def corelationMacro(self,dat_cursor=None,lst_tscode=['399002.SZ'],lst_macro=['shibor.on','PIMon.cpi'],lst_findmedian=['pe',()],renderfile=False) -> Line:
lst_findmedian[1] = tuple(self.da.df_stockTbl['ts_code']) if lst_findmedian[1] is None else lst_findmedian[1]
if dat_cursor is None:
dat_cursor = datetime.now().astimezone(timezone(cfg.STR_TIMEZONE))
int_latestViewDate = self.da.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )
df_baseCal = self.da.df_calTbl.loc[self.da.df_calTbl['cal_date']<=int_latestViewDate]['cal_date'].copy()
lst_xaxis = ['{}/{}/{}'.format(i[:4],i[4:6],i[6:]) for i in df_baseCal.values.astype('str')]
linechart = (
Line()
.add_xaxis(lst_xaxis)
)
for code in lst_tscode:
str_tscode = DataAgent.formatCode(code)
df_target = self.da.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)
if len(df_target)>0:
df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')
else:
continue
linename = '{} - {}'.format(str_tscode,self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['name'].values[0])
lst_values = list(df_toPlot['close'])
lst_values = list(Analytics.normByRange(lst_values))
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),
itemstyle_opts=opts.ItemStyleOpts(
color="#00da3c",
),
)
try:
lst_pe = df_toPlot['pe'].values.tolist()
lst_pb = df_toPlot['pb'].values.tolist()
lst_pepb = [round(lst_pe[index]/value,2) for index, value in enumerate(lst_pb) if value!=0]
linechart.add_yaxis(str_tscode+"_PE", lst_pe, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
linechart.add_yaxis(str_tscode+"_PE/PB", lst_pepb, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
except:
continue
df_shibor = self.da.query('load_shibor')
# print('Supported shibor columns: {}'.format(df_shibor.columns))
df_pimon = self.da.query('load_piMon')
# print('Supported macro eco columns: {}'.format(df_pimon.columns))
for item in lst_macro:
try:
topic,colname = item.split('.')
except:
print('WARNING! input code {} not understood...'.format(item))
if topic=='shibor':
df_target = df_shibor
maxarr = df_target.max(axis=0, skipna=True).values[1:]
minarr = df_target.min(axis=0, skipna=True).values[1:]
deltaRng = max(maxarr)-min(minarr) # overwrite the normalization function delta to put all shibor rates comparable
elif topic=='pimon':
df_target = df_pimon
deltaRng = None
else:
print('WARNING! subject code {} is not understood...'.format(topic))
if len(df_target)>0:
df_toPlot = pd.merge(df_baseCal, df_target, how='left', left_on='cal_date', right_on='date')
df_toPlot[colname] = df_toPlot[colname].bfill()
else:
continue
linename = item
lst_values = list(df_toPlot[colname])
lst_values = list(Analytics.normByRange(lst_values,delta=deltaRng))
linechart.add_yaxis(linename, lst_values, # 所有等比例缩放的折线都放在linechart里，使用左侧y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1,opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
str_cat = lst_findmedian[0]
df_result = pd.DataFrame()
for str_tscode in lst_findmedian[1]:
try:
df = self.da.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)[['ts_code','trade_date',str_cat]]
print('{} csv file loaded for {} trend analysis...'.format(str_tscode,str_cat),end='\r')
except Exception as e:
print('WARNING! {} csv file not found...'.format(str_tscode))
continue
df_result = pd.concat([df_result,df])
df_result.dropna(subset=[str_cat],inplace=True)
df_result = df_result.loc[df_result['trade_date']>19940301]
# libs.df_csv(cfg.PATH_BUGFILE,(df_result.groupby('trade_date')['ts_code'].count(),))
# print(df_result.groupby('trade_date')['ts_code'].count())
df_target = df_result.groupby('trade_date')[str_cat].median()
if len(df_target)>0:
df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')
linename = '{}-median'.format(str_cat)
lst_values = list(df_toPlot[str_cat])
lst_values = list(lst_values)
linechart.add_yaxis(linename, lst_values, yaxis_index=1,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
linechart.extend_axis( # 所有保持原比例的折线都放在linechart2里，使用右侧y轴
yaxis=opts.AxisOpts(
name="倍数",
type_="value",
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="black")
),
axislabel_opts=opts.LabelOpts(formatter="{value}倍"),
)
)
linechart.set_global_opts(
title_opts=opts.TitleOpts(
title='宏观比较',
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(name="等比例",
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",
),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'macro')
linechart.render(fname)
else:
pass
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'macro')
linechart.render(fname) if renderfile else None
self.macrochart = linechart #输出至可访问对象属性中
return self
def chart():
kline=(
Kline()
.add_xaxis(stock_date)
.add_yaxis("Kline", stock_price)
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
# 顯示分割
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
# 圖例位置
legend_opts=opts.LegendOpts(is_show=True, pos_top=10, pos_left="center"),
# DataZoom slider
datazoom_opts=[opts.DataZoomOpts(is_show=False, type_="inside",
xaxis_index=[0, 1], range_start=0, range_end=100, ),
opts.DataZoomOpts(is_show=True, xaxis_index=[0, 1], type_="slider",
pos_top="90%", range_start=0, 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")
),
# axispointer_opts.AxisPointerOpts(is_show=True,link=[{"xAxisIndex":"all"}],
# label=opts.LabelOpts(background_color=''#777'),),
# 區域選擇組件
# brush_opts=opts.BrushOpts(#指定所有數列對應的座標系x_axis_index="all",#指定哪些 series
# 可以被聯動brush_link="all",#定義顏色透明度out_of_brush={"colorAlpha":0.1},brush_type="lineX",),
)
)
line=(
Line()
.add_xaxis(stock_date)
.add_yaxis("10日移動平均線", sma_10, is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5))
.add_yaxis("120日移動平均線", sma_120, is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5))
.add_yaxis("240日移動平均線", sma_240, is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5))
.set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category"),
)
)
bar = (
Bar()
.add_xaxis(stock_date)
.add_yaxis("交易量", stock_volume_red, gap="0%",
xaxis_index=1, yaxis_index=1, label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#A73835"),
)
.add_yaxis("交易量", stock_volume_green,gap="0%",
xaxis_index=1, yaxis_index=1, label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#424A56"),
)
.add_yaxis("交易量", stock_volume_gray,gap="0%",
xaxis_index=1, yaxis_index=1, label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#404143")
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category", is_scale=True, grid_index=1, boundary_gap=False,
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=False),
split_number=20, ),
yaxis_opts=opts.AxisOpts(grid_index=1, is_scale=True, split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
overlap_kline_line = kline.overlap(line)
global grid_chart
grid_chart = (
Grid()
.add(overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
)
.add(bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="70%", height="16%"), )
)
grid_chart.render()
def kline(self):
data = self.calculate().to_dict('list')
kline = (
Kline().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="portfolio index",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
datazoom_opts=[
opts.DataZoomOpts(is_show=False,
type_="inside",
xaxis_index=[0, 0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0, 1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0, 2],
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"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
kline_line = (Line().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="MA5",
y_axis=self.calculate_ma(day_count=5, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA10",
y_axis=self.calculate_ma(day_count=10, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA20",
y_axis=self.calculate_ma(day_count=20, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA30",
