def plot_positions(self, position_list):
lst_xaxis = self.df_unitAxis.loc[
self.df_unitAxis['timeAxis'] >=
self.trade_start]['unit_intdate'].astype(str).values.tolist(
) # xaxis values has to be STRING type, int will not work
linechart = (Line().add_xaxis(lst_xaxis))
# heartbeat的dateindex个数会>=position的timeindex个数，因为并不是没一轮market event循环都会有signal，order等事件触发，由此并不是每天都会生成position
for pos in position_list:
if pos.fname is not None:
df = pd.read_csv(pos.fname, encoding=cfg.FILE_ENCODE)
df_cash = df.loc[df['ts_code'] == 'cash'].groupby(
'timeindex').agg({'market_value': 'sum'})
df = df.groupby('timeindex').agg({'market_value': 'sum'})
linename = '{}持仓'.format(pos.name)
linename_cash = '{}现金持仓'.format(pos.name)
assert lst_xaxis == df.index.astype(str).values.tolist()
lst_values = df['market_value'].values.tolist()
lst_valuesCash = df_cash['market_value'].values.tolist()
linechart.add_yaxis(
linename,
lst_values,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.3),
label_opts=opts.LabelOpts(is_show=False),
)
linechart.add_yaxis(
linename_cash,
lst_valuesCash,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.3),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#bbbbbb", ),
)
linechart.set_global_opts(
title_opts=opts.TitleOpts(title='持仓曲线', ),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
name="市场价值（元）",
is_scale=True,
),
legend_opts=opts.LegendOpts(is_show=True,
pos_top='5%',
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0],
type_="slider",
pos_top="90%",
range_start=75,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000",
font_size=10),
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
# put everything in a grid for easy resizing and positioning
gridChart = Grid()
gridChart.add(
linechart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top='25%'),
)
fname = f'{cfg.PATH_POSFILE}{pos.name}.html'
gridChart.render(fname)
self.chart = gridChart #输出至可访问对象属性中
),
xaxis_opts=opts.AxisOpts(
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
name="水量",
type_="value",
min_=0,
max_=250,
interval=50,
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
)
line = (
Line()
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="平均温度",
yaxis_index=1,
y_axis=[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
label_opts=opts.LabelOpts(is_show=False),
)
)
bar.overlap(line).render("mixed_bar_and_line.html")
def zhaopin():
zhaopin_data, zhaopin_account = pip_data(keyword='招聘')
cve_data, cve_account = pip_data(keyword='cve')
yujing_data, yujing_account = pip_data(keyword='预警')
loudong_data, loudong_account = pip_data(keyword='漏洞')
fuxain_data, fuxain_account = pip_data(keyword='复现')
zongjie_data, zongjie_account = pip_data(keyword='总结')
paixu = {}
quanneng = {}
accounts_list = list(
set(cve_data[0:20] + yujing_data[0:20] + loudong_data[0:20] +
fuxain_data[0:20] + zongjie_data[0:20]))
for i in list(
set(cve_data[0:20] + yujing_data[0:20] + loudong_data[0:20] +
fuxain_data[0:20] + zongjie_data[0:20])):
print(i)
paixu[i]=cve_account.get(i,0)/1094/5+\
yujing_account.get(i,0)/834/5+\
loudong_account.get(i,0)/5399/5+\
fuxain_account.get(i,0)/255/5+\
zongjie_account.get(i,0)/603/5
quanneng[i] = [
cve_account.get(i, 0) / 1094 * 100,
yujing_account.get(i, 0) / 834 * 100,
loudong_account.get(i, 0) / 5399 * 100,
fuxain_account.get(i, 0) / 255 * 100,
zongjie_account.get(i, 0) / 603 * 100,
]
print(
cve_account.get(i, 0) / 1094,
yujing_account.get(i, 0) / 834,
loudong_account.get(i, 0) / 5399,
fuxain_account.get(i, 0) / 255,
zongjie_account.get(i, 0) / 603,
)
from operator import itemgetter
print(sorted(paixu.items(), key=itemgetter(1), reverse=True))
print('招聘信息条数', (sum(x for x in zhaopin_account.values())))
print('cve信息条数top20', (sum(cve_account.get(x, 0) for x in cve_data[0:20])))
print('预警信息条数top20',
(sum(yujing_account.get(x, 0) for x in yujing_data[0:20])))
print('漏洞信息条数top20',
(sum(loudong_account.get(x, 0) for x in loudong_data[0:20])))
print('复现信息条数top20',
(sum(fuxain_account.get(x, 0) for x in fuxain_data[0:20])))
print('总结信息条数top20',
(sum(zongjie_account.get(x, 0) for x in zongjie_data[0:20])))
print('预警信息条数', (sum(x for x in yujing_account.values())))
print('漏洞信息条数', (sum(x for x in loudong_account.values())))
print('复现信息条数', (sum(x for x in fuxain_account.values())))
print('总结信息条数', (sum(x for x in zongjie_account.values())))
# c = (
# Pie({"width": "1700px", "height": "900px"})
#
# .add(
# "‘cve’发布统计",
#
# cve_data[0:20],
# radius=[46, 80],
# center=["30%", "30%"],
# is_clockwise=False,
# label_opts=opts.LabelOpts(is_show=False),
# )
#
# .add(
# "‘预警’发布统计",
# yujing_data[0:20],
# radius=[46, 80],
# center=["58%", "30%"],
# is_clockwise=False,
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add(
# "‘漏洞’发布统计",
# loudong_data[0:20],
# radius=[46, 80],
# center=["85%", "30%"],
# is_clockwise=False,
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add(
# "‘复现’发布统计",
# fuxain_data[0:20],
# radius=[46, 80],
# center=["30%", "70%"],
# is_clockwise=False,
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add(
# "‘总结’发布统计",
# zongjie_data[0:20],
# radius=[46, 80],
# center=["58%", "70%"],
# is_clockwise=False,
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add(
# "‘招聘’发布统计",
# zhaopin_data[0:20],
# radius=[46, 80],
# center=["85%", "70%"],
# is_clockwise=False,
# label_opts=opts.LabelOpts(is_show=False),
# )
# .set_global_opts(title_opts=opts.TitleOpts(title="2019年微信公众号文章主题数据统计图(top20)", pos_left='center'),
# legend_opts=opts.LegendOpts(type_='scroll', is_show=True, pos_left="5%", pos_top='5%',
# orient="vertical"))
# .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
# )
#
#
# # page = Page(layout=Page.SimplePageLayout)
# # 需要自行调整每个 chart 的 height/width，显示效果在不同的显示器上可能不同
# # page.add(c)
# # c.render(path='./static/year/pie.html')
# return c
c = (
Line({
"width": "1500px",
"height": "700px"
}).add_xaxis(['cve', '预警', '漏洞', '复现', '总结']).add_yaxis(
"嘶吼专业版",
quanneng['嘶吼专业版'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"FreeBuf",
quanneng['FreeBuf'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"邑安全",
quanneng['邑安全'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"山石瞭望",
quanneng['山石瞭望'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"360CERT",
quanneng['360CERT'],
is_smooth=True,
label_opts=opts.LabelOpts(
is_show=False)).add_yaxis(
"腾讯御见威胁情报中心",
quanneng['腾讯御见威胁情报中心'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).
add_yaxis(
"绿盟科技",
quanneng['绿盟科技'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"Timeline Sec",
quanneng['Timeline Sec'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"长亭安全课堂",
quanneng['长亭安全课堂'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"合天智汇",
quanneng['合天智汇'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"绿盟科技安全情报",
quanneng['绿盟科技安全情报'],
is_smooth=True,
label_opts=opts.LabelOpts(
is_show=False)).add_yaxis(
"E安全",
quanneng['E安全'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).
add_yaxis(
"信安之路",
quanneng['信安之路'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"黑白之道",
quanneng['Seebug漏洞平台'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"维他命安全",
quanneng['维他命安全'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"无级安全",
quanneng['无级安全'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"安全客",
quanneng['安全客'],
is_smooth=True,
label_opts=opts.LabelOpts(
is_show=False)).add_yaxis(
"弥天安全实验室",
quanneng['弥天安全实验室'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).
add_yaxis(
"HACK学习呀",
quanneng['HACK学习呀'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"Tide安全团队",
quanneng['Tide安全团队'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"SecPulse安全脉搏",
quanneng['SecPulse安全脉搏'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"雷神众测",
quanneng['雷神众测'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"安全祖师爷",
quanneng['安全祖师爷'],
is_smooth=True,
label_opts=opts.LabelOpts(
is_show=False)).add_yaxis(
"洛米唯熊",
quanneng['洛米唯熊'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).
