月度回报表现图

贡献分解图
个基点评图
组合对比图

app/utils/draw.py

@@ -13,25 +13,47 @@ from matplotlib import ticker
 from matplotlib.ticker import FuncFormatter
 from matplotlib.font_manager import FontProperties
 
+# 中文字体初始化
+plt.rcParams['font.sans-serif']=['Heiti TC']
+
 
 def to_percent(temp, position):
     return '%1.0f' % temp + '%'
 
-def draw_stacked_column_chart(product_list, cumulative):
-    """堆叠柱状图"""
+
+def draw_month_return_chart(xlabels, product_list, cumulative):
+    """月度回报表现图"""
 
     # plt.title('Scores by group and gender')
     # plt.ylabel('Scores')
+    figsize = (20, 12)
+    # 标签文字大小
+    fontsize = 20
     # 初始化
-    fig = plt.figure(figsize=(12, 8))
+    fig = plt.figure(figsize=figsize)
     ax1 = fig.add_subplot()
-    ax2 = ax1.twiny()
+    ax2 = ax1.twinx()
     max_x_count = max([x['data'].size for x in product_list])
     loc = np.arange(max_x_count)  # the x locations for the groups
     width = 0.35  # the width of the bars: can also be len(x) sequence
-    color_list = ['#222A77', '#6C71AA', '#E1BC95', '#102A77', '#6CB1AA', '#CCBC95']
+    color_list = ['#222A77', '#6C71AA', '#E1BC95', '#F9DBB8']
 
-    # 堆叠柱状图
+    # 坐标轴
+    ax1.tick_params(labelsize=fontsize)
+    ax2.tick_params(labelsize=fontsize)
+    # 坐标轴颜色
+    ax2.tick_params(axis='y', colors='#C6A774')
+    ax1.set_xticks(loc)
+    ax1.set_xticklabels(xlabels)
+    ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
+    ax2.yaxis.set_major_formatter(FuncFormatter(to_percent))
+    temp_rate = np.zeros(max_x_count)
+    for i in range(len(product_list)):
+        temp_rate += product_list[i]['data']
+    max_rate = np.max(np.hstack((temp_rate, cumulative['data'])))
+    ax2.set_ylim(0, max_rate + 15)
+
+    # 柱状图
     prod_legend = []
     for i in range(len(product_list)):
         ax = None
@@ -41,35 +63,180 @@ def draw_stacked_column_chart(product_list, cumulative):
         else:
             for j in range(i):
                 bottom = bottom + product_list[j]['data']
-            ax = ax1.bar(loc, product_list[i]['data'], width, bottom=bottom, color=color_list[i], alpha=0.8)
+            if i < len(color_list):
+                ax = ax1.bar(loc, product_list[i]['data'], width, bottom=bottom, color=color_list[i], alpha=0.8)
+            else:
+                ax = ax1.bar(loc, product_list[i]['data'], width, bottom=bottom, alpha=0.8)
         prod_legend.append(ax[0])
+    ax1.legend(prod_legend, [prod['name'] for prod in product_list], loc='upper left', fontsize=fontsize)
+
+    # 画折线图
+    ax2.plot(loc, cumulative['data'], color='#C6A774', marker='', linewidth=3, label=cumulative['name'])
+    ax2.legend(loc='upper center', fontsize=fontsize)
+
+    plt.show()
+
+
+def draw_contribution_chart(xlabels, product_list, cumulative):
+    """贡献分解图"""
+
+    # plt.title('Scores by group and gender')
+    # plt.ylabel('Scores')
+    figsize = (20, 12)
+    # 标签文字大小
+    fontsize = 22
+    # 初始化
+    fig = plt.figure(figsize=figsize)
+    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
+    width = 0.35  # the width of the bars: can also be len(x) sequence
+    color_list = ['#222A77', '#6C71AA', '#E1BC95', '#F9DBB8']
 
