修改objective

portfolio_diagnose.py

@@ -335,7 +335,7 @@ tamp_fund = get_tamp_fund()
 
 
 class PortfolioDiagnose(object):
-    def __init__(self, client_type, portfolio, invest_amount, expect_return=0.15,
+    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):
         """基金诊断
 
@@ -543,7 +543,7 @@ class PortfolioDiagnose(object):
                 else:
                     prod.drop(columns=proposal, inplace=True)
 
-        prod.bfill(inplace=True)
+        prod.dropna(how='all', 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])
@@ -605,13 +605,12 @@ class PortfolioDiagnose(object):
         try:
             ef = EfficientFrontier(mu, S, weight_bounds=[w_low, 1], expected_drawdown=dd)
             # ef = EfficientFrontier(mu, S, weight_bounds=[0, 1], expected_drawdown=dd)
-            # ef.add_sector_constraints(propose_risk_mapper, risk_lower, risk_upper)
+            ef.add_sector_constraints(propose_risk_mapper, risk_lower, risk_upper)
             ef.efficient_return(target_return=self.expect_return, target_drawdown=self.expect_drawdown)
             clean_weights = ef.clean_weights()
             ef.portfolio_performance(verbose=True)
             self.new_weights = np.array(list(clean_weights.values()))
-        except Exception as e:
-            print(e)
+        except:
             self.new_weights = np.asarray([1/len(self.propose_portfolio.columns)] * len(self.propose_portfolio.columns))
 
         print(self.new_weights)
@@ -708,10 +707,13 @@ class PortfolioDiagnose(object):
         # 覆盖的基金名称
         strategy_name_evaluate = "".join(uniqe_strategy_name)[:-1]
 
+        try:
             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"
+        except ZeroDivisionError:
+            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
@@ -750,7 +752,73 @@ class PortfolioDiagnose(object):
         ret = []
         for k, v in data.items():
             ret.append(sentence[k].format(*data[k]).replace("，；", "；"))
-        return ret
+
+        # 旧组合累积收益df
+        group_result_data = group_result[group_name]
+        hold_info = group_result_data["group_hoding_info"]
+        hold_info_df = pd.DataFrame(hold_info)
+        group_order_df = data_adaptor.user_customer_order_df[
+            data_adaptor.user_customer_order_df["folio_name"] == group_name]
+        group_order_start_date = pd.to_datetime(group_order_df["confirm_share_date"].min())
+
+        freq_max = group_order_df["freq"].max()
+        n_freq = freq_days(int(freq_max))
+
+        old_return_df = group_result_data["return_df"]
+        old_return_df["cum_return_ratio"] = old_return_df["cum_return_ratio"] - 1
+
+        # 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(hold_info_df["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"]
+
+        # 指数收益
+        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
+
+        # 指数收益
+        index_return = index_return[index_return.index >= group_order_start_date]
+        start_index_return = index_return[" close"].values[0]
+        index_return["new_index_return"] = (index_return[" close"] - start_index_return) / (1 + start_index_return)
+        index_return_ratio = index_return["new_index_return"].values[-1]
+        index_return_ratio_year = annual_return(index_return["new_index_return"].values[-1],
+                                                index_return["new_index_return"], n_freq)
+        index_volatility = volatility(index_return["new_index_return"] + 1, n_freq)
+        index_drawdown = max_drawdown(index_return["new_index_return"] + 1)
+        index_sim = simple_return(index_return["new_index_return"]+1)
+        index_exc = excess_return(index_sim, BANK_RATE, n_freq)
+        index_sharpe = sharpe_ratio(index_exc, index_sim, n_freq)
+
+        # 收益对比数据
+        return_compare_df = pd.merge(index_return[["new_index_return"]], old_return_df[["cum_return_ratio"]],
+                                     right_index=True,
+                                     left_index=True)
+        return_compare_df["date"] = return_compare_df.index
+        return_compare_df["date"] = return_compare_df["date"].apply(lambda x: x.strftime("%Y-%m-%d"))
+        return_compare_df.iloc[1:-1, :]["date"] = ""
+        old_return_compare_result = {
+
+            "index": {"name": "中证500", "data": return_compare_df["new_index_return"].values*100},
+            "origin_combination": {"name": "原组合", "data": return_compare_df["cum_return_ratio"].values*100},
+            "xlabels": return_compare_df["date"].values
+        }
+        # 指标对比
+        old_indicator = {"group_name": "现有持仓组合", "return_ratio": round((old_return - 1) * 100, 2),
+                         "return_ratio_year": round(old_return_ratio_year * 100, 2),
+                         "volatility": round(old_volatility * 100, 2),
+                         "max_drawdown": round(old_max_drawdown[0] * 100, 2), "sharpe": round(old_sharpe, 2)}
+
+        index_indicator = {"group_name": "中证500", "return_ratio": round(index_return_ratio * 100, 2),
+                           "return_ratio_year": round(index_return_ratio_year * 100, 2),
+                           "volatility": round(index_volatility * 100, 2),
+                           "max_drawdown": round(index_drawdown[0] * 100, 2), "sharpe": round(index_sharpe, 2)}
+        old_indicator_compare = [old_indicator, index_indicator]
+
+        return ret, old_return_compare_result, old_indicator_compare
 