add_yaxis("飓风网络安全",
quanneng['飓风网络安全'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"网信防务",
quanneng['网信防务'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"小白帽学习之路",
quanneng['小白帽学习之路'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False)).add_yaxis(
"西子实验室",
quanneng['西子实验室'],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False))
# .set_series_opts(
# areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
# )
.set_global_opts(
title_opts=opts.TitleOpts(title="2019年微信公众号主题折线图",
pos_left='43%',
pos_top='8%'),
# xaxis_opts=opts.AxisOpts(
# axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
# is_scale=False,
# boundary_gap=False,
# ),
# legend_opts=opts.LegendOpts(is_show=False),
))
c.render(path='./static/year/quanneng.html')
print('生成全能公众号')
def picture2(self):
df = pd.read_sql(
f"select * from {self.table_name} where 检查时间 LIKE '%{self.last_month}%'",
self.conn)
df['time'] = pd.to_datetime(df['检查时间'])
df['time_H'] = df['time'].apply(lambda x: x.hour)
x = df['time_H'].sort_values(ascending=True).unique()
ya = []
for i in x:
try:
bkk = df[df['time_H'] == i]['性质'].value_counts()['A类']
ya.append(str(bkk))
except:
bkk = 0
ya.append(str(bkk))
# print(ya, len(ya))
yb = []
for i in x:
try:
bkk = df[df['time_H'] == i]['性质'].value_counts()['B类']
yb.append(str(bkk))
except:
bkk = 0
yb.append(str(bkk))
# print(yb, len(yb))
yc = []
for i in x:
try:
bkk = df[df['time_H'] == i]['性质'].value_counts()['C类']
yc.append(str(bkk))
except:
bkk = 0
yc.append(str(bkk))
# print(yc, len(yc))
yd = []
for i in x:
try:
bkk = df[df['time_H'] == i]['性质'].value_counts()['D类']
yd.append(str(bkk))
except:
bkk = 0
yd.append(str(bkk))
# print(yd, len(yd))
a = []
for i in x:
m = str(i) + ':00'
a.append(m)
e = (
Line().add_xaxis(a).add_yaxis("A类", ya).add_yaxis(
"B类",
yb).add_yaxis("C类", yc).add_yaxis("D类", yd).set_global_opts(
title_opts=opts.TitleOpts(
title=f"现场安全信息性质分布(时)",
subtitle=f"统计时间：{self.last_month}"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
xaxis_opts=opts.AxisOpts(name="时间"),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={
"saveAsImage": {
'backgroundColor':
'white'
},
"dataZoom": {},
"restore": {},
"magicType": {
"show": True,
"type":
["line", "bar"]
},
"dataView": {}
}))
# .render(f"safe_message_hour_{self.table_name}.html")
)
print('已画图成功')
return e
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Line, Scatter
from pyecharts.faker import Faker
x = Faker.choose()
line = (
Line()
.add_xaxis(x)
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(title_opts=opts.TitleOpts(title="Overlap-line+scatter"))
)
scatter = (
Scatter()
.add_xaxis(x)
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
)
line.overlap(scatter)
put_html(line.render_notebook())
def TREND_render(x,y,z,y_up,y_down,max_index,min_index,x_name,y_name,query,answer):
x=list(map(str,x))
y=list(map(float,y))
bar = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="increase_value",
y_axis=y_up,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_='max')]
),
)
# .add_yaxis(series_name="increase", yaxis_data=y_up, stack="GDP",markpoint_opts=opts.MarkPointOpts(
# data=[opts.MarkPointItem(name=x[max_index], coord=[x[max_index], y[max_index]+y_up[max_index]+z[max_index]], value="Max")]
# ),)
.add_yaxis(series_name="decrease_value", y_axis=y_down, stack="GDP",markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_='max')]
),)
.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(range_start=10, range_end=90),
opts.DataZoomOpts(type_="inside")],
yaxis_opts=opts.AxisOpts(
name=y_name,
type_="value",
axislabel_opts=opts.LabelOpts(formatter="{value}"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(name=x_name),
graphic_opts=[opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=['\n'+"Q:"+' '+query+'\n'+"\n"+'A:'+' '+answer],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333"
)
)
)]
)
)
line = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name='food',
yaxis_index=0,
y_axis=y,
label_opts=opts.LabelOpts(is_show=True),
# markpoint_opts=opts.MarkPointOpts(
# data=[opts.MarkPointItem(type_='max', value='Max'),opts.MarkPointItem(type_='min',value='Max')],
# ),
)
.set_global_opts(legend_opts=opts.LegendOpts(is_show=False), )
)
# line.overlap(bar).render("TREND.html")
grid = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG, ))
grid.add(bar.overlap(line), grid_opts={'left': '20%', 'bottom': '34%'})
option1 = grid.dump_options_with_quotes()
option1 = json.loads(option1)
option = {"option": [option1], "query": query}
return option
class TsdaChart(base_chart.BaseChart):
chart = None
max_x = None
min_x = None
def __init__(self, title, xaxis_name, yaxis_name):
base_chart.BaseChart.__init__(self, title, xaxis_name, yaxis_name)
def clear(self):
self.data.clear()
def set_data(self, **data):
if ('x' in data) & ('y' in data):
self.max_x = max(data['x'])
self.min_x = min(data['x'])
if 'label' in data:
self.data.append([data['x'], data['y'], data['label']])
else:
self.data.append([data['x'], data['y']])
return self
def show(self):
return self.chart
def plot(self):
self.chart = Line(init_opts=opts.InitOpts(page_title=self.title))
# load data
self.chart.add_xaxis(self.data[0][0])
index = 0
for data_set in self.data:
if len(data_set) > 2: # get label from data set
label = data_set[2]
else:
label = self.yaxis_name + str(index)
if index == 0:
print('append line:', data_set[0])
print(data_set[1])
self.chart.add_yaxis(label, data_set[1])
else:
print('append bar:', data_set[0])
print(data_set[1])
bar = Bar()
bar.add_xaxis(data_set[0])
bar.add_yaxis(label, data_set[1])
self.chart.overlap(bar)
index += 1
# set options
self.chart.set_global_opts(
title_opts=opts.TitleOpts(title=self.title),
toolbox_opts=opts.ToolboxOpts(is_show=True),
datazoom_opts=opts.DataZoomOpts(is_show=True,
type_='slider',
range_start=0,
range_end=100),
# xaxis_opts=opts.AxisOpts(name=self.xaxis_name, type_='time', name_location='end', name_gap=15,
# splitline_opts=opts.SplitLineOpts(is_show=True),
# min_=self.min_x, max_=self.max_x),
yaxis_opts=opts.AxisOpts(
name=self.yaxis_name,
name_location='center',
name_gap=25,
splitline_opts=opts.SplitLineOpts(is_show=True)))
self.chart.set_series_opts(
label_opts=opts.LabelOpts(is_show=False, position='inside'))
return self
def report(request):
"""返回慢SQL历史趋势"""
checksum = request.GET.get('checksum')
cnt_data = ChartDao().slow_query_review_history_by_cnt(checksum)
pct_data = ChartDao().slow_query_review_history_by_pct_95_time(checksum)
cnt_x_data = [row[1] for row in cnt_data['rows']]
cnt_y_data = [int(row[0]) for row in cnt_data['rows']]
pct_y_data = [str(row[0]) for row in pct_data['rows']]
line = Line(init_opts=opts.InitOpts(width='800', height='380px'))
line.add_xaxis(cnt_x_data)
line.add_yaxis("慢查次数",
cnt_y_data,
is_smooth=True,
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="max", name='最大值'),
opts.MarkLineItem(type_="average", name='平均值')
]))
line.add_yaxis("慢查时长(95%)",
pct_y_data,
is_smooth=True,
is_symbol_show=False)
line.set_series_opts(areastyle_opts=opts.AreaStyleOpts(opacity=0.5, ))
line.set_global_opts(
title_opts=opts.TitleOpts(title='SQL历史趋势'),
legend_opts=opts.LegendOpts(selected_mode='single'),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
),
)
result = {"status": 0, "msg": '', "data": line.render_embed()}
return HttpResponse(json.dumps(result), content_type='application/json')
def draw_balance_line(xaxis, yaxis, title="消费统计", markline=None, width=2000) -> Line:
"""
x = [月_日, 月_日, 月_日, ....]
y = [(title1, [num1, num2, num3, num4, ...]), (title2, [num1, num2, num3, num4, ...])]