y_axis=self.calculate_ma(day_count=30, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
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),
),
))
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
# Bar-1
bar_1 = (Bar().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="Volumn",
y_axis=data["volume"],
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(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (Bar().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="MACD",
y_axis=data["MACD"],
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(
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=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="DIFF",
y_axis=data["DIFF"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=data["DEA"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
# 最下面的柱状图和折线图
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(
init_opts=opts.InitOpts(width="1500px", height="750px"))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 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(data["datas"]))
# K线图和 MA5 的折线图
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
height="60%"),
)
# Volumn 柱状图
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="71%",
height="10%"),
)
# MACD DIFS DEAS
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="82%",
height="14%"),
)
makedir(self.path, 'kline')
grid_chart.render(
f"{self.path}\\kline\\{str(self.names)}_{time.strftime('%Y-%m-%d', time.localtime())}.html"
)
def print_line():
x_data = range(0, len(Grid))
timeline2 = Timeline(init_opts=opts.InitOpts(bg_color="black"))
for i in range(len(Grid)):
line = (Line(init_opts=opts.InitOpts(bg_color="black")).add_xaxis(
xaxis_data=time[0:i + 1]).add_yaxis(
series_name="grid",
y_axis=Grid[0:i + 1],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(
width=3,
color="rgb(255,0,255)",
),
color="rgb(255,255,0)",
symbol="diamond",
symbol_size=2,
).add_yaxis(
series_name="pv",
y_axis=PV[0:i + 1],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3,
color="rgb(0,255,255)"),
color="rgb(255,255,255)",
symbol="circle",
symbol_size=2,
).add_yaxis(
series_name="battery",
y_axis=Battery[0:i + 1],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3,
color="rgb(255,255,255)"),
color="rgb(0,255,255)",
symbol="triangle",
symbol_size=2,
).add_yaxis(
series_name="demand",
y_axis=demand[0:i + 1],
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3,
color="rgb(255,255,0)"),
color="rgb(255,0,255)",
symbol="rect",
symbol_size=2,
).set_global_opts(
title_opts=opts.TitleOpts(
title="PV, battery, demand, grid(kW) till time {}".format(
time[i]),
title_textstyle_opts=opts.TextStyleOpts(color="white")),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="none"),
xaxis_opts=opts.AxisOpts(
boundary_gap=False,
min_='dataMin',
max_='dataMax',
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="white"), ),
axislabel_opts=opts.LabelOpts(),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value}"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="white")),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
is_piecewise=False,
dimension=0,
textstyle_opts=opts.TextStyleOpts(color="white")),
legend_opts=opts.LegendOpts(
textstyle_opts=opts.TextStyleOpts(color="white"),
pos_left="right",
pos_top="5%"),
))
timeline2.add(line, "{}年".format(i)).add_schema(play_interval=2000,
is_auto_play=True,
is_timeline_show=False)
timeline2.render("C:/Users/suqi/Desktop/allcode/linenew.html")
from pyecharts import options as opts
from pyecharts.charts import Gauge
c = (Gauge().add(
"业务指标",
[("完成率", 55.5)],
split_number=5,
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color=[(0.3, "#67e0e3"), (0.7, "#37a2da"), (1, "#fd666d")], width=30)),
detail_label_opts=opts.LabelOpts(formatter="{value}"),
).set_global_opts(
title_opts=opts.TitleOpts(title="Gauge-分割段数-Label"),
legend_opts=opts.LegendOpts(is_show=False),
).render("gauge_splitnum_label.html"))
def grid_mutil_yaxis() -> Grid:
x_data = ["{}月".format(i) for i in range(1, 13)]
bar = (
Bar()
.add_xaxis(x_data)
.add_yaxis(
"蒸发量",
[2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],
yaxis_index=0,
color="#d14a61",
)
.add_yaxis(
"降水量",
[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
yaxis_index=1,
color="#5793f3",
)
.extend_axis(
yaxis=opts.AxisOpts(
name="蒸发量",
type_="value",
min_=0,
max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#d14a61")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
type_="value",
name="温度",
min_=0,
max_=25,
position="left",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#675bba")
),
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)
),
)
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(
name="降水量",
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",
[3.3, 10.2, 20.3, 23.0, 16.5],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
)
)
bar.overlap(line)
return Grid().add(bar, opts.GridOpts(pos_left="5%", pos_right="20%"), is_control_axis_index=True)
center=['50%', '50%'],
textstyle_opts=opts.TextStyleOpts(
color='red',
),
splitline_opt=opts.SplitLineOpts(
is_show= True,
linestyle_opts=opts.LineStyleOpts(
color='#98F5FF',
width=1,
type_='dotted'
)
),
axisline_opt=opts.AxisLineOpts(
linestyle_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(
color='#98F5FF',
)
)
),
splitarea_opt=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(
color=['#141c42', '#141c42']
)
)
)
.add(
series_name=series[i],
data=vdata[i],
color=c[i],
label_opts=opts.LabelOpts({
[9, 4, 28],
[0, 5, 22],
[1, 5, 41],
[2, 5, 110],
[3, 5, 39],
[4, 5, 40],
[5, 5, 90],
[6, 5, 50],
[7, 5, 60],
[8, 5, 146],
[9, 5, 15],
]
axisOpts = opts.AxisOpts(
type_="category",
axisline_opts=opts.AxisLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(
color='#A3C7E7',
font_size=10,
),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=True,
linestyle_opts=opts.LineStyleOpts(
is_show=True,
color='black',
width=10,
)),
)
c = (
HeatMap(init_opts=opts.InitOpts(bg_color='black', )).add_xaxis(
color='#B44038', #图元样式配置项
border_color="#B44038", #颜色
border_width=10)) #图元的大小
line.add_yaxis(
"湖北新增确诊病例",
hubei_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#4E87ED'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#4E87ED',
border_color="#4E87ED",
border_width=3))
line.add_yaxis(
"其他省份新增病例",
other_statis,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#F1A846'),
label_opts=opts.LabelOpts(position='bottom'), #标签在折线的底部
itemstyle_opts=opts.ItemStyleOpts(color='#F1A846',
border_color="#F1A846",
border_width=3))
line.set_global_opts(
title_opts=opts.TitleOpts(title="新增确诊病例", subtitle='数据来源：丁香园'),
yaxis_opts=opts.AxisOpts(
max_=16000,
min_=1,
type_="log", #坐标轴配置项
splitline_opts=opts.SplitLineOpts(is_show=True), #分割线配置项
axisline_opts=opts.AxisLineOpts(is_show=True))) #坐标轴刻度线配置项
#line.render(path='/home/aistudio/data/新增确诊趋势图.html')
line.render(path='D:/pycharm/projects/data/新增确诊趋势图.html')
def line_color_with_js_func(place="上海野生动物园") -> Line:
if place is None:
weather_data = weather_spider(weather_dict.get("上海野生动物园"))
else:
if place in weather_dict.keys():
place_weather_url = weather_dict.get(place)
weather_data = weather_spider(place_weather_url)
else:
return None
x_data = [
x.replace("".join(re.findall("（.*", x)), "")
for x in weather_data.get("days")
]
y_data = [
maxt.rstrip("℃").split("/")[0]
for maxt in weather_data.get("max_min_t")
]
y_data2 = [
mint.rstrip("℃").split("/")[1]
for mint in weather_data.get("max_min_t")
]
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#6495ED'}, {offset: 1, color: '#12406d'}], false)"
)
c = (
Line(init_opts=opts.InitOpts(
width="100%", height="100%")).set_series_opts(
label_opts=opts.LabelOpts(is_show=True, color="white")).
