# 轴线配置
axisline_opts=opts.AxisLineOpts(
is_show=True, # 显示轴线
# 设置轴线风格
linestyle_opts=opts.LineStyleOpts(color='gray')),
# 设置对应的标签， ｛｝ 数据
axislabel_opts=opts.LabelOpts(formatter='{value}')))
# 设置全局配置
bar.set_global_opts(
# 标题
title_opts=opts.TitleOpts(
title='中国早期疫情变化趋势图',
subtitle='郑州大学信息工程学院TedLau',
subtitle_textstyle_opts=opts.TextStyleOpts(
font_size=12,
width='1.6',
),
pos_left='3%', # title 的位置
pos_top='1%',
),
# 图例
legend_opts=opts.LegendOpts(
pos_left='25%',
pos_top='7%',
),
# 提示框
tooltip_opts=opts.TooltipOpts(
trigger='axis', # 坐标轴。柱状图
axis_pointer_type='cross', # 十字准星指示 表示启用两个正交的轴
),
# 对纵轴的全局配置
series_name='疑似数', # 系列名称
y_axis=suspected_data, # 数据
yaxis_index=3)
# 3、全局配置
bar.set_global_opts(
# 标题配置
title_opts=opts.TitleOpts(
title='中国疫情数据', # 主标题
subtitle='广州0624Python全体', # 子标题
pos_left='1%', # 标题的左位置
pos_top='3%', # 标题的上位置
# 设置主标题样式
title_textstyle_opts=opts.TextStyleOpts(
font_style='normal', # 字体风格
font_weight='normal', # 字体的粗细
color='#FDF5E6', # 字体颜色
font_size=25, # 字体大小
),
# 设置子标题样式
subtitle_textstyle_opts=opts.TextStyleOpts(
font_style='normal', # 字体风格
font_weight='normal', # 字体的粗细
color='#FDF5E6', # 字体颜色
font_size=20, # 字体大小
)),
# 图例设置
legend_opts=opts.LegendOpts(
type_='plain', # 图例类型 ，plain---普通类型
is_show=True, # 显示图例
pos_left='25%',
pos_top='5%',
'''依赖模块
pip install jieba, pyecharts
'''
from pyecharts import options as opts
from pyecharts.charts import WordCloud
import jieba
with open('danmus.csv', encoding='utf-8') as f:
text = " ".join([line.split(',')[-1] for line in f.readlines()])
words = jieba.cut(text)
_dict = {}
for word in words:
if len(word) >= 2:
_dict[word] = _dict.get(word, 0) + 1
items = list(_dict.items())
items.sort(key=lambda x: x[1], reverse=True)
# for i in range(10):
# word, count = items[i]
# print(f'{word:<5}{count:>5}')
c = (WordCloud().add(
"",
items,
word_size_range=[20, 120],
textstyle_opts=opts.TextStyleOpts(font_family="cursive"),
).render("wordCloud.html"))
def image_host_resource(doc_path, hosts_metrics, host):
options = opts.InitOpts(js_host=os.path.join(os.getcwd(),
"scripts/javascript/"),
animation_opts=opts.AnimationOpts(animation=False))
line1 = (Line(options).add_xaxis(
hosts_metrics[host['ip']]['metrics']['datetime']
).add_yaxis(
"CPU",
hosts_metrics[host['ip']]['metrics']['cpu'],
is_symbol_show=False,
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(color=utils.JsCode(
"new echarts.graphic.LinearGradient(0,0,0,1,[{offset:0,color:'rgb(255,158,68)'},{offset:1,color:'rgb(255,70,131)'}])"
),
opacity=0.5)
).add_yaxis(
"Memory",
hosts_metrics[host['ip']]['metrics']['memory'],
is_symbol_show=False,
is_smooth=True).set_series_opts(linestyle_opts=opts.LineStyleOpts(
width=2)).set_global_opts(
title_opts=opts.TitleOpts(
title="主机资源使用率 (%s)" % (host['ip']),
title_textstyle_opts=opts.TextStyleOpts(font_size=13),
subtitle=" 数据来源-健康度平台"),
xaxis_opts=opts.AxisOpts(type_="time"),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter=utils.JsCode(
"function(val){return val + '%';}")))))
line2 = (Line(options).add_xaxis(
hosts_metrics[host['ip']]['metrics']['datetime']
).add_yaxis(
"Process",
hosts_metrics[host['ip']]['metrics']['process'],
is_symbol_show=False,
is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(color=utils.JsCode(
"new echarts.graphic.LinearGradient(0,0,0,1,[{offset:0,color:'#fff'},{offset:1,color:'#61a0a8'}])"
),
opacity=0.5)
).set_series_opts(linestyle_opts=opts.LineStyleOpts(
width=2)).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="time",
position="top",
axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(is_inverse=True),
title_opts=opts.TitleOpts(
title="应用运行数量(所有)",
pos_bottom="0%",
title_textstyle_opts=opts.TextStyleOpts(font_size=13)),
legend_opts=opts.LegendOpts(pos_bottom="0%")))
grid = (Grid(options).add(
line1,
grid_opts=opts.GridOpts(pos_top=50,
pos_left=50,
pos_right=50,
pos_bottom="50%")).add(line2,
grid_opts=opts.GridOpts(
pos_top="58%",
pos_left=50,
pos_right=50,
pos_bottom=30)))
make_snapshot(
snapshot,
grid.render(
os.path.join(doc_path,
"render_%s.html" % (host['ip'].replace(".", "_")))),
os.path.join(doc_path,
"host_res_%s.png" % (host['ip'].replace(".", "_"))),
pixel_ratio=1,
delay=1,
is_remove_html=True)
def find_aggregation_render(x, y, flag, query, table_path, answer, y_name):
colorList = ['#f36c6c', '#e6cf4e', '#20d180', '#0093ff']
x_sum = ["Sum"]
Sum = []
Sum.append(sum(y))
mean = np.mean(y)
y1 = []
y2 = []
for i in range(len(y)):
if y[i] >= mean:
y1.append(round(mean, 2))
y2.append(
opts.BarItem(name=x[i],
value=round(y[i] - mean, 2),
label_opts=opts.LabelOpts(formatter="+{c}"),
itemstyle_opts=opts.ItemStyleOpts(color="red")))
else:
y1.append(round(y[i], 2))
y2.append(
opts.BarItem(name=x[i],
value=round(mean - y[i], 2),
label_opts=opts.LabelOpts(formatter="-{c}"),
itemstyle_opts=opts.ItemStyleOpts(color="green",
opacity=0.3)))
if flag == "mean":
bar = Bar()
bar.add_xaxis(x)
bar.add_yaxis(
'',
y_axis=y1,
stack='stack1',
label_opts=opts.LabelOpts(is_show=False),
color="white",
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(name="mean:", y=round(mean, 2))],
label_opts=opts.LabelOpts(formatter="Mean:{c}", color="black"),
linestyle_opts=opts.LineStyleOpts(color="gray")),
)
bar.add_yaxis('', y_axis=y2, stack='stack1')
bar.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(range_start=10, range_end=90),
opts.DataZoomOpts(type_="inside")
],
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=40, font_size='100%'),
name_textstyle_opts=opts.TextStyleOpts(font_size="100%")),
yaxis_opts=opts.AxisOpts(
min_=int(min(y) - (max(y) - min(y)) / 10),
name=y_name,
axislabel_opts=opts.LabelOpts(font_size="100%"),
name_textstyle_opts=opts.TextStyleOpts(font_size="100%")),
graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=[
'\n' + "Q:" + ' ' + query + '\n' + "\n" + 'A:' +
' ' + answer
],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
bar.render("aggregation.html")
grid = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid.add(bar, grid_opts={'left': '15%', 'bottom': '34%'})
grid1 = Grid()
grid1.add(bar, grid_opts={'left': '15%', 'bottom': '34%'})
grid1.render("mean.html")
option1 = grid.dump_options_with_quotes()
option1 = json.loads(option1)
option = {"option": [option1], "query": query}
return option
elif flag == "sum":
bar = Bar()
bar.add_xaxis(x_sum)
bar.add_yaxis('',
Sum,
label_opts=opts.LabelOpts(position="inside",
formatter="Sum:{c}"),
color=colorList[0])
for i in range(len(y)):
Y = []
Y.append(y[i])
r = str(x[i])
bar.add_yaxis('',
Y,
stack='stack1',
label_opts=opts.LabelOpts(position="inside",
formatter=r + ":{c}"),
tooltip_opts=opts.TooltipOpts(is_show=False))
bar.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(range_start=10,
range_end=90,
orient="vertical"),
opts.DataZoomOpts(type_="inside")
],
graphic_opts=[
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center",
top="bottom",
