y = []
for i in data.loc[:, ["chicknum"]].values.tolist():
y.append(int(i[0]))
z = []
for i in data.loc[:, ["title"]].values.tolist():
z.append(i[0])
(Scatter(init_opts=opts.InitOpts(width="1600px", height="700px")).add_xaxis(
xaxis_data=x).add_yaxis(
series_name="点击数",
y_axis=[list(i) for i in zip(y, z)],
symbol_size=15,
label_opts=opts.LabelOpts(is_show=True),
).set_series_opts().set_global_opts(
title_opts=opts.TitleOpts(title="学院官网通知点击数"),
xaxis_opts=opts.AxisOpts(
type_="time", splitline_opts=opts.SplitLineOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) {return '标题 : ' + params.value[2];}")),
visualmap_opts=opts.VisualMapOpts(type_="color",
max_=5000,
min_=0,
dimension=1),
).render("showChickNum.html"))
def stack_bar_percent() -> Bar:
list2 = [
{
"value": 12,
"percent": 12 / (12 + 3)
},
{
"value": 23,
"percent": 23 / (23 + 21)
},
{
"value": 33,
"percent": 33 / (33 + 5)
},
{
"value": 3,
"percent": 3 / (3 + 52)
},
{
"value": 33,
"percent": 33 / (33 + 43)
},
]
list3 = [
{
"value": 3,
"percent": 3 / (12 + 3)
},
{
"value": 21,
"percent": 21 / (23 + 21)
},
{
"value": 5,
"percent": 5 / (33 + 5)
},
{
"value": 52,
"percent": 52 / (3 + 52)
},
{
"value": 43,
"percent": 43 / (33 + 43)
},
]
c = (Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_xaxis([
1, 2, 3, 4, 5
]).add_yaxis(
"product1", list2, stack="stack1", category_gap="50%"
).add_yaxis(
"product2",
list3, stack="stack1",
category_gap="50%"
).set_series_opts(label_opts=opts.LabelOpts(
position="right",
formatter=JsCode(
"function(x){return Number(x.data.percent * 100).toFixed() + '%';}"
),
)))
return c
def genGeo(data):
c = (
Geo(init_opts=opts.InitOpts(
width='1800px',
height='1000px',
bg_color='#0F1C3C',
))
.add_schema(
maptype='china',
itemstyle_opts=opts.ItemStyleOpts(
border_color=JsCode(
"""
new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: '#00F6FF'
}, {
offset: 1,
color: '#53D9FF'
}], false)
"""
),
color0='rgba(10,76,139,1)',
border_width=1.0,
opacity=0.8,
color="#012366",
),
label_opts=opts.LabelOpts(
is_show=False,
color='rgba(255,144,128,1)',
),
emphasis_itemstyle_opts=opts.ItemStyleOpts(
area_color='rgba(0,0,0,0)'
),
emphasis_label_opts=opts.LabelOpts(
is_show=False
),
zoom=1.2,
)
.add( # 添加散点图
"",
data,
symbol_size=20,
color='#EA7552',
label_opts=opts.LabelOpts(
is_show=True,
color="#0f0",
border_width=2.,
font_size=18,
position="inside",
formatter=JsCode(
"""
function f(param){
return param.name + ','+ param.value[2]/10
}
"""
),
)
)
.set_series_opts(
range_size=[12, 40],
itemstyle_opts=opts.ItemStyleOpts(
border_width=1.0,
border_color='#215495',
),
areastyle_opts=opts.AreaStyleOpts(
color={
"x": 0,
"y": 0,
"x2": 0,
"y2": 1,
"colorStops": [{
"offset": 0,
"color": '#073684'
}, {
"offset": 1,
"color": '#061E3D'
}],
}
)
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="全国本科院校分布图",
pos_left='40%',
pos_top='10%',
title_textstyle_opts=opts.TextStyleOpts(
color='white',
font_size=20
)
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
type_='size',
min_=4*10,
max_=80*10,
),
legend_opts=opts.LegendOpts(
is_show=False
),
tooltip_opts=opts.TooltipOpts(
trigger="item",
formatter=JsCode(
"""
function f(param){
return param.name + ','+ param.value[2]/10
}
"""
)
)
)
.render("university-geo.html")
)
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Bar, Timeline
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker
x = Faker.choose()
tl = Timeline()
for i in range(2015, 2020):
bar = (Bar().add_xaxis(x).add_yaxis("商家A", Faker.values()).add_yaxis(
"商家B", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts("某商店{}年营业额 - With Graphic 组件".format(i)),
graphic_opts=[
opts.GraphicGroup(
graphic_item=opts.GraphicItem(
rotation=JsCode("Math.PI / 4"),
bounding="raw",
right=100,
bottom=110,
z=100,
),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(left="center",
top="center",
z=100),
graphic_shape_opts=opts.GraphicShapeOpts(
width=400, height=50),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"),
),
def get_images(x_data, y_data, filename_, series_name='x1_dist'):
plot_line_path = '.'
