新组合结果

app/service/portfolio_diagnose.py

-# -*- coding: UTF-8 -*-
-# """
-# @author: Zongxi.Li
-# @file:portfolio_copy.py
-# @time:2020/12/03
-# """
 from app.utils.fund_rank import *
 from app.utils.risk_parity import *
 from app.pypfopt import risk_models
@@ -547,17 +541,17 @@ class PortfolioDiagnose(object):
         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)
+        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)
+        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()
+                                 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,
@@ -569,14 +563,13 @@ class PortfolioDiagnose(object):
             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]
+                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，"
+            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()
+        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:
@@ -592,29 +585,28 @@ class PortfolioDiagnose(object):
         else:
             no_data_fund_evaluate = "；"
 
-        group_order_df = data_adaptor.user_customer_order_df[
-            data_adaptor.user_customer_order_df["folio_name"] == group_name]
+        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:
+
+        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_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)
         evaluate_enum = [["优秀", "良好", "一般"],
-                         ["优秀", "良好", "合格", "较差"]]
+         ["优秀", "良好", "合格", "较差"]]
 
         z_score_evaluate = evaluate_enum[0][z_score_level]
         drawdown_evaluate = evaluate_enum[1][drawdown_level]
@@ -648,9 +640,13 @@ class PortfolioDiagnose(object):
 
         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())
 
         # 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)
+        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"]
@@ -668,15 +664,77 @@ class PortfolioDiagnose(object):
             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
-
 
-        sentence = "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 = "".join(set(self.portfolio) - set(self.abandon_fund_score + self.abandon_fund_corr))
-        abandon_fund = "".join(self.abandon_fund_score + self.abandon_fund_corr)
-        proposal_fund = "".join(self.proposal_fund)
-        data = [hold_fund, abandon_fund, proposal_fund, old_max_drawdown]
-        return sentence % data
+        # 基金名称，策略分级
+        propose_fund_id_name_list = [propose_fund_df[propose_fund_df["fund_id"] == fund_id]["fund_name"].values[0] for
+                                     fund_id in propose_fund_id_list]
+        propose_fund_id_strategy_name_list = [dict_substrategy[int(propose_fund_df[propose_fund_df["fund_id"] == fund_id]["substrategy"].values[0])] for
+                                     fund_id in propose_fund_id_list]
+        propose_fund_asset = [round(self.new_weights[i] * total_asset, 2) for i in range(len(propose_fund_id_name_list))]
+
+        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]}
+                        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"])
+        for hold in hold_info:
+            suggestions = {}
+            if hold["fund_strategy_name"] not in suggestions_result.keys():
+                suggestions_result[hold["fund_strategy_name"]] = {}
+            suggestions["fund_strategy_name"] = hold["fund_strategy_name"]
+            suggestions["fund_name"] = hold["fund_name"]
+            suggestions["before_optimization"] = hold["market_values"]
+            suggestions["after_optimization"] = 0
+            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
+
+        for key, value in propose_info.items():
+            if value["fund_name"] not in old_hold_fund_name_list:
+                suggestions = {}
+                if key not in suggestions_result.keys():
+                    suggestions_result[key] = {}
+                suggestions["fund_strategy_name"] = value["substrategy"]
+                suggestions["fund_name"] = value["fund_name"]
+                suggestions["before_optimization"] = 0
+                suggestions["after_optimization"] = value["asset"]
+                suggestions_result[key][suggestions["fund_name"]] = suggestions
+        suggestions_result_asset = {"before": total_asset, "after": total_asset}
+
+        # 旧组合累积收益
+        old_return_df = group_result_data["return_df"]
+        old_return_df["cum_return_ratio"] = old_return_df["cum_return_ratio"] - 1
+        # 新组合累积收益
+        propose_fund_return_limit_data = propose_fund_return[propose_fund_return.index >= group_order_start_date]
+        start_return = propose_fund_return_limit_data['return'].values[0]
+        propose_fund_return_limit_data["new_return"] = (propose_fund_return_limit_data["return"] - start_return)/(1+start_return)
+        # 指数收益
+        index_return = index_return[index_return.index >= group_order_start_date]
+        start_index_return = index_return[self.index_id].values[0]
+        index_return["new_index_return"] = (index_return[self.index_id] - start_index_return) / (1 + start_index_return)
+
+        # 新组合区间年化收益率
+        freq_max = group_order_df["freq"].max()
+        n_freq = freq_days(int(freq_max))
+        new_return_ratio_year = annual_return(propose_fund_return_limit_data["new_return"].values[-1], propose_fund_return_limit_data, n_freq)
+
+        # 新组合波动率
+        new_volatility_ = volatility(propose_fund_return_limit_data["new_return"]+1, n_freq)
+
+        # 新组合最大回撤
+        new_drawdown = max_drawdown(propose_fund_return_limit_data["new_return"]+1)
+
+        # 新组合夏普比率
+        sim = simple_return(propose_fund_return_limit_data["new_return"]+1)
+        exc = excess_return(sim, BANK_RATE, n_freq)
+        new_sharpe = sharpe_ratio(exc, sim, n_freq)
+
+
+
+        return suggestions_result, suggestions_result_asset
 
     def single_evaluation(self, fund_id):
         """

app/service/result_service.py

@@ -159,7 +159,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             index_result = self.signal_fund_profit_result(index_df[index_df.index >= pd.to_datetime(first_trade_date)],
                                                           "index")
             folio_report_data["index_result"] = index_result
-
+            folio_report_data["return_df"] = resample_df
             self.group_result_data[folio] = folio_report_data
 
         return self.group_result_data