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
# 可以通过jsCode添加js代码，也可以直接用字符串
text=[
'\n' + "Q:" + ' ' + query + '\n' + 'A:' + ' ' +
answer
],
font="14px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#333")))
])
grid = Grid(init_opts=opts.InitOpts(
width="100%",
height="100%",
renderer=globals.RenderType.SVG,
))
grid.add(bar, grid_opts={'left': '15%', 'bottom': '34%'})
option1 = grid.dump_options_with_quotes()
option1 = json.loads(option1)
option = {"option": [option1], "query": query}
return option
def get_year_chart(year: str, title1, title2):
map_data = [[[x["name"], x["value"]] for x in d["data"]] for d in data
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) + title1,
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=title2, pos_left="72%", 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_=maxNum,
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(0,0,0,0.3)"),
).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
from pyecharts import options as opts
from pyecharts.charts import WordCloud
import json
# 生成tag频率词云图
f = open('tag_wordcloud_data.json', encoding='utf-8')
res = f.read()
data = json.loads(res)
words = []
for k in data.keys():
words.append((k, data[k]))
c = (WordCloud().add(
"",
words,
word_size_range=[20, 100],
textstyle_opts=opts.TextStyleOpts(),
).set_global_opts(title_opts=opts.TitleOpts(
title="tag-WordCloud")).render("tag_wordcloud_diamond.html"))
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")
axislabel_opts=opts.LabelOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
radiusaxis_opts=opts.RadiusAxisOpts(
min_=0,
max_=7,
interval=1,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
splitline_opts=opts.series_options.SplitLineOpts(is_show=True, linestyle_opts={'color':'grey','opacity':0.8})
),
polar_opts=opts.PolarOpts(),
splitline_opt=opts.SplitLineOpts(is_show=False),
textstyle_opts=opts.TextStyleOpts(color="black"),
)
.add(
series_name="美国队长",
data=captain_america,
areastyle_opts=opts.AreaStyleOpts(opacity=0.1),
linestyle_opts=opts.LineStyleOpts(width=1),
)
.add(
series_name="钢铁侠",
data=iron_man,
areastyle_opts=opts.AreaStyleOpts(opacity=0.1),
linestyle_opts=opts.LineStyleOpts(width=1),
)
.add(
series_name="黑寡妇",
def draw_chart(df,
extend_field,
save_path="",
trade_list=None,
kline_title="买卖点K线图"):
datetime_array = df.datetime.tolist()
volume_array = df.volume.tolist()
# open close low high
kline_data = df[["open", "close", "low", "high"]].values.tolist()
point_list = []
if trade_list is None:
trade_list = []
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")
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")))
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_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",
yaxis_data=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=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.8),
label_opts=opts.LabelOpts(is_show=False),
)
# 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 draw_daily_results_chart(daily_df, save_path):
"""绘制资金曲线图"""
date_list = daily_df.index.values.tolist()
balance_list = daily_df.balance.values.tolist()
draw_down_list = daily_df.drawdown.values.tolist()
net_pnl_list = daily_df.net_pnl.values.tolist()
line1 = (Line().add_xaxis(xaxis_data=date_list).add_yaxis(
series_name="Balance",
y_axis=balance_list,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
).set_global_opts(
title_opts=opts.TitleOpts(title="回测资金曲线", pos_left="center"),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=True),
),
legend_opts=opts.LegendOpts(pos_left="1%"),
yaxis_opts=opts.AxisOpts(
type_="value",
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True),
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside",
range_start=0,
range_end=100,
xaxis_index=[0, 1, 2]),
],
))
line2 = (Line().set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=True),
position="top",
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(pos_left="9%"),
yaxis_opts=opts.AxisOpts(
type_="value",
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True),
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
).add_xaxis(xaxis_data=date_list).add_yaxis(
series_name="Draw Down",
y_axis=draw_down_list,
label_opts=opts.LabelOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
))
bar3 = (Bar().set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=True),
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(pos_left="19%"),
yaxis_opts=opts.AxisOpts(
type_="value",
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True),
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
).add_xaxis(xaxis_data=date_list).add_yaxis(
series_name="Daily Pnl",
yaxis_data=net_pnl_list,
label_opts=opts.LabelOpts(is_show=False)))
(Grid(init_opts=opts.InitOpts(width="100%", height="720px")).add(
chart=line1,
grid_opts=opts.GridOpts(pos_left=50, pos_right=50, height="35%")).add(
chart=line2,
grid_opts=opts.GridOpts(pos_left=50,
pos_right=50,
pos_top="48%",
height="20%"),
).add(
chart=bar3,
grid_opts=opts.GridOpts(pos_left=50,
pos_right=50,
pos_top="71%",
height="20%"),
).render(os.path.join(save_path, "daily_results.html")))
def draw(datelist, pricelist, title):
min_value = min(pricelist)
max_value = max(pricelist)
line = (
Line(init_opts=opts.InitOpts(
width='1800px',
height='800px',
js_host="./",
))
.set_global_opts(
title_opts=opts.TitleOpts(
title=title,
# subtitle='股票价格走势'
),
legend_opts=opts.LegendOpts(
is_show=True,
pos_top=10,
pos_left="center",
item_width=30,
item_height=15,
textstyle_opts=opts.TextStyleOpts(
font_family='Microsoft Yahei',
font_size=14,
font_style='oblique'
)
),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
xaxis_opts=opts.AxisOpts(
# type_="time",
name='日期',
split_number=10,
name_gap=35,
axispointer_opts=opts.AxisPointerOpts(is_show=True),
name_textstyle_opts=opts.TextStyleOpts(
font_size=16,
font_family='Microsoft Yahei'
)
),
yaxis_opts=opts.AxisOpts(
type_="value",
# name='价格',
min_=min_value,
max_=max_value,
split_number=4,
axispointer_opts=opts.AxisPointerOpts(is_show=True),
name_textstyle_opts=opts.TextStyleOpts(
font_size=16,
font_family='Microsoft Yahei'
),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1))
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
# xaxis_index=[0, 1],
range_start=30,
range_end=70,
),
opts.DataZoomOpts(
is_show=True,
# xaxis_index=[0, 1],
type_="slider",
pos_top="96%",
range_start=38,
range_end=70,
),
],
)
.add_xaxis(xaxis_data=datelist)
.add_yaxis(series_name="走势情况",
is_selected=True,
y_axis=pricelist,
label_opts=opts.LabelOpts(is_show=False)
)
.render(title + '.html')
)
def Candle_render_output(x_date, y_data, vol_data) -> Grid:
# ydata type : open close low high
# vol_data is for bar
print('Candle_render_output')
kline = (Kline().add_xaxis(xaxis_data=x_date).add_yaxis(
series_name="Dow-Jones index",
y_axis=y_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