add_xaxis(xaxis_data=x_data).add_yaxis(
"日最高气温",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True,
position="top",
color="white",
font_size=16),
itemstyle_opts=opts.ItemStyleOpts(color="red",
border_color="#fff",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js),
opacity=1),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
legend_opts=opts.LegendOpts(is_show=False),
)
# 第二
.add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="日最低气温",
y_axis=y_data2,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True,
position="top",
color="white",
font_size=16),
itemstyle_opts=opts.ItemStyleOpts(color="#0ff",
border_color="#fff",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js),
opacity=1),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
legend_opts=opts.LegendOpts(is_show=False),
))
return c
def dray_line_piture(self,my_line_data_x,my_line_data_y):
if os.path.isfile('.\line_color.html'):
os.remove('.\line_color.html')
if(len(my_line_data_x) != len(my_line_data_y)):
print("leng not match !!!")
exit()
x_data = my_line_data_x
y_data = my_line_data_y
#print(x_data)
#print(y_data)
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c = (
Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="注册总量",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True, position="top", color="white",formatter="{c}%"),
itemstyle_opts=opts.ItemStyleOpts(
color="red", border_color="#fff", border_width=3
),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="Yield",
pos_left="5%",
pos_top="5%",
title_textstyle_opts=opts.TextStyleOpts(color="#fff", font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
#is_show=False,
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
),
axistick_opts=opts.AxisTickOpts(
is_show=False,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=False, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
legend_opts=opts.LegendOpts(is_show=False),
)
.render("line_color.html")
)
def radar_angle_radius_axis_opts() -> Radar:
data = [{"value": [4, -4, 2, 3, 0, 1], "name": "预算分配"}]
c_schema = [
{
"name": "销售",
"max": 4,
"min": -4
},
{
"name": "管理",
"max": 4,
"min": -4
},
{
"name": "技术",
"max": 4,
"min": -4
},
{
"name": "客服",
"max": 4,
"min": -4
},
{
"name": "研发",
"max": 4,
"min": -4
},
{
"name": "市场",
"max": 4,
"min": -4
},
]
c = (Radar().set_colors(["#4587E7"]).add_schema(
schema=c_schema,
shape="circle",
center=["50%", "50%"],
radius="80%",
angleaxis_opts=opts.AngleAxisOpts(
min_=0,
max_=360,
is_clockwise=False,
interval=5,
axistick_opts=opts.AxisTickOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
radiusaxis_opts=opts.RadiusAxisOpts(
min_=-4,
max_=4,
interval=2,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
polar_opts=opts.PolarOpts(),
splitarea_opt=opts.SplitAreaOpts(is_show=False),
splitline_opt=opts.SplitLineOpts(is_show=False),
).add(
series_name="预算",
data=data,
areastyle_opts=opts.AreaStyleOpts(opacity=0.1),
linestyle_opts=opts.LineStyleOpts(width=1),
))
return c
list(val_repot.BADRATE),
yaxis_index=0,
color="red",
)
.set_global_opts(
title_opts=opts.TitleOpts(title="行为评分卡模型表现"),
)
.extend_axis(
yaxis=opts.AxisOpts(
name="累计坏人占比",
type_="value",
min_=0,
max_=0.5,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="red")
),
axislabel_opts=opts.LabelOpts(formatter="{value}"),
)
)
.add_xaxis(list(val_repot.index))
.add_yaxis(
"KS",
list(val_repot['KS']),
yaxis_index=1,
color="blue",
label_opts=opts.LabelOpts(is_show=False),
)
)
line.render_notebook()
def plot_kline(ka,
bs=None,
file_html="kline.html",
width="1400px",
height="680px"):
"""
:param ka: KlineAnalyze
:param bs: pd.DataFrame
买卖点，包含三个字段 ["操作提示", "交易时间", "交易价格"]
:param file_html: str
:param width: str
:param height: str
:return: None
"""
df = ka.to_df(use_macd=True, ma_params=(
5,
20,
))
x = df.dt.to_list()
title = "%s | %s 至 %s" % (ka.symbol, ka.start_dt, ka.end_dt)
kline = (
Kline().add_xaxis(xaxis_data=x).add_yaxis(
series_name="",
y_axis=df[['open', 'close', 'low', 'high']].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True)).set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(is_show=True,
position="inside")),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(is_show=False,
type_="inside",
xaxis_index=[0, 0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0, 1],
pos_top="96%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0, 2],
range_end=100),
],
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
kline_line = (Line().add_xaxis(xaxis_data=x).add_yaxis(
series_name="笔",
y_axis=df.bi.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='diamond',
symbol_size=8,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='dotted', width=2),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="线段",
y_axis=df.xd.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='triangle',
symbol_size=12,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='solid', width=2),
label_opts=opts.LabelOpts(is_show=True, position='right'),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
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, position="inside"),
),
))
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
c = (Scatter().add_xaxis(bs['交易时间'].to_list()).add_yaxis(
"买卖点",
bs['交易价格'].to_list(),
label_opts=opts.LabelOpts(
is_show=True,
position="left",
formatter=JsCode(
"function(params){return bsName[params.dataIndex][0];}")),
))
overlap_kline_line = overlap_kline_line.overlap(c)
# draw volume
bar_1 = (Bar().add_xaxis(xaxis_data=x).add_yaxis(
series_name="Volumn",
y_axis=df.vol.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(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=True, position='inside')),
legend_opts=opts.LegendOpts(is_show=False),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (Bar().add_xaxis(xaxis_data=x).add_yaxis(
series_name="MACD",
y_axis=df.macd.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(
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, position="inside"),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=x).add_yaxis(
series_name="DIF",
y_axis=df['diff'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=df['dea'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
# draw MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(
init_opts=opts.InitOpts(width=width, height=height, page_title=title))
grid_chart.add_js_funcs("var barData = {}".format(
df[['open', 'close', 'low', 'high']].values.tolist()))
