def draw(self,freq=""):
kdata=[]
ktime=[]
kvol=[]
for i in range(len(self.klines_merge)):
current_kline = self.klines_merge[i]
onedata=[]
onedata.append(current_kline['open'])
onedata.append(current_kline['close'])
onedata.append(current_kline['low'])
onedata.append(current_kline['high'])
kdata.append(onedata)
ktime.append(current_kline['date'])
kvol.append(current_kline['volume'])
kline=(Kline().add_xaxis(ktime)
.add_yaxis(series_name= self.klines_merge[0]['code'],y_axis= kdata,
markline_opts=opts.MarkLineOpts(label_opts=opts.LabelOpts(position="middle", color="blue", font_size=15),
data=self.split_data_part(),
symbol=["circle", "none"],),
)
.set_global_opts(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)),
),
datazoom_opts = [opts.DataZoomOpts()],
title_opts=opts.TitleOpts(title=self.klines_merge[0]['code']) ,
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True, data=self.split_data_part())
)
)
bar = (Bar().add_xaxis(ktime).add_yaxis("",kvol))
path =self.klines_merge[0]['code']+"-"+freq+".html";
kline.render(path)
def candle(self, data, label="", xaxis=None, zoom_setting=[], position=0, show_rgb=False, mark_point=[], mark_line=[]):
zoomopt = []
for setting in zoom_setting:
zoomopt.append(
opts.DataZoomOpts(**setting)
)
if not xaxis: xaxis = self.xaxis
rgb_color = {
"itemstyle_opts": opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
}
if not show_rgb: rgb_color = {}
mark_point_item = []
for point in mark_point:
mark_point_item.append(
opts.MarkPointItem(**point),
)
return (Kline(init_opts=opts.InitOpts(**self.chart_opt))
.add_xaxis(xaxis_data=xaxis)
.add_yaxis(
series_name=label,
y_axis=data,
markpoint_opts=opts.MarkPointOpts(data=mark_point_item),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(
position="middle", color="blue", font_size=15
),
data=mark_line,
symbol=["circle", "none"],
),
**rgb_color
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True, data=[])
)
.set_global_opts(
title_opts=opts.TitleOpts(title="Candle Chart", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=position,
is_scale=True,
boundary_gap=False,
axislabel_opts=opts.LabelOpts(is_show=True),
),
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"),
datazoom_opts=zoomopt,
)
)
def line_with_mark_area():
line = Line(
init_opts=opts.InitOpts(theme='light', width='1000px', height='600px'))
line.add_xaxis(x_data)
line.add_yaxis('', y_data)
line.set_series_opts(markarea_opts=opts.MarkAreaOpts(
data=[opts.MarkAreaItem(name="黄金周", x=("2020/10/1", "2020/10/8"))]))
return line
def kline(self,
data,
title="K线图",
open="open",
close="close",
high="high",
low="low",
date=None,
blist=None,
slist=None,
positlist=None):
if type(data) != pd.DataFrame:
return None
if data.empty:
return None
if date == None:
xaxis = pd.Series(
data.index).apply(lambda x: x.strftime("%Y-%m-%d")).to_list()
else:
xaxis = pd.Series(data["date"]).apply(
lambda x: x.strftime("%Y-%m-%d")).to_list()
yaxis = data[[open, close, high, low]].values.tolist()
chart = Kline(init_opts=opts.InitOpts())
chart.add_xaxis(xaxis)
chart.add_yaxis(
"kline",
yaxis,
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]),
markpoint_opts=bsopts(blist=blist, slist=slist))
chart.set_series_opts(markarea_opts=opts.MarkAreaOpts(
data=positionsArea(positlist)))
# chart = maline(data).overlap(chart)
# chart.set_global_opts(
# 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)
# ),
# ),
# # datazoom_opts=datazoom,
# title_opts=opts.TitleOpts(title=title),
# )
return chart
def __draw_zs_area(_ka):
lines = []
scatters = []
for _zs in _ka.zs_list:
x_start = _zs['start_point']['dt']
if _zs['zs_finished']:# 中枢完成
x_end = _zs['end_point']['dt']
chart_b = EffectScatter()
if 'buy3' in _zs:
chart_b.add_xaxis([_zs['buy3']['dt']])
chart_b.add_yaxis(series_name=_ka.freq if aggregation else "B", y_axis=[_zs['buy3']['xd']], is_selected=False, symbol="circle", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="red",))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
scatters.append(chart_b)
elif 'sell3' in _zs:
chart_b.add_xaxis([_zs['sell3']['dt']])
chart_b.add_yaxis(series_name=_ka.freq if aggregation else "S", y_axis=[_zs['sell3']['xd']], is_selected=False, symbol="circle", symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(color="green",))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts, legend_opts=legend_not_show_opts)
scatters.append(chart_b)
elif len(_zs['points'])>=5:# 中枢成立但未完成，有3笔或段以上
x_end = _zs['points'][-1]['dt']
else: # 中枢未完成，且未确定
continue
ZD = _zs['ZD']
ZG = _zs['ZG']
area_data=[[ {'xAxis': x_start, 'yAxis': ZD, 'value': ZD }, {'xAxis': x_end, 'yAxis': ZG, 'value': ZG }]]
line = (Line()
.add_xaxis([x_start, x_end])