).set_global_opts(
title_opts=opts.TitleOpts(
title="Kline)",
subtitle="MA(5, 10, 30, 60)",
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=0,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#ec0000"
},
{
"value": -1,
"color": "#00da3c"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
bar = (
Bar().add_xaxis(xaxis_data=x_date).add_yaxis(
series_name="Volume",
# yaxis_data=[
# [i, y_data[i][3], 1 if y_data[i][0] > y_data[i][1] else -1]
# for i in range(len(y_data))
# ],
yaxis_data=vol_data,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
lines = Kline_ma_line(x_date, y_data)
kline = kline.overlap(lines)
# Grid Overlap + Bar
grid_chart = Grid()
grid_chart.add(kline,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
height="50%"))
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="16%"),
)
return grid_chart
def show_pyecharts():
x_data = [
'0.0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9',
'1.0'
]
y_data = [
'1533', '573', '502', '491', '520', '556', '577', '788', '1252',
'20564', '6393'
]
#情感分析柱状图
bar = (Bar().add_xaxis(x_data).add_yaxis(
"情感分析",
y_data,
label_opts=opts.LabelOpts(is_show=False),
color='#3498DB').set_global_opts(title_opts={
"text": "台海舆论情感分析",
"subtext": "大于0.5为正面评价，小于0.5为负面评价"
}))
#台湾网饼图
x_data2 = [
"文化", "经贸", "媒体专栏", "网友专栏", "两岸专家", "两岸", "台商", "部委", "台海时事", "网友快言",
"海峡时评", "两岸快评"
]
y_data2 = [553, 553, 35, 39, 448, 465, 553, 321, 553, 406, 553, 556]
pie = (Pie(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add(
series_name="中国台湾网",
data_pair=[list(z) for z in zip(x_data2, y_data2)],
radius=["50%", "70%"],
label_opts=opts.LabelOpts(is_show=False, position="center"),
).set_colors([
"#E8F8F5", "#D1F2EB", "#A3E4D7", "#76D7C4", "#48C9B0", "#1ABC9C",
"#17A589", "#148F77", "#117864", "#0E6251F", "#73C6B6", "#45B39D"
]).set_global_opts(legend_opts=opts.LegendOpts(
pos_left="legft", orient="vertical")).set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"), ))
#词云图
word = (WordCloud().add(
"",
list1,
word_size_range=[20, 100],
shape=SymbolType.DIAMOND,
textstyle_opts=opts.TextStyleOpts(font_family="cursive"),
))
#折线图1
y2[0], y2[1], y2[2] = None, None, None
y4[1] = None
line = (Line().add_xaxis(xaxis_data=x1).add_yaxis(
series_name="微博",
symbol="emptyCircle",
is_symbol_show=True,
color="#F2D7D5",
y_axis=y1,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="环球网",
symbol="emptyCircle",
is_symbol_show=True,
color="#C0392B",
y_axis=y2,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="中国台湾网",
symbol="emptyCircle",
is_symbol_show=True,
color="#641E16",
y_axis=y3,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="知乎",
symbol="emptyCircle",
is_symbol_show=True,
color="#9B59B6",
y_axis=y4,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="中国日报网",
symbol="emptyCircle",
is_symbol_show=True,
color="#512E5F",
y_axis=y5,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="中新网",
symbol="emptyCircle",
is_symbol_show=True,
color="#2980B9",
y_axis=y6,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).
add_yaxis(
series_name="今日头条",
symbol="emptyCircle",
is_symbol_show=True,
color="#154360",
y_axis=y7,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="光明网",
symbol="emptyCircle",
is_symbol_show=True,
color="#3498DB",
y_axis=y8,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="凤凰网",
symbol="emptyCircle",
is_symbol_show=True,
color="#1ABC9C",
y_axis=y9,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="新华网",
symbol="emptyCircle",
is_symbol_show=True,
color="#0E6251",
y_axis=y10,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(
width=3)).set_global_opts(
tooltip_opts=opts.TooltipOpts(
trigger="axis"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(
is_show=True),
splitline_opts=opts.SplitLineOpts(
is_show=True),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(
is_on_zero=False,
linestyle_opts=opts.LineStyleOpts(
color="#d14a61"))),
))
#平台占比饼图
x_data3 = [
"微博", "知乎", "中国台湾网", "环球网", "日报网", "中新网", "今日头条", "光明网", "凤凰网", "新华网"
]
y_data3 = [5401, 157, 5035, 3245, 4296, 5574, 1891, 3131, 1052, 1997]
data_pair = [list(z) for z in zip(x_data3, y_data3)]
data_pair.sort(key=lambda x: x[1])
pie2 = (Pie(init_opts=opts.InitOpts(theme=ThemeType.ROMA)).add(
series_name="访问来源",
data_pair=data_pair,
rosetype="radius",
radius="55%",
center=["50%", "50%"],
label_opts=opts.LabelOpts(is_show=False, position="center"),
).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{d}%")))
#折线图2
line2 = (Line().add_xaxis(xaxis_data=x_data4).add_yaxis(
series_name="热度走势",
y_axis=y_data4,
color="#FF69B4",
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
symbol="emptyCircle",
is_symbol_show=True,
is_smooth=True,
label_opts=opts.LabelOpts(is_show=True),
).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(rotate=45)),
))
#条形图
line3 = (Bar().add_xaxis(x_data5).add_yaxis(
"平均值",
y_data5,
label_opts=opts.LabelOpts(is_show=False),
color='#6A5ACD').reversal_axis().extend_axis(yaxis=opts.AxisOpts(
type_='value',
name='转发次数',
position='left',
)).set_global_opts(
title_opts={"text": "评论与转发关系图"},
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter='{value}(评论数量)')),
))
#圆
radius = (Polar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_schema(
radiusaxis_opts=opts.RadiusAxisOpts(data=x_data6, type_="category"),
angleaxis_opts=opts.AngleAxisOpts(is_clockwise=True, max_=100000),
).add("点赞数量", y_data6, type_="bar").set_global_opts(
title_opts=opts.TitleOpts(title="")).set_series_opts(
label_opts=opts.LabelOpts(is_show=True)))
return render_template("index2.html",
bar_data=bar.dump_options(),
word_data=word.dump_options(),
pie_data=pie.dump_options(),
pie2_data=pie2.dump_options(),
line_data=line.dump_options(),
line2_data=line2.dump_options(),
line3_data=line3.dump_options(),
radius_data=radius.dump_options())
["河口县", '没有数据']]
m = (Map(init_opts=opts.InitOpts()).add(
series_name="红河州各县市人口密度",
maptype="红河哈尼族彝族自治州",
data_pair=MAP_DATA,
is_map_symbol_show=True,
zoom=0.8).set_global_opts(
tooltip_opts=opts.TooltipOpts(trigger="item",
formatter="{b}<br/>{c} (人 / km2)"),
visualmap_opts=opts.VisualMapOpts(
range_color=["lightskyblue", "yellow", "orangered"], ),
legend_opts=opts.LegendOpts(pos_top="15%",
item_height=24,
item_width=30,
textstyle_opts=opts.TextStyleOpts(
font_size=18, color='#28b2c7'))))
# 词云 词条热词
words = get_count('honghe.txt')
yun = (WordCloud().add(
"",
words,
word_size_range=[5, 40],
shape='star',
width=640,
height=480,
pos_top='50%',
pos_right='5%',
pos_left='66%').set_global_opts(title_opts=opts.TitleOpts(title="红河热词")))
grid = Grid(init_opts=opts.InitOpts(width="1920px",
def candlechart(self,str_tscode=None,subset='masterCandle',dat_cursor=None,MA=[5,10,21], renderfile=False) -> Grid:
str_tscode = DataAgent.formatCode(str_tscode)
int_chartsize = 500
if str_tscode == False or str_tscode is None:
print('invalid sec code provided...')