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
grid_chart.add_js_funcs("var bsName = {}".format(
bs[["操作提示", "交易价格"]].values.tolist()))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="71%",
height="10%"),
)
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="82%",
height="14%"),
)
grid_chart.render(path=file_html)
def generate_line_html(rows, select=None):
# 用散点图展示
line = Line(
opts.InitOpts(height='700px', width='1424px', theme=ThemeType.LIGHT))
x = []
y1 = []
y2 = []
y3 = []
y4 = []
y5 = []
for row in rows:
x.append(row['时间'])
y3.append(row['低溢价率策略累积收益率'])
y2.append(row['高收益率策略累积收益率'])
y1.append(row['低余额+双低策略累积收益率'])
y1.append(row['低溢价率+双低策略累积收益率'])
# y4.append(row['可转债指数累积收益率'])
# y5.append(row['沪深300累积收益率'])
line.add_xaxis(x)
line.add_yaxis("低溢价率策略", y3)
line.add_yaxis("高收益率策略", y2)
line.add_yaxis("低余额+双低策略", y1)
line.add_yaxis("低溢价+双低策略", y1)
# line.add_yaxis("可转债指数", y4)
# line.add_yaxis("沪深300", y5)
line.set_global_opts(
title_opts=opts.TitleOpts(title="策略组合收益率",
pos_left='center',
pos_top=-5),
tooltip_opts=opts.TooltipOpts(
trigger='axis',
formatter=JsCode(
"function (params) {"
"return '<table style=\"width:150px;\">'+"
"'<tr ><td style=\"height:20px;background-color:white;border:0px\" colspan=2>'+ params[0].data[0] +'</td></tr>' +"
"'<tr><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[1].color+'\">高收益率策略</td><td style=\"height:15px;background-color:white;border:0px\">' + params[1].value[1] + '%</td></tr>' +"
"'<tr><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[2].color+'\">低溢价率策略</td><td style=\"height:15px;background-color:white;border:0px\">' + params[2].value[1] + '%</td></tr>' +"
"'<tr ><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[0].color+'\">低余额+双低策略</td><td style=\"height:15px;background-color:white;border:0px\">' + params[3].value[1] + '%</td></tr>' +"
"'<tr ><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[0].color+'\">低溢价率+双低策略</td><td style=\"height:15px;background-color:white;border:0px\">' + params[4].value[1] + '%</td></tr>' +"
"'<tr><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[3].color+'\">可转债指数</td><td style=\"height:15px;background-color:white;border:0px\">' + params[5].value[1] + '%</td></tr>' +"
"'<tr><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[4].color+'\">沪深300</td><td style=\"height:15px;background-color:white;border:0px\">' + params[6].value[1] + '%</td></tr>' +"
"'</table>';}")),
legend_opts=opts.LegendOpts(pos_top=50,
# selected_mode='single'
),
datazoom_opts={
'start': 0,
'end': 100
},
toolbox_opts=opts.ToolboxOpts(feature={
'dataZoom': {},
}),
# visualmap_opts=opts.VisualMapOpts(
# type_="color", max_=150, min_=20, dimension=1
# ),
xaxis_opts=opts.AxisOpts(
# data=None,
type_='time',
name='时间',
name_gap=30,
is_scale=True,
name_location='middle',
splitline_opts=opts.SplitLineOpts(is_show=False),
# axislabel_opts=opts.LabelOpts(formatter="{value}"), #echarts.format.formatTime('yy-MM-dd', value*1000)
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
symbol=['none', 'arrow'])),
yaxis_opts=opts.AxisOpts(
type_='value',
name='收益率(%)',
name_rotate=90,
name_gap=55,
name_location='middle',
is_scale=True,
axislabel_opts=opts.LabelOpts(formatter='{value}%'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
symbol=['none', 'arrow'])))
line.set_series_opts(
symbol='none',
smooth=False,
label_opts=opts.LabelOpts(is_show=False),
)
line_html = line.render_embed('template.html', env)
return line_html
"蒸发量",
[2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],
yaxis_index=0,
color="#d14a61",
).add_yaxis(
"降水量",
[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
yaxis_index=1,
color="#5793f3",
).extend_axis(yaxis=opts.AxisOpts(
name="蒸发量",
type_="value",
min_=0,
max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#d14a61")),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
)).extend_axis(yaxis=opts.AxisOpts(
type_="value",
name="温度",
min_=0,
max_=25,
position="left",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#675bba")),
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)),
)).set_global_opts(
yaxis_opts=opts.AxisOpts(
name="降水量",
def kline(self):
""""""
if "_" in self.bar_data.symbol[0]:
symbol_split = self.bar_data.symbol[0].split("_")
series_name = "_".join(
[symbol_split[0], symbol_split[1][-2:], symbol_split[2][-2:]])
else:
series_name = self.bar_data.symbol[0]
kline = Kline(init_opts=opts.InitOpts(width="1400px", height="800px"))
kline.add_xaxis(xaxis_data=self.bar_data_datetime)
kline.add_yaxis(
series_name=series_name,
yaxis_index=0,
y_axis=self.bar_data[[
"open_price", "close_price", "low_price", "high_price"
]].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(color="#ef232a",
color0="#14b143",
border_color="#8A0000",
border_color0="#008F28",
opacity=0.8),
)
kline.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 0],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
type_="slider",
xaxis_index=[0, 1],
pos_top="95%",
range_start=0,
range_end=100,
),
],
xaxis_opts=opts.AxisOpts(
is_scale=True,
type_="category",
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.8)),
),
brush_opts=opts.BrushOpts(
tool_box=[
"rect", "polygon", "keep", "lineX", "lineY", "clear"
],
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
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=12,
font_family="Arial",
font_weight="lighter",
),
),
toolbox_opts=opts.ToolboxOpts(orient="horizontal",
pos_left="right",
pos_top="0%"),
legend_opts=opts.LegendOpts(
is_show=True,
type_="scroll",
selected_mode="multiple",
pos_left="left",
pos_top="0%",
legend_icon="roundRect",
),
# 多图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
)
self.kline_chart = kline
def generate_bar_html(rows, title):
x = []
y = []
for row in rows:
x.append(row[0])
y.append({'value': row[1], 'range': [row[2], row[3]]})
bar = Bar(init_opts=opts.InitOpts(height='700px',
width='1424px',
theme=ThemeType.SHINE,
chart_id="cb_tree_map"))
bar.add_xaxis(x)
bar.add_yaxis(
"个数",
y,
yaxis_index=0,
bar_width=50,
category_gap='1%',
gap='1%',
label_opts=opts.LabelOpts(
position="top",
formatter=JsCode("function(x){return x.data[0];}")),
)
bar.set_series_opts(itemstyle_opts=opts.ItemStyleOpts(color=JsCode(
"function(x){return x.data['range'][0]=='0'?'lightgray':(x.data['range'][0] < 0 ? 'lightgreen':'lightcoral')}"
)))
bar.set_global_opts(
title_opts=opts.TitleOpts(