.add_yaxis(series_name=_ka.freq if aggregation else "ZS", y_axis=[ZD, ZG], symbol='none'
"""
, markpoint_opts = opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="ZG"),
opts.MarkPointItem(type_="min", name="ZD"),
]
)
"""
, markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(
position="middle", color="blue", font_size=15,
),
linestyle_opts=opts.LineStyleOpts(type_="dashed"),
data=area_data,
symbol=["none", "none"],
)
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(data=area_data, itemstyle_opts=opts.ItemStyleOpts(color="#dcdcdc",opacity=0.1))
))
lines.append(line)
return lines, scatters
def line_with_mark_area():
line = Line(init_opts=opts.InitOpts(theme='essos', width='1600px', height='450px'))
line.add_xaxis(x_data)
line.add_yaxis('百天情感趋势图', y_data)
# 设置标记区域
line.set_series_opts(
markarea_opts=opts.MarkAreaOpts(
data=[
opts.MarkAreaItem(name="情绪低落期", x=("1.20", "2.8"))
]
)
)
return line
def kline(data, buy=[], sell=[], zone=[], title="K线图", height="250px"):
if data.empty:
return Kline(init_opts=opts.InitOpts(width="100%", height=height))
xaxis = pd.Series(
data.index).apply(lambda x: x.strftime("%Y-%m-%d")).to_list()
yaxis = data[["open", "close", "high", "low"]].values.tolist()
chart = Kline(init_opts=opts.InitOpts(width="100%", height=height))
chart.add_xaxis(xaxis)
chart.add_yaxis(
"kline",
yaxis,
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
# markline_opts=opts.MarkLineOpts(
# data=[opts.MarkLineItem(type_="max", value_dim="close")]
# ),
markpoint_opts=opts.MarkPointOpts(
data=buy.apply(buypoint, axis=1).values.tolist() +
(sell.apply(sellpoint, axis=1).values.tolist())),
)
chart.set_series_opts(markarea_opts=opts.MarkAreaOpts(
data=zone.apply(bszone, axis=1).values.tolist()))
# chart = chart.overlap(maline(data))
# chart.set_global_opts(
# 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)
# ),
# ),
# datazoom_opts=[opts.DataZoomOpts(
# pos_bottom="0px",
# range_start= 100.00-(5000.00/len(xaxis)),
# range_end= 100.00,
# )],
# title_opts=opts.TitleOpts(title=title),
# )
return chart
def kline(data, title="K线图", height="250px"):
# logging.debug(data)
if data.empty:
return Kline(init_opts=opts.InitOpts(width="100%", height=height))
xaxis = pd.Series(
data.index).apply(lambda x: x.strftime("%Y-%m-%d")).to_list()
yaxis = data[["open", "close", "high", "low"]].values.tolist()
chart = Kline(init_opts=opts.InitOpts(width="100%", height=height))
chart.add_xaxis(xaxis)
chart.add_yaxis(
"kline",
yaxis,
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="max", value_dim="close")]),
markpoint_opts=opts.MarkPointOpts(
data=bspoint(data),
label_opts=opts.LabelOpts(
position="top",
color="#000",
font_size=8,
),
),
)
chart.set_series_opts(markarea_opts=opts.MarkAreaOpts(data=bsArea))
chart = maline(data).overlap(chart)
chart.set_global_opts(
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)),
),
datazoom_opts=datazoom,
title_opts=opts.TitleOpts(title=title),
)
return chart
def geoStation(self, data) -> Geo:
"""
-- 全国火车站及开行列车数量分布图
设置主题：theme=ThemeType.DARK，背景颜色：bg_color="#e0f0e9"，设置高亮状态下的样式
:param data:list:[(车站名称，值)]
:return: Geo
"""
station_map = (
Geo(init_opts=opts.InitOpts(
width='1000px', height='500px',
theme=ThemeType.DARK)).add_schema(
maptype="china",
zoom=1.2,
itemstyle_opts=opts.ItemStyleOpts(color="#323c48",
border_color="#111"),
emphasis_itemstyle_opts=opts.ItemStyleOpts(
color="#323c48"), # 高亮状态下的多边形样式(省份)
emphasis_label_opts=opts.LabelOpts(
is_show=False) # 高亮状态下的标签样式(省份名称)
).add_coordinate_json(self.file_json) # 从json文件导入坐标值
.add('', data[:2990], symbol_size=2,
color='#057748').set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markarea_opts=opts.MarkAreaOpts(
is_silent=False)) # 关闭各省名称显示
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_='color',
min_=0,
max_=100),
title_opts=opts.TitleOpts(
title='全国火车站及开行列车数量分布图',
title_textstyle_opts=opts.TextStyleOpts(
font_size=20), # 设置标题大小
subtitle='全国3500+个车站，开行列车最多的前三个车站是：{}，{}，{}... {}'.format(
data[0][0], data[1][0], data[2][0],
self.data_source))))
return station_map
axispointer_opts=opts.AxisPointerOpts(is_show=True)
# 分隔线
splitline_opts=opts.SplitLineOpts(is_show=True),
)
)
.set_series_opts(
# 区域标记
markarea_opts=opts.MarkAreaOpts(
is_silent=False,
data=[
opts.MarkAreaItem(
name='',
x=(year-0.5,year+0.5),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color="#aaaaaa", opacity=0.1
),
)
]
)
)
)
#------------------------------------- 配件及其他常用格式详解 -------------------------------------#
line=(
Line( # 官方说明链接 下同
# 初始化 https://pyecharts.org/#/zh-cn/global_options?id=initopts%ef%bc%9a%e5%88%9d%e5%a7%8b%e5%8c%96%e9%85%8d%e7%bd%ae%e9%a1%b9
width = "900px",
height = "500px",
def positionsArea(list):
# 持仓范围
return opts.MarkAreaOpts(
data=list.apply(positionsitem, axis=1).values.tolist())