return False
dat_cursor = datetime.now().astimezone(timezone(cfg.STR_TIMEZONE)) if dat_cursor is None else dat_cursor
closestTradeDate = self.da.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )
tpl_dateindex = tuple(str(i) for i in self.da.df_calTbl.loc[self.da.df_calTbl['cal_date'] <= closestTradeDate]['cal_date'][-int_chartsize:] )
# 连接储存对象
if subset=='masterCandle':
data_store = libs.tryOpenH5(cfg.H5_FILE_PRE,mode='r')
else:
data_store = libs.tryOpenH5('{}{}.dat'.format(cfg.H5_FILE_PATH,subset),mode='r')
# 初始化交易日表格，作为标准交易日序列join个股交易数据
df_toPlot = data_store['masterCandle'].loc[data_store['masterCandle']['ts_code']==str_tscode]
df_dateindex = pd.DataFrame({'trade_caldate': tpl_dateindex}).astype(int)
data_store.close()
try:
str_secname = self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['name'].values[0]
str_sectype = self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['sec_type'].values[0]
except Exception as e:
return libs.log_csv(str_cat='WARNING',str_op='candlechart',str_desc='security code {} does not exist in basic info table...'.format(str_tscode))
str_plotname = '{} - {}'.format(str_tscode,str_secname)
# 交易日时间轴标准化，补全停牌日,当日live数据等k线
'''------ 获取实时交易数据部分--------------------'''
int_liveDate = self.da.int_findClosestTradeDate(datetime.strftime(datetime.now().astimezone(timezone(cfg.STR_TIMEZONE)),'%Y%m%d') )
if int_liveDate<=closestTradeDate and int_liveDate>df_toPlot['trade_date'].max():
df_liveQuote = self.da.query('quote_now',[self.da.formatCode(str_tscode,False),])
if len(df_liveQuote)>0:
df_liveQuote.rename(columns={'open':'adj_open',
'high':'adj_high',
'low':'adj_low',
'price':'adj_close',
'volume':'vol',
'amount':'amount'}, inplace=True)
df_liveQuote=df_liveQuote.astype(dtype= {'adj_open':'float','adj_high':'float','adj_low':'float','adj_close':'float','vol':'float','amount':'float'})
df_liveQuote['trade_date'] = int_liveDate
df_liveQuote['ts_code'] = str_tscode
df_liveQuote['vol'] = df_liveQuote['vol']/100 if str_sectype==1 or str_tscode[:3]=='399' else df_liveQuote['vol']
df_liveQuote['amount'] = df_liveQuote['amount']/1000
df_toPlot = df_toPlot.append(df_liveQuote[['ts_code','trade_date','adj_open','adj_high','adj_low','adj_close','vol','amount']],sort=False)
'''------ 结束 获取实时交易数据部分--------------------'''
df_toPlot = pd.merge(df_dateindex, df_toPlot, left_on='trade_caldate', right_on='trade_date', how='left')
df_toPlot['close'].fillna(method='ffill',inplace=True)
df_toPlot['ts_code'].fillna(method='ffill',inplace=True)
df_toPlot['klineSML'].fillna('NNNN',inplace=True)
df_toPlot['adj_close'].fillna(method='ffill',inplace=True)
df_toPlot['vol'].fillna(value=0,inplace=True)
df_toPlot['amount'].fillna(value=0,inplace=True)
df_toPlot['open'].fillna(df_toPlot['close'],inplace=True)
df_toPlot['high'].fillna(df_toPlot['close'],inplace=True)
df_toPlot['low'].fillna(df_toPlot['close'],inplace=True)
df_toPlot['adj_open'].fillna(df_toPlot['adj_close'],inplace=True)
df_toPlot['adj_high'].fillna(df_toPlot['adj_close'],inplace=True)
df_toPlot['adj_low'].fillna(df_toPlot['adj_close'],inplace=True)
self.kchartdf = df_toPlot #输出至可访问对象属性中
'''-----------------画图部分---------------------'''
lst_ohlcv=df_toPlot.loc[:,['adj_open','adj_close','adj_low','adj_high']].values.tolist()
lst_vol=list(df_toPlot['vol'].values)
lst_amount=list(df_toPlot['amount'].values)
lst_peaks=list(abs(df_toPlot['peaks'].values))
# lst_pivotdown=[float('nan') if i<0 else i for i in list(df_toPlot['pivots'].values)]
lst_pivotdown=[float('nan') if i<0 else i for i in list(df_toPlot['valid_pivots'].values)]
# lst_pivotup=[float('nan') if i>0 else abs(i) for i in list(df_toPlot['pivots'].values)]
lst_pivotup=[float('nan') if i>0 else abs(i) for i in list(df_toPlot['valid_pivots'].values)]
lst_xaxis=list(df_toPlot['trade_caldate'].astype(str))
def calculate_ma(day_count: int, d):
result: List[Union[float, str]] = []
for i in range(len(d)):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(d[i - j][1])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
kline = (Kline()
.add_xaxis(lst_xaxis)
.add_yaxis(series_name=str_secname, y_axis=lst_ohlcv,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min",value_dim="close"),
opts.MarkPointItem(type_="max",value_dim="close")],
symbol_size = [20, 20], #表示标记宽为 20，高为 10
),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#ec0000",
border_color0="#00da3c",
),)
.set_global_opts(
title_opts=opts.TitleOpts(
title=str_plotname,
subtitle='MA='+str(MA),
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
legend_opts=opts.LegendOpts(is_show=True, pos_top='10%', pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0,1,2],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0,1,2],
type_="slider",
pos_top="90%",
range_start=75,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000",font_size=10),
),
# 阴量绿阳量红
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=list(map(lambda x: x+len(MA),[4,5])), #动态算出vol和amt柱状图的series index
is_piecewise=True,
pieces=[
{"value": 1, "color": "#ec0000"},
{"value": -1, "color": "#00da3c"},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
)
trendline = (
Line()
.add_xaxis(lst_xaxis)
.add_yaxis('高低点', lst_peaks,
itemstyle_opts=opts.ItemStyleOpts(color="green"))
)
for i in MA:
if i is not None:
trendline.add_yaxis(
series_name='MA'+str(i),
y_axis=calculate_ma(day_count=i, d=lst_ohlcv),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
trendline.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
trendline.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
keyPoints = (
EffectScatter()
.add_xaxis(lst_xaxis)
.add_yaxis("末跌高", lst_pivotdown,symbol=SymbolType.ARROW,symbol_rotate=180,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color="purple"))
.add_yaxis("末升低", lst_pivotup,symbol=SymbolType.ARROW,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color="blue"))
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
vol_bar = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="交易量",
yaxis_data=[
[i, lst_vol[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]
for i in range(len(lst_vol))
],
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_="最低",
max_="最高",
),
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),
)
# .add_yaxis("交易量", lst_vol,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
amnt_bar = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="交易额",
yaxis_data=[
[i, lst_amount[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]
for i in range(len(lst_amount))
],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=2,
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_="最低",
max_="最高",
),
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),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
