def draw_bar3D(cls, title: str, data: pd.DataFrame) -> Bar3D:
"""
根据df内容绘制3D柱状图
:param title: 标题
:param data: 包含三轴数据的dataframe index为x轴 column为Y轴 value为z轴
:return:
"""
data_list = []
index_list = data.index.tolist()
column_list = data.columns.tolist()
# 获取dataframe最大最小值
min_data = data.min().min()
max_data = data.max().max()
# 遍历dataframe，准备待操作数组
for i in range(len(index_list)):
for j in range(len(column_list)):
# 记录 XYZ
temp_list = [index_list[i], column_list[j], data.iloc[i, j]]
# print(i,j,index_list[i],column_list[j])
data_list.append(temp_list)
c = (
Bar3D(init_opts=opts.InitOpts(
width=DEFAULT_WIDTH,
animation_opts=opts.AnimationOpts(
animation_delay=200,
animation_easing="bounceOut"), # 增加启动动效
)).add(
series_name=title,
data=data_list,
xaxis3d_opts=opts.Axis3DOpts(type_="category",
data=index_list),
yaxis3d_opts=opts.Axis3DOpts(type_="category",
data=column_list),
zaxis3d_opts=opts.Axis3DOpts(type_="value"),
).set_series_opts(label_opts=opts.LabelOpts(is_show=True)).
set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left="0%"),
toolbox_opts=opts.ToolboxOpts(), # 显示工具箱
tooltip_opts=opts.TooltipOpts(is_show=True),
axispointer_opts=opts.AxisPointerOpts(
is_show=True, type_="none"), # 指针移动时显示所有数值
legend_opts=opts.LegendOpts(
is_show=True,
selected_mode="multiple",
# pos_bottom="0%",
# pos_right="0%",
# orient="vertical",
), # 显示图例说明
# datazoom_opts=[
# opts.DataZoomOpts(
# range_start=0, range_end=100, orient="vertical", pos_left="2%"
# ),
# opts.DataZoomOpts(range_start=0, range_end=100, orient="horizontal"),
# ], # 增加缩放配置横纵轴都支持缩放
visualmap_opts=opts.VisualMapOpts(max_=max_data, min_=min_data)
# visualmap_opts=opts.VisualMapOpts(type_="color", max_=1, min_=-1),
))
return c
def gen_kline(df1):
dt_list = list(df1['datetime'])
kline_data = df1.apply(lambda record: [
float(record['open']),
float(record['close']),
float(record['low']),
float(record['high'])
],
axis=1).tolist()
#kline = Kline(init_opts=opts.InitOpts(width='1000px'))
kline = Kline()
kline.add_xaxis(list(df1['datetime']))
kline.add_yaxis('日K', kline_data)
kline.set_global_opts(
title_opts=opts.TitleOpts(title='K线'),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
xaxis_index=[0, 1, 2],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1, 2],
range_start=0,
range_end=100,
),
],
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1))),
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=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
)
return kline
def draw_line(cls, title: str, xaxis_data: List[str],
yaxis_data: pd.DataFrame) -> Line:
"""
根据df内容绘制折线图
:param title: 标题
:param xaxis_data: 横轴数据
:param yaxis_data: 纵轴绘制数据
:return:
"""
# 获取dataframe最大最小值
min_data = yaxis_data.min().min()
max_data = yaxis_data.max().max()
c = (
Line(init_opts=opts.InitOpts(
width=DEFAULT_WIDTH)).add_xaxis(xaxis_data).set_series_opts(
label_opts=opts.LabelOpts(is_show=True)).
