
+
+        # 建议基金数据
+        index_return, propose_fund_return = self.return_compare()
+        propose_fund_id_list = list(propose_fund_return.columns)
+        propose_fund_id_list.remove("return")
+        with TAMP_SQL(tamp_product_engine) as tamp_product:
+            tamp_product_session = tamp_product.session
+            sql_product = "select distinct `id`, `fund_short_name`, `nav_frequency`, `substrategy` from `fund_info`"
+            cur = tamp_product_session.execute(sql_product)
+            data = cur.fetchall()
+            product_df = pd.DataFrame(list(data), columns=['fund_id', 'fund_name', 'freq', 'substrategy'])
+        propose_fund_df = product_df[product_df["fund_id"].isin(propose_fund_id_list)]
+        propose_fund_id_list_name = []  # 基金名称，策略分级
+
+
+
+
+        # hold_fund = set(self.portfolio) - set(self.abandon_fund)
+        # abandon_fund = self.abandon_fund
+        # proposal_fund = self.proposal_fund
+        # data = [hold_fund, abandon_fund, proposal_fund]
+        # return data
 
     def single_evaluation(self, fund_id):
         """
@@ -745,8 +774,8 @@ class PortfolioDiagnose(object):
 portfolio = ['HF00002JJ2', 'HF00005DBQ', 'HF0000681Q', 'HF00006693', 'HF00006AZF', 'HF00006BGS']
 portfolio_diagnose = PortfolioDiagnose(client_type=1, portfolio=portfolio, invest_amount=10000000)
 portfolio_diagnose.optimize()
-if __name__ == '__main__':
-    print(portfolio_diagnose.single_fund_radar())
+# if __name__ == '__main__':
+#     print(portfolio_diagnose.single_fund_radar())
     # print(portfolio_diagnose.propose_fund_radar())
     # print(portfolio_diagnose.old_portfolio_evaluation())
     # print('旧组合相关性：', portfolio_diagnose.old_correlation)