添加个基评价

app/pypfopt/base_optimizer.py

@@ -305,8 +305,6 @@ class BaseConvexOptimizer(BaseOptimizer):
             is_sector = [sector_mapper[t] == sector for t in self.tickers]
             self._constraints.append(cp.sum(self._w[is_sector]) >= sector_lower[sector])
 
-        print(self._constraints)
-
     def convex_objective(self, custom_objective, weights_sum_to_one=True, **kwargs):
         """
         Optimise a custom convex objective function. Constraints should be added with

app/service/portfolio_diagnose.py

@@ -16,6 +16,7 @@ def cal_correlation(prod):
     """
     prod_return = prod.iloc[:, :].apply(lambda x: simple_return(x))
     correlation = prod_return.corr()
+    correlation = correlation.round(2)
     return correlation.mask(np.eye(correlation.shape[0], dtype=np.bool))
 
 
@@ -92,7 +93,8 @@ def choose_good_evaluation(evaluation):
 
     if v1[0] > 1:
         del evaluation[1]
-    if v2[0] > 1:
+    # if v2[0] > 1:
+    if v2:
         del evaluation[2]
     if v3[0] > 1:
         del evaluation[3]
@@ -112,7 +114,8 @@ def choose_bad_evaluation(evaluation):
 
     if v1[0] < 2:
         del evaluation[1]
-    if v2[0] < 2:
+    # if v2[0] < 2:
+    if v2:
         del evaluation[2]
     if v3[0] < 2:
         del evaluation[3]
@@ -207,7 +210,7 @@ def get_fund_name(fund):
 # 获取排名信息
 fund_rank = get_fund_rank()
 # 获取探普产品池
-tamp_fund = get_fund_rank()
+tamp_fund = get_tamp_fund()
 
 
 class PortfolioDiagnose(object):
@@ -337,6 +340,10 @@ class PortfolioDiagnose(object):
             if np.any(self.old_correlation[fund] > 0.8):
                 self.abandon_fund_corr.append(fund)
                 prod = prod.drop(fund, axis=1)
+
+        self.old_correlation = self.old_correlation.fillna(1).round(2)
+        self.old_correlation.columns = self.old_correlation.columns.map(lambda x: get_fund_name(x).values[0][0])
+        self.old_correlation.index = self.old_correlation.index.map(lambda x: get_fund_name(x).values[0][0])
         return prod
 
     def proposal(self, prod):
@@ -349,17 +356,17 @@ class PortfolioDiagnose(object):
 
         """
         # 组合内已包含的策略
-        included_strategy = set()
+        # included_strategy = set()
         # 按每种基金最少投资100w确定组合包含的最大基金数量
         max_len = self.invest_amount // 1e6 - len(prod.columns)
 
         # 排名表内包含的所有策略
-        all_strategy = set(fund_rank['substrategy'].to_list())
-        if prod is not None:
-            included_strategy = set([search_rank(fund_rank, fund, metric='substrategy') for fund in prod.columns])
+        # all_strategy = set(fund_rank['substrategy'].to_list())
+        # if prod is not None:
+        #     included_strategy = set([search_rank(fund_rank, fund, metric='substrategy') for fund in prod.columns])
 
         # 待添加策略为所有策略-组合已包含策略
-        add_strategy = all_strategy - included_strategy
+        # add_strategy = all_strategy - included_strategy
 
         # 遍历产品池，推荐得分>80且与组合内其他基金相关度低于0.8的属于待添加策略的基金
         for proposal in tamp_fund['fund_id']:
@@ -370,8 +377,8 @@ class PortfolioDiagnose(object):
             else:
                 continue
 
-            if proposal_z_score > 80 and proposal_strategy in add_strategy:
-                # if proposal_z_score > 80:
+            # if proposal_z_score > 80 and proposal_strategy in add_strategy:
+            if proposal_z_score > 60:
 
                 proposal_nav = get_nav(proposal, self.start_date, invest_type=self.invest_type)
                 # 忽略净值周期大于周更的产品
@@ -387,29 +394,42 @@ class PortfolioDiagnose(object):
                 prod.ffill(inplace=True)
                 prod = resample(prod, get_trade_cal(), min(self.freq_list))
 
-                _correlation = cal_correlation(prod)
-                _correlation = _correlation.fillna(0)
+                self.new_correlation = cal_correlation(prod)
+                judge_correlation = self.new_correlation.fillna(0)
 
-                if np.all(_correlation < 0.8):
+                if np.all(judge_correlation < 0.8):
                     self.proposal_fund.append(proposal)
                     max_len -= 1
 