set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left="0%"),
toolbox_opts=opts.ToolboxOpts(), # 显示工具箱
tooltip_opts=opts.TooltipOpts(is_show=True),
axispointer_opts=opts.AxisPointerOpts(
is_show=True, type_="none"), # 指针移动时显示所有数值
legend_opts=opts.LegendOpts(
is_show=True,
selected_mode="multiple",
# pos_bottom="0%",
# pos_right="0%",
# orient="vertical",
), # 显示图例说明
datazoom_opts=[
opts.DataZoomOpts(
range_start=0,
range_end=100,
orient="vertical",
pos_left=DATAZOOM_VERTICAL_POS_LEFT,
),
opts.DataZoomOpts(range_start=0,
range_end=100,
orient="horizontal"),
], # 增加缩放配置横纵轴都支持缩放
# visualmap_opts = opts.VisualMapOpts(max_ = max_data, min_ = min_data, pos_bottom="10%")
))
# 遍历dataframe 依次添加数据到y轴
column_list = yaxis_data.columns.tolist()
for column in column_list:
c.add_yaxis(
column,
yaxis_data[column].tolist(),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="min", symbol_size=60),
opts.MarkPointItem(type_="max", symbol_size=60),
]),
)
return c
def gen_line(df1, s1, price_min, price_max):
df1['datetime'] = df1['date'] + ' ' + df1['time']
# df1['datetime'] = df1['date']
dt_list1 = list(df1['datetime'])
# print( len(dt_list1) )
# dt_list1 = [s[5:10] for s in dt_list1]
close_list1 = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list1 = np.array(close_list1)
# print(close_list1)
#line1 = Line(init_opts=opts.InitOpts(width='1500px', height='600px'))
line1 = Line()
line1.set_global_opts(
yaxis_opts=opts.AxisOpts(
min_=price_min,
max_=price_max,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
],
#legend_opts=opts.LegendOpts(is_show=False),
#tooltip_opts=opts.TooltipOpts( trigger="axis",axis_pointer_type="cross" ),
tooltip_opts=opts.TooltipOpts(axis_pointer_type="cross"),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
),
)
line1.add_xaxis(xaxis_data=dt_list1)
line1.add_yaxis(
s1,
y_axis=close_list1,
)
line1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return line1
def bar_single(x, y_name, y_bar_value, title, subtitle):
bar = (
Bar(init_opts=opts.InitOpts(width='800px', height='400px'))
.add_xaxis(x)
.add_yaxis(y_name, y_bar_value, color="#5793f3")
.set_global_opts(
title_opts=opts.TitleOpts(title=title, subtitle=subtitle),
axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
)
)
return bar
def line_bar_draw(data,html_name):
x_data = data.index.tolist()
bars = data.iloc[:,1].values.tolist()
change = data.iloc[:,0].values.tolist()
bar = (
Bar(init_opts=opts.InitOpts(width="1200px", height="700px"))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="Stock_Price",
yaxis_data=bars,
label_opts=opts.LabelOpts(is_show=False),
)
.extend_axis(
yaxis=opts.AxisOpts(
name="Value_Series",
type_="value",
)
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="axis", axis_pointer_type="cross"
),
xaxis_opts=opts.AxisOpts(
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
name="Stock_Price",
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(formatter="￥ {value}"),
),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside", range_start=0, range_end=100),
],
)
)
line = (
Line()
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="Value_Series",
yaxis_index=1,
y_axis=change,
label_opts=opts.LabelOpts(is_show=False),
)
)
bar.overlap(line).render('./output/'+html_name+'.html')
def line_two(x, y_line1_name, y2_name, y1_line_value, y2_line_value, title, subtitle):
line = (
Line(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
.add_xaxis(x)
.add_yaxis(y_line1_name, y1_line_value)
.add_yaxis(y2_name, y2_line_value)
.set_global_opts(title_opts=opts.TitleOpts(title=title, subtitle=subtitle))
.set_series_opts(
label_opts=opts.LabelOpts(is_show=True), # 显示标签
axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow")
# , markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值")])
)
)
return line
def descripMixBar(colname):
date, like, comment, repost = get_tenweibo_data(colname)
x_data = date
bar = (Bar(
init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(
xaxis_data=x_data).add_yaxis(
series_name="转发量",
yaxis_data=repost,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="评论量",
yaxis_data=comment,
label_opts=opts.LabelOpts(is_show=False),
).extend_axis(yaxis=opts.AxisOpts(
name="评论",
type_="value",
min_=0,
max_=4000,
interval=500,
axislabel_opts=opts.LabelOpts(formatter="{value}"),
)).set_global_opts(
title_opts=opts.TitleOpts(title="近10个微博数据对比"),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True,
type_="shadow"),
axislabel_opts=opts.LabelOpts(rotate=-15)),
yaxis_opts=opts.AxisOpts(
name="转发",
type_="value",
min_=0,
max_=8000,
interval=800,