import datetime
import math
import pyecharts.options as opts
import random
from pyecharts.charts import *
from pyecharts.components import Table
from pyecharts.faker import POPULATION
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = "https://cdn.kesci.com/lib/pyecharts_assets/"
# %%
# 直角坐标系图表
# 直方图
x_data = ['apple', 'huawei', 'xiaomi', 'oppo', 'vivo', 'meizu']
y_data = [123, 312, 89, 107, 82, 23]
bar = (Bar().add_xaxis(x_data).add_yaxis('', y_data))
bar.render()
# %%
# 折线图
x_data = ['apple', 'huawei', 'xiaomi', 'oppo', 'vivo', 'meizu']
y_data = [123, 312, 89, 107, 82, 23]
bar = (Line().add_xaxis(x_data).add_yaxis('', y_data))
bar.render()
# %%
# 折线图
x_data = ['apple', 'huawei', 'xiaomi', 'oppo', 'vivo', 'meizu']
y_data = [[random.randint(100, 200) for i in range(10)] for item in x_data]
Box = Boxplot()
Box.add_xaxis(x_data)
Box.add_yaxis('', Box.prepare_data(y_data))
bar.render()
)
bar.add_xaxis(month_columns)
bar.add_yaxis(
'降水量', # 设置系列名。
data_precipitation, # 配置数据源。
itemstyle_opts=opts.ItemStyleOpts( # 配置图形。
color='CornFlowerBlue'))
bar.add_yaxis('蒸发量', data_evaporation)
bar.set_global_opts(
title_opts=opts.TitleOpts( # 设置标题和副标题，或用字典形式也可。
title='条形图示例', subtitle='降水量、蒸发量显示'),
datazoom_opts=opts.DataZoomOpts(is_show=True), # 设置显示滚动条。
yaxis_opts=opts.AxisOpts(max_=200), # 配置y轴最大值为200，类似方法可配置x轴。
toolbox_opts=opts.ToolboxOpts(), # 配置工具箱栏。
)
bar.set_series_opts(
label_opts=opts.LabelOpts(is_show=False), # 设置在图表上显示具体数值，默认为True，此处设置为不显示。
markpoint_opts=opts.MarkAreaOpts( # 设置显示数据标记点。
data=[
opts.MarkPointItem(type_='max'), # 标记最大值点。
opts.MarkPointItem(type_='min'), # 标记最小值点。
]),
markline_opts=opts.MarkLineOpts( # 设置显示数据标记线。
data=[
opts.MarkLineItem(type_='average',
name='平均') # 标记平均值线和线名称，线名称会在鼠标浮于其上时显示。
]))
bar.render('{}.html'.format(__file__))
.set_global_opts(
title_opts=opts.TitleOpts(title="一天用电量分布", subtitle="纯属虚构"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(boundary_gap=False),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} W"),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
dimension=0,
pieces=[
{"lte": 6, "color": "green"},
{"gt": 6, "lte": 8, "color": "red"},
{"gt": 8, "lte": 14, "color": "green"},
{"gt": 14, "lte": 17, "color": "red"},
{"gt": 17, "color": "green"},
],
),
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(
data=[
opts.MarkAreaItem(name="早高峰", x=("07:30", "10:00")),
opts.MarkAreaItem(name="晚高峰", x=("17:30", "21:15")),
]
)
)
.render("distribution_of_electricity.html")
)
),
title_opts=opts.TitleOpts(title="各时段的订单数量与销售额"),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
))
line = (
Line().add_xaxis(x_data).add_yaxis(
"销售额",
t3["analyze_t3.sales"].values.tolist(),
yaxis_index=0,
#color="#ff5798",
#color="#BFEFFF",
label_opts=opts.LabelOpts(is_show=False),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(data=[
opts.MarkAreaItem(name="午高峰", x=("12", "15")),
opts.MarkAreaItem(name="晚高峰", x=("17", "20")),
])))
# x_data = ["{}月".format(i) for i in range(1, 13)]
# line = (
# Line()
# .add_xaxis(x_data)
# .add_yaxis(
# "平均温度",
# [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
# # yaxis_index=2,
# # color="#675bba",
# # label_opts=opts.LabelOpts(is_show=False),
# )
# )
pieces=[
{
"lte": 6,
"color": "green"
},
{
"gt": 6,
"lte": 8,
"color": "red"
},
{
"gt": 8,
"lte": 14,
"color": "green"
},
{
"gt": 14,
"lte": 17,
"color": "red"
},
{
"gt": 17,
"color": "green"
},
],
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(data=[
opts.MarkAreaItem(name="早高峰", x=("07:30", "10:00")),
opts.MarkAreaItem(name="晚高峰", x=("17:30", "21:15")),
])).render("distribution_of_electricity.html"))
def draw_chart(df,
extend_field,
save_path="",
trade_list=None,
point_list=None,
area_list=None,
kline_title="买卖点K线图",
is_smooth=True,
is_connect_nones=False):
datetime_array = df.datetime.tolist()
volume_array = df.volume.tolist()
# open close low high
kline_data = df[["open", "close", "low", "high"]].values.tolist()
# 绘制买卖点
_point_list = []
# 绘制标志点
if point_list is not None and len(point_list) > 1:
for point in point_list:
if type(point["datetime"]) == str:
point_datetime = point["datetime"]
else:
point_datetime = point["datetime"].strftime(
"%Y-%m-%d %H:%M:%S.%f")
point_name = point.get("name", "")
_point_list.append(
opts.MarkPointItem(
name=point_name,
coord=[point_datetime, point["y"]],
value=point_name if point_name else point["y"],
itemstyle_opts=opts.ItemStyleOpts(
color=point.get("color", "#ef232a"))))
else:
if trade_list is not None and len(trade_list) > 1:
for trade in trade_list:
# 开多
if type(trade[0]) == str:
trade_datetime = trade[0]
else:
trade_datetime = trade[0].strftime(
"%Y-%m-%d %H:%M:%S.%f")
if trade_datetime < datetime_array[0]:
continue