filename = plot_line_path + filename_
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c = (Scatter(init_opts=opts.InitOpts(bg_color=JsCode(
background_color_js))).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name=series_name,
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True,
position="top",
color="white"),
itemstyle_opts=opts.ItemStyleOpts(color="red",
border_color="#fff",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js),
opacity=1),
).set_global_opts(
title_opts=opts.TitleOpts(
title="show %s in one image" % series_name,
pos_top="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff",
font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
min_='dataMin',
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside", range_start=0,
range_end=100),
],
legend_opts=opts.LegendOpts(is_show=False),
))
c.render(filename)
def anylize_to_html_01(data={}, info=''):
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.charts import Scatter, Bar, Timeline, Line, Page, Tab
from pyecharts.components import Table
tab0 = Tab()
data.loc[(data['max_back_1'] != 0),
'赢撤比'] = -1 * data['end_1'] / data['max_back_1']
data.loc[(data['max_back_1'] == 0), '赢撤比'] = 0
data = data[data['end_1'] != 0]
clm = list(set(data['celue_name'].tolist()))
print(clm)
title = f'所有参数的，每个指标的统计状态:'
tjdf = pd.DataFrame()
i = 0
tj_index = {
'end_1': '最终收益',
'sharp_rate_1': '夏普率',
'max_back_1': '最大回撤',
'win_rate_2': '胜率',
'trade_nums_1': '交易次数',
'profit_days_1': '盈利天数',
'赢撤比': '赢撤比'
}
s_tj_index = []
for clm0 in clm:
print(clm0)
df = data[data['celue_name'] == clm0].copy()
for k, v in df.groupby('para1'):
print(k)
index_0 = i + 1
i = i + 1
tjdf.loc[index_0, 'para1'] = k
tjdf.loc[index_0, 'clm'] = clm0
for tj_ix in tj_index.keys():
tjdf.loc[index_0, tj_index[tj_ix]] = v[tj_ix].mean()
tjdf.loc[index_0, '赢撤比2'] = -1 * v['end_1'].mean(
) / v['max_back_1'].mean() if v['max_back_1'].mean() != 0 else 0
print(tjdf)
print(tjdf.keys())
tj_index2 = list(tjdf.keys())
tj_index2.remove('para1')
tj_index2.remove('clm')
clm2 = tj_index2
for x, clm0 in enumerate(clm):
df0 = tjdf[tjdf['clm'] == clm0].copy()
tl = Timeline()
for k, i in enumerate(tj_index2):
xdata, ydata1 = df0['para1'], df0[['para1', i]]
print(xdata)
print(ydata1)
# exit()
bar0 = (Bar().add_xaxis(xdata.tolist()).add_yaxis(
'均值',
ydata1.values.tolist(),
label_opts=opts.LabelOpts(is_show=False, ),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
offset=0,
name='参数名：',
type_="value",
is_show=True,
axistick_opts=opts.AxisTickOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
offset=0,
name='均值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
title_opts=opts.TitleOpts(title=f"{title}==:{i}"),
legend_opts=opts.LegendOpts(pos_right=True),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) { return '==para1：'+params.value[0] +' <br/>== 指标：'+ params.value[1]}"
), ),
))
tl.add(bar0, f"{i}")
tab0.add(tl, clm0)
for x, clm0 in enumerate(clm2):
tl = Timeline()
tjdf0 = tjdf[tjdf['最终收益'] != 0].copy()
tjdf0.sort_values(clm0, ascending=True, inplace=True)
df0 = tjdf0.iloc[-1 * int(tjdf0.shape[0] * 0.12):].copy()
index_list = list(df0.keys())
index_list.remove('clm')
df0 = df0[index_list]
for k, i in enumerate(index_list):
xdata, ydata1 = df0['para1'], df0[[i, 'para1']]
print(ydata1)
sca0 = (Scatter().add_xaxis(xdata.tolist()).add_yaxis(
'分布值',
ydata1.values.tolist(),
label_opts=opts.LabelOpts(is_show=False, ),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
offset=0,
name='参数名：',
type_="value",
is_show=True,
axistick_opts=opts.AxisTickOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
offset=0,
name='分布值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
title_opts=opts.TitleOpts(title=f"{title}==:{i}"),
legend_opts=opts.LegendOpts(pos_right=True),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) { return '==para1：'+params.value[0] +' <br/>== 指标：'+ params.value[1]}"
), ),
))
tl.add(sca0, f"{i}")
tab0.add(tl, clm0)
tab0.render(f"{info}.html")
print(f"{info}.html")
return
line_color_js = """
new echarts.graphic.LinearGradient(
0,0,0,1,
[{offset: 0,
color: '#2e9afe'},
{
offset: 0.3,
color: '#01a9db'},
{
offset: 0.6,
color: '#f5bca9'},
{
offset: 1,
color: '#b40404'}]
)
"""
# 执行绘图
knowledgeGraph = (Graph().set_global_opts(title_opts=opts.TitleOpts(
title="Python知识图谱")).add(
series_name="",
links=linkList,
nodes=nodeList,
layout="circular",
is_rotate_label=True,
linestyle_opts=opts.LineStyleOpts(color=JsCode(js_code=line_color_js),
curve=0.3,
width=1.5),
itemstyle_opts=opts.ItemStyleOpts(color="#474747")).render(
path=os.path.join(resultPath, resultFileName)))
"rating": movie["rating"]
})
data_pair = []
tooltip = {}
for i in range(len(genres)):
data_pair.append([genres[i], len(meta_data[i])])
tmp = sorted([[
t["title"].replace("\"", """).replace("\'", "&apos"),
t["rating"]
] for t in meta_data[i]],
key=lambda t: t[1],
reverse=True)[:5]
tooltip[genres[i]] = tmp
tooltip_json = json.dumps(tooltip)
# print(tooltip["Drama"])
pie = Pie()
pie.add_js_funcs("data = JSON.parse(\'" + tooltip_json + "\');")
pie.add(
series_name="",
data_pair=data_pair,
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode(
"function(params){ text = \'\'; for (i = 0; i < 5; i++) { title = data[params.name][i][0]; rating = data[params.name][i][1]; text += title + \': \' + rating + '<br>'}; return text;}"
)))
pie.set_global_opts(title_opts=opts.TitleOpts(title="IMDB Pie"),
legend_opts=opts.LegendOpts(is_show=False))
pie.render("pie.html")
def kline(self):
data = self.calculate().to_dict('list')
kline = (
Kline().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="portfolio index",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
datazoom_opts=[
opts.DataZoomOpts(is_show=False,
type_="inside",
xaxis_index=[0, 0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0, 1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0, 2],
range_end=100),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
kline_line = (Line().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="MA5",
y_axis=self.calculate_ma(day_count=5, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA10",
y_axis=self.calculate_ma(day_count=10, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA20",
y_axis=self.calculate_ma(day_count=20, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA30",
y_axis=self.calculate_ma(day_count=30, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
))
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
# Bar-1
bar_1 = (Bar().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="Volumn",
