Merge branch 'dev' of http://47.100.44.39:10001/pengxiong/fund_report into dev

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.utils.week_evaluation import *
 
 
 def cal_correlation(prod):
@@ -494,20 +495,109 @@ class PortfolioDiagnose(object):
         propose_fund_return.to_csv('new_port.csv', encoding='gbk')
         return index_return, propose_fund_return
 
-    def old_evaluation(self):
-        start_year = self.start_date.year
-        start_month = self.start_date.month
+    def old_evaluation(self, group_name, group_result, data_adaptor):
+        start_year = data_adaptor.start_date.year
+        start_month = data_adaptor.start_date.month
         current_year = datetime.datetime.now().year
         current_month = datetime.datetime.now().month
         current_day = datetime.datetime.now().day
         past_month = (current_year - start_year) * 12 + current_month - start_month
+
+        # 投入成本(万元)
+        input_cost = round(group_result[group_name]["total_cost"]/10000, 2)
+        # 整体盈利（万元)
+        total_profit = round(group_result[group_name]["cumulative_profit"]/10000, 2)
+        # 整体表现 回撤能力
+        fund_rank_data = fund_rank[fund_rank["fund_id"].isin(self.portfolio)]
+        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 >= 80,
+                                 70 <= z_score < 80,
+                                 z_score < 70], [0, 1, 2]).item()
+        drawdown_level = np.select([drawdown_rank >= 0.8,
+                                    0.7 <= drawdown_rank < 0.8,
+                                    0.6 <= drawdown_rank < 0.7,
+                                    drawdown_rank < 0.6], [0, 1, 2, 3]).item()
+        # 收益稳健
+        fund_rank_re = fund_rank_data[fund_rank_data["annual_return_rank"] > 0.8]
+        return_rank_evaluate = ""
+        if len(fund_rank_re) > 0:
+            num = len(fund_rank_re)
+            fund_id_rank_list = list(fund_rank_re["fund_id"])
+            for f_id in fund_id_rank_list:
+                name = data_adaptor.user_customer_order_df[data_adaptor.user_customer_order_df["fund_id"] == f_id]["fund_name"].values[0]
+                return_rank_evaluate = return_rank_evaluate + name + "、"
+            return_rank_evaluate = return_rank_evaluate[:-1] +"等" + str(num) + "只产品稳健，对组合的收益率贡献明显，"
+
+        # 正收益基金数量
+        group_hold_data = pd.DataFrame(group_result[group_name]["group_hoding_info"])
+        profit_positive_num = group_hold_data[group_hold_data["profit"]>0]["profit"].count()
+        if profit_positive_num > 0:
+            profit_positive_evaluate = str(profit_positive_num) + "只基金取的正收益，"
+        else:
+            profit_positive_evaluate = ""
+
+        # 综合得分较低数量
+        abandon_num = len(self.abandon_fund_score)
+        abandon_evaluate = str(abandon_num) + "只基金综合得分较低建议更换，"
+
+        # 成立时间短
+        if len(self.no_data_fund) > 0:
+            no_data_fund_evaluate = str(len(self.no_data_fund)) + "只基金因为成立时间较短，暂不做评价；"
+        else:
+            no_data_fund_evaluate = "；"
+
+        group_order_df = data_adaptor.user_customer_order_df[data_adaptor.user_customer_order_df["folio_name"] == group_name]
+        strategy_list = group_order_df["substrategy"]
+        uniqe_strategy = list(strategy_list.unique())
+        uniqe_strategy_name = [dict_substrategy[int(x)] + "、" for x in uniqe_strategy]
+        # 覆盖的基金名称
+        strategy_name_evaluate = "".join(uniqe_strategy_name)[:-1]
+
+
+        if len(uniqe_strategy)/float(len(strategy_list)) > 0.6:
+            strategy_distribution_evaluate = "策略上有一定分散"
+        else:
+            strategy_distribution_evaluate = "策略分散程度不高"
+        # 相关性
+        if len(self.abandon_fund_corr) > 0:
+            fund_corr_name = [str(group_order_df[group_order_df["fund_id"] == f_id]["fund_name"].values[0]) + "和" for f_id in self.abandon_fund_corr]
+            fund_corr_evaluate = "".join(fund_corr_name)[:-1] + "相关性较高，建议调整组合配比；"
+        else:
+            fund_corr_evaluate = "；"
+
         num_fund = len(self.portfolio)
-        abandon_fund = [[x, self.invest_type] for x in self.abandon_fund_score + self.abandon_fund_corr]
-        old_strategy = set([search_rank(fund_rank, x, metric='substrategy') for x in self.portfolio])
+        evaluate_enum = [["优秀", "良好", "一般"],
+         ["优秀", "良好", "合格", "较差"]]
 
-        data = [start_year, start_month, past_month, self.invest_amount,
-                current_year, current_month, current_day]
-        return data
+        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"]:
+            z_score_evaluate = """<span class="self_description_red">{}</span>""".format(z_score_evaluate)
+        else:
+            z_score_evaluate = """<span class="self_description_green">{}</span>""".format(z_score_evaluate)
+
+        if drawdown_evaluate in ["优秀", "良好"]:
+            drawdown_evaluate = """<span class="self_description_red">{}</span>""".format(drawdown_evaluate)
+        else:
+            drawdown_evaluate = """<span class="self_description_green">{}</span>""".format(drawdown_evaluate)
+
+        sentence = {
+            1: "1、组合构建于{}年{}月，至今已运行{}个月。投入成本为{}万元，截止{}年{}月{}日，整体盈利{}万元，整体表现{}，回撤控制能力{}；\n",
+            2: "2、组合共持有{}只基金，{}{}{}{}\n",
+            3: "3、策略角度来看，组合涵盖了{}， {}{}\n"
+        }
+
+        data = {1: [start_year, start_month, past_month, input_cost, current_year, current_month, current_day,
+                    total_profit, z_score_evaluate, drawdown_evaluate],
+                2: [num_fund, return_rank_evaluate, profit_positive_evaluate, abandon_evaluate, no_data_fund_evaluate],
+                3: [strategy_name_evaluate, strategy_distribution_evaluate, fund_corr_evaluate]
+                }
+        ret = []
+        for k, v in data.items():
+            ret.append(sentence[k].format(*data[k]).replace("，；", "；"))
+        return ret
 
     def new_evaluation(self):
         hold_fund = set(self.portfolio) - set(self.abandon_fund)
@@ -657,10 +747,10 @@ portfolio = ['HF00002JJ2', 'HF00005DBQ', 'HF0000681Q', 'HF00006693', 'HF00006AZF
 portfolio_diagnose = PortfolioDiagnose(client_type=1, portfolio=portfolio, invest_amount=10000000)
 portfolio_diagnose.optimize()
 if __name__ == '__main__':
-    # print(portfolio_diagnose.single_fund_radar())
+    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)
-    print('新组合相关性：', portfolio_diagnose.new_correlation)
-    print('旧组合个基评价：', portfolio_diagnose.old_portfolio_evaluation())
-    print('新组合个基评价：', portfolio_diagnose.propose_fund_evaluation())
+    # print('旧组合相关性：', portfolio_diagnose.old_correlation)
+    # print('新组合相关性：', portfolio_diagnose.new_correlation)
+    # print('旧组合个基评价：', portfolio_diagnose.old_portfolio_evaluation())
+    # print('新组合个基评价：', portfolio_diagnose.propose_fund_evaluation())