if trade[1] == "多" and trade[4] == "开":
_point_list.append(
opts.MarkPointItem(
name="开多",
coord=[trade_datetime, trade[6]],
value="开多",
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a")))
# 开空
elif trade[1] == "空" and trade[4] == "开":
_point_list.append(
opts.MarkPointItem(
name="开空",
coord=[trade_datetime, trade[6]],
value="开空",
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a")))
# 平多
elif trade[1] == "多" and (trade[4] == "平" or trade[4]
== "平今" or trade[4] == "平昨"):
_point_list.append(
opts.MarkPointItem(
name="平空",
coord=[trade_datetime, trade[6]],
value="平空",
itemstyle_opts=opts.ItemStyleOpts(
color="#14b143")))
# 平空
elif trade[1] == "空" and (trade[4] == "平" or trade[4]
== "平今" or trade[4] == "平昨"):
_point_list.append(
opts.MarkPointItem(
name="平多",
coord=[trade_datetime, trade[6]],
value="平多",
itemstyle_opts=opts.ItemStyleOpts(
color="#14b143")))
# 绘制矩形
_area_list = []
if area_list is not None and len(area_list) > 1:
for item in area_list:
_area_list.append(
opts.MarkAreaItem(
x=item["x"],
y=item["y"],
itemstyle_opts=opts.ItemStyleOpts(
color="transparent",
border_color=item.get("border_color", "#4D0099"),
border_width=3,
border_type="dashed"),
))
kline = (Kline().add_xaxis(xaxis_data=datetime_array).add_yaxis(
series_name="Kline",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
markpoint_opts=opts.MarkPointOpts(data=_point_list),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
data=_area_list, )).set_global_opts(
title_opts=opts.TitleOpts(title=kline_title,
pos_left="2%",
pos_top="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="88%",
range_start=0,
range_end=100,
),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
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": "#00da3c"
},
{
"value": -1,
"color": "#ec0000"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
bar = (Bar().add_xaxis(xaxis_data=datetime_array).add_yaxis(
series_name="Volume",
y_axis=volume_array,
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),
))
if len(extend_field) > 0:
line = (Line().add_xaxis(
xaxis_data=datetime_array).set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category"),
legend_opts=opts.LegendOpts(is_show=True),
))
for field in extend_field:
field_value_array = df[field].tolist()
line.add_yaxis(
series_name=field,
y_axis=field_value_array,
is_smooth=is_smooth,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.8),
label_opts=opts.LabelOpts(is_show=False),
is_connect_nones=is_connect_nones,
)
# Kline And Line
kline = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid(init_opts=opts.InitOpts(
width="100%",
height="760px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="3.5%",
pos_right="3.5%",
height="60%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="3.5%",
pos_right="3.5%",
pos_top="70%",
height="16%"),
)
grid_chart.render(os.path.join(save_path, "KLineChart.html"))
def signal_overview(price,
trades=None,
supplements=None,
plot_options={},
f_name="1213.html"):
figs_counts = len(supplements)
legends_heights = (figs_counts * 5)
left = 90 - legends_heights
height = left / figs_counts
sub_plots = []
fig_size = plot_options.setdefault("fig_size", ("1000px", "600px"))
x_data = price.index.tolist()
y_data = price.values.tolist()
marks = supplements.get("marks")
with_marks = True if marks is not None else False
if with_marks:
_ = pd.Series(index=price.index, data=np.arange(len(price)))
marks_data = [
opts.MarkPointItem(coord=[(x_data[int(l)]), (y_data[int(l)])],
itemstyle_opts=opts.ItemStyleOpts(
color=plot_options.setdefault(
v[0], colors[i]),
opacity=0.6),
symbol_size=20,
symbol="pin")
for i, v in enumerate(marks.items())
for l in _.reindex(v[1]).dropna().values.tolist()
]
else:
marks_data = []
with_trades = True if isinstance(trades, pd.Series) else False
if with_trades:
e_loc = [
price.index.get_loc(i)
for i in trades.index.get_level_values(0).unique()
]
ex_loc = [
price.index.get_loc(i)
for i in trades.index.get_level_values(1).unique()
]
pairs_loc = list(zip(e_loc, ex_loc))
(pairs_loc)
_b = [{"gt": p[0], "lte": p[1], "color": "#CD6839"} for p in pairs_loc]
_c = [{
"gt": pairs_loc[i][-1],
"lte": p[0],
"color": "#4A708B"
} for i, p in enumerate(pairs_loc[1:])]
_a = [{
"lte": pairs_loc[0][0],
"color": "#4A708B"
}, {
"gt": pairs_loc[-1][-1],
"color": colors[0]
}]
pieces = _a + _b + _c
else:
pieces = []
ds = np.unique(price.index.date)
decor_ = price.groupby(
price.index.date).apply(lambda x: [x.index[0], x.index[-1]]).loc[[
l[1] for l in list(filter(lambda x: x[0] % 2 == 0, enumerate(ds)))
]]
data_zoom_axis = np.arange(figs_counts).astype(int).tolist()
line = (Line(
init_opts=opts.InitOpts(width=fig_size[0], height=fig_size[1])
).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="价格",