y_axis=data["volume"],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (Bar().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="MACD",
y_axis=data["MACD"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="DIFF",
y_axis=data["DIFF"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=data["DEA"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
# 最下面的柱状图和折线图
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(
init_opts=opts.InitOpts(width="1500px", height="750px"))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))
# K线图和 MA5 的折线图
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
height="60%"),
)
# Volumn 柱状图
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="71%",
height="10%"),
)
# MACD DIFS DEAS
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="82%",
height="14%"),
)
makedir(self.path, 'kline')
grid_chart.render(
f"{self.path}\\kline\\{str(self.names)}_{time.strftime('%Y-%m-%d', time.localtime())}.html"
)
),
datazoom_opts=opts.DataZoomOpts(
is_show=True,
pos_bottom='2%',
pos_top='94%'
)
)
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="本科院校",
y_axis=y1_data,
label_opts=opts.LabelOpts(
is_show=False
),
bar_width='25%',
itemstyle_opts={
"normal": {
"color": JsCode(
"""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: '#fccb05'
}, {
offset: 1,
color: '#f5804d'
}])"""
),
"barBorderRadius": 11,
}
}
)
).render("university-top.html")
x_data = ["14", "15", "16", "17", "18", "19", "20", "21", "22", "23"]
y_data = [393, 438, 485, 631, 689, 824, 987, 1000, 1100, 1200]
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c = (
Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="注册总量",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True, position="top", color="white"),
itemstyle_opts=opts.ItemStyleOpts(
color="red", border_color="#fff", border_width=3
),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),
"value": 3,
"percent": 3 / (12 + 3)
},
{
"value": 21,
"percent": 21 / (23 + 21)
},
{
"value": 5,
"percent": 5 / (33 + 5)
},
{
"value": 52,
"percent": 52 / (3 + 52)
},
{
"value": 43,
"percent": 43 / (33 + 43)
},
]
c = (Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_xaxis([
1, 2, 3, 4, 5
]).add_yaxis("product1", list2, stack="stack1", category_gap="50%").add_yaxis(
"product2", list3, stack="stack1", category_gap="50%"
).set_series_opts(label_opts=opts.LabelOpts(
position="right",
formatter=JsCode(
"function(x){return Number(x.data.percent * 100).toFixed() + '%';}"),
)).render("stack_bar_percent.html"))
def create_mixed_line_and_bar(type, title, x_list, data_list, data_unit1, data_unit2 = None):
"""
:param type: 1-折线用于表示变化率and单柱状图+单折线 2-折线用于表示变化率and多柱状图+多折线
3-折线表示占比and单柱状图+单折线 4-折线表示占比and多柱状图+多折线
5-折线用于描述另一维度指标（此时data_list中最后一个k-v为折线数据）
:param title: 图表名称
:param x_list: 横坐标内容为时间序列，纵坐标高度为多个(同级)指标的总和，具体各个指标的数值、比例以不同颜色标出
:param data_list: 字典{key1:value1,key2:value2...}，构成柱状图的多个不同类型的bar
:param data_unit1: 第一根y轴的数值单位
:param data_unit2: 第二根y轴的数值单位
:return:
"""
c = Bar(init_opts=opts.InitOpts(height="600px",width="900px",theme=ThemeType.MACARONS))
c.add_xaxis(x_list)
if type == 1: # 因为有overlap，所以需要自适应调整刻度范围，是的bar和line都能显示出来
all_rate_list = []
for key in data_list.keys(): # 单柱状图只有一个bar
change_rate_list = [0]
c.add_yaxis(key, data_list[key], bar_width="20%",label_opts=opts.LabelOpts(is_show=True))
for i in range(1,len(data_list[key])):
if data_list[key][i-1] == 0 or data_list[key][i-1] is None or data_list[key][i] is None: # 注意分母不为0
change_rate_list.append(None)
else:
change_rate_list.append(round((data_list[key][i]-data_list[key][i-1])/data_list[key][i-1]*100,2))
all_rate_list.append(change_rate_list)
c.extend_axis(yaxis=opts.AxisOpts(
name="变化率",
type_="value",
min_=int(round((getMinRate(all_rate_list)-(getMaxRate(all_rate_list)-getMinRate(all_rate_list)))/10)*10),
max_=int(round((getMaxRate(all_rate_list)/10)*10)),
interval=10,
axislabel_opts=opts.LabelOpts(formatter="{value} %"),
))
line = (
Line()
.add_xaxis(xaxis_data=x_list)
.add_yaxis(
series_name="增长率",
yaxis_index=1,
y_axis=all_rate_list[0],
label_opts=opts.LabelOpts(is_show=True, formatter=JsCode(
"function(x){console.log(x);return Number(x.data[1]).toFixed(2) + '%';}"
)),
)
)
c.set_global_opts(title_opts=opts.TitleOpts(title=title,# subtitle="数据来源国家统计局",
# pos_left="center",pos_bottom="0px",
# item_gap=2,
# subtitle_textstyle_opts=opts.TextStyleOpts(color="black")
),
legend_opts=opts.LegendOpts(pos_left="right"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit1,
# name_location="end",
# name_gap=5,
# # name_rotate=45,
min_=0, # 基本上指标数据都是为正的
max_=int(round(getMaxData(data_list)/10)*10)*2,
interval=10,
))
c.overlap(line)
elif type == 2:
count = len(data_list.keys())
all_rate_list = []
keys_list = []
for key in data_list.keys():
keys_list.append(key)
change_rate_list = [0]
c.add_yaxis(key, data_list[key], label_opts=opts.LabelOpts(is_show=True))
for i in range(1,len(data_list[key])):
if data_list[key][i-1] == 0 or data_list[key][i-1] is None or data_list[key][i] is None: # 注意分母不为0
change_rate_list.append(None)
else:
change_rate_list.append(round((data_list[key][i]-data_list[key][i-1])/data_list[key][i-1]*100,2))
all_rate_list.append(change_rate_list)
c.extend_axis(yaxis=opts.AxisOpts(
name="变化率",
type_="value",
min_=int(
round((getMinRate(all_rate_list) - (getMaxRate(all_rate_list) - getMinRate(all_rate_list))) / 10) * 10),
max_=int(round((getMaxRate(all_rate_list) / 10) * 10)),
interval=10,
axislabel_opts=opts.LabelOpts(formatter="{value} %"),
))
all_line = []
for i in range(count):
line = (
Line()
.add_xaxis(xaxis_data=x_list)
.add_yaxis(
series_name=keys_list[i] + "增长率",
yaxis_index=1,
y_axis=all_rate_list[i],
label_opts=opts.LabelOpts(is_show=True, formatter=JsCode(
"function(x){console.log(x);return Number(x.data[1]).toFixed(2) + '%';}"
)),
)
)
all_line.append(line)
c.set_global_opts(title_opts=opts.TitleOpts(title=title, # subtitle="数据来源国家统计局",
pos_left="center",pos_bottom="0px",
# item_gap=2,
# subtitle_textstyle_opts=opts.TextStyleOpts(color="black")
),
legend_opts=opts.LegendOpts(pos_left="right"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit1,
# name_location="end",
# name_gap=5,
# # name_rotate=45,
min_=0,
max_=int(round(getMaxData(data_list)/10)*10)*2,
interval=10,
))
for line in all_line:
c.overlap(line)
src_path = "./指标-年度-图片生成/"
html_file_name = src_path + title + ".html"
img_file_name = src_path + title + ".png"
make_snapshot(snapshot, c.render(html_file_name), img_file_name)
print(img_file_name+":生成完毕...")