     def new_evaluation(self, group_name, group_result, data_adaptor):
         try:
@@ -793,7 +861,6 @@ class PortfolioDiagnose(object):
                                  "substrategy": propose_fund_id_strategy_name_list[i],
                                  "asset": propose_fund_asset[i]}
                             for i in range(len(propose_fund_id_list))}
-
             # 调仓建议
             suggestions_result = {}
             old_hold_fund_name_list = list(hold_info_df["fund_name"])
@@ -857,7 +924,7 @@ class PortfolioDiagnose(object):
             index_return_ratio_year = annual_return(index_return["new_index_return"].values[-1], index_return["new_index_return"], n_freq)
             index_volatility = volatility(index_return["new_index_return"]+1, n_freq)
             index_drawdown = max_drawdown(index_return["new_index_return"]+1)
-            index_sim = simple_return(propose_fund_return_limit_data["new_return"]+1)
+            index_sim = simple_return(index_return["new_index_return"]+1)
             index_exc = excess_return(index_sim, BANK_RATE, n_freq)
             index_sharpe = sharpe_ratio(index_exc, index_sim, n_freq)
 
@@ -870,9 +937,9 @@ class PortfolioDiagnose(object):
             return_compare_df["date"] = return_compare_df["date"].apply(lambda x: x.strftime("%Y-%m-%d"))
             return_compare_df.iloc[1:-1,:]["date"] = ""
             return_compare_result = {
-                "new_combination": {"name": "新组合", "data": return_compare_df["new_return"].values},
-                "index": {"name": "中证500", "data": return_compare_df["new_index_return"].values},
-                "origin_combination": {"name": "原组合", "data": return_compare_df["cum_return_ratio"].values},
+                "new_combination": {"name": "新组合", "data": return_compare_df["new_return"].values*100},
+                "index": {"name": "中证500", "data": return_compare_df["new_index_return"].values*100},
+                "origin_combination": {"name": "原组合", "data": return_compare_df["cum_return_ratio"].values*100},
                 "xlabels": return_compare_df["date"].values
             }
 
@@ -885,6 +952,7 @@ class PortfolioDiagnose(object):
                              "volatility": round(index_volatility*100, 2), "max_drawdown": round(index_drawdown[0]*100, 2), "sharpe": round(index_sharpe, 2)}
             indicator_compare = [new_indicator, old_indicator, index_indicator]
 
+
             # 在保留{}的基础上，建议赎回{}，并增配{}后，整体组合波动率大幅降低，最大回撤从{}降到不足{}，年化收益率提升{}个点
             hold_fund = set(self.portfolio) - set(self.abandon_fund_score + self.abandon_fund_corr + self.no_data_fund)
             hold_fund_name = [get_fund_name(x).values[0][0] for x in hold_fund]
@@ -913,7 +981,7 @@ class PortfolioDiagnose(object):
             repr(e)
             return None, None, None, None, None
 
-    def single_evaluation(self, fund_id, objective):
+    def single_evaluation(self, fund_id, objective=False):
         """
            1、该基金整体表现优秀/良好/一般，收益能力优秀/良好/合格/较差，回撤控制能力优秀/良好/合格/较差，风险收益比例较高/一般/较低；
            2、在收益方面，该基金年化收益能力高于/持平/低于同类基金平均水平，有x%区间跑赢大盘/指数，绝对收益能力优秀/一般；
@@ -1038,19 +1106,22 @@ class PortfolioDiagnose(object):
                 pass
 
         evaluation_dict = {'name': fund_name, 'data': ret}
+
         if objective:
             if fund_id in self.abandon_fund_score + self.abandon_fund_corr:
                 evaluation_dict['status'] = "换仓"
             elif fund_id in self.portfolio:
                 evaluation_dict['status'] = "保留"
+        else:
+            evaluation_dict['status'] = ""
         return evaluation_dict
 
-    def old_portfolio_evaluation(self, ):
+    def old_portfolio_evaluation(self, objective=False):
         try:
             result = []
             for fund in self.portfolio:
                 try:
-                    result.append(self.single_evaluation(fund))
+                    result.append(self.single_evaluation(fund, objective))
                 except IndexError:
                     continue
             return result