axislabel_opts=opts.LabelOpts(formatter="{value}"),
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="点赞量",
yaxis_index=1,
y_axis=like,
label_opts=opts.LabelOpts(is_show=False),
))
return bar.overlap(line)
def music_comments_rating_line(id, num) -> Line:
(rt,qu,num) = music_comments_rating(id, num)
if rt == 0:
return 0
l1 = (
Line()
.add_xaxis(xaxis_data=qu)
.add_yaxis(
series_name="评分",
y_axis=rt,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]
),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="评论时间季度与评分的关系",subtitle="数据量：" + str(num),),
tooltip_opts=opts.TooltipOpts(trigger="axis", is_show=True),
axispointer_opts=opts.AxisPointerOpts(
is_show=True, link=[{"xAxisIndex": "all"}]
),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
toolbox_opts=opts.ToolboxOpts(is_show=True,
# orient="vertical",
pos_left="95%",
feature={
"dataZoom": {"yAxisIndex": "none"},
"dataView": {},
"magicType": {
"show": True,
"title": "切换",
"type": ['line', 'bar'],
# 启用的动态类型，包括'line'（切换为折线图）, 'bar'（切换为柱状图）, 'stack'（切换为堆叠模式）, 'tiled'（切换为平铺模式）
},
"restore": {},
"saveAsImage": {},
},
),
xaxis_opts=opts.AxisOpts(name="评论时间季度", type_="category", boundary_gap=False),
yaxis_opts=opts.AxisOpts(name_gap=50,name_rotate=90,name_location="center",name="评分"),
)
.dump_options_with_quotes()
)
return l1
def program_lang(df):
data = analyze(df, 'LanguageRecommendationSelect').head(10)
data_x = [y for y in data['LanguageRecommendationSelect']]
data_y = [int(y) for y in data['num']]
data_per = data['percent'] * 100
c = (Bar(init_opts=opts.InitOpts(width="1200px", height="500px"))
.add_xaxis(xaxis_data=data_x)
.add_yaxis(series_name='编程语言', yaxis_data=data_y,
label_opts=opts.LabelOpts(is_show=False),
)
.extend_axis(
yaxis=opts.AxisOpts(
name="百分比", type_="value", min_=0,
axislabel_opts=opts.LabelOpts(formatter="{value} %"),
position='top'
))
.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="axis", axis_pointer_type="cross"
),
xaxis_opts=opts.AxisOpts(
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
axislabel_opts={"rotate": 45}, name_rotate=45
),
yaxis_opts=opts.AxisOpts(
# name="水量", type_="value",
# min_=0, max_=250, interval=50,
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
name_rotate=45, axislabel_opts={"rotate": 45}
),
title_opts=opts.TitleOpts(title="编程语言"),
)
)
line = (
Line()
.add_xaxis(xaxis_data=data_x)
.add_yaxis(
z_level=10,
series_name="占比",
yaxis_index=1,
y_axis=data_per,
label_opts=opts.LabelOpts(is_show=False, formatter="{value} %"),
)
)
c.overlap(line).render('program.html')
def books_durations_bar(id, num) -> Bar:
(key,val,num) = books_durations(id, num)
if key == 0:
return 0
c = (
Bar()
.add_xaxis(list(map(str, key)))
.add_yaxis("图书数", list(map(str, val)),
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]
),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="不同图书页数的图书产量", subtitle="数据量：" + str(num),),
tooltip_opts=opts.TooltipOpts(trigger="axis", is_show=True),
yaxis_opts=opts.AxisOpts(name_gap=50,name_rotate=90,name_location="center",name="图书数（个）"),
xaxis_opts=opts.AxisOpts(name="图书页数（页）"),
axispointer_opts=opts.AxisPointerOpts(
is_show=True, link=[{"xAxisIndex": "all"}]
),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
toolbox_opts=opts.ToolboxOpts(is_show=True,
# orient="vertical",
pos_left="95%",
feature={
"dataZoom": {"yAxisIndex": "none"},
"dataView": {},
"magicType": {
"show": True,
"title": "切换",
"type": ['line', 'bar'],
# 启用的动态类型，包括'line'（切换为折线图）, 'bar'（切换为柱状图）, 'stack'（切换为堆叠模式）, 'tiled'（切换为平铺模式）
},
"restore": {},
"saveAsImage": {},
},
),
)
.dump_options_with_quotes()
)
return c
def test_kline_axispointer_opts(fake_writer):
c = (Kline().add_xaxis(
["2017/7/{}".format(i + 1)
for i in range(10)]).add_yaxis("kline", data).set_global_opts(
yaxis_opts=opts.AxisOpts(is_scale=True),
xaxis_opts=opts.AxisOpts(is_scale=True),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
c.render()
_, content = fake_writer.call_args[0]
assert_in("axisPointer", content)
def analysis_struct():
# 处理数据
x_data = pdata['年份'].map(lambda x: "%d" % x).tolist()
y_data1 = pdata['0-14岁人口(万人)'].map(lambda x: "%.2f" % x).tolist()
y_data2 = pdata['15-64岁人口(万人)'].map(lambda x: "%.2f" % x).tolist()
y_data3 = pdata['65岁及以上人口(万人)'].map(lambda x: "%.2f" % x).tolist()
# 人口结构折线图
line = Line()
line.add_xaxis(x_data)
line.add_yaxis('0-14岁人口',
y_data1,
label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('15-64岁人口',
y_data2,
label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('65岁及以上人口',
y_data3,
label_opts=opts.LabelOpts(is_show=False))
line.set_global_opts(