def draw_chart(ts_code, start_date, end_date):
'''
pyecharts V1 版本开始支持链式调用
文档地址 https://pyecharts.org/#/zh-cn/
'''
stock_data = get_process_datas(ts_code, start_date, end_date)
stock_data.index = pd.to_datetime(stock_data['TIME'], format="%Y/%m/%d")
x = stock_data["TIME"].values.tolist()
stock_basic_data = stock_data[[
"TIME", "OPEN", "CLOSE", "LOW", "HIGH", "VOLUME"
]]
# stock_data = stock_data.sort_index(ascending=True) # 倒序，看时间顺序是否正常决定是不是要用
# k线图
kline = (
Kline().add_xaxis(x).add_yaxis(
"K线图",
stock_basic_data.iloc[:, 1:5].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True, is_show=False),
# axis_opts=opts.AxisOpts(is_scale=True,min_=0), #y轴起始坐标可以设为0
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
), # y轴起始坐标可自动调整
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='20%'),
title_opts=opts.TitleOpts(title="股票价格",
subtitle=ts_code,
pos_left='22%',
pos_top="20%"),
#把所有的x轴连接在一起
# axispointer_opts=opts.AxisPointerOpts(is_show=True,
# link=[{"xAxisIndex": "all"}],
# label=opts.LabelOpts(background_color="#777"),
# ),
datazoom_opts=[ # 设置zoom参数后即可缩放
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1, 2, 3, 4], # 设置第0轴和第1轴同时缩放
range_start=0,
range_end=100,
),
],
))
# 成交量柱形图
bar_volumn = (Bar().add_xaxis(x).add_yaxis(
"成交量",
stock_basic_data["VOLUME"].values.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
title_opts=opts.TitleOpts(title="成交量", pos_left='22%', pos_top="48%"),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='48%'),
))
#绘制均线图
line_ma = (
Line().add_xaxis(x).add_yaxis(
series_name="MA5",
y_axis=stock_data["MA5"].values.tolist(),
is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="MA10",
y_axis=stock_data["MA10"].values.tolist(),
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="MA20",
y_axis=stock_data["MA20"].values.tolist(),
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
title_opts=opts.TitleOpts(title="MA",
pos_left='22%',
pos_top="88%"),
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
is_scale=True),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='85%'),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
#绘制jdk线
line_kdj = (Line().add_xaxis(x).add_yaxis(
series_name="K",
y_axis=stock_data["K"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="D",
y_axis=stock_data["D"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="J",
y_axis=stock_data["J"].values.tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
title_opts=opts.TitleOpts(title="KDJ", pos_left='22%', pos_top="62%"),
datazoom_opts=[opts.DataZoomOpts(type_="inside", )],
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="7%",
pos_top='60%'),
xaxis_opts=opts.AxisOpts(
is_scale=True,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
#绘制macd柱状图
bar_macd = (Bar().add_xaxis(x).add_yaxis(
series_name='MACD',
yaxis_data=stock_data['MACD'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
title_opts=opts.TitleOpts(title="MACD", pos_left='22%', pos_top="75%"),
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='70%'),
))
#绘制DIF和DEA
line_macd = (Line().add_xaxis(x).add_yaxis(
series_name="DIF",
y_axis=stock_data['DIF'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="DEA",
y_axis=stock_data['DEA'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
#绘制布林线
line_boll = (Line().add_xaxis(x).add_yaxis(
series_name="MID",
y_axis=stock_data["MID"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="UPPER",
y_axis=stock_data["UPPER"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="LOWER",
y_axis=stock_data["LOWER"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
datazoom_opts=[opts.DataZoomOpts(type_="inside", )],
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
overlap_kline_linema = kline.overlap(line_boll)
overlap_macd = bar_macd.overlap(line_macd)
# 使用网格将多张图标组合到一起显示
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
# 为了把 data.datas 的数据写入到html中作为全局变量,目前无法跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(
stock_basic_data.iloc[:, 1:5].values.tolist()))
grid_chart.add(
overlap_kline_linema,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="3%",
height="40%"),
)
grid_chart.add(
bar_volumn,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="45%",
height="10%"),
)
grid_chart.add(line_kdj,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="60%",
height="10%"))
grid_chart.add(overlap_macd,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="72%",
height="10%"))
grid_chart.add(line_ma,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="85%",
height="10%"))
grid_chart.render('stock_{}/stock_{}.html'.format(
ts_code, ts_code)) # 保存成用股票代码命名的文档
return 0
def plot_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 = pd.DataFrame(ka.kline)
df = macd(df)
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)
"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(
series_name="2016 降水量",
"globalCoord":
False
})
).set_global_opts(title_opts=opts.TitleOpts(
title="拜登和川普最新支持率",
title_link="https://new.qq.com/omn/20201004/20201004A0098Z00.html",
pos_left="32%",
pos_top="15%",
title_textstyle_opts=opts.TextStyleOpts(
color="white",
font_size=30,
))))
return l1
l1 = buildLiquid("拜登", [0.65], ["30%", "50%"])
l2 = buildLiquid("川普", [0.35], ["70%", "50%"])
grid = (Grid(init_opts=opts.InitOpts(bg_color=JsCode("""