y_axis=y_data,
yaxis_index=0,
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2),
markpoint_opts=opts.MarkPointOpts(data=marks_data, ),
).set_global_opts(
title_opts=opts.TitleOpts(title="价格",
pos_left='center',
pos_top=str(figs_counts * 5) + "%"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
border_width=1,
),
legend_opts=opts.LegendOpts(pos_left="left"),
axispointer_opts=opts.AxisPointerOpts(is_show=True,
link=[{
"xAxisIndex": "all"
}]),
datazoom_opts=[
opts.DataZoomOpts(is_show=True,
is_realtime=True,
xaxis_index=data_zoom_axis,
pos_top="5%",
range_start=0,
range_end=100,
orient="horizontal",
type_="inside"),
opts.DataZoomOpts(type_="slider",
xaxis_index=data_zoom_axis,
pos_bottom="bottom"),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
axistick_opts=opts.AxisTickOpts(is_inside=False),
axispointer_opts=opts.AxisPointerOpts(is_show=True),
axisline_opts=opts.AxisLineOpts(symbol='none'),
splitline_opts=opts.SplitLineOpts(is_show=True)),
xaxis_opts=opts.AxisOpts(is_show=False, ),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
is_piecewise=with_trades,
dimension=0,
pieces=pieces,
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(data=[
opts.MarkAreaItem(
name="",
x=(d[0], d[1]),
itemstyle_opts=opts.ItemStyleOpts(color="#A1A9AF", opacity=0.2),
) for d in decor_
])))
sub_plots.append(line)
if with_marks:
_ = supplements.pop("marks")
if len(supplements) > 0:
last_pic = list(supplements.keys())[-1]
for i, s_1 in enumerate(supplements.items()):
k, v = s_1[0], s_1[1]
print(k)
ls = Line(
init_opts=opts.InitOpts(width=fig_size[0], height=fig_size[1]))
ls = ls.add_xaxis(xaxis_data=price.index.tolist())
for j, s_2 in enumerate(v.items()):
n, l = s_2[0], s_2[1]
print(n)
num = i * len(s_1) + j
ls.add_yaxis(
series_name=n,
y_axis=l.tolist(),
yaxis_index=len(sub_plots),
linestyle_opts=opts.LineStyleOpts(
width=2, color=plot_options.setdefault(n,
colors[num])),
label_opts=opts.LabelOpts(is_show=False),
)
s = legends_heights + height * len(sub_plots)
ls = ls.set_global_opts(
title_opts=opts.TitleOpts(title=k,
pos_left='center',
pos_top=str(s) + "%"),
legend_opts=opts.LegendOpts(pos_left="left",
pos_top=str(len(sub_plots) * 5) +
"%"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
axistick_opts=opts.AxisTickOpts(is_inside=False),
axispointer_opts=opts.AxisPointerOpts(is_show=True),
axisline_opts=opts.AxisLineOpts(symbol='none'),
splitline_opts=opts.SplitLineOpts(is_show=True)),
xaxis_opts=opts.AxisOpts(is_show=(k == last_pic), ),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
data=[
opts.MarkAreaItem(
name="",
x=(d[0], d[1]),
itemstyle_opts=opts.ItemStyleOpts(color="#A1A9AF",
opacity=0.2),
) for d in decor_
],
is_silent=False,
))
sub_plots.append(ls)
pos = [
i + "%" for i in (height * np.arange(len(sub_plots)) +
legends_heights + 4.3).astype(str)
]
g = (Grid(init_opts=opts.InitOpts(width=fig_size[0], height=fig_size[1])))
for i, v in enumerate(sub_plots):
g.add(
chart=v,
grid_opts=opts.GridOpts(pos_left=50,
pos_right=50,
pos_top=pos[i],
height=str(height - 5) + "%"),
)
g.render(f_name)
def line(delivery_dates, df):
# line the vix
FLINE_OPT = opts.LineStyleOpts(opacity=1, width=1.5)
OLINE_OPT = opts.LineStyleOpts(opacity=0.9, width=1.2, type_='dashed')
df['delivery'] = [10 + 5 * (idx in delivery_dates) for idx in df.index]
warning_areas = get_warning_areas(df)
# there is a bug in pyecharts, the colors are reversed
# line.colors = ['crimson', 'gold', 'green', 'fuchsia', 'orchid', 'plum']
line = (
Line().add_xaxis(xaxis_data=df.index).add_yaxis(
"vix",
df[0],
is_symbol_show=False,
areastyle_opts=opts.AreaStyleOpts(opacity=0.2),
linestyle_opts=FLINE_OPT,
color='powderblue',
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="min", name="ivl"),
opts.MarkLineItem(type_="max", name="ivh"),
])).add_yaxis('gvz',
df['gvz'],
is_symbol_show=False,
linestyle_opts=FLINE_OPT,
color='olive',
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="min", name="ivl"),
opts.MarkLineItem(type_="max", name="ivh"),
])).add_yaxis(
'ovx',
df['ovx'],
is_symbol_show=False,
linestyle_opts=FLINE_OPT,
color='tan',
# is_selected = False,
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="min", name="ivl"),
opts.MarkLineItem(type_="max", name="ivh"),
])).add_yaxis(
"1",
df[1],
is_symbol_show=False,
color='plum',
linestyle_opts=OLINE_OPT).add_yaxis(
"2",
df[2],
is_symbol_show=False,
color='orchid',
linestyle_opts=OLINE_OPT,
is_selected=False).add_yaxis(
"3",
df[3],
is_symbol_show=False,
color='fuchsia',
linestyle_opts=OLINE_OPT).add_yaxis(
"4",
df[4],
is_symbol_show=False,
color='green',
linestyle_opts=OLINE_OPT,
is_selected=False).add_yaxis(
"5",
df[5],
is_symbol_show=False,
color='gold',
linestyle_opts=OLINE_OPT)