title_opts=opts.TitleOpts(title="人口结构",
pos_bottom="bottom",
pos_left="center"),
xaxis_opts=opts.AxisOpts(
name='年份',
name_location='end',
type_="category",
# axislabel_opts=opts.LabelOpts(is_show=True, color="#000", interval=0, rotate=90),
axistick_opts=opts.AxisTickOpts(is_show=True,
is_align_with_label=True),
axispointer_opts=opts.AxisPointerOpts(
type_="shadow", label=opts.LabelOpts(is_show=True))),
# y轴相关选项设置
yaxis_opts=opts.AxisOpts(name='人口数（万人）',
type_="value",
position="left",
axislabel_opts=opts.LabelOpts(is_show=True)),
legend_opts=opts.LegendOpts(is_show=True))
# 渲染图像，将多个图像显示在一个html中
# DraggablePageLayout表示可拖拽
page = Page(layout=Page.DraggablePageLayout)
page.add(line)
page.render('population_struct.html')
def gen_kline(self, df1, symbol):
df1['datetime'] = df1['date']
dt_list = list(df1['datetime'])
k_plot_value = df1.apply(
lambda record:
[record['open'], record['close'], record['low'], record['high']],
axis=1).tolist()
#print(k_plot_value)
kline = Kline(init_opts=opts.InitOpts(
width='1500px',
height="700px",
))
kline.add_xaxis(dt_list)
kline.add_yaxis(symbol, k_plot_value)
kline.set_global_opts(
title_opts=opts.TitleOpts(title='日线'),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(axis_pointer_type="cross"),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
),
)
return kline
def dayloss_line_base(self, x, y, t, st):
c = (Line(
init_opts=opts.InitOpts(width="1000px", height="500px")
).add_xaxis(x).add_yaxis("线损率(%)", y, is_smooth=True).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
title_opts=opts.TitleOpts(title=t, subtitle=st),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-90),
max_interval=366,
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True,
type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} %"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
pixel_ratio=2, background_color='white'),
restore=opts.ToolBoxFeatureRestoreOpts(is_show=False),
data_view=opts.ToolBoxFeatureDataViewOpts(is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(is_show=False),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_='clear'),
))).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(y=6),
],
label_opts=opts.LabelOpts(is_show=False),
),
))
return c
def analyse(sheet_title):
data = pd.read_excel('B站{0}综合排行榜前100视频.xlsx'.format(sheet_title),
sheet_name=sheet_title)
x_data = data['AV号']
y_data_pts = data['综合评分']
y_data_playnums = list(data['总播放量'] / 10)
y_data_coins = list(data['投币数量'])
y_data_views = list(data['弹幕总数'])
bar = (Bar(
init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(
xaxis_data=x_data, ).add_yaxis(
'总播放量', yaxis_data=y_data_playnums).add_yaxis(
'投币数量', yaxis_data=y_data_coins, stack=1).add_yaxis(
'弹幕总数', yaxis_data=y_data_views, stack=1).extend_axis(
yaxis=opts.AxisOpts(name='综合评分', type_='value')).
set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(
title="B站{0}综合排行榜前100视频".format(sheet_title),
pos_left="center",
pos_top="bottom"),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True,
type_="shadow"),
axislabel_opts=opts.LabelOpts(rotate=-30)),
yaxis_opts=opts.AxisOpts(name='总播放量*0.1/投币数量/弹幕总数',
type_='value'),
datazoom_opts=opts.DataZoomOpts()))
line = (Line().add_xaxis(xaxis_data=data['AV号']).add_yaxis(
series_name='综合评分',
yaxis_index=1,
y_axis=y_data_pts,
label_opts=opts.LabelOpts(is_show=False)))
bar.overlap(line).render("B站{0}综合排行榜前100视频数据.html".format(sheet_title))
def draw_pic(self, x_data, y_data1, y_data2, y_data3):
# 处理数据
yd1 = ["%.2f" % (x * 100) for x in y_data1]
yd2 = ["%.2f" % (x * 100) for x in y_data2]
yd3 = ["%.2f" % (x * 100) for x in y_data3]
# 人口结构折线图
line = Line(init_opts=opts.InitOpts(width='1600px', height='500px'))
line.add_xaxis(x_data)
line.add_yaxis('乐观上涨点数', yd1, label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('平均上涨点数', yd2, label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis('悲观上涨点数', yd3, label_opts=opts.LabelOpts(is_show=False))
line.set_global_opts(
title_opts=opts.TitleOpts(title="短线操作收益",
pos_bottom="bottom",
pos_left="center"),
xaxis_opts=opts.AxisOpts(
name='日期',
name_location='end',
type_="category",
# axislabel_opts=opts.LabelOpts(is_show=True, color="#000", interval=0, rotate=90),
axistick_opts=opts.AxisTickOpts(is_show=True,
is_align_with_label=True),
axispointer_opts=opts.AxisPointerOpts(
type_="shadow", label=opts.LabelOpts(is_show=True))),
# y轴相关选项设置
yaxis_opts=opts.AxisOpts(
name='收益点数',
type_="value",
position="left",
axislabel_opts=opts.LabelOpts(is_show=True)),
legend_opts=opts.LegendOpts(is_show=True))