:param xaxis: x轴
:param yaxis: y轴
:param title: 标题
:param markline: 标记辅助线
:param width: 宽
:return: Line
"""
line = Line()
line.add_xaxis(xaxis)
for name, axis in yaxis:
line.add_yaxis(name, axis)
line.set_global_opts(title_opts=opts.TitleOpts(title=title, ),
datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside")],
tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='shadow'))
line.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
if markline is not None:
line.set_series_opts(markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(
y=markline,
name='预算')
])
)
return line
def draw_pnl(trader_sim_obj) -> Grid:
# 【1】 画Returns
trader_name = trader_sim_obj.name
df_Pnl = pd.DataFrame(trader_sim_obj.Data.Pnl)
df_Pnl = df_Pnl.sort_values(by='Date')
df_Pnl['cumsum'] = df_Pnl['Returns'].cumsum()
x_list = df_Pnl['Date'].dt.date.to_list()
y_list = df_Pnl['cumsum'].to_list()
line_returns = (Line().add_xaxis(x_list).add_yaxis(
series_name=trader_name, y_axis=y_list).set_global_opts(
title_opts=opts.TitleOpts(title='Returns', pos_top='10%'),
legend_opts=opts.LegendOpts(pos_top='0%'),
datazoom_opts=opts.DataZoomOpts(is_show=True,
xaxis_index=[0, 1],
pos_top='60%'),
xaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(is_show=True),
is_scale=True,
),
yaxis_opts=opts.AxisOpts(is_scale=True),
).set_series_opts(
label_opts='False',
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', symbol='circle'),
]),
))
# 【2】 画MDD
df_Pnl['cummax'] = df_Pnl['cumsum'].cummax()
df_Pnl['drawDown'] = df_Pnl['cumsum'] - df_Pnl['cummax']
x_list = df_Pnl['Date'].dt.date.to_list()
round_n = get_round_pos(df_Pnl['drawDown'])
y_list = round(df_Pnl['drawDown'], round_n).to_list()
line_mdd = (
Line().add_xaxis(x_list).add_yaxis(
series_name=trader_name,
y_axis=y_list,
).set_global_opts(
title_opts=opts.TitleOpts(title='MDD', pos_top='75%'),
legend_opts=opts.LegendOpts(is_show=False),
datazoom_opts=opts.DataZoomOpts(is_show=False, xaxis_index=[0, 1]),
xaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
# max_=0,
# min_=y_min - abs((y_max - y_min)) * 0.1 ,
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True, )),
).set_series_opts(
label_opts='False',
markpoint_opts=opts.MarkPointOpts(data=[
# opts.MarkPointItem(type_='max', symbol='roundRect'),
opts.MarkPointItem(type_='min', symbol='circle'),
]),
))
# 组合gird
grid = (Grid(init_opts=opts.InitOpts(width='1200px', height='800px')).add(
line_returns, grid_opts=opts.GridOpts(pos_bottom='40%')).add(
line_mdd, grid_opts=opts.GridOpts(pos_top='65%', pos_bottom='5%')))
return grid
def draw_band_and_trades(trader_sim_obj) -> Grid:
def band1(x):
l = list(x)
l.sort()
m = sum(l[1:3]) / 2
l2 = [i for i in l if (i > m * 0.95) * (i < m * 1.05)]
return max(l2)
def band2(x):
l = list(x)
l.sort()
m = sum(l[1:3]) / 2
l2 = [i for i in l if (i > m * 0.95) * (i < m * 1.05)]
return min(l2)
# 【1】 Band
trader_name = trader_sim_obj.name
# 拿数据
df_band = pd.DataFrame(trader_sim_obj.Data.Band)
df_band.set_index(keys='DateTime', drop=True, inplace=True)
df_bar = pd.DataFrame(trader_sim_obj.Data.Bar)
# 转换 计算 BarPx
df_bar_open_pv = df_bar.pivot_table(values='Open',
index='DateTime',
columns='ticker',
aggfunc='mean')
ticker1 = df_bar[df_bar['tickerNum'] == '1']['ticker'].to_list()[-1]
ticker2 = df_bar[df_bar['tickerNum'] == '2']['ticker'].to_list()[-1]
df_bar_open_pv['px'] = df_bar_open_pv[ticker1] / df_bar_open_pv[ticker2]
# 筛选Band 合并 Band和BarPx
df_band_px = pd.concat([df_band, df_bar_open_pv[['px']]], axis=1)
df_band_px['Band1'] = df_band_px[[
'BuyEntry', 'BuyExit', 'SellEntry', 'SellExit'
]].apply(func=band1, axis=1)
df_band_px['Band2'] = df_band_px[[
'BuyEntry', 'BuyExit', 'SellEntry', 'SellExit'
]].apply(func=band2, axis=1)
df_band_px = df_band_px[['Band1', 'Band2', 'px']]
round_n = get_round_pos(df_band_px['px'])
df_band_px['px'] = round(df_band_px['px'], round_n)
x_list = df_band_px.index.to_list()
line_band = (
Line().add_xaxis(xaxis_data=x_list).add_yaxis(
series_name='Band1',
y_axis=df_band_px['Band1'].to_list(),
).add_yaxis(
series_name='Band2',
y_axis=df_band_px['Band2'].to_list(),
).add_yaxis(
series_name='BarPx',
y_axis=df_band_px['px'].to_list(),
).set_global_opts(
title_opts=opts.TitleOpts(title='Band', pos_top='10%'),
legend_opts=opts.LegendOpts(pos_top='5%'),
datazoom_opts=opts.DataZoomOpts(is_show=True,
xaxis_index=[0, 1, 2],
pos_top='50%'),
yaxis_opts=opts.AxisOpts(is_scale=True,
splitline_opts=opts.SplitLineOpts(
is_show=True, )),
xaxis_opts=opts.AxisOpts(is_scale=True,
splitline_opts=opts.SplitLineOpts(
is_show=True, )),
# yaxis_opts=opts.AxisOpts(
# max_=y_max+abs((y_max-y_min))*0.15,
# min_=y_min-abs((y_max-y_min))*0.15
# )
))
# 【2】 画Trades
# 数据
df_position = pd.DataFrame()
df_trades = pd.DataFrame(trader_sim_obj.Data.Trades)
list_ticker = df_trades['Ticker'].unique().tolist()
# 用trades 计算 position
# 使用的方法比较复杂，
# 能处理 对有跨交易日持仓的情况；
# 用于显示持仓，而不是成交，因为只是成交的话无法对成交盈亏有所了然
ticker_position = {}
for ticker_i in list_ticker[0]:
ticker_position[ticker_i] = {'Buy': [0], 'Sell': [0], 'netPos': [0]}
df_i_trades = df_trades[df_trades['Ticker'] == ticker_i]
df_i_trades = df_i_trades[[
'DateTime', 'Direction', 'OffsetFlag', 'TradedPrice',
'TradedVolume'
]]
df_i_trades.sort_values(by='DateTime', inplace=True)
l_data = df_i_trades[['OffsetFlag', 'Direction',
'TradedVolume']].to_dict('record')
for d in l_data:
offset_flag = d['OffsetFlag']
direction = d['Direction']
traded_volume = d['TradedVolume']
tv = ((offset_flag == 'Open') * 2 - 1) * traded_volume
td = ['Sell', 'Buy'][int(({
'Buy': 1,
'Sell': -1
}[direction] * ((offset_flag == 'Open') * 2 - 1)) / 2 + 0.5)]
volume0 = {}
volume0['Buy'] = ticker_position[ticker_i]['Buy'][-1]
volume0['Sell'] = ticker_position[ticker_i]['Sell'][-1]
volume0['netPos'] = ticker_position[ticker_i]['netPos'][-1]
if volume0 == 0 and tv < 0:
ticker_position[ticker_i]['Buy'].append(volume0['Buy'])
ticker_position[ticker_i]['Sell'].append(volume0['Sell'])
ticker_position[ticker_i]['netPos'].append(volume0['netPos'])
else:
for bs in ['Buy', 'Sell']:
if bs == td:
ticker_position[ticker_i][bs].append(volume0[bs] + tv)
else:
ticker_position[ticker_i][bs].append(volume0[bs])
ticker_position[ticker_i]['netPos'].append(
ticker_position[ticker_i]['Buy'][-1] -
ticker_position[ticker_i]['Sell'][-1])
# print(df_position, ticker_position[ticker_i])
df_position[ticker_i] = ticker_position[ticker_i]['netPos'][1:]
# position 数据处理， 规整index轴
df_position['DateTime'] = df_trades['DateTime']
df_position.drop_duplicates(subset='DateTime', keep='last', inplace=True)
df_position.set_index('DateTime', inplace=True, drop=True)
df_position_all = df_position.reindex(index=df_band_px.index.to_list(),
method='ffill')
df_position_all.fillna(value=0, inplace=True)
x = df_position_all.index.to_list()
# 画图
# 仅显示 某一个 ticker的 trades和px
# TODO arb（pair成交）的情况，用arbPx图，也是一个图啊
i_ticker = df_position_all.columns.to_list()[0]
y_trades = df_position_all[i_ticker].to_list()
line_trades = (Line().add_xaxis(x).add_yaxis(
series_name=i_ticker,
y_axis=y_trades,
yaxis_index=0,
).set_global_opts(
title_opts=opts.TitleOpts(title='Trades', pos_top='65%'),
legend_opts=opts.LegendOpts(pos_top='60%'),
datazoom_opts=opts.DataZoomOpts(is_show=False, xaxis_index=[0, 1, 2]),
yaxis_opts=opts.AxisOpts(is_scale=True,
splitline_opts=opts.SplitLineOpts(
is_show=True, )),
xaxis_opts=opts.AxisOpts(is_scale=True,
splitline_opts=opts.SplitLineOpts(
is_show=True, )),
))
# ticker 的bar px
df_ticker_i_px = pd.DataFrame(df_bar.loc[df_bar['ticker'] == i_ticker,
['DateTime', 'Open']])
df_ticker_i_px.set_index(keys='DateTime', drop=True, inplace=True)
df_ticker_i_px = df_ticker_i_px.reindex(index=df_band_px.index.to_list(),
