def draw_order_signals(trader_name, render='html'):
df_account = get_account(trader_name=trader_name)
start_timestamp = df_account['timestamp'][0]
end_timestamp = df_account['timestamp'][-1]
df_orders = get_orders(trader_name=trader_name)
grouped = df_orders.groupby('security_id')
page = Page()
for security_id, order_df in grouped:
kdata = get_kdata(security_id=security_id,
provider='netease',
start_timestamp=start_timestamp,
end_timestamp=end_timestamp)
mark_points = order_df
kline = draw_kline(df_list=[kdata],
markpoints_list=[mark_points],
render=None)
page.add(kline)
if render == 'html':
file_name = '{}_signals'.format(trader_name)
page.render(get_ui_path(file_name))
elif render == 'notebook':
page.render_notebook()
return page
def draw_account_details(trader_name, render='html'):
page = Page()
account_summary = draw_account_list([trader_name], render=None)
# klines = draw_order_signals(trader_name=trader_name, render=None)
positions = draw_positions(trader_name=trader_name, render=None)
page.add(account_summary, positions)
if render == 'html':
file_name = '{}_details'.format(trader_name)
page.render(get_ui_path(file_name))
elif render == 'notebook':
page.render_notebook()
return page
def page_default_layout():
page = Page()
page.add(
bar_datazoom_slider(),
line_markpoint(),
pie_rosetype(),
grid_mutil_yaxis(),
liquid_data_precision(),
table_base(),
)
put_html(page.render_notebook())
def test_page_render_notebook():
page = Page()
page.add(_create_line(), _create_bar(), _create_table())
html = page.render_notebook().__html__()
assert_in("City name", html)
pie_2 = (Pie().add("", [list(z) for z in zip(risk, h_20)], rosetype="radius",radius=["30%", "55%"])
.set_colors(color).set_global_opts(title_opts=opts.TitleOpts("고객 투자성향 분포: 20대")).set_series_opts(label))
tl.add(pie_2, "20대 자산 상위그룹")
tl2 = Timeline()
pie_0 = (Pie().add("", [list(z) for z in zip(risk, l_30)], rosetype="radius",radius=["30%", "55%"])
.set_colors(color).set_global_opts(title_opts=opts.TitleOpts("고객 투자성향 분포: 30대")).set_series_opts(label))
tl2.add(pie_0, "30대 자산 하위그룹")
pie_1 = (Pie().add("", [list(z) for z in zip(risk, m_30)], rosetype="radius",radius=["30%", "55%"])
.set_colors(color).set_global_opts(title_opts=opts.TitleOpts("고객 투자성향 분포: 30대")).set_series_opts(label))
tl2.add(pie_1, "30대 자산 중위그룹")
pie_2 = (Pie().add("", [list(z) for z in zip(risk, h_30)], rosetype="radius",radius=["30%", "55%"])
.set_colors(color).set_global_opts(title_opts=opts.TitleOpts("고객 투자성향 분포: 30대")).set_series_opts(label))
tl2.add(pie_2, "30대 자산 상위그룹")
page = Page().add(tl,tl2)
page.render_notebook()
# 20대, 30대의 자산 규모 별 그룹에 따른 위험 회피정도를 시각화 해보았습니다.
#
# 20대의 경우 자산이 적으면 위험을 회피하려고 하지만, 30대는 자산이 적더라도 위험을 감수하려고 합니다.
# 20대와 30대 모두 자산 상위그룹의 경우 위험을 감수하고자 하며, 30대의 경우 그 비율이 더 높습니다.
# 이를 통해 Y&Z세대 내에서 위험을 감수하는 정도는 나이가 많아질수록, 또 자산 규모가 커질수록 높아진다고 볼 수 있습니다.
# In[12]:
#계좌 개수 시각화
fig = px.strip(cus_info, x="age_cat", y="num_act", color="class",
category_orders={"age_cat": ["10대 미만","20대","30대","40대","50대","60대","70대 이상"],
def foregin_city_all():
file = os.getcwd() # 读取当前文件路径
path = file + '\\data\\各国各地区疫情信息'
path1 = file + '\\daliy_changes_view'
isExists = os.path.exists(path1) # 判断当前目录是否有文件夹photos，如果没有则创建
if not isExists:
os.mkdir(path1) # 创建photos,用于存放生成的图像数据
path2 = file + '\\daliy_changes_view\\各国疫情严重程度排名前十地区信息'
isExists = os.path.exists(path2) # 判断当前目录是否有文件夹daliy_changes_view，如果没有则创建
if not isExists:
os.mkdir(path2) # 创建daliy_changes_view,用于存放生成的图像数据
Filelist = [] # 将当前文件夹内的所有表名存放此列表
for home, dirs, files in os.walk(path):
for filename in files:
Filelist.append(filename)
# 判断数据是否存在
def checknan(name):
if np.any(pd.isnull(name)) == True:
name.fillna(value="0", inplace=True)
# 随机颜色生成用于制作南丁格尔玫瑰图
def randomcolor(kind):
colors = []
for i in range(kind):
colArr = [
'1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C',
'D', 'E', 'F'
]
color = ""
for i in range(6):
color += colArr[random.randint(0, 14)]
colors.append("#" + color)
return colors
for i in Filelist:
data = pd.read_excel(file + '\\data\\各国各地区疫情信息\\%s' % (i), index=False)
data_sort = data.sort_values(axis=0, ascending=False, by=['confirm'])
data_message = data_sort.head(10) # 提取疫情严重程度排名前十地区的信息
n = i[:-5] # 只提取国家名，不要后缀（.xlsx）
checknan(data_message['confirm'])
checknan(data_message['heal'])
checknan(data_message['dead'])
y1_confirm = data_message['confirm']
y1_confirm = list(y1_confirm)
y1_confirm = [int(i) for i in y1_confirm]
y2_dead = data_message['dead']
y2_dead = list(y2_dead)
y2_dead = [int(i) for i in y2_dead]
y3_heal = data_message['heal']
y3_heal = list(y3_heal)
y3_heal = [int(i) for i in y3_heal]
name_list = []
for j in data_message['name']:
name_list.append(j)
x = name_list
color_series = randomcolor(len(x))