# 渲染图像，将多个图像显示在一个html中
# DraggablePageLayout表示可拖拽
page = Page(layout=Page.DraggablePageLayout)
page.add(line)
page.render('up.html')
def analysis_urban():
x_data = pdata['年份'].map(lambda x: "%d" % x).tolist()
# total = pdata['年末总人口(万人)'].map(lambda x: "%.2f" % (x / 1000)).tolist()
y_data1 = pdata['城镇人口(万人)'].map(lambda x: "%.2f" % (x / 1000)).tolist()
y_data2 = pdata['乡村人口(万人)'].map(lambda x: "%.2f" % (x / 1000)).tolist()
# 城镇化比例
# y_data_rate = pdata['城镇人口(万人)'] * 100 / pdata['年末总人口(万人)']
bar = Bar()
bar.add_xaxis(x_data)
bar.add_yaxis("城镇人口", y_data1, stack="stack1", category_gap="10%")
bar.add_yaxis("乡村人口", y_data2, stack="stack1", category_gap="10%")
bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=True, position="inside", rotate=90))
bar.set_global_opts(
title_opts=opts.TitleOpts(title="中国城镇化进程"),
xaxis_opts=opts.AxisOpts(
name='年份',
name_location='end',
type_="category",
# axislabel_opts=opts.LabelOpts(is_show=True, color="#000", interval=0, rotate=90),
axistick_opts=opts.AxisTickOpts(is_show=True,
is_align_with_label=True),
axispointer_opts=opts.AxisPointerOpts(
type_="shadow", label=opts.LabelOpts(is_show=True))),
# y轴相关选项设置
yaxis_opts=opts.AxisOpts(name='人口数(千万人)',
type_="value",
position="left",
axislabel_opts=opts.LabelOpts(is_show=True)),
legend_opts=opts.LegendOpts(is_show=True))
# 渲染图像，将多个图像显示在一个html中
page = Page(layout=Page.DraggablePageLayout)
page.add(bar)
page.render('population_urban.html')
def updateBar(win, draw, lose, team):
c = (Bar().add_xaxis(team).add_yaxis(
"胜场", win, stack="stack1", color="#000079").add_yaxis(
"平局", draw, stack="stack1", color="#7B7B7B").add_yaxis(
"败场", lose, stack="stack1", color="#FF0000").set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
type_="category",
axispointer_opts=opts.AxisPointerOpts(
is_show=True, type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
name="场数",
type_="value",
interval=5,
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
))
return c
def kline_pro(kline: List[dict],
fx: List[dict] = None,
xd=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
:param xd: 线段识别结果
:param zs: 中枢
: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"
}])
range_start = int(100 - 216 / len(kline) * 100)
dz_inside = opts.DataZoomOpts(False,
"inside",
xaxis_index=[0, 1, 2],
range_start=range_start,
range_end=100)
dz_slider = opts.DataZoomOpts(True,
"slider",
xaxis_index=[0, 1, 2],
pos_top="96%",
pos_bottom="0%",
range_start=range_start,
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.get('dt', x['date']) for x in kline]
try:
dts = [x['date'] for x in kline]
except:
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['volume'],
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)
ma55 = SMA(close, timeperiod=55)
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)
if xd:
index = 0
zs_colors = [
"yellow", "white", '#f034c1', "#7944b7", "#468b58", "#c17f2f",
"#9EA0A1"
]
data = []
temp_xd = xd
while temp_xd:
zs_color = zs_colors[index % len(zs_colors)]
data = data + [
opts.MarkAreaItem(
name='XD{}'.format(index),
x=(x['xd_list'][0]['date'], x['xd_list'][-1]['date']),
y=(x['ZG']['value'], x['ZD']['value']),
label_opts=opts.LabelOpts(color=zs_color),
itemstyle_opts=opts.ItemStyleOpts(
color=zs_color,
opacity=0.2,
)) for x in temp_xd.zs_list
]
temp_xd = temp_xd.next
index = index + 1
chart_k.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True, data=data))
# 均线图
# ------------------------------------------------------------------------------------------------------------------
chart_ma = Line()
chart_ma.add_xaxis(xaxis_data=dts)
ma_keys = {"MA5": ma5, "MA34": ma34, "MA55": ma55, "MA233": ma233}
ma_colors = ["#39afe6", "#da6ee8", "#A02128", "#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:
try:
fx_dts = [x['date'] for x in fx]
except:
fx_dts = [x['dt'] for x in fx]
fx_val = [x['value'] 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 xd:
index = 0
xd_colors = zs_colors
while xd:
xd_dts = [x['date'] for x in xd]
xd_val = [x['value'] for x in xd]
chart_xd = Line()
chart_xd.add_xaxis(xd_dts)
xd_color = xd_colors[index % len(xd_colors)]
chart_xd.add_yaxis(
series_name="XD{}".format(index),
y_axis=xd_val,
is_selected=True,
symbol="triangle",
symbol_size=10,
linestyle_opts=opts.LineStyleOpts(color=xd_color,
width=index + 1,
type_="solid"),
itemstyle_opts=opts.ItemStyleOpts(color=xd_color))
chart_xd.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_xd)
xd = xd.next
index = index + 1
if bs:
b_dts = [x['date'] for x in bs if x['bs'] == 'buy']
if len(b_dts) > 0:
