123

app/service/portfolio_diagnose.py

@@ -622,5 +622,8 @@ portfolio_diagnose = PortfolioDiagnose(client_type=1, portfolio=portfolio, inves
 portfolio_diagnose.optimize()
 
 if __name__ == '__main__':
+    # 相关性
     print(portfolio_diagnose.old_correlation)
+    # 个基点评
+    # print(portfolio_diagnose.single_fund_radar())
     # print(portfolio_diagnose.propose_fund_evaluation())

app/utils/draw.py

@@ -105,7 +105,7 @@ def draw_contribution_chart(xlabels, product_list, cumulative):
     fontsize = 22
     # 初始化
     fig = plt.figure(figsize=figsize)
-    ax1 = fig.add_subplot(111)
+    ax1 = fig.add_subplot()
     ax2 = ax1.twiny()
     max_x_count = max([x['data'].size for x in product_list])
     loc = np.arange(max_x_count)  # the x locations for the groups
@@ -145,12 +145,12 @@ def draw_contribution_chart(xlabels, product_list, cumulative):
     ax2.plot(loc, cumulative['data'], color='#C6A774', marker='', linewidth=3, label=cumulative['name'])
     ax2.legend(loc='upper left', fontsize=fontsize)
 
-    # plt.show()
-    imgdata = BytesIO()
-    fig.savefig(imgdata, format='png')
-    imgdata.seek(0)  # rewind the data
-    month_return_img = 'data:image/png;base64,' + base64.b64encode(imgdata.getvalue()).decode('utf-8')
-    return month_return_img
+    plt.show()
+    # imgdata = BytesIO()
+    # fig.savefig(imgdata, format='png')
+    # imgdata.seek(0)  # rewind the data
+    # month_return_img = 'data:image/png;base64,' + base64.b64encode(imgdata.getvalue()).decode('utf-8')
+    # return month_return_img
 
 
 def draw_comment_chart(xlabels, source_prod, target_prod):