method='ffill')
y_px_open = df_ticker_i_px['Open'].to_list()
line_ticker_px = (
Line().add_xaxis(x).add_yaxis(
"价格",
y_px_open,
yaxis_index=1,
).set_global_opts(
title_opts=opts.TitleOpts(title='Px-Open',
pos_top='65%',
pos_right='0%'),
legend_opts=opts.LegendOpts(pos_top='63%'),
datazoom_opts=opts.DataZoomOpts(is_show=False,
xaxis_index=[0, 1, 2]),
# yaxis_opts=opts.AxisOpts(is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True, )),
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(position='right',
is_scale=True,
name='Px'),
))
# 组合gird
grid = (Grid(init_opts=opts.InitOpts(width='1200px', height='800px')).add(
line_band,
grid_opts=opts.GridOpts(pos_top='5%', pos_bottom='50%')).add(
line_trades,
grid_opts=opts.GridOpts(pos_top='55%', pos_bottom='5%')).add(
line_ticker_px,
grid_opts=opts.GridOpts(pos_top='55%', pos_bottom='5%')))
return grid
def kline_pro(kline: List[dict],
fx: List[dict] = None,
bi: List[dict] = None,
xd: List[dict] = None,
bs: List[dict] = None,
title: str = "缠中说禅K线分析",
width: str = "1400px",
height: str = '580px') -> Grid:
"""绘制缠中说禅K线分析结果
:param kline: K线
:param fx: 分型识别结果
:param bi: 笔识别结果
{'dt': Timestamp('2020-11-26 00:00:00'),
'fx_mark': 'd',
'start_dt': Timestamp('2020-11-25 00:00:00'),
'end_dt': Timestamp('2020-11-27 00:00:00'),
'fx_high': 144.87,
'fx_low': 138.0,
'bi': 138.0}
:param xd: 线段识别结果
:param bs: 买卖点
:param title: 图表标题
:param width: 图表宽度
:param height: 图表高度
:return: 用Grid组合好的图表
"""
# 配置项设置
# ------------------------------------------------------------------------------------------------------------------
bg_color = "#1f212d" # 背景
up_color = "#F9293E"
down_color = "#00aa3b"
init_opts = opts.InitOpts(bg_color=bg_color, width=width, height=height, animation_opts=opts.AnimationOpts(False))
title_opts = opts.TitleOpts(title=title, pos_top="1%",
title_textstyle_opts=opts.TextStyleOpts(color=up_color, font_size=20),
subtitle_textstyle_opts=opts.TextStyleOpts(color=down_color, font_size=12))
label_not_show_opts = opts.LabelOpts(is_show=False)
legend_not_show_opts = opts.LegendOpts(is_show=False)
red_item_style = opts.ItemStyleOpts(color=up_color)
green_item_style = opts.ItemStyleOpts(color=down_color)
k_style_opts = opts.ItemStyleOpts(color=up_color, color0=down_color, border_color=up_color,
border_color0=down_color, opacity=0.8)
legend_opts = opts.LegendOpts(is_show=True, pos_top="1%", pos_left="30%", item_width=14, item_height=8,
textstyle_opts=opts.TextStyleOpts(font_size=12, color="#0e99e2"))
brush_opts = opts.BrushOpts(tool_box=["rect", "polygon", "keep", "clear"],
x_axis_index="all", brush_link="all",
out_of_brush={"colorAlpha": 0.1}, brush_type="lineX")
axis_pointer_opts = opts.AxisPointerOpts(is_show=True, link=[{"xAxisIndex": "all"}])
dz_inside = opts.DataZoomOpts(False, "inside", xaxis_index=[0, 1, 2], range_start=80, range_end=100)
dz_slider = opts.DataZoomOpts(True, "slider", xaxis_index=[0, 1, 2], pos_top="96%",
pos_bottom="0%", range_start=80, range_end=100)
yaxis_opts = opts.AxisOpts(is_scale=True,
axislabel_opts=opts.LabelOpts(color="#c7c7c7", font_size=8, position="inside"))
grid0_xaxis_opts = opts.AxisOpts(type_="category", grid_index=0, axislabel_opts=label_not_show_opts,
split_number=20, min_="dataMin", max_="dataMax",
is_scale=True, boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False))
tool_tip_opts = opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
position=JsCode("""
function (pos, params, el, elRect, size) {
var obj = {top: 10};
obj[['left', 'right'][+(pos[0] < size.viewSize[0] / 2)]] = 30;
return obj;
}
"""),
textstyle_opts=opts.TextStyleOpts(color="#000"),
)
# 数据预处理
# ------------------------------------------------------------------------------------------------------------------
dts = [x['dt'] for x in kline]
# k_data = [[x['open'], x['close'], x['low'], x['high']] for x in kline]
k_data = [opts.CandleStickItem(name=i, value=[x['open'], x['close'], x['low'], x['high']])
for i, x in enumerate(kline)]
vol = []
for i, row in enumerate(kline):
item_style = red_item_style if row['close'] > row['open'] else green_item_style
bar = opts.BarItem(name=i, value=row['vol'], itemstyle_opts=item_style, label_opts=label_not_show_opts)
vol.append(bar)
close = np.array([x['close'] for x in kline], dtype=np.double)
diff, dea, macd = MACD(close)
ma5 = SMA(close, timeperiod=5)
ma34 = SMA(close, timeperiod=34)
ma233 = SMA(close, timeperiod=233)
macd_bar = []
for i, v in enumerate(macd.tolist()):
item_style = red_item_style if v > 0 else green_item_style
bar = opts.BarItem(name=i, value=round(v, 4), itemstyle_opts=item_style,
label_opts=label_not_show_opts)
macd_bar.append(bar)
diff = diff.round(4)
dea = dea.round(4)
# K 线主图
# ------------------------------------------------------------------------------------------------------------------
chart_k = Kline()
chart_k.add_xaxis(xaxis_data=dts)
chart_k.add_yaxis(series_name="Kline", y_axis=k_data, itemstyle_opts=k_style_opts)
chart_k.set_global_opts(
legend_opts=legend_opts,
datazoom_opts=[dz_inside, dz_slider],
yaxis_opts=yaxis_opts,
tooltip_opts=tool_tip_opts,
axispointer_opts=axis_pointer_opts,
brush_opts=brush_opts,
title_opts=title_opts,
xaxis_opts=grid0_xaxis_opts
)
# 均线图
# ------------------------------------------------------------------------------------------------------------------
chart_ma = Line()
chart_ma.add_xaxis(xaxis_data=dts)
ma_keys = {"MA5": ma5, "MA34": ma34, "MA233": ma233}
ma_colors = ["#39afe6", "#da6ee8", "#00940b"]
for i, (name, ma) in enumerate(ma_keys.items()):
chart_ma.add_yaxis(series_name=name, y_axis=ma, is_smooth=True,
is_selected=False, symbol_size=0, label_opts=label_not_show_opts,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color=ma_colors[i]))
chart_ma.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_ma)
# 缠论结果
# ------------------------------------------------------------------------------------------------------------------
if fx:
fx_dts = [x['dt'] for x in fx]
fx_val = [x['fx'] for x in fx]
chart_fx = Scatter()
chart_fx.add_xaxis(fx_dts)
chart_fx.add_yaxis(series_name="FX", y_axis=fx_val, is_selected=False,
symbol="circle", symbol_size=6, label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(color="rgba(152, 147, 193, 1.0)", ))
chart_fx.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_fx)
if bi:
bi_dts = [x['dt'] for x in bi]
bi_val = [x['bi'] for x in bi]
chart_bi = Line()
chart_bi.add_xaxis(bi_dts)
chart_bi.add_yaxis(series_name="BI", y_axis=bi_val, is_selected=True,
symbol="diamond", symbol_size=10, label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(color="rgba(184, 117, 225, 1.0)", ),
linestyle_opts=opts.LineStyleOpts(width=1.5))
chart_bi.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_bi)
if xd:
xd_dts = [x['dt'] for x in xd]
xd_val = [x['xd'] for x in xd]
chart_xd = Line()
chart_xd.add_xaxis(xd_dts)
chart_xd.add_yaxis(series_name="XD", y_axis=xd_val, is_selected=True, symbol="triangle", symbol_size=10,
itemstyle_opts=opts.ItemStyleOpts(color="rgba(37, 141, 54, 1.0)", ))
chart_xd.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_xd)
if bs:
b_dts = [x['dt'] for x in bs if x['mark'] == 'buy']
if len(b_dts) > 0:
b_val = [x['price'] for x in bs if x['mark'] == 'buy']
chart_b = Scatter()
chart_b.add_xaxis(b_dts)
chart_b.add_yaxis(series_name="BUY", y_axis=b_val, is_selected=False, symbol="arrow", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="#f31e1e", ))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_b)
s_dts = [x['dt'] for x in bs if x['mark'] == 'sell']
if len(s_dts) > 0:
s_val = [x['price'] for x in bs if x['mark'] == 'sell']
chart_s = Scatter()