b_val = [x['value'] for x in bs if x['bs'] == '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['date'] for x in bs if x['bs'] == 'sell']
if len(s_dts) > 0:
s_val = [x['value'] for x in bs if x['bs'] == '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 draw(datelist, pricelist, title):
min_value = min(pricelist)
max_value = max(pricelist)
line = (
Line(init_opts=opts.InitOpts(
width='1800px',
height='800px',
js_host="./",
)).set_global_opts(
title_opts=opts.TitleOpts(title=title,
# subtitle='股票价格走势'
),
legend_opts=opts.LegendOpts(is_show=True,
pos_top=10,
pos_left="center",
item_width=30,
item_height=15,
textstyle_opts=opts.TextStyleOpts(
font_family='Microsoft Yahei',
font_size=14,
font_style='oblique')),
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"),
),
xaxis_opts=opts.AxisOpts(
# type_="time",
name='日期',
split_number=10,
name_gap=35,
axispointer_opts=opts.AxisPointerOpts(is_show=True),
name_textstyle_opts=opts.TextStyleOpts(
font_size=16, font_family='Microsoft Yahei')),
yaxis_opts=opts.AxisOpts(
type_="value",
# name='价格',
min_=min_value,
max_=max_value,
split_number=4,
axispointer_opts=opts.AxisPointerOpts(is_show=True),
name_textstyle_opts=opts.TextStyleOpts(
font_size=16, font_family='Microsoft Yahei'),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1))),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
# xaxis_index=[0, 1],
range_start=30,
range_end=70,
),
opts.DataZoomOpts(
is_show=True,
# xaxis_index=[0, 1],
type_="slider",
pos_top="96%",
range_start=38,
range_end=70,
),
],
).add_xaxis(xaxis_data=datelist).add_yaxis(
series_name="走势情况",
is_selected=True,
y_axis=pricelist,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="average", name="平均值")
])).render(title + '.html'))
"2015-6",
"2015-7",
"2015-8",
"2015-9",
"2015-10",
"2015-11",
"2015-12",
],
xaxis=opts.AxisOpts(
type_="category",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
axisline_opts=opts.AxisLineOpts(
is_on_zero=False,
linestyle_opts=opts.LineStyleOpts(color="#6e9ef1")),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
label=opts.LabelOpts(formatter=JsCode(js_formatter))),
),
).add_yaxis(
series_name="2015 降水量",
is_smooth=True,
symbol="emptyCircle",
is_symbol_show=False,
# xaxis_index=1,
color="#d14a61",
y_axis=[
2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3
],
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
def show_xd_kline(kline_raw, show=True):
freq = '5min'
start_dt = kline_raw.iloc[0]["dt"]
end_dt = kline_raw.iloc[-1]["dt"]
x_data = kline_raw.dt.values.tolist()
oclh = kline_raw[['open', 'close', 'low', 'high']].values.tolist()
symbol = kline_raw.iloc[0]['symbol']
kline = (
Kline(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name=symbol,
y_axis=oclh,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="缠论分析结果：%s-%s" % (symbol, freq),
subtitle="时间区间：%s 至 %s" % (start_dt, end_dt)
),
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=True, pos_top=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",
),
)
)
chan_fx = (
Scatter(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis("分型标记", kline_raw.fx.values.tolist(),
label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
)
)
chan_bi = (
Scatter(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis("笔标记", kline_raw.bi.values.tolist(),
label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
)
)
chan_xd = (
Scatter(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis("线段标记", kline_raw.xd.values.tolist(),
label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
)
)
bar = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="Volume",
yaxis_data=kline_raw.vol.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
kline = kline.overlap(chan_fx)
kline = kline.overlap(chan_bi)
kline = kline.overlap(chan_xd)
# Grid Overlap + Bar
grid_chart = Grid(opts.InitOpts(width="1400px", height="800px", theme=ThemeType.WHITE))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(
pos_left="8%", pos_right="8%", height="60%"
),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(
pos_left="8%", pos_right="8%", pos_top="70%", height="16%"
),
)
graph_path = os.path.join(cache_path, "%s_kline_%s.html" % (symbol, "fx"))
grid_chart.render(path=graph_path)
# 调用浏览器打开可视化结果
if show:
webbrowser.open(graph_path)
label_opts=opts.LabelOpts(is_show=False),
)
.extend_axis(
yaxis=opts.AxisOpts(
name="温度",
type_="value",
min_=0,
max_=25,
interval=5,
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
)
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),
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 = (
def kline_profession_example() -> Grid:
data = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
def calculate_ma(day_count: int, d):
result: List[Union[float, str]] = []
for i in range(len(d)):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(d[i - j][1])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
x_data = ["2017-7-{}".format(i + 1) for i in range(31)]
kline = (Kline().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="Dow-Jones index",
y_axis=data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
).set_global_opts(
title_opts=opts.TitleOpts(
title="复杂版 Kline 示例 (Kline + Line + Bar)",
subtitle="MA 就以(2, 4, 6, 8为例)",
),
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=x_data).add_yaxis(
series_name="MA2",
y_axis=calculate_ma(day_count=2, d=data),
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="MA4",
y_axis=calculate_ma(day_count=4, d=data),
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="MA6",
y_axis=calculate_ma(day_count=6, d=data),
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="MA8",
y_axis=calculate_ma(day_count=8, d=data),
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=x_data).add_yaxis(
series_name="Volume",
yaxis_data=[[i, data[i][3], 1 if data[i][0] > data[i][1] else -1]
for i in range(len(data))],
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
def kline_pro(kline: List[dict],
ma: List[dict],
macd: 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 = "1200px",
height: str = '680px') -> Grid:
"""绘制缠中说禅K线分析结果
:param kline: K线
:param ma: 均线
:param macd: MACD
:param fx: 分型识别结果
:param bi: 笔识别结果
: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])
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",
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]
vol = []
for row in kline:
item_style = red_item_style if row['close'] > row[
'open'] else green_item_style
bar = opts.BarItem(value=row['vol'],
itemstyle_opts=item_style,
label_opts=label_not_show_opts)
vol.append(bar)
macd_bar = []
for row in macd:
item_style = red_item_style if row['macd'] > 0 else green_item_style
bar = opts.BarItem(value=round(row['macd'], 4),
itemstyle_opts=item_style,
label_opts=label_not_show_opts)
macd_bar.append(bar)
diff = [round(x['diff'], 4) for x in macd]
dea = [round(x['dea'], 4) for x in macd]
# 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 = [x for x in ma[0].keys() if "ma" in x][:3]
ma_colors = ["#39afe6", "#da6ee8", "#00940b"]
for i, k in enumerate(ma_keys):
y_data = [x[k] for x in ma]
chart_ma.add_yaxis(series_name=k.upper(),
y_axis=y_data,
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 = Scatter()
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)", ))
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 = Scatter()
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['buy'] 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['sell'] 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 quanguo_wuhan_compare(city_list):
time = set()
for item in city_list:
time.add(item[0])
TIME = {}
for x in time:
TIME.update({x: (0, 0)})
for item in city_list:
if item[1] in province:
if item[1] != "湖北":
TIME.update(
{item[0]: (TIME[item[0]][0] + item[3], TIME[item[0]][1])})
else:
TIME.update(
{item[0]: (TIME[item[0]][0], TIME[item[0]][1] + item[3])})
time_others_wuhan = []
for k, v in TIME.items():
time_others_wuhan.append([k, v[0], v[1]])
time_others_wuhan.sort()
L1 = (Line().add_xaxis(
xaxis_data=[x[0] for x in time_others_wuhan]).add_yaxis(
series_name="全国其他地区",
y_axis=[x[1] for x in time_others_wuhan],
symbol_size=8,
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=1.5),
is_smooth=True,
).set_global_opts(
title_opts=opts.TitleOpts(title="湖北地区和全国其他地区确诊人数对比图",
subtitle="截止至2020年5月21日",
pos_left="center"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
axispointer_opts=opts.AxisPointerOpts(is_show=True,
link=[{
"xAxisIndex": "all"
}]),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
is_realtime=True,
start_value=30,
end_value=70,
xaxis_index=[0, 1],
)
],
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=True),
),
yaxis_opts=opts.AxisOpts(is_inverse=False, name="人数"),
legend_opts=opts.LegendOpts(pos_left="left"),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
feature={
"dataZoom": {
"yAxisIndex": "none"
},
"restore": {},
"saveAsImage": {},
},
),
))
L2 = (Line().add_xaxis(
xaxis_data=[x[0] for x in time_others_wuhan]).add_yaxis(
series_name="湖北地区",
y_axis=[x[2] for x in time_others_wuhan],
xaxis_index=1,
yaxis_index=1,
symbol_size=8,
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=1.5),
is_smooth=True,
).set_global_opts(