# .add_yaxis("交易额", lst_amount,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
priceChart = kline.overlap(trendline).overlap(keyPoints)
gridChart = Grid()
gridChart.add(
priceChart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_bottom='40%'),
)
gridChart.add(
vol_bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="60%", height='15%'),
)
gridChart.add(
amnt_bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="75%"),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'kline')
gridChart.render(fname) if renderfile else None
self.kchart = gridChart # 将结果输出到analytics对象属性中用于function以外的调用
return self
def plot_positions(self, position_list):
lst_xaxis = self.df_unitAxis.loc[
self.df_unitAxis['timeAxis'] >=
self.trade_start]['unit_intdate'].astype(str).values.tolist(
) # xaxis values has to be STRING type, int will not work
linechart = (Line().add_xaxis(lst_xaxis))
# heartbeat的dateindex个数会>=position的timeindex个数，因为并不是没一轮market event循环都会有signal，order等事件触发，由此并不是每天都会生成position
for pos in position_list:
if pos.fname is not None:
df = pd.read_csv(pos.fname, encoding=cfg.FILE_ENCODE)
df_cash = df.loc[df['ts_code'] == 'cash'].groupby(
'timeindex').agg({'market_value': 'sum'})
df = df.groupby('timeindex').agg({'market_value': 'sum'})
linename = '{}持仓'.format(pos.name)
linename_cash = '{}现金持仓'.format(pos.name)
assert lst_xaxis == df.index.astype(str).values.tolist()
lst_values = df['market_value'].values.tolist()
lst_valuesCash = df_cash['market_value'].values.tolist()
linechart.add_yaxis(
linename,
lst_values,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.3),
label_opts=opts.LabelOpts(is_show=False),
)
linechart.add_yaxis(
linename_cash,
lst_valuesCash,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.3),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#bbbbbb", ),
)
linechart.set_global_opts(
title_opts=opts.TitleOpts(title='持仓曲线', ),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
name="市场价值（元）",
is_scale=True,
),
legend_opts=opts.LegendOpts(is_show=True,
pos_top='5%',
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0],
type_="slider",
pos_top="90%",
range_start=75,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000",
font_size=10),
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
# put everything in a grid for easy resizing and positioning
gridChart = Grid()
gridChart.add(
linechart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top='25%'),
)
fname = f'{cfg.PATH_POSFILE}{pos.name}.html'
gridChart.render(fname)
self.chart = gridChart #输出至可访问对象属性中
def corelationMacro(self,dat_cursor=None,lst_tscode=['399002.SZ'],lst_macro=['shibor.on','PIMon.cpi'],lst_findmedian=['pe',()],renderfile=False) -> Line:
lst_findmedian[1] = tuple(self.da.df_stockTbl['ts_code']) if lst_findmedian[1] is None else lst_findmedian[1]
if dat_cursor is None:
dat_cursor = datetime.now().astimezone(timezone(cfg.STR_TIMEZONE))
int_latestViewDate = self.da.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )
df_baseCal = self.da.df_calTbl.loc[self.da.df_calTbl['cal_date']<=int_latestViewDate]['cal_date'].copy()
lst_xaxis = ['{}/{}/{}'.format(i[:4],i[4:6],i[6:]) for i in df_baseCal.values.astype('str')]
linechart = (
Line()
.add_xaxis(lst_xaxis)
)
for code in lst_tscode:
str_tscode = DataAgent.formatCode(code)
df_target = self.da.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)
if len(df_target)>0:
df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')
else:
continue
linename = '{} - {}'.format(str_tscode,self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['name'].values[0])
lst_values = list(df_toPlot['close'])
lst_values = list(Analytics.normByRange(lst_values))
linechart.add_yaxis(linename, lst_values,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.3),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color="#00da3c",
),
)
try:
lst_pe = df_toPlot['pe'].values.tolist()
lst_pb = df_toPlot['pb'].values.tolist()
lst_pepb = [round(lst_pe[index]/value,2) for index, value in enumerate(lst_pb) if value!=0]
linechart.add_yaxis(str_tscode+"_PE", lst_pe, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
linechart.add_yaxis(str_tscode+"_PE/PB", lst_pepb, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
except:
continue
df_shibor = self.da.query('load_shibor')
# print('Supported shibor columns: {}'.format(df_shibor.columns))
df_pimon = self.da.query('load_piMon')
# print('Supported macro eco columns: {}'.format(df_pimon.columns))
for item in lst_macro:
try:
topic,colname = item.split('.')
except:
print('WARNING! input code {} not understood...'.format(item))
if topic=='shibor':
df_target = df_shibor
maxarr = df_target.max(axis=0, skipna=True).values[1:]
minarr = df_target.min(axis=0, skipna=True).values[1:]
deltaRng = max(maxarr)-min(minarr) # overwrite the normalization function delta to put all shibor rates comparable
elif topic=='pimon':
df_target = df_pimon
deltaRng = None
else:
print('WARNING! subject code {} is not understood...'.format(topic))
if len(df_target)>0:
df_toPlot = pd.merge(df_baseCal, df_target, how='left', left_on='cal_date', right_on='date')
df_toPlot[colname] = df_toPlot[colname].bfill()
else:
continue
linename = item
lst_values = list(df_toPlot[colname])
lst_values = list(Analytics.normByRange(lst_values,delta=deltaRng))
linechart.add_yaxis(linename, lst_values, # 所有等比例缩放的折线都放在linechart里，使用左侧y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1,opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
str_cat = lst_findmedian[0]
df_result = pd.DataFrame()
for str_tscode in lst_findmedian[1]:
try:
df = self.da.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)[['ts_code','trade_date',str_cat]]
print('{} csv file loaded for {} trend analysis...'.format(str_tscode,str_cat),end='\r')
except Exception as e:
print('WARNING! {} csv file not found...'.format(str_tscode))
continue
df_result = pd.concat([df_result,df])
df_result.dropna(subset=[str_cat],inplace=True)
df_result = df_result.loc[df_result['trade_date']>19940301]
# libs.df_csv(cfg.PATH_BUGFILE,(df_result.groupby('trade_date')['ts_code'].count(),))
# print(df_result.groupby('trade_date')['ts_code'].count())
df_target = df_result.groupby('trade_date')[str_cat].median()
if len(df_target)>0:
df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')
linename = '{}-median'.format(str_cat)
lst_values = list(df_toPlot[str_cat])
lst_values = list(lst_values)
linechart.add_yaxis(linename, lst_values, yaxis_index=1,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
linechart.extend_axis( # 所有保持原比例的折线都放在linechart2里，使用右侧y轴
yaxis=opts.AxisOpts(
name="倍数",
type_="value",
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="black")
),
axislabel_opts=opts.LabelOpts(formatter="{value}倍"),
)
)