chart_s.add_xaxis(s_dts)
chart_s.add_yaxis(series_name="SELL", y_axis=s_val, is_selected=False, symbol="pin", symbol_size=12,
itemstyle_opts=opts.ItemStyleOpts(color="#45b97d", ))
chart_s.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_s)
# 成交量图
# ------------------------------------------------------------------------------------------------------------------
chart_vol = Bar()
chart_vol.add_xaxis(dts)
chart_vol.add_yaxis(series_name="Volume", y_axis=vol, bar_width='60%')
chart_vol.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=True, font_size=8, color="#9b9da9"),
),
yaxis_opts=yaxis_opts, legend_opts=legend_not_show_opts,
)
# MACD图
# ------------------------------------------------------------------------------------------------------------------
chart_macd = Bar()
chart_macd.add_xaxis(dts)
chart_macd.add_yaxis(series_name="MACD", y_axis=macd_bar, bar_width='60%')
chart_macd.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, color="#c7c7c7"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
line = Line()
line.add_xaxis(dts)
line.add_yaxis(series_name="DIFF", y_axis=diff, label_opts=label_not_show_opts, is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color="#da6ee8"))
line.add_yaxis(series_name="DEA", y_axis=dea, label_opts=label_not_show_opts, is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color="#39afe6"))
chart_macd = chart_macd.overlap(line)
grid0_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="12%", height="58%")
grid1_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="74%", height="8%")
grid2_opts = opts.GridOpts(pos_left="0%", pos_right="1%", pos_top="86%", height="10%")
grid_chart = Grid(init_opts)
grid_chart.add(chart_k, grid_opts=grid0_opts)
grid_chart.add(chart_vol, grid_opts=grid1_opts)
grid_chart.add(chart_macd, grid_opts=grid2_opts)
return grid_chart
def line_step() -> Line:
c = (Line().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values(), is_step=True).set_global_opts(
title_opts=opts.TitleOpts(title="Line-阶梯图")))
return c
def line_base() -> Line:
c = (Line().add_xaxis(Faker.choose()).add_yaxis(
"商家A",
Faker.values()).add_yaxis("商家B", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts(title="Line-基本示例")))
return c
def draw_chart(stock_data):
'''
pyecharts V1 版本开始支持链式调用
文档地址 https://pyecharts.org/#/zh-cn/
'''
stock_data.index = pd.to_datetime(stock_data['TIME'], format="%Y/%m/%d")
x = stock_data["TIME"].values.tolist()
stock_basic_data = stock_data[[
"TIME", "OPEN", "CLOSE", "LOW", "HIGH", "VOLUME"
]]
# stock_data = stock_data.sort_index(ascending=True) # 倒序，看时间顺序是否正常决定是不是要用
# k线图
kline = (
Kline().add_xaxis(x).add_yaxis(
"K线图",
stock_basic_data.iloc[:, 1:5].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True, is_show=False),
# axis_opts=opts.AxisOpts(is_scale=True,min_=0), #y轴起始坐标可以设为0
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
), # y轴起始坐标可自动调整
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='20%'),
title_opts=opts.TitleOpts(title="股票价格",
subtitle=ts_code,
pos_left='22%',
pos_top="20%"),
#把所有的x轴连接在一起
# axispointer_opts=opts.AxisPointerOpts(is_show=True,
# link=[{"xAxisIndex": "all"}],
# label=opts.LabelOpts(background_color="#777"),
# ),
datazoom_opts=[ # 设置zoom参数后即可缩放
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1, 2, 3, 4], # 设置第0轴和第1轴同时缩放
range_start=0,
range_end=100,
),
],
))
# 成交量柱形图
bar_volumn = (Bar().add_xaxis(x).add_yaxis(
"成交量",
stock_basic_data["VOLUME"].values.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
title_opts=opts.TitleOpts(title="成交量", pos_left='22%', pos_top="48%"),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='48%'),
))
#绘制均线图
line_ma = (
Line().add_xaxis(x).add_yaxis(
series_name="MA5",
y_axis=stock_data["MA5"].values.tolist(),
is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="MA10",
y_axis=stock_data["MA10"].values.tolist(),
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="MA20",
y_axis=stock_data["MA20"].values.tolist(),
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
title_opts=opts.TitleOpts(title="MA",
pos_left='22%',
pos_top="88%"),
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
is_scale=True),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='85%'),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
#绘制jdk线
line_kdj = (Line().add_xaxis(x).add_yaxis(
series_name="K",
y_axis=stock_data["K"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="D",
y_axis=stock_data["D"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="J",
y_axis=stock_data["J"].values.tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
title_opts=opts.TitleOpts(title="KDJ", pos_left='22%', pos_top="62%"),
datazoom_opts=[opts.DataZoomOpts(type_="inside", )],
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="7%",
pos_top='60%'),
xaxis_opts=opts.AxisOpts(
is_scale=True,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
#绘制macd柱状图
bar_macd = (Bar().add_xaxis(x).add_yaxis(
series_name='MACD',
yaxis_data=stock_data['MACD'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
title_opts=opts.TitleOpts(title="MACD", pos_left='22%', pos_top="75%"),
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='70%'),
))
#绘制DIF和DEA
line_macd = (Line().add_xaxis(x).add_yaxis(
series_name="DIF",
y_axis=stock_data['DIF'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="DEA",
y_axis=stock_data['DEA'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
#绘制布林线
line_boll = (Line().add_xaxis(x).add_yaxis(
series_name="MID",
y_axis=stock_data["MID"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="UPPER",
y_axis=stock_data["UPPER"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="LOWER",
y_axis=stock_data["LOWER"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
datazoom_opts=[opts.DataZoomOpts(type_="inside", )],
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
overlap_kline_linema = kline.overlap(line_boll)
overlap_macd = bar_macd.overlap(line_macd)
# 使用网格将多张图标组合到一起显示
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
# 为了把 data.datas 的数据写入到html中作为全局变量,目前无法跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(
stock_basic_data.iloc[:, 1:5].values.tolist()))
grid_chart.add(
overlap_kline_linema,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="3%",
height="40%"),
)
grid_chart.add(
bar_volumn,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="45%",
height="10%"),
)
grid_chart.add(line_kdj,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="60%",
height="10%"))
grid_chart.add(overlap_macd,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="72%",
height="10%"))
grid_chart.add(line_ma,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="85%",
height="10%"))
grid_chart.render('stock_{}/stock_{}.html'.format(
ts_code, ts_code)) # 保存成用股票代码命名的文档
return 0
(Line().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="QPS",
# stack="总量",
y_axis=[0, 6512.09, 11408.77, 17198.11, 21451.15, 26426.46, 25311.42],
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=True),
).add_yaxis(
series_name="请求总量",
# stack="总量",
y_axis=[0, 3908017, 6845695, 10320806, 12871444, 15867653, 15188519],
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=True),
).add_yaxis(
series_name="TPS",
# stack="总量",
y_axis=[0, 6512.09, 11408.77, 17198.11, 21451.15, 26426.46, 25311.42],
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=True),
).add_yaxis(
series_name="事物总量",
# stack="总量",
y_axis=[0, 3908017, 6845695, 10320806, 12871444, 15867653, 15188519],
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=True),
).set_global_opts(
title_opts=opts.TitleOpts(title="oltp_point_select-索引查询"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