axispointer_opts=opts.AxisPointerOpts(is_show=True,
link=[{
"xAxisIndex": "all"
}]),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
xaxis_opts=opts.AxisOpts(
grid_index=1,
type_="category",
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=True),
position="top",
),
datazoom_opts=[
opts.DataZoomOpts(
is_realtime=True,
type_="inside",
start_value=30,
end_value=70,
xaxis_index=[0, 1],
)
],
yaxis_opts=opts.AxisOpts(is_inverse=True, name="确诊人数"),
legend_opts=opts.LegendOpts(pos_left="15%"),
))
(Grid(init_opts=opts.InitOpts(width="1024px", height="768px")).add(
chart=L1,
grid_opts=opts.GridOpts(pos_left=50, pos_right=50, height="35%")).add(
chart=L2,
grid_opts=opts.GridOpts(pos_left=50,
pos_right=50,
pos_top="55%",
height="35%"),
).render(path="compare.html"))
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",
yaxis_data=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",
yaxis_data=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 draw_kine(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")
stock_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
# k线图
kline = (
Kline().add_xaxis(stock_data[["TIME"]].values.tolist()).add_yaxis(
"K线图",
stock_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
title_opts=opts.TitleOpts(title="价格",
subtitle=ts_code,
pos_top="20%"),
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], # 设置第0轴和第1轴同时缩放
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,
),
],
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
# 成交量柱形图
x = stock_data[["TIME"]].values.tolist()
y = stock_data[["VOLUME"]].values[:, 0].tolist()
bar = (Bar().add_xaxis(x).add_yaxis(
"成交量",
y,
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_top="70%"),
legend_opts=opts.LegendOpts(is_show=False),
))
# 使用网格将多张图标组合到一起显示
grid_chart = Grid(init_opts=opts.InitOpts(
width="1200px",
height="600px",
animation_opts=opts.AnimationOpts(animation=False),
))
# 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(
stock_data.iloc[:, 1:5].values.tolist()))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="15%", pos_right="15%", height="55%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="15%",
pos_right="15%",
pos_top="70%",
height="20%"),
)
grid_chart.render('stock_{}.html'.format(ts_code)) #保存成用股票代码命名的文档
def daily_issue_plot(daily,cummulative):
import pyecharts.options as opts
from pyecharts.charts import Bar, Line
# color = ["#36648B","tomato"]
daily = daily.round(2)
cummulative = cummulative.round(2)
x_data = daily.index.astype(str).tolist()
bar = (
Bar(init_opts=opts.InitOpts(width=fig_width, height=fig_height))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="每日发行数量",
yaxis_index=2,
yaxis_data=daily['发行数量'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="每日发行总额",
yaxis_index=3,
yaxis_data=daily['发行总额'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="axis", axis_pointer_type="cross"
),
xaxis_opts=opts.AxisOpts(
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
name="数量",
type_="value",
# min_=0,
# max_=250,
# interval=50,
axislabel_opts=opts.LabelOpts(formatter="{value}"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
)
)
)
line = (
Line(init_opts=opts.InitOpts(width=fig_width, height=fig_height))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="累计发行数量",
yaxis_index=0,
y_axis=cummulative['累计发行数量'].tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(width=2),
).add_yaxis(
series_name="累计发行总额",
yaxis_index=1,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(width=2),
y_axis=cummulative['累计发行总额'].tolist(),
label_opts=opts.LegendOpts(is_show=False),
)
.extend_axis(yaxis=opts.AxisOpts(
name= "总额",
type_="value",
position='right',
offset=60,
axislabel_opts=opts.LabelOpts(formatter="{value}亿",position="right"),
),).extend_axis(
yaxis=opts.AxisOpts(
name="数量",
type_="value",
position="left",
offset=0,
axislabel_opts=opts.LabelOpts(formatter="{value}"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
name="总额",
type_="value",
position="left",
offset=60,
axislabel_opts=opts.LabelOpts(formatter="{value}亿"),
)
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#E8E8E8")
),
),
yaxis_opts=opts.AxisOpts(
name = "数量",
type_="value",
position="right",
offset=0,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=1)
),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(color='#E8E8E8')
),
),
toolbox_opts=opts.ToolboxOpts(is_show=True,
feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
background_color='white',
connected_background_color="white",
pixel_ratio=pixel_ratio,
name="pic",
),
)
),
)
)
line.overlap(bar)
return line