-                    add_strategy -= {proposal_strategy}
-                    if len(add_strategy) == 0 or max_len == 0:
-                        # if max_len == 0:
+                    # add_strategy -= {proposal_strategy}
+                    # if len(add_strategy) == 0 or max_len == 0:
+                    if max_len == 0:
                         break
                 else:
                     prod.drop(columns=proposal, 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).values[0][0])
+        self.new_correlation.index = self.new_correlation.index.map(lambda x: get_fund_name(x).values[0][0])
         return prod
 
     def optimize(self, ):
+        import time
+        start = time.time()
         origin_portfolio = self.get_portfolio()
+        end1 = time.time()
+        print("原始组合数据获取时间：", end1 - start)
         abandoned_portfolio = self.abandon(origin_portfolio)
+        end2 = time.time()
+        print("计算换仓基金时间：", end2 - end1)
         propose_portfolio = self.proposal(abandoned_portfolio)
+        end3 = time.time()
+        print("遍历产品池获取候选推荐时间：", end3 - end2)
         # propose_portfolio.to_csv('test_portfolio.csv', encoding='gbk')
-        returns = propose_portfolio.pct_change().dropna()
-        mu = expected_returns.mean_historical_return(propose_portfolio)
-        S = risk_models.sample_cov(propose_portfolio)
+
+        mu = expected_returns.mean_historical_return(propose_portfolio, frequency=min(self.freq_list))
+        S = risk_models.sample_cov(propose_portfolio, frequency=min(self.freq_list))
+        dd = expected_returns.drawdown_from_prices(propose_portfolio)
         # if self.client_type == 1:
         # proposal_risk = [[x, get_risk_level(search_rank(fund_rank, x, metric='substrategy'))] for x in
         #                  self.proposal_fund]
@@ -422,13 +442,16 @@ class PortfolioDiagnose(object):
         risk_upper = {"H": 0.0}
         risk_lower = {"L": 0.6, "M": 0.4}
 
-        ef = EfficientFrontier(mu, S)
+        ef = EfficientFrontier(mu, S, expected_drawdown=dd)
         ef.add_sector_constraints(propose_risk_mapper, risk_lower, risk_upper)
         # weights = ef.nonconvex_objective(deviation_risk_parity, ef.cov_matrix)
-        ef.efficient_return(0.2)
+        ef.efficient_drawdown(drawdown_limit=0.5)
         clean_weights = ef.clean_weights()
         # ef.portfolio_performance(verbose=True)
         self.new_weights = np.array(list(clean_weights.values()))
+        print(clean_weights)
+        end4 = time.time()
+        print("模型计算一次时间：", end4 - end3)
         # S = np.asmatrix(S)
         # w_origin = np.asarray([i for i in w_origin.values()])
         # risk_target = np.asarray([1 / len(w_origin)] * len(w_origin))
@@ -471,7 +494,7 @@ class PortfolioDiagnose(object):
         current_day = datetime.datetime.now().day
         past_month = (current_year - start_year) * 12 + current_month - start_month
         num_fund = len(self.portfolio)
-        abandon_fund = [[x, self.invest_type] for x in self.abandon_fund]
+        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])
 
         data = [start_year, start_month, past_month, self.invest_amount,
@@ -532,11 +555,11 @@ class PortfolioDiagnose(object):
                                  sharp_rank < 0.6], [0, 1, 2]).item()
 
         data = {1: [total_level, return_level, drawdown_level, sharp_level],
-                2: [return_triple, "TO DO", return_triple],
-                3: [drawdown_triple, drawdown_triple, drawdown_value, drawdown_triple],
+                2: [return_triple, "12", return_bool],
+                3: [drawdown_triple, drawdown_triple, format(drawdown_value, '.2%'), drawdown_triple],
                 4: [return_bool, drawdown_bool, drawdown_bool, return_bool, drawdown_bool]}
 
-        if fund_id in self.abandon_fund:
+        if fund_id in self.abandon_fund_score:
             data['remove'] = True
         elif fund_id in self.proposal_fund:
             data[5] = [1] * 7
@@ -545,33 +568,34 @@ class PortfolioDiagnose(object):
             data['remove'] = False
 