).render("oltp_point_select.html"))
)
.set_global_opts(legend_opts=opts.LegendOpts(pos_left="legft", orient="vertical"))
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
),
label_opts=opts.LabelOpts(formatter="{b}: {c}")
)
)
pie.render_notebook()
# 折线图
line = (
Line()
.add_xaxis(x)
.add_yaxis('china', data_china)
.set_global_opts(title_opts=opts.TitleOpts(title="Line - 基本示例"))
)
line.render_notebook()
line = (
Line()
.add_xaxis(x)
.add_yaxis('china', data_china)
.add_yaxis('russis', data_russia)
.set_global_opts(title_opts=opts.TitleOpts(title="Line - 双折线图"))
)
line.render_notebook()
Line()
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="邮件营销",
stack="总量",
y_axis=[120, 132, 101, 134, 90, 230, 210],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="联盟广告",
stack="总量",
y_axis=[220, 182, 191, 234, 290, 330, 310],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="视频广告",
stack="总量",
y_axis=[150, 232, 201, 154, 190, 330, 410],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="直接访问",
stack="总量",
y_axis=[320, 332, 301, 334, 390, 330, 320],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="搜索引擎",
stack="总量",
y_axis=[820, 932, 901, 934, 1290, 1330, 1320],
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="折线图堆叠"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
.render("stacked_line_chart.html")
min_=0,
max_=250,
position="right",
offset=80,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#5793f3")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
),
title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
)
)
line = (
Line()
.add_xaxis(x_data)
.add_yaxis(
"平均温度",
[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
)
)
bar.overlap(line)
grid = Grid()
grid.add(bar, opts.GridOpts(pos_left="5%", pos_right="20%"), is_control_axis_index=True)
grid.render("grid_multi_yaxis.html")
[(0, 730), (1, 1471), (2, 1768), (3, 3914), (4, 5929), (5, 15821),
(6, 34954), (7, 76019), (8, 69751), (9, 21495), (10, 2288)],
sort_="ascending",
label_opts=opts.LabelOpts(position="inside"),
).set_global_opts(title_opts=opts.TitleOpts(title="用户等级分布")))
# 歌单数和粉丝数的关系
d = (Scatter().add_xaxis([i for i in range(0, 13)]).add_yaxis(
"粉丝数", [j for j in range(100, 1300, 100)]).set_global_opts(
title_opts=opts.TitleOpts(title="歌单数和粉丝数关系")))
# 男女粉丝的粉丝数TOP10
ee = (Line().add_xaxis([
str(i) for i in range(1, 11)
]).add_yaxis("男性", [
2331948, 436372, 328108, 254534, 146967, 78094, 76079, 67013, 65588, 63676
]).add_yaxis(
"女性",
[273930, 207547, 138289, 132198, 88809, 47306, 39272, 36877, 35043, 34579
]).set_global_opts(title_opts=opts.TitleOpts(title="男女粉丝的粉丝数TOP20")))
e = (Line().add_xaxis(
[i for i in range(1, 9)]).add_yaxis("A", Faker.values()).add_yaxis(
"B",
Faker.values()).set_global_opts(title_opts=opts.TitleOpts(title="测试")))
# e.render('mycharts.html')
# ee.render('test.html')
# make_snapshot(snapshot, pie.render(), "pie.png")
pemysql.gender(5771)
pemysql.level(5771)
def to_grid(ka,
kline_mode: str = "new",
with_bi: bool = False,
with_xd: bool = False,
with_zs: bool = False,
with_bs: bool = False,
with_ma: bool = False,
with_vol: bool = False,
with_macd: bool = False,
title: str = "ChanLun In Practise",
width: str = "1440px",
height: str = '900px') -> Grid:
"""绘制缠中说禅K线分析结果
:param kline: K线 new / raw，new标识标准化后的k线，raw标识原始k线
:param with_bi: 是否显示笔识别结果，默认True，不输出False
:param with_xd: 是否显示线段识别结果
:param with_zs: 是否显示中枢识别结果
:param with_bs: 是否显示买卖点
:param with_ma: 是否显示均线，默认True，不输出False
:param with_macd: 是否显示macd，默认True，不输出False
:param with_vol: 是否显示成交量，默认True，不输出False
:param title: 图表标题
:param width: 图表宽度
:param height: 图表高度
:return: 用Grid组合好的图表
"""
# 配置项设置
# ------------------------------------------------------------------------------------------------------------------
bg_color = "#1f212d" # 背景
up_color = "#F9293E"
down_color = "#00aa3b"
init_opts = opts.InitOpts(bg_color=bg_color, width='1400px', height='800px')
title_opts = opts.TitleOpts(title=title, pos_top="1%",
title_textstyle_opts=opts.TextStyleOpts(color=up_color, font_size=20),
subtitle_textstyle_opts=opts.TextStyleOpts(color=down_color, font_size=12))
label_not_show_opts = opts.LabelOpts(is_show=False)
legend_not_show_opts = opts.LegendOpts(is_show=False)
red_item_style = opts.ItemStyleOpts(color=up_color)
green_item_style = opts.ItemStyleOpts(color=down_color)
k_style_opts = opts.ItemStyleOpts(color=up_color, color0=down_color, border_color=up_color,
border_color0=down_color, opacity=0.8)
legend_opts = opts.LegendOpts(is_show=True, pos_top="1%", pos_left="30%", item_width=14, item_height=8,
textstyle_opts=opts.TextStyleOpts(font_size=12, color="#0e99e2"))
brush_opts = opts.BrushOpts(tool_box=["rect", "polygon", "keep", "clear"],
x_axis_index="all", brush_link="all",
out_of_brush={"colorAlpha": 0.1}, brush_type="lineX")
axis_pointer_opts = opts.AxisPointerOpts(is_show=True, link=[{"xAxisIndex": "all"}])
dz_inside = opts.DataZoomOpts(False, "inside", xaxis_index=[0, 1, 2])
dz_slider = opts.DataZoomOpts(True, "slider", xaxis_index=[0, 1, 2], pos_top="96%", pos_bottom="0%")
yaxis_opts = opts.AxisOpts(is_scale=True, axislabel_opts=opts.LabelOpts(color="#c7c7c7", font_size=8, position="inside"))
grid0_xaxis_opts = opts.AxisOpts(type_="category", grid_index=0, axislabel_opts=label_not_show_opts,
split_number=20, min_="dataMin", max_="dataMax",
is_scale=True, boundary_gap=False, axisline_opts=opts.AxisLineOpts(is_on_zero=False))
tool_tip_opts = opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
)
# 数据预处理
# ------------------------------------------------------------------------------------------------------------------
kline = ka.kline_new if kline_mode == 'new' else ka.kline_raw
dts = [x['dt'] for x in kline]
k_data = [[x['open'], x['close'], x['low'], x['high']] for x in kline]
# seriesname
aggregation = len(ka.ka_list)>0
# {'freq': {'kline', 'ma', 'vol', 'macd'}}
agg_dict = {}
agg_dict[ka.freq] = {
"kline": k_data,
"ma": ka.ma,
"vol": kline,
"macd": ka.macd
}
if aggregation:
for high_ka in ka.ka_list:
agg_dict[high_ka.freq] = {}
_high_k_data = high_ka.kline_new if kline_mode == 'new' else high_ka.kline_raw
high_k_data = []
high_ma_data = []
high_vol_data = []
high_macd_data = []
for i in range(len(dts)):
high_k_data.append([])
high_ma_data.append({})
high_vol_data.append(None)
high_macd_data.append(None)
for ki in range(len(kline)):
k = kline[ki]
_start = 0
for h_ki in range(_start, len(_high_k_data)):
h_k = _high_k_data[h_ki]
if (h_k['dt'] == k['dt']):
high_k_data[ki] = [h_k['open'], h_k['close'], h_k['low'], h_k['high']]
high_ma_data[ki] = high_ka.ma[h_ki]
high_vol_data[ki] = h_k
high_macd_data[ki] = high_ka.macd[h_ki]
_start = h_ki+1
break
agg_dict[high_ka.freq] = {
"kline": high_k_data,
"ma": high_ma_data,
"vol": high_vol_data,
"macd": high_macd_data
}
# K 线主图
# ------------------------------------------------------------------------------------------------------------------
chart_k = Kline()
chart_k.add_xaxis(xaxis_data=dts)
for k, v in agg_dict.items():
chart_k.add_yaxis(series_name=k if aggregation else 'kline', y_axis=v['kline'], itemstyle_opts=k_style_opts)
is_selected = False
chart_k.set_global_opts(
legend_opts=legend_opts,
datazoom_opts=[dz_inside, dz_slider],
yaxis_opts=yaxis_opts,
tooltip_opts=tool_tip_opts,
axispointer_opts=axis_pointer_opts,
brush_opts=brush_opts,
title_opts=title_opts,
xaxis_opts=grid0_xaxis_opts,
)
if with_ma:
# 均线图
# ------------------------------------------------------------------------------------------------------------------
ma_keys = [x for x in agg_dict[ka.freq]['ma'][0].keys() if "ma" in x][:3]
ma_colors = ["#39afe6", "#da6ee8", "#00940b"]