linechart.set_global_opts(
title_opts=opts.TitleOpts(
title='宏观比较',
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(name="等比例",
is_scale=True,
),
legend_opts=opts.LegendOpts(is_show=True, pos_top='5%', pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0],
type_="slider",
pos_top="90%",
range_start=75,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000",font_size=10),
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'macro')
linechart.render(fname)
else:
pass
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'macro')
linechart.render(fname) if renderfile else None
self.macrochart = linechart #输出至可访问对象属性中
return self
def render_wordcloud_py():
with st.echo("below"):
data = [
("生活资源", "999"),
("供热管理", "888"),
("供气质量", "777"),
("生活用水管理", "688"),
("一次供水问题", "588"),
("交通运输", "516"),
("城市交通", "515"),
("环境保护", "483"),
("房地产管理", "462"),
("城乡建设", "449"),
("社会保障与福利", "429"),
("社会保障", "407"),
("文体与教育管理", "406"),
("公共安全", "406"),
("公交运输管理", "386"),
("出租车运营管理", "385"),
("供热管理", "375"),
("市容环卫", "355"),
("自然资源管理", "355"),
("粉尘污染", "335"),
("噪声污染", "324"),
("土地资源管理", "304"),
("物业服务与管理", "304"),
("医疗卫生", "284"),
("粉煤灰污染", "284"),
("占道", "284"),
("供热发展", "254"),
("农村土地规划管理", "254"),
("生活噪音", "253"),
("供热单位影响", "253"),
("城市供电", "223"),
("房屋质量与安全", "223"),
("大气污染", "223"),
("房屋安全", "223"),
("文化活动", "223"),
("拆迁管理", "223"),
("公共设施", "223"),
("供气质量", "223"),
("供电管理", "223"),
("燃气管理", "152"),
("教育管理", "152"),
("医疗纠纷", "152"),
("执法监督", "152"),
("设备安全", "152"),
("政务建设", "152"),
("县区、开发区", "152"),
("宏观经济", "152"),
("教育管理", "112"),
("社会保障", "112"),
("生活用水管理", "112"),
("物业服务与管理", "112"),
("分类列表", "112"),
("农业生产", "112"),
("二次供水问题", "112"),
("城市公共设施", "92"),
("拆迁政策咨询", "92"),
("物业服务", "92"),
("物业管理", "92"),
("社会保障保险管理", "92"),
("低保管理", "92"),
("文娱市场管理", "72"),
("城市交通秩序管理", "72"),
("执法争议", "72"),
("商业烟尘污染", "72"),
("占道堆放", "71"),
("地上设施", "71"),
("水质", "71"),
("无水", "71"),
("供热单位影响", "71"),
("人行道管理", "71"),
("主网原因", "71"),
("集中供热", "71"),
("客运管理", "71"),
("国有公交（大巴）管理", "71"),
("工业粉尘污染", "71"),
("治安案件", "71"),
("压力容器安全", "71"),
("身份证管理", "71"),
("群众健身", "41"),
("工业排放污染", "41"),
("破坏森林资源", "41"),
("市场收费", "41"),
("生产资金", "41"),
("生产噪声", "41"),
("农村低保", "41"),
("劳动争议", "41"),
("劳动合同争议", "41"),
("劳动报酬与福利", "41"),
("医疗事故", "21"),
("停供", "21"),
("基础教育", "21"),
("职业教育", "21"),
("物业资质管理", "21"),
("拆迁补偿", "21"),
("设施维护", "21"),
("市场外溢", "11"),
("占道经营", "11"),
("树木管理", "11"),
("农村基础设施", "11"),
("无水", "11"),
("供气质量", "11"),
("停气", "11"),
("市政府工作部门（含部门管理机构、直属单位）", "11"),
("燃气管理", "11"),
("市容环卫", "11"),
("新闻传媒", "11"),
("人才招聘", "11"),
("市场环境", "11"),
("行政事业收费", "11"),
("食品安全与卫生", "11"),
("城市交通", "11"),
("房地产开发", "11"),
("房屋配套问题", "11"),
("物业服务", "11"),
("物业管理", "11"),
("占道", "11"),
("园林绿化", "11"),
("户籍管理及身份证", "11"),
("公交运输管理", "11"),
("公路（水路）交通", "11"),
("房屋与图纸不符", "11"),
("有线电视", "11"),
("社会治安", "11"),
("林业资源", "11"),
("其他行政事业收费", "11"),
("经营性收费", "11"),
("食品安全与卫生", "11"),
("体育活动", "11"),
("有线电视安装及调试维护", "11"),
("低保管理", "11"),
("劳动争议", "11"),
("社会福利及事务", "11"),
("一次供水问题", "11"),
]
c = (
WordCloud()
.add(series_name="热点分析", data_pair=data, word_size_range=[6, 66])
.set_global_opts(
title_opts=opts.TitleOpts(
title="热点分析", title_textstyle_opts=opts.TextStyleOpts(font_size=23)
),
tooltip_opts=opts.TooltipOpts(is_show=True),
)
)
st_pyecharts(c)
"""
page = Page(page_title='iDesignLab')
# 省级现存确诊
chart = Map(init_opts=opts.InitOpts(width='1500px', height='800px'))
chart.add('现存确诊',
[list(z) for z in zip(province_name, p_current_confirmed_count)],
'china')
chart.set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True, pos_top='20px'),
title_opts=opts.TitleOpts(
title='2019-nCoV疫情地图：{}（{}）'.format('现存确诊', time.asctime()),
subtitle='2019-nCoV Map：{}（{}）'.format('Current Diagnosis',
time.asctime()),
subtitle_textstyle_opts=opts.TextStyleOpts(font_size=15),
pos_left='center',
pos_top='20px'),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces=[{
'min': 10000
}, {
'min': 500,
'max': 9999
}, {
'min': 100,
'max': 499
}, {
'min': 10,
'max': 99
(Radar(
init_opts=opts.InitOpts(width="1280px", height="720px", bg_color="white")).
set_colors(["red"]).add_schema(
schema=[
opts.RadarIndicatorItem(name="HTML语音", max_=100),
opts.RadarIndicatorItem(name="CSS语言", max_=100),
opts.RadarIndicatorItem(name="JavaScript语言", max_=100),
opts.RadarIndicatorItem(name="FLASK模块", max_=100),
opts.RadarIndicatorItem(name="Django模块", max_=100),
opts.RadarIndicatorItem(name="Java语言", max_=100),
opts.RadarIndicatorItem(name="python-tkinter模块", max_=100),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="black", font_size=20),
).add(
series_name="感觉要求程度",
data=v1,
linestyle_opts=opts.LineStyleOpts(color="green", width=2),
).add(series_name="实际学习程度",
data=v2,
linestyle_opts=opts.LineStyleOpts(
color="red",
width=2,
),
areastyle_opts=opts.AreaStyleOpts(opacity=0.1)).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
title_opts=opts.TitleOpts(title="学习进度"),
legend_opts=opts.LegendOpts()).render("basic_radar_chart.html"))
def buildLiquid(name, data, center):
l1 = (Liquid().add(
name,
data,
center=center,
label_opts=opts.LabelOpts(
position="inside",
color="white",
),
background_color={
"type":
'linear',
"x":
1,
"y":
0,
"x2":
0.5,
"y2":
1,
"colorStops": [{
"offset": 1,
"color": 'rgba(68, 145, 253, 0)'
}, {
"offset": 0.5,
"color": 'rgba(68, 145, 253, .25)'
}, {
"offset": 0,
"color": 'rgba(68, 145, 253, 1)'
}],
"globalCoord":
False
},
color=[{
"type":
'linear',
"x":
0,
"y":
0,
"x2":
0,
"y2":
1,
"colorStops": [{
"offset": 1,
"color": 'rgba(58, 71, 212, 0)'
}, {
"offset": 0.5,
"color": 'rgba(31, 222, 225, .2)'
}, {
"offset": 0,
"color": 'rgba(31, 222, 225, 1)'
}],
"globalCoord":
False
}],
outline_border_distance=0.,
outline_itemstyle_opts=opts.ItemStyleOpts(
border_width=20,
border_color={
"type":
'linear',
"x":
0,
"y":
0,
"x2":
0,
"y2":
1,
"colorStops": [{
"offset": 0,
"color": 'rgba(69, 73, 240, 0)'
}, {
"offset": 0.5,
"color": 'rgba(69, 73, 240, .25)'
}, {
"offset": 1,
"color": 'rgba(69, 73, 240, 1)'
}],
"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
("物业服务", "11"),
("物业管理", "11"),
("占道", "11"),
("园林绿化", "11"),
("户籍管理及身份证", "11"),
("公交运输管理", "11"),
("公路（水路）交通", "11"),
("房屋与图纸不符", "11"),
("有线电视", "11"),
("社会治安", "11"),
("林业资源", "11"),
("其他行政事业收费", "11"),
("经营性收费", "11"),
("食品安全与卫生", "11"),
("体育活动", "11"),
("有线电视安装及调试维护", "11"),
("低保管理", "11"),
("劳动争议", "11"),
("社会福利及事务", "11"),
("一次供水问题", "11"),
]
(WordCloud().add(
series_name="热点分析", data_pair=data,
word_size_range=[6, 66]).set_global_opts(
title_opts=opts.TitleOpts(
title="热点分析",
title_textstyle_opts=opts.TextStyleOpts(font_size=23)),
tooltip_opts=opts.TooltipOpts(is_show=True),
).render("basic_wordcloud.html"))
def generate_kchart(self, MA=[5, 10, 21], renderfile=False) -> Grid:
if self.exists == False:
return None
# reassign cursor tick to trigger populate attributes like self.histBar and etc.
self.cursorTick = self._cursorTick
df_toPlot = self.allBars.loc[
self.allBars['unit_intdate'] <= self.cursorTick['unit_intdate']]
# 输入时间序列中带有未来日期，则取当下能取到的最新online数据模拟
# if len(self.allBars.loc[self.allBars['unit_intdate'] == self.cursorTick['unit_intdate']]) == 0:
# df_liveQuote = self.get_livebar(assign_timestamp=self.cursorTick['timeAxis'])
# if len(df_liveQuote) > 0:
# df_toPlot = df_toPlot.append(df_liveQuote, ignore_index = True, sort=False)
str_secname = self.sec_name
str_plotname = '{} - {}'.format(self.ts_code, str_secname)
'''-----------------画图部分---------------------'''
lst_ohlcv = df_toPlot.loc[:, ['open', 'close', 'low', 'high'
]].values.tolist()
lst_vol = list(df_toPlot['vol'].values)
lst_amount = list(df_toPlot['amount'].values)
# 需从处理过的histbar中获得，由于长度比allbar短1，需将值list添加一个空值补齐
lst_peaks = list(abs(self.histBars['peaks'].values))
# lst_pivotdown=[*(float('nan') if i<0 else i for i in list(self.histBars['pivots'].values)),float('nan')]
lst_pivotdown = [
*(float('nan') if i < 0 else i
for i in list(self.histBars['valid_pivots'].values)),
float('nan')
]
# lst_pivotup=[*(float('nan') if i>0 else abs(i) for i in list(self.histBars['pivots'].values)),float('nan')]
lst_pivotup = [
*(float('nan') if i > 0 else abs(i)
for i in list(self.histBars['valid_pivots'].values)),
float('nan')
]
lst_xaxis = list(df_toPlot['unit_intdate'].astype(
str)) # xaxis values has to be STRING type, int will not work
def calculate_ma(day_count: int, d):
result: List[Union[float, str]] = []
for i in range(len(d)):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(d[i - j][1])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
kline = (
Kline().add_xaxis(lst_xaxis).add_yaxis(
series_name=str_secname,
y_axis=lst_ohlcv,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="min", value_dim="close"),
opts.MarkPointItem(type_="max", value_dim="close")
],
symbol_size=[20, 20], #表示标记宽为 20，高为 10
),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#ec0000",
border_color0="#00da3c",
),
).set_global_opts(
title_opts=opts.TitleOpts(
title=str_plotname,
subtitle='MA=' + str(MA),
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
legend_opts=opts.LegendOpts(is_show=True,
pos_top='10%',
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1, 2],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1, 2],
type_="slider",
pos_top="90%",
range_start=75,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000",
font_size=10),
),
# 阴量绿阳量红
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=list(
map(lambda x: x + len(MA),
[4, 5])), #动态算出vol和amt柱状图的series index
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#ec0000"
},
{
"value": -1,
"color": "#00da3c"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
trendline = (Line().add_xaxis(lst_xaxis).add_yaxis(
'高低点', lst_peaks,
itemstyle_opts=opts.ItemStyleOpts(color="green")))
for i in MA:
if i is not None:
trendline.add_yaxis(
series_name='MA' + str(i),
y_axis=calculate_ma(day_count=i, d=lst_ohlcv),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
trendline.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
trendline.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
keyPoints = (EffectScatter().add_xaxis(lst_xaxis).add_yaxis(
"末跌高",
lst_pivotdown,
symbol=SymbolType.ARROW,
symbol_rotate=180,
symbol_size=5,
itemstyle_opts=opts.ItemStyleOpts(color="purple")).add_yaxis(
"末升低",
lst_pivotup,
symbol=SymbolType.ARROW,
symbol_size=5,
itemstyle_opts=opts.ItemStyleOpts(
color="blue")).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)))
vol_bar = (
Bar().add_xaxis(lst_xaxis).add_yaxis(
series_name="交易量",
y_axis=[[
i, lst_vol[i],
1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1
] for i in range(len(lst_vol))],
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_="最低",
max_="最高",
),
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),
)
# .add_yaxis("交易量", lst_vol,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
amnt_bar = (
Bar().add_xaxis(lst_xaxis).add_yaxis(
series_name="交易额",
y_axis=[[
i, lst_amount[i],
1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1
] for i in range(len(lst_amount))],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=2,
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_="最低",
max_="最高",
),
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),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
# .add_yaxis("交易额", lst_amount,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
priceChart = kline.overlap(trendline).overlap(keyPoints)
gridChart = Grid()
gridChart.add(
priceChart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_bottom='40%'),
)
gridChart.add(
vol_bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="60%",
height='15%'),
)
gridChart.add(
amnt_bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="75%"),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE, 'kline')
gridChart.render(fname) if renderfile else None
self.kchart = gridChart # 将结果输出到analytics对象属性中用于function以外的调用
return self
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 generate_mchart(self,
lst_tscode=[],
lst_macro=[
'shibor.1w', 'pimon.nt_yoy', 'pimon.ppi_yoy',
'pimon.m1_yoy', 'pimon.m2_yoy',
'pimon.allstock_circ_mv_sum'
],
lst_findmedian=['pe', ()],
renderfile=False) -> Grid:
if self.exists == False:
return None
''' testing findmedian with a given index '''
# df=self._dataagent.query('index_weight',index_code='000300.SH',start_date='20200630', end_date='20200630')
# lst_findmedian=['pb',tuple(df['con_code'].values)]
''' ----------test end--------------------'''
lst_tscode = list(set([self.ts_code, *lst_tscode]))
# lst_findmedian[1] = tuple(self._dataagent.registry.loc[self._dataagent.registry['sec_type']=='stk']['ts_code']) if lst_findmedian[1] is None or len(lst_findmedian[1])==0 else lst_findmedian[1]
dat_cursor = self.cursorTick['timeAxis']
int_latestViewDate = self._dataagent.int_findClosestTradeDate(
datetime.strftime(dat_cursor, '%Y%m%d'))
df_baseCal = self._dataagent.df_calTbl.loc[
self._dataagent.df_calTbl['cal_date'] <=
int_latestViewDate]['cal_date'].copy()
lst_xaxis = [
'{}/{}/{}'.format(i[:4], i[4:6], i[6:])
for i in df_baseCal.values.astype('str')
] # xaxis values has to be STRING type, int will not work
linechart = (Line().add_xaxis(lst_xaxis))
# 生成主线，即目标证券/指数，及比较用的指数
for str_tscode in lst_tscode:
# str_tscode = DataAgent.formatCode(code)
df_target = self._dataagent.query('load_alldaily',
tpl_dateindex=tuple(df_baseCal),
ts_code=str_tscode)
if len(df_target) > 0:
df_toPlot = pd.merge(left=df_baseCal,
right=df_target,
how='left',
left_on='cal_date',
right_on='trade_date')
else:
continue
'''=====主图画图部分包括主标的和副标的价格曲线，在主图中续把主图及后面叠加图的option，y轴等设置好==='''
linename = '{} - {}'.format(str_tscode, self.sec_name)
lst_values = list(df_toPlot['close'])
lst_values = list(libs.normByRange(lst_values))
linechart.add_yaxis(
linename,
lst_values,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.3),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="#00da3c", ),
)
linechart.extend_axis( # 所有保持原比例的折线都放在linechart2里，使用右侧y轴
yaxis=opts.AxisOpts(
name="倍数",
type_="value",
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="black")),
axislabel_opts=opts.LabelOpts(formatter="{value}倍"),
))
linechart.set_global_opts(
title_opts=opts.TitleOpts(title='宏观比较', ),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
name="等比例",
is_scale=True,
),
legend_opts=opts.LegendOpts(is_show=True,
pos_top='5%',
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0],
type_="slider",
pos_top="90%",
range_start=75,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000",
font_size=10),
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
'''=============================主图画图部分结束================='''
'''=========叠加图1 尝试读取pe等股票才有的属性，如失败则说明不是股票，循环继续============='''
try:
lst_pe = df_toPlot['pe'].values.tolist()
lst_pb = df_toPlot['pb'].values.tolist()
lst_pepb = [
round(lst_pe[index] / value, 2)
for index, value in enumerate(lst_pb) if value != 0
]
linechart.add_yaxis(
str_tscode + "_PE",
lst_pe,
yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
linechart.add_yaxis(
str_tscode + "_PE/PB",
lst_pepb,
yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
except:
continue
'''==========================叠加图1结束================================'''
df_shibor = self._dataagent.query('load_shibor')
# print('Supported shibor columns: {}'.format(df_shibor.columns))
df_aggDay = self._dataagent.query('load_aggDayKPI')
# print('Supported macro eco columns: {}'.format(df_aggDay.columns))
df_pimon = self._dataagent.query('load_piMon')
df_pimon = pd.merge(
df_pimon,
df_aggDay,
how='left',
left_on='date',
right_on='trade_date') #与aggday按每月末日期合并以获得如“流通市值”等kpi
# print('Supported macro eco columns: {}'.format(df_pimon.columns))
'''=================叠加图2 所有股票的倍数类KPI，使用倍数类y轴============='''
lst_aggDayKPIloop = [
'allstock_pe_median', 'allstock_pe_ttm_median',
'allstock_pb_median', 'allstock_pepb_median'
]
for kpi in lst_aggDayKPIloop:
lst_values = df_aggDay[kpi].values.tolist()
linechart.add_yaxis(
kpi,
lst_values,
yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
'''==========================叠加图2结束================================'''
'''===========叠加图3 宏观数据如shibor，cpi，m1等，按“。”分割类别进行图表生成，使用等比类y轴 ========'''
for item in lst_macro:
try:
topic, colname = item.split('.')