         x = '30%'
-        # 第一个评价
-        content = {1: [["优秀", "良好", "一般"],
-                       ["优秀", "良好", "合格", "较差"],
-                       ["优秀", "良好", "合格", "较差"],
-                       ["高", "一般", "较低"]],
-                   # 第二个评价
-                   2: [["高于", "持平", "低于"],
-                       x,
-                       ["优秀", "一般"]],
-                   # 第三个评价
-                   3: [["优秀", "良好", "一般"],
-                       ["高", "中", "低"], x,
-                       ["高于", "持平", "低于"]],
-                   # 第四个评价
-                   4: [["较好", "较差"],
-                       ["较小", "较大"],
-                       ["较小", "较小"],
-                       ["较大", "较小"],
-                       ["较低", "较高"]],
-                   5: [["TO DO"] * 7]}
+        content = {
+            # 第一个评价
+            1: [["优秀", "良好", "一般"],
+                ["优秀", "良好", "合格", "较差"],
+                ["优秀", "良好", "合格", "较差"],
+                ["高", "一般", "较低"]],
+            # 第二个评价
+            2: [["高于", "持平", "低于"],
+                x,
+                ["优秀", "一般"]],
+            # 第三个评价
+            3: [["优秀", "良好", "一般"],
+                ["高", "中", "低"], x,
+                ["高于", "持平", "低于"]],
+            # 第四个评价
+            4: [["较好", "较差"],
+                ["较小", "较大"],
+                ["较小", "较小"],
+                ["较大", "较小"],
+                ["较低", "较高"]],
+            5: [["TO DO"]] * 7}
 
         sentence = {
-            1: "1、该基金整体表现%s，收益能力%s，回撤控制能力%s，风险收益比例%s；\n",
-            2: "2、在收益方面，该基金年化收益能力%s同类基金平均水平，有%s区间跑赢指数，绝对收益能力%s；\n",
-            3: "3、在风险方面，该基金抵御风险能力%s，在同类基金中处于%s等水平，最大回撤为%s，%s同类基金平均水平；\n",
-            4: "4、该基金收益%s的同时回撤%s，也就是说，该基金在用%s风险换取%s收益，存在%s风险；\n",
-            5: "5、基金经理，投资年限%s年，经验丰富；投资能力较强，生涯中共管理过%s只基金，历任的%s只基金平均业绩在同类中处于上游水平，其中%s只排名在前%s；生涯年化回报率%s，同期大盘只有%s；"}
+            1: "该基金整体表现%s，收益能力%s，回撤控制能力%s，风险收益比例%s；\n",
+            2: "在收益方面，该基金年化收益能力%s同类基金平均水平，有%s区间跑赢指数，绝对收益能力%s；\n",
+            3: "在风险方面，该基金抵御风险能力%s，在同类基金中处于%s等水平，最大回撤为%s，%s同类基金平均水平；\n",
+            4: "该基金收益%s的同时回撤%s，也就是说，该基金在用%s风险换取%s收益，存在%s风险；\n",
+            5: "基金经理，投资年限%s年，经验丰富；投资能力较强，生涯中共管理过%s只基金，历任的%s只基金平均业绩在同类中处于上游水平，其中%s只排名在前%s；生涯年化回报率%s，同期大盘只有%s；"}
 
         remove = data["remove"]
         del data["remove"]
@@ -582,18 +606,21 @@ class PortfolioDiagnose(object):
         # 剔除，选择坏的话术
         else:
             evaluation = choose_bad_evaluation(data)
-        print(evaluation)
 
         ret = ""
         for k, v in evaluation.items():
             # print(translate_single(content[k], v))
             ret = ret + sentence[k] % translate_single(content[k], v)
-        return {fund_id: ret}
+        fund_name = get_fund_name(fund_id).values[0][0]
+        return {'name': fund_name, 'data': ret}
 
     def old_portfolio_evaluation(self, ):
         result = []
         for fund in self.portfolio:
-            result.append(self.single_evaluation(fund))
+            try:
+                result.append(self.single_evaluation(fund))
+            except IndexError:
+                continue
         return result
 
     def propose_fund_evaluation(self, ):
@@ -622,6 +649,10 @@ 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.propose_fund_radar())
-    # print(portfolio_diagnose.propose_fund_evaluation())
+    # 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())

app/utils/fund_rank.py

 # import pymysql
 from sqlalchemy import create_engine
+
 db = create_engine(
-    'mysql+pymysql://tamp_fund:@imeng408@tamper.mysql.polardb.rds.aliyuncs.com:3306/tamp_fund?charset=utf8mb4')
+    'mysql+pymysql://tamp_fund:@imeng408@tamper.mysql.polardb.rds.aliyuncs.com:3306/tamp_fund?charset=utf8mb4',
+    pool_size=50,
+    pool_recycle=3600,
+    pool_pre_ping=True)
 con = db.connect()
 
 import logging
-logging.basicConfig(level=logging.INFO)
 
+logging.basicConfig(level=logging.INFO)
 
 from app.utils.week_evaluation import *
 
@@ -297,4 +301,4 @@ if __name__ == '__main__':
     # fund_rank.to_csv("fund_rank.csv", encoding='gbk')
     # df = pd.read_csv('fund_rank.csv')
     # df.to_sql("fund_rank", con, if_exists='replace')
-    con.close()
+    con.close()
\ No newline at end of file