new echarts.graphic.RadialGradient(0.3, 0.3, 0.8, [{
offset: 0,
color: '#431ab8'
}, {
offset: 1,
color: '#471bba'
}])
"""))).add(l1, grid_opts=opts.GridOpts()).add(
l2, grid_opts=opts.GridOpts()))
grid.render("multiple_liquid.html")
# 多个水球图显示
l1 = (Liquid().add("lq", [0.6, 0.7], center=["60%", "50%"],
shape="diamond").set_global_opts(title_opts=opts.TitleOpts(
title="多个Liquid显示")))
# JsCode里面的%只能是单引号，双引号不会报错，但是结果也不会显示
# Math.floor() 向下取整
l2 = Liquid().add(
"lq",
[0.3254],
center=["25%", "50%"],
shape="diamond",
label_opts=opts.LabelOpts(
font_size=50,
formatter=JsCode("""function (param){
return (Math.floor(param.value * 10000)/100) + '%';
}"""),
position="inside",
),
)
grid = Grid().add(l1, grid_opts=opts.GridOpts()).add(l2,
grid_opts=opts.GridOpts())
grid.render("multiple_liquid.html")
# Section2 平行坐标系
from pyecharts.charts import Parallel
data = [
[1, 91, 45, 125, 0.82, 34, 23, "良"],
[2, 65, 27, 78, 0.86, 45, 29, "良"],
def anylize_to_html_02(data={}, fl_style='para1', info='', add_info=''):
# 根据生成的原始数据
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.charts import Scatter, Bar, Timeline, Line, Page, Tab
from pyecharts.components import Table
tab0 = Tab()
data.loc[(data['max_back_1'] != 0),
'赢撤比'] = -1 * data['end_1'] / data['max_back_1']
data.loc[(data['max_back_1'] == 0), '赢撤比'] = 0
data = data[data['end_1'] != 0]
clm = list(set(data['celue_name'].tolist()))
print(clm)
title = f'所有参数的，每个指标的统计状态:'
tjdf = pd.DataFrame()
tj_dict = {}
# 生成分类数据tjdf
i = 0
tj_index = {
'end_1': '最终收益',
'sharp_rate_1': '夏普率',
'max_back_1': '最大回撤',
'win_rate_2': '胜率',
'trade_nums_1': '交易次数',
'profit_days_1': '盈利天数',
'赢撤比': '赢撤比'
}
for clm0 in clm:
print(clm0)
df = data[data['celue_name'] == clm0].copy()
for k, v in df.groupby(fl_style):
print(k)
index_0 = i + 1
i = i + 1
tjdf.loc[index_0, fl_style] = k
tjdf.loc[index_0, 'clm'] = clm0
for tj_ix in tj_index.keys():
tjdf.loc[index_0, tj_index[tj_ix]] = v[tj_ix].mean()
tjdf.loc[index_0, '赢撤比2'] = -1 * v['end_1'].mean(
) / v['max_back_1'].mean() if v['max_back_1'].mean() != 0 else 0
print(tjdf)
print(tjdf.keys())
# exit()
filter_col = '夏普率'
df0_dict = {} #策略名+参数的字典。
tjdf2 = pd.DataFrame()
for x, clm0 in enumerate(clm):
# 每个策略类，按照filter_col排序，根据每个策略回测数量，选取比较高的排名的策略
df0 = tjdf[tjdf['clm'] == clm0].copy()
df0.sort_values(filter_col, ascending=True, inplace=True)
df0 = df0.iloc[-1 * int(df0.shape[0] * 0.35):]
df0_dict[str(df0.iloc[-1]['clm'])] = list(
sorted(set(df0[fl_style].tolist())))
tjdf2 = tjdf2.append(df0, ignore_index=True)
tjdf2.sort_values('最终收益', ascending=True, inplace=True)
tjdf2 = tjdf2.iloc[int(tjdf2.shape[0] * 0.1):]
print(tjdf2)
tj_index2 = list(tjdf2.keys())
tj_index2.remove(fl_style)
tj_index2.remove('clm')
clm2 = tj_index2
for x, clm0 in enumerate(clm2):
tl = Timeline()
tjdf0 = tjdf2[tjdf2['最终收益'] != 0].copy()
tjdf0.sort_values(clm0, ascending=True, inplace=True)
# df0 = tjdf0.iloc[-1*int(tjdf0.shape[0]*0.12):].copy()
df0 = tjdf0
index_list = list(df0.keys())
index_list.remove('clm')
df0 = df0[index_list]
for k, i in enumerate(index_list):
xdata, ydata1 = df0[fl_style], df0[[i, fl_style]]
print(add_info)
# print( ydata1)
sca0 = (Scatter().add_js_funcs(
f"document.write('%s')" % add_info
).add_xaxis(xdata.tolist()).add_yaxis(
'分布值',
ydata1.values.tolist(),
label_opts=opts.LabelOpts(is_show=False, ),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
offset=0,
name='参数名：',
type_="value",
is_show=True,
axistick_opts=opts.AxisTickOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(
offset=0,
name='分布值：',
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True)),
title_opts=opts.TitleOpts(title=f"{title}==:{i}"),
legend_opts=opts.LegendOpts(pos_right=True),
tooltip_opts=opts.TooltipOpts(formatter=JsCode(
"function (params) { return '==canshu：'+params.value[0] +' <br/>== 指标：'+ params.value[1]}"
), ),
))
tl.add(sca0, f"{i}")
tab0.add(tl, clm0)
tl.render(f"{info}.html")
print(f"{info}.html")
return df0_dict
RADAR: str = "radar"
SANKEY: str = "sankey"
SCATTER: str = "scatter"
SCATTER3D: str = "scatter3D"
SUNBURST: str = "sunburst"
THEMERIVER: str = "themeRiver"
TREE: str = "tree"
TREEMAP: str = "treemap"
WORDCLOUD: str = "wordCloud"
CUSTOM: str = "custom"
ToolTipFormatterType = {
_ChartType.GEO:
JsCode("""function (params) {
return params.name + ' : ' + params.value[2];
}"""),
_ChartType.GAUGE:
"{a} <br/>{b} : {c}%",
}
class _ThemeType:
BUILTIN_THEMES = ["light", "dark", "white"]
LIGHT = "light"
DARK = "dark"
WHITE = "white"
CHALK: str = "chalk"
ESSOS: str = "essos"
INFOGRAPHIC: str = "infographic"
MACARONS: str = "macarons"
("海南", [110.3893, 19.8516, 300]),
("上海", [121.4648, 31.2891, 1300]),
]
c = (
Map3D()
.add_schema(
itemstyle_opts=opts.ItemStyleOpts(
color="rgb(5,101,123)",
opacity=1,
border_width=0.8,
border_color="rgb(62,215,213)",
),
map3d_label=opts.Map3DLabelOpts(
is_show=False,
formatter=JsCode("function(data){return data.name + " " + data.value[2];}"),
),
emphasis_label_opts=opts.LabelOpts(
is_show=False,
color="#fff",
font_size=10,
background_color="rgba(0,23,11,0)",
),
light_opts=opts.Map3DLightOpts(
main_color="#fff",
main_intensity=1.2,
main_shadow_quality="high",
is_main_shadow=False,
main_beta=10,
ambient_intensity=0.3,
),
197.20820681141507,
],
],
"outliers": [],
},
]