except:
cfg.applogger.warning(f'input code {item} not recognized...')
if topic == 'shibor':
df_target = df_shibor
maxarr = df_target.max(axis=0, skipna=True).values[1:]
minarr = df_target.min(axis=0, skipna=True).values[1:]
deltaRng = max(maxarr) - min(
minarr
) # overwrite the normalization function delta to put all shibor rates comparable
elif topic == 'pimon':
df_target = df_pimon
deltaRng = None
else:
cfg.applogger.warning(
f'subject code {topic} is not recognized...')
if len(df_target) > 0:
df_toPlot = pd.merge(df_baseCal,
df_target,
how='left',
left_on='cal_date',
right_on='date')
df_toPlot[colname] = df_toPlot[colname].bfill()
else:
continue
linename = item
lst_values = list(df_toPlot[colname])
lst_values = list(libs.normByRange(lst_values, delta=deltaRng))
linechart.add_yaxis(
linename,
lst_values, # 所有等比例缩放的折线都放在linechart里，使用左侧y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
'''==========================叠加图3结束================================'''
'''========叠加图4 如参数给出需要寻找中位数的股票列表则读取相应所有本地文件后计算并生成叠加图标========'''
if lst_findmedian[1] is not None and len(lst_findmedian[1]) > 0:
str_cat = lst_findmedian[0]
df_result = pd.DataFrame()
for str_tscode in lst_findmedian[1]:
try:
df = self._dataagent.query(
'load_alldaily',
tpl_dateindex=tuple(df_baseCal),
ts_code=str_tscode)[['ts_code', 'trade_date', str_cat]]
print('{} csv file loaded for {} trend analysis...'.format(
str_tscode, str_cat),
end='\r')
except Exception as e:
cfg.applogger.warning(
f'{str_tscode} csv file not found...')
continue
df_result = pd.concat([df_result, df])
df_result.dropna(subset=[str_cat], inplace=True)
df_result = df_result.loc[df_result['trade_date'] > 19940301]
# libs.df_csv(cfg.PATH_BUGFILE,(df_result.groupby('trade_date')['ts_code'].count(),))
# print(df_result.groupby('trade_date')['ts_code'].count())
df_target = df_result.groupby('trade_date')[str_cat].median()
if len(df_target) > 0:
df_toPlot = pd.merge(left=df_baseCal,
right=df_target,
how='left',
left_on='cal_date',
right_on='trade_date')
linename = '{}-median'.format(str_cat)
lst_values = list(df_toPlot[str_cat])
lst_values = list(lst_values)
linechart.add_yaxis(
linename,
lst_values,
yaxis_index=1,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
else:
pass
'''==========================叠加图4结束================================'''
# put everything in a grid for easy resizing and positioning
gridChart = Grid()
gridChart.add(
linechart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top='25%'),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE, 'macro')
gridChart.render(fname) if renderfile else None
self.mchart = gridChart #输出至可访问对象属性中
return self
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)
opts.BarItem(
name=area[i],
value=nums[i],
itemstyle_opts=opts.ItemStyleOpts(color=colors[i]) # 设置每根柱子的颜色
))
bar.add_xaxis(xaxis_data=area)
bar.add_yaxis("上榜美女数", y)
bar.set_global_opts(
xaxis_opts=opts.AxisOpts(name='国家',
axislabel_opts=opts.LabelOpts(rotate=45)),
yaxis_opts=opts.AxisOpts(
name='上榜美女数',
min_=0,
max_=55, # y轴刻度的最小值 最大值
),
title_opts=opts.TitleOpts(title="各地区上榜美女数",
title_textstyle_opts=opts.TextStyleOpts(
font_family="KaiTi",
font_size=25,
color="black")))
# 标记最大值 最小值 平均值 标记平均线
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="average", name="平均值")
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]))
bar.render("女神地区分布.html")
(
Radar(init_opts=opts.InitOpts(width="1600px", height="1000px", bg_color="#CCCCCC"))
.add_schema(
schema=[
opts.RadarIndicatorItem(name="销售（sales）", max_=6500),
opts.RadarIndicatorItem(name="管理（Administration）", max_=16000),
opts.RadarIndicatorItem(name="信息技术（Information Technology）", max_=30000),
opts.RadarIndicatorItem(name="客服（Customer Support）", max_=38000),
opts.RadarIndicatorItem(name="研发（Development）", max_=52000),
opts.RadarIndicatorItem(name="市场（Marketing）", max_=25000),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
.add(
series_name="预算分配（Allocated Budget）",
data=v1,
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
)
.add(
series_name="实际开销（Actual Spending）",
data=v2,
linestyle_opts=opts.LineStyleOpts(color="#5CACEE"),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="基础雷达图"), legend_opts=opts.LegendOpts()
)
def kline_profession_example() -> Grid:
data = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
def calculate_ma(day_count: int, d):
result: List[Union[float, str]] = []
for i in range(len(d)):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(d[i - j][1])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
x_data = ["2017-7-{}".format(i + 1) for i in range(31)]
kline = (Kline().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="Dow-Jones index",
y_axis=data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
).set_global_opts(
title_opts=opts.TitleOpts(
title="上证指数",
subtitle="MA5",
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=0,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#ec0000"
},
{
"value": -1,
"color": "#00da3c"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
line = (Line().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5, d=data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
bar = (Bar().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="Volume",
yaxis_data=[[i, data[i][3], 1 if data[i][0] > data[i][1] else -1]
for i in range(len(data))],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Kline And Line
overlap_kline_line = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid()
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="16%"),
)
return grid_chart