title="=========" + title + "=========",
subtitle_textstyle_opts=opts.TextStyleOpts(font_weight='bold',
font_size='15px'),
pos_left='center',
pos_top='-1px',
),
tooltip_opts=opts.TooltipOpts(is_show=False),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
# data=None,
# type_='category',
# name_gap=0,
# name_rotate=90,
# axislabel_opts=opts.LabelOpts(
# rotate=-60,
# ),
name='价格涨跌区间',
name_gap=35,
is_scale=True,
name_location='middle',
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=True,
# symbol=['none', 'arrow']
)),
yaxis_opts=opts.AxisOpts(
type_='value',
name='个数',
# name_rotate=90,
name_gap=35,
name_location='middle',
# min_=0,
# max_=max_value,
is_scale=True,
axislabel_opts=opts.LabelOpts(formatter='{value}'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
# symbol=['none', 'arrow']
)),
)
bar_html = bar.render_embed('template.html', html_utils.env)
return bar_html
def draw_chart(ts_code, start_date, end_date):
'''
pyecharts V1 版本开始支持链式调用
文档地址 https://pyecharts.org/#/zh-cn/
'''
stock_data = get_process_datas(ts_code, start_date, end_date)
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
def plot_lines(self, x_data, y_data, filename_, series_name='x1_dist'):
filename = self.plot_line_path + filename_
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c = (Line(
init_opts=opts.InitOpts(bg_color=JsCode(background_color_js))
).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name=series_name,
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True,
position="top",
color="white"),
itemstyle_opts=opts.ItemStyleOpts(color="red",
border_color="#fff",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js),
opacity=1),
).set_global_opts(
title_opts=opts.TitleOpts(
title="show %s in one image" % series_name,
pos_top="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff",
font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside", range_start=0,
range_end=100),
],
legend_opts=opts.LegendOpts(is_show=False),
))
c.render(filename)
textstyle_opts=opts.TextStyleOpts(
color='#fff'
)
),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="shadow",
textstyle_opts=opts.TextStyleOpts(
color="#fff"
)
),
xaxis_opts=opts.AxisOpts(
type_="category",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(
color="white"
)
),
axislabel_opts=opts.LabelOpts(
)
),
yaxis_opts=opts.AxisOpts(
type_="value",
max_=90,
axistick_opts=opts.AxisTickOpts(
is_show=False
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(
color='rgba(255,255,255,0.3)'
pos_top='15',
pos_left='40%',
title_textstyle_opts=opts.TextStyleOpts(font_size=20)),
legend_opts=opts.LegendOpts(
legend_icon='circle',
orient='vertical',
align='left',
item_width=15,
item_height=8,
item_gap=7,
pos_top="55",
pos_left="110",
textstyle_opts=opts.TextStyleOpts(font_size=10)),
xaxis_opts=opts.AxisOpts(
name_textstyle_opts=opts.TextStyleOpts(font_size=9, color='#525252'),
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color='#525252')),
axistick_opts=opts.AxisTickOpts(linestyle_opts=opts.LineStyleOpts(
color='#525252')),
axislabel_opts=opts.LabelOpts(font_size=8, color='#525252')),
yaxis_opts=opts.AxisOpts(
name_textstyle_opts=opts.TextStyleOpts(font_size=9, color='#525252'),
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color='#525252')),
axistick_opts=opts.AxisTickOpts(linestyle_opts=opts.LineStyleOpts(
color='#525252')),
axislabel_opts=opts.LabelOpts(font_size=8, color='#525252')),
tooltip_opts=opts.TooltipOpts(
trigger='axis', textstyle_opts=opts.TextStyleOpts(font_size=9.5)),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(type_='jpeg',
pixel_ratio=2.5))))
def tradeDataShowKLine(product_code, ma=None, autoType=None) -> Grid:
'''
展示该产品的K线图，默认前复权，等值等比例的展示
:param product_code: 产品代码
:param ma=[30,60,99,120,250] 均线
:param autoType: autoType=qfq-前复权 hfq-后复权 None-不复权
:param *avg_line: 均线 暂时不做
:return:
'''
ma = [30, 60, 99, 120, 250] if ma is None else ma
(productname,
tradeDataDf) = sttradepb.getTradeDataFromDataBase(product_code,
ma,
autotype=autoType)
tradeDataDf = tradeDataDf.where(tradeDataDf.notnull(), None)
workDateList = tradeDataDf['trade_date'].tolist()
workDateList = list(map(str, workDateList))
# open,close,low,high数据获取
new_data = tradeDataDf.loc[:, gc.PRICE_COLS].values
tradeDataList = new_data.tolist()
kline = (Kline().add_xaxis(xaxis_data=workDateList).add_yaxis(
series_name="kline",
y_axis=tradeDataList,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
).set_global_opts(
title_opts=opts.TitleOpts(
title=productname + "(%s)" % product_code,
subtitle="MA%s" % str(ma),
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=0,
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"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#ec0000"
},
{
"value": -1,
"color": "#00da3c"
},
],
),
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",
),
))
line = (Line().add_xaxis(xaxis_data=workDateList).add_yaxis(
series_name="MA%s" % ma[0],
y_axis=tradeDataDf.loc[:, 'ma%s' % ma[0]].values.tolist(),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA%s" % ma[1],
y_axis=tradeDataDf.loc[:, 'ma%s' % ma[1]].values.tolist(),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA%s" % ma[2],
y_axis=tradeDataDf.loc[:, 'ma%s' % ma[2]].values.tolist(),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA%s" % ma[3],
y_axis=tradeDataDf.loc[:, 'ma%s' % ma[3]].values.tolist(),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA%s" % ma[4],
y_axis=tradeDataDf.loc[:, 'ma%s' % ma[4]].values.tolist(),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
bar = (Bar().add_xaxis(xaxis_data=workDateList).add_yaxis(
series_name="Volume",
yaxis_data=tradeDataDf.loc[:, "volume"].values.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
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=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Kline And Line
overlap_kline_line = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid()
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="16%"),
)
return grid_chart
xaxis_data=x_data).add_yaxis(
series_name="蒸发量",
y_axis=evaporation_capacity,
yaxis_index=0,
color=colors[1]).add_yaxis(
series_name="降水量",
y_axis=rainfall_capacity,
yaxis_index=1,
color=colors[0]).extend_axis(
yaxis=opts.AxisOpts(name="蒸发量",
type_="value",
min_=0,
max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(
color=colors[1])),
axislabel_opts=opts.LabelOpts(
formatter="{value} ml"))).