chart_ma = Line()
chart_ma.add_xaxis(xaxis_data=dts)
for key, vals in agg_dict.items():
for i, k in enumerate(ma_keys):
y_data = [x[k] if k in x else None for x in v['ma']]
chart_ma.add_yaxis(series_name=key if aggregation else k.upper(), y_axis=y_data, is_smooth=True,
is_selected=True, is_connect_nones=True, symbol_size=0, label_opts=label_not_show_opts,
linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color=ma_colors[i]))
chart_ma.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_ma)
# 缠论结果
# ------------------------------------------------------------------------------------------------------------------
def __draw_bi_line(_ka):
bi_dts = [x['dt'] for x in _ka.bi_list]
bi_val = [x['bi'] for x in _ka.bi_list]
chart_bi = Line()
chart_bi.add_xaxis(bi_dts)
chart_bi.add_yaxis(series_name=_ka.freq if aggregation else "BI", y_axis=bi_val, is_selected=True,
symbol="diamond", symbol_size=10, label_opts=label_not_show_opts,)
chart_bi.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
return chart_bi
if with_bi:
chark_k = chart_k.overlap(__draw_bi_line(ka))
for _ka in ka.ka_list:
chark_k = chart_k.overlap(__draw_bi_line(_ka))
def __draw_xd_line(_ka):
xd_dts = [x['dt'] for x in _ka.xd_list]
xd_val = [x['xd'] for x in _ka.xd_list]
chart_xd = Line()
chart_xd.add_xaxis(xd_dts)
chart_xd.add_yaxis(series_name=_ka.freq if aggregation else "XD", y_axis=xd_val, is_selected=True, symbol="triangle", symbol_size=10,)
chart_xd.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
return chart_xd
if with_xd:
chark_k = chart_k.overlap(__draw_xd_line(ka))
for _ka in ka.ka_list:
chark_k = chart_k.overlap(__draw_xd_line(_ka))
def __draw_zs_area(_ka):
lines = []
scatters = []
for _zs in _ka.zs_list:
x_start = _zs['start_point']['dt']
if _zs['zs_finished']:# 中枢完成
x_end = _zs['end_point']['dt']
chart_b = EffectScatter()
if 'buy3' in _zs:
chart_b.add_xaxis([_zs['buy3']['dt']])
chart_b.add_yaxis(series_name=_ka.freq if aggregation else "B", y_axis=[_zs['buy3']['xd']], is_selected=False, symbol="circle", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="red",))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
scatters.append(chart_b)
elif 'sell3' in _zs:
chart_b.add_xaxis([_zs['sell3']['dt']])
chart_b.add_yaxis(series_name=_ka.freq if aggregation else "S", y_axis=[_zs['sell3']['xd']], is_selected=False, symbol="circle", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="green",))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
scatters.append(chart_b)
elif len(_zs['points'])>=5:# 中枢成立但未完成，有3笔或段以上
x_end = _zs['points'][-1]['dt']
else: # 中枢未完成，且未确定
continue
ZD = _zs['ZD']
ZG = _zs['ZG']
area_data=[[ {'xAxis': x_start, 'yAxis': ZD, 'value': ZD }, {'xAxis': x_end, 'yAxis': ZG, 'value': ZG }]]
line = (Line()
.add_xaxis([x_start, x_end])
.add_yaxis(series_name=_ka.freq if aggregation else "ZS", y_axis=[ZD, ZG], symbol='none'
"""
, markpoint_opts = opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="ZG"),
opts.MarkPointItem(type_="min", name="ZD"),
]
)
"""
, markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(
position="middle", color="blue", font_size=15,
),
linestyle_opts=opts.LineStyleOpts(type_="dashed"),
data=area_data,
symbol=["none", "none"],
)
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(data=area_data, itemstyle_opts=opts.ItemStyleOpts(color="#dcdcdc",opacity=0.1))
))
lines.append(line)
return lines, scatters
if with_zs:
_areas, _scatters = __draw_zs_area(ka)
for _ka in ka.ka_list:
_a, _s = __draw_zs_area(_ka)
_areas.extend(_a)
_scatters.extend(_s)
for _area in _areas:
chark_k = chart_k.overlap(_area)
for _scatter in _scatters:
chark_k = chart_k.overlap(_scatter)
# if with_vol:
# 成交量图
# ------------------------------------------------------------------------------------------------------------------
chart_vol = Bar()
chart_vol.add_xaxis(dts)
for k, v in agg_dict.items():
vol = []
for row in v['vol']:
if not row:
row = {'close': 0, 'open': 0, 'vol': 0}
item_style = red_item_style if row['close'] > row['open'] else green_item_style
bar = opts.BarItem(name=None, value=row['vol'], itemstyle_opts=item_style, label_opts=label_not_show_opts)
vol.append(bar)
chart_vol.add_yaxis(series_name=k if aggregation else "Volume", y_axis=vol, bar_width='60%')
chart_vol.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=True, font_size=8, color="#9b9da9"),
),
yaxis_opts=yaxis_opts, legend_opts=legend_not_show_opts,
)
#if with_macd:
# MACD图
# ------------------------------------------------------------------------------------------------------------------
chart_macd = Bar()
chart_macd.add_xaxis(dts)
for k, v in agg_dict.items():
macd_bar = []
for row in v['macd']:
if not row:
row = {'macd': 0}
item_style = red_item_style if row['macd'] > 0 else green_item_style
bar = opts.BarItem(name=None, value=round(row['macd'], 4), itemstyle_opts=item_style, label_opts=label_not_show_opts)
macd_bar.append(bar)
chart_macd.add_yaxis(series_name=k if aggregation else "MACD", y_axis=macd_bar, bar_width='60%')
chart_macd.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, color="#c7c7c7"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
line = Line()
line.add_xaxis(dts)
for k, v in agg_dict.items():
macd = v['macd']
diff = [round(x['diff'], 4) if x else None for x in macd]
dea = [round(x['dea'], 4) if x else None for x in macd]
line.add_yaxis(series_name=k if aggregation else "DIFF", y_axis=diff, label_opts=label_not_show_opts, is_symbol_show=False
, is_connect_nones=True, linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color="#EDCB89"))
line.add_yaxis(series_name=k if aggregation else "DEA", y_axis=dea, label_opts=label_not_show_opts, is_symbol_show=False
, is_connect_nones=True, linestyle_opts=opts.LineStyleOpts(opacity=0.8, width=1.0, color="#FFFFFF"))
chart_macd = chart_macd.overlap(line)
grid0_opts = opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="12%", height="58%")
grid1_opts = opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="74%", height="8%")
grid2_opts = opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="86%", height="10%")
grid_chart = Grid(init_opts)
grid_chart.add(chart_k, grid_opts=grid0_opts)
grid_chart.add(chart_vol, grid_opts=grid1_opts)
grid_chart.add(chart_macd, grid_opts=grid2_opts)
return grid_chart
def _create_line():
return Line().add_xaxis([
'Sunday', 'Saturday', 'Friday', 'Thursday', 'Wednesday', 'Tuesday',
'Monday'
]).add_yaxis("商家A", [7, 6, 5, 4, 3, 2, 1])
def picture1(self):
df = pd.read_sql(
f"select * from {self.table_name} where 检查时间 LIKE '%{self.last_month}%'",
self.conn)
df['time'] = pd.to_datetime(df['检查时间'])
df['time'] = df['time'].apply(lambda x: x.strftime('%Y%m%d'))
ya = []
for i in df['time'].unique():
i = str(i)
try:
bkk = df[df['time'] == i]['性质'].value_counts()['A类']
ya.append(str(bkk))
except:
bkk = 0
ya.append(str(bkk))
# print(ya, len(ya))
yb = []
for i in df['time'].unique():
i = str(i)
try:
bkk = df[df['time'] == i]['性质'].value_counts()['B类']
yb.append(str(bkk))
except:
bkk = 0
yb.append(str(bkk))
# print(yb, len(yb))
yc = []
for i in df['time'].unique():
i = str(i)
try:
bkk = df[df['time'] == i]['性质'].value_counts()['C类']
yc.append(str(bkk))
print(yc)
except:
bkk = 0
yc.append(str(bkk))
print(yc, len(yc))
yd = []
for i in df['time'].unique():
i = str(i)
try:
bkk = df[df['time'] == i]['性质'].value_counts()['D类']
yd.append(str(bkk))
except:
bkk = 0
yd.append(str(bkk))
x = df['time'].unique()
# chart = picture(x,ya,yb,yc,yd)
a = []
for i in x:
a.append(str(i))
c = (
Line(init_opts=opts.InitOpts(
page_title=f"现场安全信息性质分布(月)")).add_xaxis(a).add_yaxis(
"A类", ya).add_yaxis("B类", yb).add_yaxis("C类", yc).