# Bars = (
# Bar(init_opts=opts.InitOpts(width='1080px',height='700px'))
# .add_xaxis(xaxis_data=x)
# .add_yaxis(series_name='确诊人数', yaxis_data=y1_confirm)
# )
page = Page() # 创建一个分页用于放所有的图像在这一分页上
#####画南丁格尔玫瑰图##########
# 画出确诊人数的图
fig = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig.add("", [list(z) for z in zip(x, y1_confirm)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig.set_global_opts(title_opts=opts.TitleOpts(title=n +
'疫情严重程度排名前十地区的确诊人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig.set_series_opts(label_opts=opts.LabelOpts(
is_show=True,
position='inside',
font_size=12,
formatter='{b}:{c}例',
font_style='italic',
font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
fig.set_colors(color_series)
# 画出死亡人数的图
fig1 = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig1.add("", [list(z) for z in zip(x, y2_dead)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig1.set_global_opts(title_opts=opts.TitleOpts(title=n +
'疫情严重程度排名前十地区的死亡人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig1.set_series_opts(label_opts=opts.LabelOpts(
is_show=True,
position='inside',
font_size=12,
formatter='{b}:{c}例',
font_style='italic',
font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
# 画出治愈人数的图
fig2 = Pie(init_opts=opts.InitOpts(width='500px', height='700px'))
fig2.add("", [list(z) for z in zip(x, y3_heal)],
radius=['30%', '135%'],
center=['50%', '65%'],
rosetype='area')
fig2.set_global_opts(title_opts=opts.TitleOpts(title=n +
'疫情严重程度排名前十地区的治愈人数'),
legend_opts=opts.LegendOpts(is_show=False))
fig2.set_series_opts(label_opts=opts.LabelOpts(
is_show=True,
position='inside',
font_size=12,
formatter='{b}:{c}例',
font_style='italic',
font_weight='bold',
font_family='Microsoft YaHei')) # b:province;c:num
page.add(fig) # 将图像加入同一页
page.add(fig1) # 将图像加入同一页
page.add(fig2) # 将图像加入同一页
page.render_notebook()
page.render(file + '\\daliy_changes_view\\各国疫情严重程度排名前十地区信息\\%s.html' %
(n),
index=False)
def plot(self, total_df):
page = Page(layout=Page.DraggablePageLayout)
dates = [date.strftime('%Y-%m-%d') for date in total_df.index]
for column in total_df.columns[:]:
try:
tmp = total_df[[column]]
tmp.fillna(method='bfill', inplace=True) # 应该用前一天，但是为了画图，这里用了后一天，对总体影响不大
ma5 = tmp.rolling(5).mean()
ma5.fillna(method='bfill', inplace=True)
ma5 = round(ma5, 4)
ma10 = tmp.rolling(10).mean()
ma10.fillna(method='bfill', inplace=True)
ma10 = round(ma10, 4)
ma25 = tmp.rolling(25).mean()
ma25.fillna(method='bfill', inplace=True)
ma25 = round(ma25, 4)
ma40 = tmp.rolling(40).mean()
ma40.fillna(method='bfill', inplace=True)
ma40 = round(ma40, 4)
upper, middle, lower = ta.BBANDS(tmp[column].values, timeperiod=25, nbdevup=2, nbdevdn=2, matype=0)
c = (
Line(init_opts=opts.InitOpts(height="300px", width='100%', bg_color="white"))
.add_xaxis(dates)
.add_yaxis(
'',
tmp.values.tolist(),
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="#FF0000", width=2),
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min"), opts.MarkPointItem(type_="max")])
)
.add_yaxis(
'',
upper,
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="#404040", width=0.5)
# markpoint_opts=opts.MarkPointOpts(),
)
.add_yaxis(
'',
middle,
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="#404040", width=0.5)
# markpoint_opts=opts.MarkPointOpts(),
)
.add_yaxis(
'',
lower,
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="#404040", width=0.5)
# markpoint_opts=opts.MarkPointOpts(),
)
.add_yaxis(
"ma5",
ma5.values.tolist(),
symbol="emptyCircle",
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="#0000FF", width=1)
)
.add_yaxis(
"ma10",
ma10.values.tolist(),
symbol="emptyCircle",
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="#000000", width=1)
)
.add_yaxis(
"ma25",
ma25.values.tolist(),
symbol="emptyCircle",
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="#006400", width=1)
)
.add_yaxis(
"ma40",
ma40.values.tolist(),
symbol="emptyCircle",
is_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(color="#800080", width=1)
)
.set_global_opts(title_opts=opts.TitleOpts(title=column),
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(is_scale=True,
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True), ),
toolbox_opts=opts.ToolboxOpts(
# feature=opts.ToolBoxFeatureOpts(
# save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
# type_="png",
# background_color='white',
# pixel_ratio=15))
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside"), ]
)
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
# markpoint_opts=opts.MarkPointOpts(
# data=[
# opts.MarkPointItem(type_="max", name="最大值"),
# opts.MarkPointItem(type_="min", name="最小值"),
# opts.MarkPointItem(type_="average", name="平均值"),
# ]
# ),
)
)
page.add(c)
except:
logger.warning(f'{column}画图失败')
continue
page.load_javascript()
page.render_notebook()
return page