extend_axis(yaxis=opts.AxisOpts(
type_="value",
name="温度",
min_=0,
max_=25,
position="left",
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color=colors[2])),
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)),
)).set_global_opts(yaxis_opts=opts.AxisOpts(
),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="OCTOBER 2015",
pos_bottom="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff", font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
from pyecharts import charts
from pyecharts import options as opts
value = 200
gu = charts.Gauge(init_opts=opts.InitOpts(width="640px", height="480px"))
gu.add(
"指标",
[("昨日单位面积能耗", value)],
split_number=10,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color=[(
0.2, "#00B050"), (0.4, "#67e0e3"), (0.7, "#FF6600"), (1,
"#fd666d")],
width=25)),
min_=0, # 最小刻度
max_=1400, # 最大刻度
)
gu.set_global_opts(
title_opts=opts.TitleOpts(
title="楼宇能耗指标表盘\n昨日单位面积能耗{0}Wh/m^2".format(value)),
legend_opts=opts.LegendOpts(is_show=False),
)
gu.render(r"C:\Users\CQ375\Desktop\ex\Guage-eg.html")
def Pycharts():
with open('reviews/pos.txt', "r") as f: # 训练集可持续化，读取上次写文件名，这次接着来
total_reviews_ups = f.read()
if total_reviews_ups == '':
print('请爬取训练集')
else:
total_reviews_up = int(total_reviews_ups)
with open('reviews/neg.txt', "r") as f: # 训练集可持续化
total_reviews_downs = f.read()
if total_reviews_downs == '':
print('请爬取训练集')
else:
total_reviews_down = int(total_reviews_downs)
file = open('reviews_over.txt', 'r', encoding='UTF-8') # 路径自定义
text = file.read()
file.close()
cut_text = " ".join(jieba.cut(text))
words = cut_text.split()
words1 = [re.sub("[\，\。\！\；\？\、\”\“]", '', word) for word in words] # 去掉标点符号
#停用词 自定义词典
jieba.load_userdict("dict.txt")
stopwords_file = "stopwords.txt"
stop_f = open(stopwords_file, "r", encoding='utf-8')
stop_words = list()
for line in stop_f.readlines():
line = line.strip()
if not len(line):
continue
stop_words.append(line)
stop_f.close
DIY_stopwords = ['一个','没有','说','玩','做','里','中','-','⣿','','感','spoiler','2','点','完','·','\\','4','走','一款',"'",'会','没',',','游戏','帧','/']
for line in range(0,len(DIY_stopwords)):
stop_words.append(DIY_stopwords[line])
words2 = [word for word in words1 if word not in stop_words] #停用词
wordcounts = collections.Counter(words2).most_common(200)
#print(wordcounts)
#词云
cloud = (
WordCloud(opts.InitOpts(width="100%", height="950px"))
.add("词频统计", wordcounts, word_size_range=[20, 300], )
.set_global_opts(
title_opts=opts.TitleOpts(
title="词频统计", title_textstyle_opts=opts.TextStyleOpts(font_size=23)
),
tooltip_opts=opts.TooltipOpts(is_show=True),
)
.render("html/wordcloud_diamond.html")
)
UP = (
Gauge(init_opts=opts.InitOpts(width="500px", height="500px"))
.add(series_name="推荐指数", data_pair=[["", sa.pos/(sa.pos+sa.neg)*100]],
title_label_opts=opts.LabelOpts(font_size=40, color="blue", font_family="Microsoft YaHei"),
)
.set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{a} <br/>{b} : {c}%"),
)
.set_series_opts(axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color=[[0.3, "#67e0e3"], [0.7, "#37a2da"], [1, "#fd666d"]], width=30)
)
)
.render("html/gauge_up.html")
)
DOWN = (
Gauge(init_opts=opts.InitOpts(width="500px", height="500px"))
.add(series_name="推荐指数", data_pair=[["", sa.neg/(sa.pos+sa.neg)*100]],
title_label_opts=opts.LabelOpts(font_size=40, color="blue", font_family="Microsoft YaHei"),
)
.set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{a} <br/>{b} : {c}%"),
)
.set_series_opts(axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color=[[0.3, "#67e0e3"], [0.7, "#37a2da"], [1, "#fd666d"]], width=30)
)
)
.render("html/gauge_down.html")
)
pie = (
Pie()
.add(
"1",
[['正面训练集数量',total_reviews_up ], ['负面训练集数量', total_reviews_down]],
radius=["40%", "75%"],
)
.set_global_opts(
title_opts=opts.TitleOpts(title="Pie-Radius"),
legend_opts=opts.LegendOpts(orient="vertical", pos_top="15%", pos_left="2%"),
)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
.render("html/pie.html")
)
bar = (
Bar()
.add_xaxis(['推荐', '不推荐'])
.add_yaxis("识别成功", [pospic,negpic], stack="stack1")
.add_yaxis("识别失败", [totpos-pospic,totneg-negpic], stack="stack1")
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="准确度为%s" %str(RecommendCent)))
.render("html/bar.html")
)
webbrowser.open('file:///F:/Project/WorkSpace/2077CommentAnalyze/Python/html/index.html')
from pyecharts import options as opts
from pyecharts.charts import PictorialBar
from pyecharts.globals import SymbolType
location = ["山西", "四川", "西藏", "北京", "上海", "内蒙古", "云南", "黑龙江", "广东", "福建"]
values = [13, 42, 67, 81, 86, 94, 166, 220, 249, 262]
c = (PictorialBar().add_xaxis(location).add_yaxis(
"",
values,
label_opts=opts.LabelOpts(is_show=False),
symbol_size=18,
symbol_repeat="fixed",
symbol_offset=[0, 0],
is_symbol_clip=True,
symbol=SymbolType.ROUND_RECT,