# add delivery date mark, not a good idea, but worked.
.add_yaxis("delivery",
df['delivery'],
is_symbol_show=False,
color='crimson',
is_step=True).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markarea_opts=opts.MarkAreaOpts(
is_silent=True,
data=[
opts.MarkAreaItem(name="warn", x=(xs, xe))
for xs, xe in warning_areas
],
itemstyle_opts=opts.ItemStyleOpts(
color='#FFA54F10'))).set_global_opts(
title_opts=opts.TitleOpts(title="vix",
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)),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="line"),
datazoom_opts=opts.DataZoomOpts(
type_="slider",
range_start=50,
range_end=100),
))
return line
def draw_chart(cls, data):
kline = (
Kline().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
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(
label_opts=opts.LabelOpts(position="middle",
color="blue",
font_size=15),
data=cls.split_data_part(data),
symbol=["circle", "none"],
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True, data=cls.split_data_part(data))).
set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", 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)),
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="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0,
2],
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0,
3],
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=data["times"])
# # EMA10
# .add_yaxis(
# series_name="EMA10",
# y_axis=data["ema10"],
# xaxis_index=2,
# yaxis_index=2,
# label_opts=opts.LabelOpts(is_show=False),
# )
# EMA144
# .add_yaxis(
# series_name="EMA144",
# y_axis=data["ema144"],
# xaxis_index=2,
# yaxis_index=2,
# label_opts=opts.LabelOpts(is_show=False),
# )
# boll_median
.add_yaxis(
series_name="median",
y_axis=data["boll_median"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
# boll_upper
.add_yaxis(
series_name="upper",
y_axis=data["boll_upper"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
# boll_lower
.add_yaxis(
series_name="lower",
y_axis=data["boll_lower"],
xaxis_index=2,
yaxis_index=2,
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["times"]).add_yaxis(
series_name="Volumn",
yaxis_data=data["vols"],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
# 根据 echarts demo 的原版是这么写的
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode("""
# function(params) {
# var colorList;
# if (data.datas[params.dataIndex][1]>data.datas[params.dataIndex][0]) {
# colorList = '#ef232a';
# } else {
# colorList = '#14b143';
# }
# return colorList;
# }
# """)
# )
# 改进后在 grid 中 add_js_funcs 后变成如下
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["times"]).add_yaxis(
series_name="MACD",
yaxis_data=data["macds"],
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["times"]).add_yaxis(
series_name="DIF",
y_axis=data["difs"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=data["deas"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
rsi_line = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="rsi6",
y_axis=data["rsi6"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="rsi12",
y_axis=data["rsi12"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="rsi24",
y_axis=data["rsi24"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
# MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(
init_opts=opts.InitOpts(width="1400px", height="800px"))
# 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="4%",
pos_right="1%",
height="45%"),
)
# Volumn 柱状图
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
pos_top="56%",
height="10%"),
)
# MACD DIFS DEAS
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
pos_top="68%",
height="14%"),
)
# rsi6
grid_chart.add(
rsi_line,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
pos_top="84%",
height="10%"),
)
return grid_chart.render_embed()
def draw(df, file_png="test.png"):
x = df.dt.to_list()
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="K线周期图表", 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="97%", 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=1.5),
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=1.5),
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)
df1 = df[df['基准价格'] > 0]
c = (
Scatter()
.add_xaxis(df1['出现时间'].to_list())
.add_yaxis(
"买卖点",
df1['基准价格'].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="1400px", height="680px"))
grid_chart.add_js_funcs("var barData = {}".format(df[['open', 'close', 'low', 'high']].values.tolist()))
grid_chart.add_js_funcs("var bsName = {}".format(df1[["操作提示", "基准价格"]].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%"),
)
file_html = file_png.replace(".png", '.html')
make_snapshot(snapshot, grid_chart.render(path=file_html), file_png)
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_chan(stock, stock_df, kc, end_time, period): #
stock_df_original = stock_df[[
'date', 'open', 'close', 'low', 'high', 'money'
]]
stock_df_bi = kc.getFenBI_df()[['date', 'chan_price']]
stock_df_xd = kc.getFenDuan_df()[['date', 'chan_price']]
kline = (
# Kline({"theme": ThemeType.DARK})
Kline().add_xaxis(
xaxis_data=stock_df_original['date'].tolist(),
).add_yaxis(
series_name=stock,
y_axis=stock_df_original[['open', 'close', 'low',