     # 坐标轴
+    ax1.tick_params(labelsize=fontsize)
     ax1.set_xticks(loc)
-    ax1.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))
-    plt.gca().yaxis.set_major_formatter(FuncFormatter(to_percent))
-    ax1.legend(prod_legend, [prod['name'] for prod in product_list], bbox_to_anchor=(0.7, -0.1), ncol=4)
-
-    # 画折线图
-    # 最大收益
+    ax1.set_xticklabels(xlabels)
+    ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
     temp_rate = np.zeros(max_x_count)
     for i in range(len(product_list)):
         temp_rate += product_list[i]['data']
     max_rate = np.max(np.hstack((temp_rate, cumulative['data'])))
     ax2.set_xticks([])
     ax2.set_ylim(0, max_rate + 10)
+
+    # 堆叠柱状图
+    prod_legend = []
+    for i in range(len(product_list)):
+        ax = None
+        bottom = np.zeros(max_x_count)
+        if i == 0:
+            ax = ax1.bar(loc, product_list[i]['data'], width, color=color_list[i], alpha=0.8)
+        else:
+            for j in range(i):
+                bottom = bottom + product_list[j]['data']
+            if i < len(color_list):
+                ax = ax1.bar(loc, product_list[i]['data'], width, bottom=bottom, color=color_list[i], alpha=0.8)
+            else:
+                ax = ax1.bar(loc, product_list[i]['data'], width, bottom=bottom, alpha=0.8)
+        prod_legend.append(ax[0])
+    ax1.legend(prod_legend, [prod['name'] for prod in product_list], bbox_to_anchor=(0.8, -0.1), ncol=4, fontsize=fontsize)
+
+    # 画折线图
     ax2.plot(loc, cumulative['data'], color='#C6A774', marker='', linewidth=3, label=cumulative['name'])
-    ax2.legend(loc='upper left')
+    ax2.legend(loc='upper left', fontsize=fontsize)
+
+    plt.show()
+
+
+def draw_comment_chart(xlabels, source_prod, target_prod):
+    """个基点评图"""
+    figsize = (20, 12)
+    # 标签文字大小
+    fontsize = 22
+    # 初始化
+    fig = plt.figure(figsize=figsize)
+    ax1 = fig.add_subplot()
+    ax2 = ax1.twiny()
+    # ax = plt.gca()  # gca:get current axis得到当前轴
+    # ax.spines['bottom'].set_position(('data', 0))  # data表示通过值来设置x轴的位置，将x轴绑定在y=0的位置
+    product_list = [source_prod, target_prod]
+    max_x_count = max([x['data'].size for x in product_list])
+    loc = np.arange(max_x_count)  # the x locations for the groups
+
+    # 坐标轴
+    ax1.tick_params(labelsize=fontsize)
+    ax2.tick_params(labelsize=fontsize)
+    ax1.set_xticks(loc)
+    ax1.set_xticklabels(xlabels)
+    ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
+    max_rate = np.max(np.hstack((source_prod['data'], target_prod['data'])))
+    ax2.set_xticks([])
+
+    # 个基折线图
+    ax1.plot(loc, source_prod['data'], color='#C6A774', marker='', linewidth=3, label=source_prod['name'])
+    ax1.legend(loc='upper left', fontsize=fontsize)
+
+    # 指数折线图
+    ax2.plot(loc, target_prod['data'], color='black', marker='', linewidth=3, label=target_prod['name'])
+    ax2.legend(loc='upper center', fontsize=fontsize)
+
+    plt.show()
+
+
+def draw_combination_chart(xlabels, new_combination, origin_combination, index):
+    """组合对比图"""
+    figsize = (20, 12)
+    # 标签文字大小
+    fontsize = 22
+    # 初始化
+    fig = plt.figure(figsize=figsize)
+    ax1 = fig.add_subplot()
+    ax2 = ax1.twiny()
+    ax3 = ax1.twiny()
+    # ax = plt.gca()  # gca:get current axis得到当前轴
+    # ax.spines['bottom'].set_position(('data', 0))  # data表示通过值来设置x轴的位置，将x轴绑定在y=0的位置
+    product_list = [origin_combination, new_combination, index]
+    max_x_count = max([x['data'].size for x in product_list])
+    loc = np.arange(max_x_count)  # the x locations for the groups
+
+    # 坐标轴
+    ax1.tick_params(labelsize=fontsize)
+    ax2.tick_params(labelsize=fontsize)
+    ax3.tick_params(labelsize=fontsize)
+    ax1.set_xticks(loc)
+    ax1.set_xticklabels(xlabels)
+    ax1.yaxis.set_major_formatter(FuncFormatter(to_percent))
+    ax2.set_xticks([])
+    ax3.set_xticks([])
+
+    # 新组合折线图
+    ax1.plot(loc, new_combination['data'], color='#C6A774', marker='', linewidth=3, label=origin_combination['name'])
+    ax1.legend(loc='upper left', fontsize=fontsize)
+
+    # 原组合折线图
+    ax2.plot(loc, origin_combination['data'], color='#222A77', marker='', linewidth=3, label=new_combination['name'])
+    ax2.legend(loc='upper center', fontsize=fontsize)
+
+    # 指数折线图
+    ax3.plot(loc, index['data'], color='black', marker='', linewidth=3, label=index['name'])
+    ax3.legend(loc='upper right', fontsize=fontsize)
 
     plt.show()
 