).reversal_axis().set_global_opts(
title_opts=opts.TitleOpts(title="PictorialBar-各省份人口数量（虚假数据）"),
xaxis_opts=opts.AxisOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
opacity=0)),
),
).render("Pictorialbar_base.html"))
def bar_line(request):
"""各种类型房屋的平均价格和数量 柱状图——折线图"""
districts = [] # 区县名称
infos = {} # 各类新房的总数和均价
# {'商业': [821, 21854707.0], '住宅': [567, 6424626.0], '别墅': [79, 1200119.0], '写字楼': [77, 1120913.0]}
cursor = Mongo().connect_mongo('runoob', 'test').find()
for data in cursor:
if data["district"] not in districts: # 获取区县
districts.append(data["district"])
if data["type"] not in infos:
infos[data["type"]] = [1, float(data["price"])]
else:
infos[data["type"]][0] += 1
infos[data["type"]][-1] += float(data["price"])
x_data = [x for x in infos.keys()]
bar = (
Bar()
.add_xaxis(x_data)
.add_yaxis(
"平均价格",
["{:.2f}".format(x[-1]/x[0]) for x in infos.values()],
yaxis_index=0,
color="#675bba",
category_gap="65%",
)
.extend_axis(
yaxis=opts.AxisOpts(
name="",
type_="value",
# min_=0,
# max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#675bba")
),
axislabel_opts=opts.LabelOpts(formatter="{value}"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
type_="value",
name="",
# min_=0,
# max_=25,
position="none",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#d14a61")
),
axislabel_opts=opts.LabelOpts(formatter="{value}"),
splitline_opts=opts.SplitLineOpts(
is_show=False, linestyle_opts=opts.LineStyleOpts(opacity=1)
)
)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="成都各种类型新房价格与数量")
)
)
line = (
Line()
.add_xaxis(x_data)
.add_yaxis(
"新房数量",
["{}".format(x[0]) for x in infos.values()],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=True),
)
)
bar.overlap(line)
bar.overlap(line).render("templates/barline.html")
return HttpResponse(bar.overlap(line).render_embed())
def pollsummary(self,subset='masterCandle',dat_cursor=None,renderfile=False):
# 初始化交易时间x轴
heartbeat = 300
# 如果未提供复盘时间游标，则默认使用最近交易日至收盘收盘时间的数据
if dat_cursor is None:
dat_cursor = datetime.now().astimezone(timezone(cfg.STR_TIMEZONE))
int_latestViewDate = self.da.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )
int_cursorDateTime = int(str(int_latestViewDate)+'150100')
int_cursorDateStart = int(str(int_latestViewDate)+'000000')
# 如提供复盘游标时间，则该时间为游标卡尺的最后时间，如日期为非交易日则自动调整为最近交易日的收盘时间
else:
int_cursorDate = datetime.strftime(dat_cursor,'%Y%m%d')
int_latestViewDate = self.da.int_findClosestTradeDate(int_cursorDate)
if int_cursorDate==int_latestViewDate: # 如相等说明提供日期为有效交易日，则使用提供的小时分钟秒为游标终点时间，否则将自动替换为最近交易日的收盘时间点
int_cursorDateTime = int(datetime.strftime(dat_cursor,'%Y%m%d%H%M%S'))
int_cursorDateStart = int(datetime.strftime(dat_cursor,'%Y%m%d')+'000000')
else:
int_cursorDateTime = int(str(int_latestViewDate) +'150100')
int_cursorDateStart = int(str(int_latestViewDate) +'000000')
beatRange = Radar.pop_pulseRange(int_latestViewDate,heartbeat)
df_stockPoll = pd.read_sql(DataAgent.dbsession.query(Stock_poll).filter(Stock_poll.time_index>=int_cursorDateStart,
Stock_poll.time_index<=int_cursorDateTime,
Stock_poll.volume!=0).statement,
DataAgent.dbsession.bind)
df_indexPoll = pd.read_sql(DataAgent.dbsession.query(Index_poll).filter(Index_poll.time_index>=int_cursorDateStart,
Index_poll.time_index<=int_cursorDateTime,
Index_poll.volume!=0).statement,
DataAgent.dbsession.bind)
'''
data_store = libs.tryOpenH5(cfg.H5_FILE_POLL,mode='r')
if 'stockPoll' in data_store:
df_stockPoll = data_store['stockPoll']
df_stockPoll = df_stockPoll[df_stockPoll['volume']!=0] # 过滤掉数据中非交易中的证券
df_stockPoll = df_stockPoll[(df_stockPoll['time_index']<=int_cursorDateTime)&(df_stockPoll['time_index']>=int_cursorDateStart)] # 过滤掉和画图期间无关的数据
else:
df_stockPoll = pd.DataFrame()
df_indexPoll = data_store['indexPoll'] if 'indexPoll' in data_store else pd.DataFrame()
data_store.close()
'''
data_store = libs.tryOpenH5(cfg.H5_FILE_PRE,mode='r')
# 如果按照输入日期没有找到当日任何盘中数据，则从入库历史数据中找到当日收盘数据用于填充
if len(df_indexPoll)==0:
df_indexPoll = data_store['masterCandle'][data_store['masterCandle']['trade_date']==int_latestViewDate]
df_indexPoll['time_index'] = int_cursorDateTime
df_indexPoll['pct_change'] = round(df_indexPoll['adj_close']/df_indexPoll['pre_close']*100,2)
df_indexPoll['per'] = np.nan
if len(df_stockPoll)==0:
df_stockPoll = data_store['masterCandle'][data_store['masterCandle']['trade_date']==int_latestViewDate]
df_stockPoll['time_index'] = int_cursorDateTime
df_stockPoll['pct_change'] = round((df_stockPoll['adj_close']-df_stockPoll['pre_close'])/df_stockPoll['pre_close']*100,2)
df_stockPoll.rename(columns={'pe': 'per'}, inplace=True)
# 如果入库历史数据中仍未找到任何数据则退出分析程序
if any([len(df_stockPoll)==0,len(df_indexPoll)==0]):
data_store.close()
print('stockPoll or indexPoll does not exist for the cursor date in the offline data store, process exiting...')