'high']].tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c"), #添加用 Hex 字符串表示的红和绿两种颜色，对应着 K线涨和跌的颜色。
).set_global_opts(
title_opts=opts.TitleOpts(title="缠论", ),
xaxis_opts=opts.AxisOpts(type_="category"),
# 分割线
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),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
#xaxis_index=[0, 1],
range_start=
0, #添加两个「数据区域缩放」功能，一个看的到（用鼠标拉缩图最下面的 slider），一个看不到（用鼠标直接在图中拉缩），并且设置 xaxis_index =[0,1]，表示用 K 线图（index 0）来控制柱状图（index 1）。
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
#xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=
0, #index为1和2的两幅图的数据局部伸缩跟着index0那幅图，这样就实现了用一根x轴的slider可以任意缩放三幅图的数据
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross", #将两幅图的提示框合并在一起。
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=3,
is_piecewise=True,
pieces=[
{
"value": -1,
"color": "#ec0000"
},
{
"value": 1,
"color": "#00da3c"
},
],
), #坐标轴指示器配置和区域选择组件配置使得数据和轴可以一起联动。
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",
),
))
Biline = (
Line().add_xaxis(xaxis_data=stock_df_bi['date'].tolist()).add_yaxis(
series_name='笔',
y_axis=stock_df_bi['chan_price'].tolist(),
is_smooth=False,
is_connect_nones=True,
is_hover_animation=False,
#linestyle_opts=opts.LineStyleOpts(color="red",width=2, type_="dashed"),
linestyle_opts=opts.LineStyleOpts(color="red",
width=2,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=True),
)
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
overlap = kline.overlap(Biline)
crp = CentralRegionProcess(kc.getFenDuan_df(), kc, isdebug=False)
crp.define_central_region()
stock_zs_x, stock_zs_y = crp.convert_to_graph_data()
if (stock_df_xd is not None) and (len(stock_zs_x) != 0):
# print('if')
for i in range(0, len(stock_zs_x), 2):
XD_line = (
Line().add_xaxis(xaxis_data=stock_df_xd['date'].tolist()).
add_yaxis(
series_name='段',
y_axis=stock_df_xd['chan_price'].tolist(),
is_smooth=False,
is_connect_nones=True,
is_hover_animation=False,
#linestyle_opts=opts.LineStyleOpts(color="red",width=2, type_="dashed"),
linestyle_opts=opts.LineStyleOpts(color="blue",
width=2,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True,
data=[[{
'xAxis': stock_zs_x[i],
'yAxis': stock_zs_y[i]
}, {
'xAxis': stock_zs_x[i + 1],
'yAxis': stock_zs_y[i + 1]
}]]))
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
elif stock_df_xd is not None:
# print('elif')
XD_line = (
Line().add_xaxis(xaxis_data=stock_df_xd['date'].tolist()).
add_yaxis(
series_name='段',
y_axis=stock_df_xd['chan_price'].tolist(),
is_smooth=False,
is_connect_nones=True,
is_hover_animation=False,
#linestyle_opts=opts.LineStyleOpts(color="red",width=2, type_="dashed"),
linestyle_opts=opts.LineStyleOpts(color="blue",
width=2,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
))
overlap = overlap.overlap(XD_line)
# MA_line = (
# Line()
# .add_xaxis(xaxis_data=stock_df_original['date'].tolist(),)
# .add_yaxis(
# series_name="MA5",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 5).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add_yaxis(
# series_name="MA10",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 10).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add_yaxis(
# series_name="MA13",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 13).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add_yaxis(
# series_name="MA34",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 34).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
# )
# Volume_Bar = (
# Bar()
# .add_xaxis(xaxis_data=stock_df_original['date'].tolist())
# .add_yaxis(
# series_name="money",
# yaxis_data=stock_df_original['money'].tolist(),
# xaxis_index=2,
# yaxis_index=2,
# label_opts=opts.LabelOpts(is_show=False),
# markline_opts=opts.MarkLineOpts(
# data=[opts.MarkLineItem(type_="average", name="平均值")]
# ),
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode(
# """
# function(params) {
# var colorList;
# if (params.data >= 0) {
# colorList = '#ef232a';
# } else {
# colorList = '#14b143';
# }
# return colorList;
# }
# """
# # """
# # 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='',
# ),
# xaxis_opts=opts.AxisOpts(
# type_="category",
# grid_index=2,
# is_scale=True,
# # axislabel_opts=opts.LabelOpts(is_show=False),
# 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=2,
# 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),
# splitarea_opts=opts.SplitAreaOpts(
# is_show=True,
# areastyle_opts=opts.AreaStyleOpts(opacity=1)
# ),
# ),
# # xaxis_opts=opts.AxisOpts(is_scale=True),
# legend_opts=opts.LegendOpts(is_show=False),
# datazoom_opts=opts.DataZoomOpts(type_="inside"),
# )
# )
#
# overlap = overlap.overlap(Volume_Bar)
print("it's here")
if len(stock_zs_x) != 0:
for i in range(0, len(stock_zs_x), 2):
zs1_line = (Line().add_xaxis(
xaxis_data=stock_zs_x[i:i + 2], ).add_yaxis(
series_name="中枢",
y_axis=np.append(stock_zs_y[i], stock_zs_y[i]),
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs2_line = (Line().add_xaxis(
xaxis_data=stock_zs_x[i:i + 2], ).add_yaxis(
series_name="中枢",
y_axis=np.append(stock_zs_y[i + 1:i + 2],
stock_zs_y[i + 1:i + 2]),
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs3_line = (Line().add_xaxis(xaxis_data=np.append(