(Boxplot(init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(
xaxis_data=axis_data).add_yaxis(
series_name="category0",
y_axis=data[0]["boxData"],
tooltip_opts=opts.TooltipOpts(
formatter=JsCode("""function(param) { return [
'Experiment ' + param.name + ': ',
'upper: ' + param.data[0],
'Q1: ' + param.data[1],
'median: ' + param.data[2],
'Q3: ' + param.data[3],
'lower: ' + param.data[4]
].join('<br/>') }""")),
).add_yaxis(
series_name="category1",
y_axis=data[1]["boxData"],
tooltip_opts=opts.TooltipOpts(
formatter=JsCode("""function(param) { return [
'Experiment ' + param.name + ': ',
'upper: ' + param.data[0],
'Q1: ' + param.data[1],
'median: ' + param.data[2],
'Q3: ' + param.data[3],
'lower: ' + param.data[4]
].join('<br/>') }""")),
def line_color_with_js_func() -> Line:
x_data = ["14", "15", "16", "17", "18", "19", "20", "21", "22", "23"]
y_data = [393, 438, 485, 631, 689, 824, 987, 1000, 1100, 1200]
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c = (Line(init_opts=opts.InitOpts(bg_color=JsCode(
background_color_js))).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="注册总量",
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True,
position="top",
color="white"),
itemstyle_opts=opts.ItemStyleOpts(color="red",
border_color="#fff",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js),
opacity=1),
).set_global_opts(
title_opts=opts.TitleOpts(
title="OCTOBER 2015",
pos_bottom="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff",
font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
legend_opts=opts.LegendOpts(is_show=False),
))
return c
def new_label_opts():
return opts.LabelOpts(formatter=JsCode(fn), position="center")
def plot_kline(df, rendered=True, ucolor="#ef232a", dcolor="#14b143", col=""):
"""
针对 dataframe 直接画出标准看盘软件的上k线图下成交量图的形式
:param df:
:param rendered:
:param ucolor: str for color when going up, default red in A stock as "#ef232a"
:param dcolor: str for color when going down, default green in A stock as "#14b143"
:param col:
:return:
"""
# TODO: color changing seems to make no effect, possible issue with pyecharts
kline = (
Kline().add_xaxis(xaxis_data=list(df["date"])).add_yaxis(
series_name="",
itemstyle_opts=opts.ItemStyleOpts(
color=ucolor,
color0=dcolor,
border_color=ucolor,
border_color0=dcolor,
),
y_axis=list(zip(df["open"], df["close"], df["low"], df["high"])),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
],
symbol="pin",
symbol_size=[56, 40],
# label_opts=opts.LabelOpts(color="#CCFFFF",position=["top", "bottom"])
),
).set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=50,
range_end=100,
xaxis_index=[0, 1],
),
opts.DataZoomOpts(
is_show=False,
type_="inside",
range_start=50,
range_end=100,
xaxis_index=1,
),
],
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
))
if col:
line = (Line().add_xaxis(xaxis_data=list(df["date"])).add_yaxis(
series_name="",
y_axis=list(df[col]),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
))
kline = kline.overlap(line)
if "volume" in df.columns:
vl = list(df["volume"])
elif "amount" in df.columns:
vl = list(df["amount"])
else:
vl = [0 for _ in range(len(df))]
bar = (
Bar().add_js_funcs("var barData = {}".format(
list(df["close"] - df["open"]))).add_xaxis(
xaxis_data=list(df["date"])).add_yaxis(
series_name="",
yaxis_data=vl,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode("""
function(params) {{
var colorList;
if (barData[params.dataIndex]>0) {{
colorList = '{ucolor}';
}} else {{
colorList = '{dcolor}';
}}
return colorList;
}}
""".format(ucolor=ucolor,
dcolor=dcolor)) # escape {} when using format
),
).set_global_opts(tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
), ))
grid_chart = Grid()
grid_chart.add_js_funcs("var barData = {}".format(
list(df["close"] - df["open"])))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="1%",
pos_top="2%",
height="65%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="1%",
pos_top="71%",
height="22%"),
)
if rendered:
return grid_chart.render_notebook()
else:
return grid_chart
def draw_charts(echarts_data):
kline_data = echarts_data["data"]
date = echarts_data["date"]
kline = (
Kline().add_xaxis(xaxis_data=date).add_yaxis(
series_name="Dow-Jones index",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
# 其中inside是支持鼠标缩放
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1, 2, 3, 4],
range_start=50,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1, 2, 3, 4],
type_="slider",
range_start=50,
range_end=100,
),
],
# # 不知道为什么要分开三个写，可能是想通过xaxis_index分别配置
# 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),
# ],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
title_opts=opts.TitleOpts(title="my_echart"),
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"
},
],
),
# 所有图的axis 连在一块
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=date).add_yaxis(
series_name="MA5",
y_axis=echarts_data['ma5'],
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False, # 去掉折线上的小圆点
).add_yaxis(
series_name="MA20",
y_axis=echarts_data['ma20'],
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False, # 去掉折线上的小圆点
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
# Kline And Line
overlap_kline_line = kline.overlap(line)
bar = (
Bar().add_xaxis(xaxis_data=date).add_yaxis(
series_name="Volume",