return False
''' ------读取security code级别的聚合数据------- '''
if subset=='masterCandle':
pass #data_store = libs.tryOpenH5(cfg.H5_FILE_PRE,mode='r')
else:
data_store.close() # 关闭之前打开的master data file
data_store = libs.tryOpenH5('{}{}.dat'.format(cfg.H5_FILE_PATH,subset),mode='r')
df_histRec = data_store['AggByCode']
data_store.close()
self.poll_aggHistRec = df_histRec # sec code 级别的聚合数据，集合包含证券及指数,输出至对象变量
''' ------读取security code级别的聚合结束------- '''
sr_intBeatIndex = pd.Series(beatRange.strftime('%Y%m%d%H%M%S').astype('int64')).rename(index='time_index')
df_index = pd.merge(sr_intBeatIndex,df_indexPoll,how='left',left_on='time_index',right_on='time_index',suffixes=['','1'])
df_stock = pd.merge(sr_intBeatIndex,df_stockPoll,how='left',left_on='time_index',right_on='time_index',suffixes=['','1'])
df_stock = pd.merge(df_stock,df_histRec,how='left',left_on='ts_code',right_index=True,suffixes=['','1'])
self.poll_index = df_index #将结果输出至analyitics对象用于方法外的访问
self.poll_stock = df_stock #将结果输出至analyitics对象用于方法外的访问
# libs.df_csv(cfg.PATH_BUGFILE,(df_stock,))
''' --------所有by time index级别的聚合在这里完成 -----'''
def popPctList(x):
# 移除涨跌幅超过+- 10%的, 新股不受10%涨跌幅限制因此会干扰box chart显示
return [0]*3 if len(x)<3 else [i for i in x if abs(i)<11]
def bigBox(x, up=True):
# 中阳（阴）以上k线数量
if up:
return list(x.loc[(x['box_size']>0) & (x['klineSML'].str.match(DataAgent.re_bigUpBoxPat))]['ts_code'])
else:
return list(x.loc[(x['box_size']<0) & (x['klineSML'].str.match(DataAgent.re_bigDnBoxPat))]['ts_code'])
def breakThrough(x,up=True):
if up: # 找到上穿有效末跌高点的
return list(x.loc[x['close']>=x['validPiv_dnHigh']]['ts_code'])
else: # 找到跌破有效末升低点的
return list(x.loc[x['close']<=x['validPiv_upLow']]['ts_code'])
df_stockBeatGrp = df_stock.groupby('time_index')
df_stockBeatRollup = df_stockBeatGrp.agg({'ts_code': ['count'],
'pct_change': [popPctList],
'per': ['median']}) # function放入中括号内会生成两个层级列名，方便后面join成新列名.如无中括号将只产生一层列名
df_stockBeatRollup.columns = ['_'.join(x) for x in df_stockBeatRollup.columns.ravel()]
df_stockBeatRollup['upThruList'] = df_stockBeatGrp.apply(lambda x: breakThrough(x,up=True)) if len(df_histRec) > 0 else np.nan
df_stockBeatRollup['dnThruList']= df_stockBeatGrp.apply(lambda x: breakThrough(x,up=False)) if len(df_histRec) > 0 else np.nan
df_stockBeatRollup['upThruCount'] = df_stockBeatRollup.apply(lambda row: len(row['upThruList']),axis=1) if len(df_histRec) > 0 else np.nan
df_stockBeatRollup['dnThruCount']= df_stockBeatRollup.apply(lambda row: len(row['dnThruList']),axis=1) if len(df_histRec) > 0 else np.nan
df_stockBeatRollup['upBigBoxList'] = df_stockBeatGrp.apply(lambda x: bigBox(x,up=True))
df_stockBeatRollup['dnBigBoxList'] = df_stockBeatGrp.apply(lambda x: bigBox(x,up=False))
df_stockBeatRollup['upBigBoxCount'] = df_stockBeatRollup.apply(lambda row: len(row['upBigBoxList']),axis=1) if len(df_histRec) > 0 else np.nan
df_stockBeatRollup['dnBigBoxCount'] = df_stockBeatRollup.apply(lambda row: len(row['dnBigBoxList']),axis=1) if len(df_histRec) > 0 else np.nan
''' --------by time index级别的聚合结束 -------------------------------'''
df_stockBeatRollup.astype(dtype= {'ts_code_count':'int32','upThruCount':'int32','dnThruCount':'int32','upBigBoxCount':'int32','dnBigBoxCount':'int32'})
self.poll_aggStkPoll = df_stockBeatRollup.loc[df_stockBeatRollup['ts_code_count']>0] # 只将有效结果输出到analytics对象属性中用于function以外的调用
''' ------------------作图区域---------------------------'''
per_median = round(self.poll_aggStkPoll.loc[self.poll_aggStkPoll.index==self.poll_aggStkPoll.index.max()].per_median.values[0],2)
lst_xaxis = ['{}:{}:{}'.format(i.hour,i.minute,i.second) for i in beatRange]
marketSummaryBoxPlot = Boxplot()
lst_yaxis = np.around(marketSummaryBoxPlot.prepare_data(df_stockBeatRollup['pct_change_popPctList'].values), decimals=2).tolist()
marketSummaryBoxPlot.add_xaxis(lst_xaxis)
marketSummaryBoxPlot.add_yaxis("%", lst_yaxis)
marketSummaryBoxPlot.set_global_opts(title_opts=opts.TitleOpts(title='整体涨跌幅分布 - {} (最后市盈率中位数:{})'.format(int_latestViewDate,str(per_median))),
yaxis_opts=opts.AxisOpts(name="涨幅",min_=-10,max_=10),
xaxis_opts=opts.AxisOpts(name="",
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=True),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=False),),
legend_opts=opts.LegendOpts(is_show=False),
)
lst_upThruBar = df_stockBeatRollup['upThruCount'].values.tolist()
lst_dnThruBar = df_stockBeatRollup['dnThruCount'].values.tolist()
lst_bigUpBoxBar = df_stockBeatRollup['upBigBoxCount'].values.tolist()
lst_bigDnBoxBar = df_stockBeatRollup['dnBigBoxCount'].values.tolist()
pivotCountChart = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="向上突破数量",
yaxis_data=lst_upThruBar,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#FD625E"),
)
.add_yaxis(
series_name="向下突破数量",
xaxis_index=1,
yaxis_index=1,
yaxis_data=lst_dnThruBar,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#01B8AA"),
)
.add_yaxis(
series_name="长阳线数量",
xaxis_index=1,
yaxis_index=1,
yaxis_data=lst_bigUpBoxBar,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="red"),
)
.add_yaxis(
series_name="长阴线数量",
xaxis_index=1,
yaxis_index=1,
yaxis_data=lst_bigDnBoxBar,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="green"),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(name="小时/分钟",),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=True),
axisline_opts=opts.AxisLineOpts(is_show=True),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=True,pos_bottom='0%', pos_left="center"),
)
)
gridChart = Grid()
gridChart.add(
marketSummaryBoxPlot,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_bottom='45%'),
)
gridChart.add(
pivotCountChart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="55%"),
)
fname = '{}marketSummary{}.html'.format(cfg.PATH_ANAFILE,int_latestViewDate)
gridChart.render(fname) if renderfile else None
return gridChart