stock_zs_x[i:i + 1], stock_zs_x[i:i + 1])).add_yaxis(
series_name="中枢",
y_axis=stock_zs_y[i:i + 2],
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs4_line = (Line().add_xaxis(xaxis_data=np.append(
stock_zs_x[i + 1:i + 2], stock_zs_x[i + 1:i + 2])).add_yaxis(
series_name="中枢",
y_axis=stock_zs_y[i:i + 2],
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs1 = zs1_line.overlap(zs2_line)
zs2 = zs3_line.overlap(zs4_line)
zs = zs1.overlap(zs2)
overlap.overlap(zs)
overlap.render("diagram/{0}@{1}#{2}.html".format(stock[0:6],
end_time[0:10], period))
print("it's done")
def draw_chart():
kline = (Kline().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143"),
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(
label_opts=opts.LabelOpts(position="middle",
color="blue",
font_size=15),
data=split_data_part(),
symbol=["circle", "none"],
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True, data=split_data_part())).set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", 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)),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(is_show=True,
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),
],
))
kline_line = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5),
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",
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
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 = kline.overlap(kline_line)
bar_1 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="Volumn",
y_axis=data["vols"],
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",
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
bar_2 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MACD",
y_axis=data["macds"],
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",
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
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["times"]).add_yaxis(
series_name="DIF",
y_axis=data["difs"],
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
overlap_bar_line = bar_2.overlap(line_2)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px", height="800px", theme=ThemeType.ESSOS))
grid_chart.add_js_funcs("var barData={}".format(data["datas"]))
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("professional_kline_chart.html")
def grid_vertical(xaxis, taixin_yaxis, yali_yaxis, td_yaxis):
max_taixin = max(taixin_yaxis) + 20
min_taixin = min(taixin_yaxis) - 60
if min_taixin <= 0:
min_taixin = 0
line1 = (Line().add_xaxis(xaxis).add_yaxis(
"胎心率",
taixin_yaxis,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="胎心率平均值")]),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="胎心率最大值"),
]),
).add_yaxis("下限", [120, 120],
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")])).add_yaxis(
"上限", [160, 160],
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average")])).
add_yaxis(
"胎动",
td_yaxis,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="None", name="胎动"),
]),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_='value',
interval=120,
splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(formatter="{value}秒"),
),
yaxis_opts=opts.AxisOpts(
type_='value',
interval=10,
min_=min_taixin,
max_=max_taixin,
splitline_opts=opts.SplitLineOpts(is_show=True)),
title_opts=opts.TitleOpts(title="eFM-60超声多普勒胎儿监护仪监测曲线图"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True, ),
).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markarea_opts=opts.MarkAreaOpts(data=[
opts.MarkAreaItem(name="合理区间",
y=(120, 160),
itemstyle_opts=opts.ItemStyleOpts(
color="#A0D897", opacity=0.2)),
])))
line2 = (
Line().add_xaxis(xaxis).add_yaxis(
"宫内压力",
yali_yaxis,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_='value',
interval=120,
splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(formatter="{value}秒"),
),
yaxis_opts=opts.AxisOpts(
type_='value',
interval=10,
min_=0,
max_=100,
splitline_opts=opts.SplitLineOpts(is_show=True)),
# title_opts=opts.TitleOpts(title="")
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
legend_opts=opts.LegendOpts(pos_top="50%"),
).set_series_opts(label_opts=opts.LabelOpts(is_show=False), ))
# 把上面生成的两个图放进grid中并通过pos_top，pos_bottom, pos_left, pos_right设置其位置
grid = (Grid(init_opts=opts.InitOpts(width="3000px", height="600px")).add(
line2, grid_opts=opts.GridOpts(pos_top='60%', pos_left='2%')).add(
line1, grid_opts=opts.GridOpts(pos_bottom="60%", pos_left='2%')))
# 生成到本地网页形式打开，也可自己设置保存成png图片，因为网页的使用更方便，自己按情况使用
grid.render('1.html')
# 输出保存为图片
make_snapshot(snapshot, grid.render(), "1.png")
print(">>图表生成完毕！")
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
oclh,
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
# markpoint_opts=opts.MarkPointOpts(
# data=[
# opts.MarkPointItem(type_="max", name="最大值"),
# opts.MarkPointItem(type_="min", name="最小值"),
# ]
# )
)
kline.set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True,
data=markareadata,
itemstyle_opts=opts.ItemStyleOpts(opacity=0.5, )))
kline.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True),
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(type_="inside", range_start=-100),
opts.DataZoomOpts(pos_bottom="0%"),
],
# title_opts=opts.TitleOpts(title=stock_code),
)
