组合分解收益图数据

app/service/result_service.py

@@ -41,14 +41,22 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
             # return_ratio_df, contribution_decomposition= self.combination_yield(cur_folio_result_cnav_data, fund_id_list)
             # resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
             resample_cur_folio_result_cnav_data = resample(cur_folio_result_cnav_data, self.trade_cal_date, freq_max)
-            return_ratio_df, contribution_decomposition = self.combination_yield(resample_cur_folio_result_cnav_data,
+            return_ratio_df, month_return_ratio_df, contribution_decomposition = self.combination_yield(resample_cur_folio_result_cnav_data,
                                                                                  fund_id_list)
             resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
 
 
             # 收益分解df
             contribution_decomposition_df = contribution_decomposition.fillna(0)*100
-            folio_report_data["contribution_decomposition"] = contribution_decomposition_df
+            p_plot_data = []
+            for a_fund_id in list(contribution_decomposition_df.columns):
+                a_name = cur_folio_order_data[cur_folio_order_data["fund_id"]==a_fund_id]["fund_name"].values[0]
+                plot_data = {'name': a_name, 'data': contribution_decomposition_df[a_fund_id].astype(np.float64).values}
+                p_plot_data.append(plot_data)
+            x_lables_data = list(contribution_decomposition_df.index)
+            cumulative_data = {'name': '总收益', 'data': ((month_return_ratio_df["cum_return_ratio"] - 1)*100).values}
+            folio_report_data["contribution_decomposition"] = {"xlabels": x_lables_data, "product_list": p_plot_data,
+                                                               "cumulative": cumulative_data}
 
             # 总成本
             total_cost = float(cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_amount"].sum() - \
@@ -176,7 +184,7 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         # resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
 
         resample_cur_folio_result_cnav_data = resample(cur_folio_result_cnav_data, self.trade_cal_date, freq_max)
-        return_ratio_df, contribution_decomposition = self.combination_yield(resample_cur_folio_result_cnav_data, fund_id_list)
+        return_ratio_df, month_return_ratio_df, contribution_decomposition = self.combination_yield(resample_cur_folio_result_cnav_data, fund_id_list)
         resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)
 
         # 总成本
@@ -363,7 +371,26 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
         signal_fund_cum_weight = nav_net_amount_df[weight_name_list]
         re_name = {x: x.replace("_profit_cum_ratio_weight", "") for x in weight_name_list}
         signal_fund_cum_weight.rename(columns=re_name, inplace=True)
-        return cum_return_ratio_df, signal_fund_cum_weight
+
+        # 月度分组
+        def year_month(x):
+            a = x.year
+            b = x.month
+            return str(a) + "/" + str(b)
+
+        profit_df_cp = signal_fund_cum_weight.copy()
+        profit_df_cp["date"] = profit_df_cp.index
+        grouped = profit_df_cp.groupby(profit_df_cp["date"].apply(year_month))
+        month_signal_fund_cum = grouped.last().sort_values(by="date")
+        del month_signal_fund_cum["date"]
+
+        p_cum_df = cum_return_ratio_df.copy()
+        p_cum_df["date"] = p_cum_df.index
+        cum_grouped = p_cum_df.groupby(p_cum_df["date"].apply(year_month))
+        month_fund_cum = cum_grouped.last().sort_values(by="date")
+        del month_fund_cum["date"]
+
+        return cum_return_ratio_df, month_fund_cum, month_signal_fund_cum
 
     @staticmethod
     def signal_fund_profit_result(p_fund_nav_df, cur_fund_id):