y_axis=echarts_data['volume'],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
#
# # 变色
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",
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),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar().add_xaxis(xaxis_data=date).add_yaxis(
series_name="MACD",
y_axis=echarts_data['macd'],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
# axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度
# splitline_opts=opts.SplitLineOpts(is_show=False), # 不显示刻度
# axislabel_opts=opts.LabelOpts(is_show=False),
# split_number=20,
),
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),
))
macd_line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="DIFF",
y_axis=echarts_data['macd_diff'],
# xaxis_index=2,
# yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=echarts_data["macd_dea"],
# xaxis_index=2,
# yaxis_index=2,
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
)
# .set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
# MACD BAR And MACD Line
bar_macd_line = bar_2.overlap(macd_line)
# KDJ
kdj_line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="K",
y_axis=echarts_data['kdj_k'],
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="D",
y_axis=echarts_data["kdj_d"],
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="J",
y_axis=echarts_data["kdj_j"],
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
# markline_opts=opts.MarkLineOpts(
# data=[100]
# ),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
# axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度
# splitline_opts=opts.SplitLineOpts(is_show=False), # 不显示刻度
# axislabel_opts=opts.LabelOpts(is_show=False),
# split_number=20,
), ))
######################
## kdj的macd
# Bar-2 (Overlap Bar + Line)
macd_kdj_bar = (
Bar().add_xaxis(xaxis_data=date).add_yaxis(
series_name="MACD_KDJ",
y_axis=echarts_data['kdj_macd'],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
# axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度
# splitline_opts=opts.SplitLineOpts(is_show=False), # 不显示刻度
# axislabel_opts=opts.LabelOpts(is_show=False),
# split_number=20,
),
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),
))
macd_kdj_line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="KDJ_DIFF",
y_axis=echarts_data['kdj_macd_diff'],
# xaxis_index=2,
# yaxis_index=2,
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="KDJ_DEA",
y_axis=echarts_data["kdj_macd_dea"],
# xaxis_index=2,
# yaxis_index=2,
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
)
# .set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
# MACD BAR And MACD Line
bar_macd_kdj_line = macd_kdj_bar.overlap(macd_kdj_line)
######################
# Grid Overlap + Bar
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="800px",
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(kline_data))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="30%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="35%",
height="10%"),
)
# 加上MACD
grid_chart.add(
bar_macd_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top="45%",
height="10%",
),
)
# 加上KDJ
grid_chart.add(
kdj_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top="55%",
height="10%",
),
)
# 加上KDJ的MACD
grid_chart.add(
bar_macd_kdj_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top="65%",
height="10%",
),
)
grid_chart.render("300368.html")
def get_year_chart(year: str):
map_data = [[[x["name"], x["value"]] for x in d["data"]] for d in MapData
if d["time"] == year][0]
min_data, max_data = (minNum, maxNum)
data_mark: List = []
i = 0
for x in time_list:
if x == year:
data_mark.append(total_num[i])
else:
data_mark.append("")
i = i + 1
map_chart = (Map().add(
series_name="",
data_pair=map_data,
zoom=1,
center=[119.5, 34.5],
is_map_symbol_show=False,
itemstyle_opts={
"normal": {
"areaColor": "#323c48",
"borderColor": "#404a59"
},
"emphasis": {
"label": {
"show": Timeline
},
"areaColor": "rgba(255,255,255, 0.5)",
},
},
).set_global_opts(
title_opts=opts.TitleOpts(
title="" + str(year) +
"全国各省份NCP实时动态(数据来源:丁香园; 数据仓库:BlankerL/DXY-2019-nCoV-Data)",
subtitle="",
pos_left="center",
pos_top="top",
title_textstyle_opts=opts.TextStyleOpts(
font_size=25, color="rgba(255,255,255, 0.9)"),
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode("""function(params) {
if ('value' in params.data) {
return params.data.value[2] + ': ' + params.data.value[0];
}
}"""),
),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="30",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
))
line_chart = (Line().add_xaxis(time_list).add_yaxis(
"", total_num).add_yaxis(
"",
data_mark,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(
title="全国各省份NCP实时动态(单位: K)", pos_left="62%", pos_top="5%")))
bar_x_data = [x[0] for x in map_data]
bar_y_data = [{"name": x[0], "value": x[1][0]} for x in map_data]
bar = (Bar().add_xaxis(xaxis_data=bar_x_data).add_yaxis(
series_name="",
yaxis_data=bar_y_data,
label_opts=opts.LabelOpts(is_show=True,
position="right",
formatter="{b} : {c}"),
).reversal_axis().set_global_opts(
xaxis_opts=opts.AxisOpts(max_=40000,
axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=False)),
tooltip_opts=opts.TooltipOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="10",
pos_top="top",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