+
+def draw_correlation_chart():
+    """相关性分析图"""
+    pass
+
+
 if __name__ == '__main__':
-    product1 = {'name': 'product1', 'data': np.array([10, 20, 30, 40, 50])}
-    product2 = {'name': 'product2', 'data': np.array([20, 20, 20, 20, 20])}
-    product3 = {'name': 'product3', 'data': np.array([20, 20, 20, 20, 20])}
-    product4 = {'name': 'product4', 'data': np.array([20, 20, 20, 20, 20])}
-    product5 = {'name': 'product5', 'data': np.array([20, 20, 20, 20, 20])}
-    product6 = {'name': 'product6', 'data': np.array([20, 20, 20, 20, 20])}
-    product_list = [product1, product2, product3, product4, product5, product6]
-    cumulative = {'name': 'totalincome', 'data': np.array([10, 50, 120, 100, 36])}
-    draw_stacked_column_chart(product_list, cumulative)
+    # xlabels = ('2020-1', '2020-2', '2020-3', '2020-4', '2020-5', '2020-6', '2020-7', '2020-8', '2020-9', '2020-10', '2020-11', '2020-12')
+    # product = {'name': '月度回报率', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    # contrast = {'name': '同比上涨', 'data': np.array([10, 50, 120, 100, 36, 0, 50, 120, 100, 36, 23, 98])}
+    # draw_month_return_chart(xlabels, [product], contrast)
+
+    # xlabels = ('2020-1', '2020-2', '2020-3', '2020-4', '2020-5', '2020-6', '2020-7', '2020-8', '2020-9', '2020-10', '2020-11', '2020-12')
+    # product1 = {'name': '塞亚成长1号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    # product2 = {'name': '塞亚成长2号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    # product3 = {'name': '塞亚成长3号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    # product4 = {'name': '塞亚成长4号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    # product5 = {'name': '塞亚成长5号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    # product6 = {'name': '塞亚成长6号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    # product7 = {'name': '塞亚成长7号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    # product8 = {'name': '塞亚成长8号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    # product_list = [product1, product2, product3, product4, product5, product6, product7, product8]
+    # cumulative = {'name': '总收益', 'data': np.array([10, 50, 120, 100, 36, 0, 50, 120, 100, 36, 23, 98])}
+    # draw_contribution_chart(xlabels, product_list, cumulative)
+
+    # xlabels = ('2020-1', '2020-2', '2020-3', '2020-4', '2020-5', '2020-6', '2020-7', '2020-8', '2020-9', '2020-10', '2020-11', '2020-12')
+    # source_prod = {'name': '远澜银杏1号', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    # target_prod = {'name': '上证指数', 'data': np.array([-10, 10, 5, 55, 24, 10, 20, 8, 10, 31, 40, 32])}
+    # draw_comment_chart(xlabels, source_prod, target_prod)
+
+    xlabels = ('2020-1', '2020-2', '2020-3', '2020-4', '2020-5', '2020-6', '2020-7', '2020-8', '2020-9', '2020-10', '2020-11', '2020-12')
+    new_combination = {'name': '新组合', 'data': np.array([20, 30, 40, 50, 60, 20, 30, 40, 50, 60, 50, 60])}
+    origin_combination = {'name': '原组合', 'data': np.array([10, 20, 30, 40, 50, 10, 20, 30, 40, 50, 40, 50])}
+    index = {'name': '上证指数', 'data': np.array([-10, 10, 5, 55, 24, 10, 20, 8, 10, 31, 40, 32])}
+    draw_combination_chart(xlabels, new_combination, origin_combination, index)

app/utils/html_to_pdf.py

@@ -24,9 +24,9 @@ def html_to_pdf():
     }
     url1 = 'http://www.qimontech.com'
     # url = 'http://baidu.com'
-    # url2 = 'https://www.cnblogs.com/zzb-yp/p/11512616.html'
-    # pdfkit.from_url(url1, '/Users/pengxiong/Desktop/out5.pdf', options=options)
-    pdfkit.from_file('/Users/pengxiong/Desktop/monthReport.html', '/Users/pengxiong/Desktop/out5.pdf', options=options)
+    url2 = 'https://manage.meerkat.top'
+    pdfkit.from_url(url1, '/Users/pengxiong/Desktop/out5.pdf', options=options)
+    # pdfkit.from_file('/Users/pengxiong/Desktop/monthReport.html', '/Users/pengxiong/Desktop/out5.pdf', options=options)
 def merge_pdf(pdfFiles, target_file='/Users/pengxiong/Desktop/combine.pdf'):
     """"""
     merger = PdfFileMerger()