add_yaxis("D类", yd).set_global_opts(
title_opts=opts.TitleOpts(title=f"现场安全信息性质分布(月)",
subtitle=f"统计时间：{self.last_month}"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=45), name="日期"),
# toolbox_opts=opts.ToolBoxFeatureSaveAsImageOpts(background_color= "auto",type_='jpg'),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature={
"saveAsImage": {
'backgroundColor':
'white'
},
"dataZoom": {},
"restore": {},
"magicType": {
"show": True,
"type": ["line", "bar"]
},
"dataView": {}
}),
))
print('已画图成功')
return c
def draw_line_with_two_y(xaxis_data, y1_series_name, y1_axis, y2_series_name,
y2_axis, y3_series_name, y3_axis, y1_name, y2_name,
to_file, unit, svg_name):
"""
左边是 两条线； 右边是 一条线。
:param svg_name: svg文件
:param xaxis_data: 横轴
:param y1_series_name: 左边第一条线的系列名
:param y1_axis: 左边第一条线的数值
:param y2_series_name: 左边第二条线的系列名
:param y2_axis: 左边第二条线的数值
:param y3_series_name: 右边第一条线的系列名
:param y3_axis: 右边第一条线的数值
:param y1_name: 左边的纵轴名称
:param y2_name: 右边的纵轴名称
:param to_file: 结果文件
:param unit: 横轴的数值的单位（加在每个横轴值后面，比如 月）
:return:
"""
xaxis_data = [str(i) + unit for i in xaxis_data]
bar = Line(init_opts=opts.InitOpts(
width="800px", height="600px", bg_color='white'
)).add_xaxis(
xaxis_data=xaxis_data,
# add_xaxis=opts.LabelOpts(formatter="{value}" + unit),
).add_yaxis(
series_name=y1_series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y1_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
series_name=y2_series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y2_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
series_name=y3_series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y3_axis,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).extend_axis(yaxis=opts.AxisOpts(
type_="value",
name=y2_name,
# min_=0,
# max_=25,
position="right",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=15),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)),
name_textstyle_opts=opts.TextStyleOpts(font_size=15))).set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="value",
name=y1_name,
# min_=0,
# max_=250,
position="left",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}",
font_size=15),
name_textstyle_opts=opts.TextStyleOpts(font_size=15)),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}", font_size=15), ),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
legend_opts=opts.LegendOpts(
item_width=25,
item_height=10,
textstyle_opts=opts.TextStyleOpts(font_size=15)),
)
make_snapshot(snapshot, bar.render(to_file), svg_name) # 生成svg图片
def Line_ele(name, model_names, datas, y_name, y_max):
x = [n for n in range(len(datas[0]))]
line = Line(init_opts=opts.InitOpts(bg_color='rgba(255,250,205,0.2)',
width='1500px',
height='600px',
page_title=name))
line.add_xaxis(xaxis_data=x)
average = []
for i in range(len(datas)):
sum = 0
num = 0
y = [n[0] for n in datas[i]]
line.add_yaxis(series_name=model_names[i],
y_axis=y,
symbol="arrow",
is_symbol_show=False)
for j in datas[i]:
sum += j[0]
num = num + 1 if j[0] > 0 else num
average.append(sum / num)
print(sum, num, average[i])
line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="max", name="最大值"),
]),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(name="multi", y=average[0]),
opts.MarkLineItem(name="multis", y=average[1]),
opts.MarkLineItem(name="plans", y=average[2])
]),
)
line.set_global_opts(
title_opts=opts.TitleOpts(title=name),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=30),
name='TEST_PICS'),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=30),
name=y_name,
min_=0,
max_=y_max),
legend_opts=opts.LegendOpts(textstyle_opts=opts.TextStyleOpts(
font_size=30)))
util.mkdir('./analysis')
line.render('./analysis/' + name + '.html')
def draw_line_picture(xaxis_data, data, to_file, unit, svg_name, stack,
y_name):
"""
多条线，是否堆叠要确认下
:param label_right:
:param y_name: y轴名称
:param stack: boolean, 是否要堆叠
:param xaxis_data: x轴
:param data: {series_name, data}
:param svg_name: svg文件
:param to_file: 结果文件
:param unit: 横轴的数值的单位（加在每个横轴值后面，比如 月）
:return:
"""
xaxis_data = [str(i) + unit for i in xaxis_data]
bar = Line(init_opts=opts.InitOpts(
width="800px", height="600px", bg_color='white')).add_xaxis(
xaxis_data=xaxis_data,
# add_xaxis=opts.LabelOpts(formatter="{value}" + unit),
)
for series_name, y_axis in data.items():
if stack:
bar = bar.add_yaxis(
series_name=series_name,
is_smooth=True,
symbol="circle",
# symbol_size=8,
stack='1',
is_symbol_show=False,
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
# linestyle_opts=opts.LineStyleOpts(width=2),
areastyle_opts=opts.AreaStyleOpts(opacity=1))
else:
bar = bar.add_yaxis(
series_name=series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
)
bar = bar.set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="value",
name=y_name,
name_textstyle_opts=opts.TextStyleOpts(font_size=25),
# min_=0,
# max_=250,
position="left",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}", font_size=25),
),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}", font_size=25, interval=0), ),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
)),
legend_opts=opts.LegendOpts(
pos_left="12%",
pos_top="10%",
orient="vertical",
# backgroundColor='rgb(255, 255, 255)',
item_width=40,
item_height=20,
textstyle_opts=opts.TextStyleOpts(font_size=25)),
)
make_snapshot(snapshot, bar.render(to_file), svg_name) # 生成svg图片
def line_smooth() -> Line:
c = (Line().add_xaxis(b).add_yaxis(
"住宅", [
len(z4),
len(z6),
len(z8),
len(z10),
len(z12),
len(z14),
len(z16),
len(z18),
len(z20),
len(z22),
len(z24),
len(z26),
len(z28),
len(z30),
len(z40)
],
is_smooth=True).add_yaxis(
"商业", [
len(s4),
len(s6),
len(s8),
len(s10),
len(s12),
len(s14),
len(s16),
len(s18),
len(s20),
len(s22),
len(s24),
len(s26),
len(s28),
len(s30),
len(s40)
],
is_smooth=True).add_yaxis(
"写字楼", [
len(x4),
len(x6),
len(x8),
len(x10),
len(x12),
len(x14),
len(x16),
len(x18),
len(x20),
len(x22),
len(x24),
len(x26),
len(x28),
len(x30),
len(x40)
],
is_smooth=True).add_yaxis(
"别墅", [
len(b4),
len(b6),
len(b8),
len(b10),
len(b12),
len(b14),
len(b16),
len(b18),
len(b20),
len(b22),
len(b24),
len(b26),
len(b28),
len(b30),
len(b40)
],
is_smooth=True).set_global_opts(title_opts=opts.TitleOpts(
title="各区域各区间房价房源数量")))
return c
def draw_line_picture_right_legend(xaxis_data,
data,
to_file,
unit,
svg_name,
stack,
y_name,
pos_right,
width='800px'):
"""
多条线，是否堆叠要确认下
:param width:
:param pos_right:
:param y_name: y轴名称
:param stack: boolean, 是否要堆叠
:param xaxis_data: x轴
:param data: {series_name, data}
:param svg_name: svg文件
:param to_file: 结果文件
:param unit: 横轴的数值的单位（加在每个横轴值后面，比如 月）
:return:
"""
xaxis_data = [str(i) + unit for i in xaxis_data]
bar = Line(init_opts=opts.InitOpts(
width="800px", height="600px", bg_color='white')).add_xaxis(
xaxis_data=xaxis_data,
# add_xaxis=opts.LabelOpts(formatter="{value}" + unit),
)
for series_name, y_axis in data.items():
if stack:
bar = bar.add_yaxis(
series_name=series_name,
is_smooth=True,
symbol="circle",
# symbol_size=8,
stack='1',
is_symbol_show=False,
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
# linestyle_opts=opts.LineStyleOpts(width=2),
areastyle_opts=opts.AreaStyleOpts(opacity=1))
else:
bar = bar.add_yaxis(
series_name=series_name,
is_smooth=True,
symbol="circle",
symbol_size=8,
is_symbol_show=True,
# color="#d14a61",
y_axis=y_axis,
yaxis_index=0,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
)
bar = bar.set_global_opts(
yaxis_opts=opts.AxisOpts(
type_="value",
name=y_name,
# min_=0,
# max_=250,
position="left",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts()),
axislabel_opts=opts.LabelOpts(formatter="{value}"),
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
# toolbox_opts=opts.ToolboxOpts(
# feature=opts.ToolBoxFeatureOpts(
# data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
# # brush=opts.ToolBoxFeatureBrushOpts(is_show=False),
# )
# ),
legend_opts=opts.LegendOpts(pos_right='10%',
pos_top="2%",
orient="vertical"),
)
grid = (Grid(init_opts=opts.InitOpts(
width=width, height="600px", bg_color='rgb(255, 255, 255)')).add(
bar,
grid_opts=opts.GridOpts(pos_right=pos_right),
is_control_axis_index=True))
grid.render(to_file)
to_svg(to_file, svg_name)
category_gap='30%')
blue_bar.set_global_opts(
title_opts=opts.TitleOpts(title='大乐透彩票', subtitle='开奖至今数据'),
# toolbox_opts=opts.ToolboxOpts(),
# datazoom_opts=opts.DataZoomOpts(),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}/次")),
xaxis_opts=opts.AxisOpts(name='开奖号码',
axislabel_opts=opts.LabelOpts(rotate=-90)))
blue_bar.set_series_opts(markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='最大值'),
opts.MarkPointItem(type_='min', name='最小值')
]))
blue_line = Line(init_opts=opts.InitOpts(width="1800px", height="800px"))
blue_line.set_global_opts(
title_opts=opts.TitleOpts(title="篮球"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
datazoom_opts=opts.DataZoomOpts(),
xaxis_opts=opts.AxisOpts(type_="category",
boundary_gap=False,
name="开奖期数",
axislabel_opts=opts.LabelOpts(rotate=-90)),
)
blue_line.add_xaxis(xaxis_data=blue_num)
blue_line.add_yaxis(
series_name="篮球号码",
y_axis=blue_balls,
symbol="emptyCircle",
is_symbol_show=True,
import pyecharts.options as opts
from pyecharts.charts import Line
"""
Gallery 使用 pyecharts 1.1.0
参考地址: https://echarts.baidu.com/examples/editor.html?c=line-simple
目前无法实现的功能:
暂无
"""
x_data = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
y_data = [820, 932, 901, 934, 1290, 1330, 1320]
(Line().set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="",
y_axis=y_data,
symbol="emptyCircle",
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
).render("render/basic_line_chart.html"))