))
pie_data = [[x[0], x[1][0]] for x in map_data]
pie = (Pie().add(
series_name="",
data_pair=pie_data,
radius=["15%", "35%"],
center=["80%", "82%"],
itemstyle_opts=opts.ItemStyleOpts(border_width=1,
border_color="rgba(0,0,0,0.5)"),
).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{b} {d}%"),
legend_opts=opts.LegendOpts(is_show=False),
))
grid_chart = (Grid().add(
bar,
grid_opts=opts.GridOpts(pos_left="10",
pos_right="45%",
pos_top="50%",
pos_bottom="5"),
).add(
line_chart,
grid_opts=opts.GridOpts(pos_left="65%",
pos_right="80",
pos_top="10%",
pos_bottom="50%"),
).add(pie, grid_opts=opts.GridOpts(pos_left="45%", pos_top="60%")).add(
map_chart, grid_opts=opts.GridOpts()))
return grid_chart
iron_man= [{"value": [6, 3, 5, 3, 5, 3], "name": "钢铁侠"}]
black_widow = [{"value": [3, 3, 2, 3, 2, 7], "name": "黑寡妇"}]
hawkeye = [{"value": [3, 3, 3, 2, 3, 7], "name": "鹰眼"}]
hulk = [{"value": [2, 7, 3, 7, 1, 3], "name": "绿巨人"}]
thor = [{"value": [2, 7, 6, 7, 7, 6], "name": "雷神"}]
myschema = [
{"name": '智力', "max": 7, "min": 0},
{"name": '力量', "max": 7, "min": 0},
{"name": '速度', "max": 7, "min": 0},
{"name": '耐力', "max": 7, "min": 0},
{"name": '能量发射', "max": 7, "min": 0},
{"name": '战斗技能', "max": 7, "min": 0}
] #设置雷达图的属性
r = Radar(init_opts=opts.InitOpts(
bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"}
)
) #初始化雷达图
r.add_js_funcs(
"""
var img = new Image(); img.src = 'a5.png';
"""
) #执行js代码
(
r.add_schema(
schema=myschema,
shape="circle", #图片形状
center=["50%", "50%"], #图片中心位置
radius="80%", #图片半径大小
angleaxis_opts=opts.AngleAxisOpts(
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A", Faker.values(), category_gap="60%").set_series_opts(
itemstyle_opts={
"normal": {
"color":
JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: 'rgba(0, 244, 255, 1)'
}, {
offset: 1,
color: 'rgba(0, 77, 167, 1)'
}], false)"""),
"barBorderRadius": [30, 30, 30, 30],
"shadowColor":
"rgb(0, 160, 221)",
}
}).set_global_opts(title_opts=opts.TitleOpts(
title="Bar-渐变圆柱")).render("Bar_border_radius.html"))
def generate_line_html(rows, select=None):
# 用散点图展示
line = Line(
opts.InitOpts(height='700px', width='1424px', theme=ThemeType.LIGHT))
x = []
y1 = []
y2 = []
y3 = []
y4 = []
y5 = []
for row in rows:
x.append(row['时间'])
y3.append(row['低溢价率策略累积收益率'])
y2.append(row['高收益率策略累积收益率'])
y1.append(row['低余额+双低策略累积收益率'])
y1.append(row['低溢价率+双低策略累积收益率'])
# y4.append(row['可转债指数累积收益率'])
# y5.append(row['沪深300累积收益率'])
line.add_xaxis(x)
line.add_yaxis("低溢价率策略", y3)
line.add_yaxis("高收益率策略", y2)
line.add_yaxis("低余额+双低策略", y1)
line.add_yaxis("低溢价+双低策略", y1)
# line.add_yaxis("可转债指数", y4)
# line.add_yaxis("沪深300", y5)
line.set_global_opts(
title_opts=opts.TitleOpts(title="策略组合收益率",
pos_left='center',
pos_top=-5),
tooltip_opts=opts.TooltipOpts(
trigger='axis',
formatter=JsCode(
"function (params) {"
"return '<table style=\"width:150px;\">'+"
"'<tr ><td style=\"height:20px;background-color:white;border:0px\" colspan=2>'+ params[0].data[0] +'</td></tr>' +"
"'<tr><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[1].color+'\">高收益率策略</td><td style=\"height:15px;background-color:white;border:0px\">' + params[1].value[1] + '%</td></tr>' +"
"'<tr><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[2].color+'\">低溢价率策略</td><td style=\"height:15px;background-color:white;border:0px\">' + params[2].value[1] + '%</td></tr>' +"
"'<tr ><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[0].color+'\">低余额+双低策略</td><td style=\"height:15px;background-color:white;border:0px\">' + params[3].value[1] + '%</td></tr>' +"
"'<tr ><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[0].color+'\">低溢价率+双低策略</td><td style=\"height:15px;background-color:white;border:0px\">' + params[4].value[1] + '%</td></tr>' +"
"'<tr><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[3].color+'\">可转债指数</td><td style=\"height:15px;background-color:white;border:0px\">' + params[5].value[1] + '%</td></tr>' +"
"'<tr><td style=\"height:15px;background-color:white;border:0px;text-align: left;color:'+params[4].color+'\">沪深300</td><td style=\"height:15px;background-color:white;border:0px\">' + params[6].value[1] + '%</td></tr>' +"
"'</table>';}")),
legend_opts=opts.LegendOpts(pos_top=50,
# selected_mode='single'
),
datazoom_opts={
'start': 0,
'end': 100
},
toolbox_opts=opts.ToolboxOpts(feature={
'dataZoom': {},
}),
# visualmap_opts=opts.VisualMapOpts(
# type_="color", max_=150, min_=20, dimension=1
# ),
xaxis_opts=opts.AxisOpts(
# data=None,
type_='time',
name='时间',
name_gap=30,
is_scale=True,
name_location='middle',
splitline_opts=opts.SplitLineOpts(is_show=False),
# axislabel_opts=opts.LabelOpts(formatter="{value}"), #echarts.format.formatTime('yy-MM-dd', value*1000)
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
symbol=['none', 'arrow'])),
yaxis_opts=opts.AxisOpts(
type_='value',
name='收益率(%)',
name_rotate=90,
name_gap=55,
name_location='middle',
is_scale=True,
axislabel_opts=opts.LabelOpts(formatter='{value}%'),
splitline_opts=opts.SplitLineOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_on_zero=False,
symbol=['none', 'arrow'])))
line.set_series_opts(
symbol='none',
smooth=False,
label_opts=opts.LabelOpts(is_show=False),
)
line_html = line.render_embed('template.html', env)
return line_html
