def base_kline_grid_chart(df, signal_infos=[]):
""" 最最最基础的 K 线图，传入的 df 只需要有 open high low close volume 即可绘制 """
# OHLC
kline_chart = ohlc_kline_chart(df, signal_infos=signal_infos)
# Volume
volume_chart = volume_bar_chart(df)
# 把 ohlc 和 volume 图组合起来
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="800px",
bg_color="#ffffff",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline_chart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="55%"),
)
grid_chart.add(
volume_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="15%"),
)
return grid_chart
def bolling_backtest_grid_chart(df):
"""完整布林线回测图，传入的 df 需要有 open high low close volume upper median lower signal equity_curve 即可绘制 """
# 布林线的 OHLC 图
kline_chart = bolling_kline_chart(df, 3) # 这里要控制3个轴同时缩放
# Volume
volume_chart = volume_bar_chart(df)
# 资金曲线
equity_chart = equity_line_chart(df)
# 组合起来
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="800px",
bg_color="#ffffff",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline_chart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="40%"),
)
grid_chart.add(
volume_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="55%",
height="10%"),
)
grid_chart.add(
equity_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="75%",
height="10%"),
)
return grid_chart
def get_date_chart(date: str):
grid_chart = (
Grid()
.add(
get_provinces_daily_bar_chart(date),
grid_opts=opts.GridOpts(
pos_left="10", pos_right="45%", pos_top="50%", pos_bottom="5"
),
)
.add(
get_China_sum_line_chart(date),
grid_opts=opts.GridOpts(
pos_left="65%", pos_right="80", pos_top="5%", pos_bottom="65%"
),
)
.add(
get_provinces_daily_pie_chart(date),
grid_opts=opts.GridOpts(
pos_left="45%", pos_top="65%"
)
)
.add(
get_provinces_daily_map_chart(date),
grid_opts=opts.GridOpts(
pos_left='10'
)
)
)
return grid_chart
def index_bar_every_X():
ted = pd.read_csv("./static/data/ted_main.csv")
ted['film_date'] = ted['film_date'].apply(
lambda x: datetime.datetime.fromtimestamp(int(x)).strftime('%d-%m-%Y'))
ted['published_date'] = ted['published_date'].apply(
lambda x: datetime.datetime.fromtimestamp(int(x)).strftime('%d-%m-%Y'))
month_order = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
'Nov', 'Dec'
]
# day_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
ted['month'] = ted['film_date'].apply(
lambda x: month_order[int(x.split('-')[1]) - 1])
month_df = pd.DataFrame(ted['month'].value_counts()).reset_index()
month_df.columns = ['month', 'talk_times']
ted['years'] = ted['published_date'].apply(lambda x: x.split('-')[2])
year_df = pd.DataFrame(ted['years'].value_counts().reset_index())
year_df.columns = ['years', 'times']
bar = (Bar().add_xaxis(month_df.month.values.tolist()).add_yaxis(
"", month_df.talk_times.values.tolist()).set_global_opts(
title_opts=opts.TitleOpts()).set_series_opts(
label_opts=opts.LabelOpts(is_show=True), ))
bar1 = (Line().add_xaxis(year_df.years.values.tolist()[::-1]).add_yaxis(
"",
year_df.times.values.tolist()[::-1]).set_global_opts(
title_opts=opts.TitleOpts()).set_series_opts(
label_opts=opts.LabelOpts(is_show=True), ))
grid = (Grid().add(bar, grid_opts=opts.GridOpts(pos_bottom="60%")).add(
bar1, grid_opts=opts.GridOpts(pos_top="60%")))
return render_template('index.html',
myechart=grid.render_embed(),
select=list(res.keys()))
def draw(self, symbol, fn_render):
fn = get_dss() +'fut/bar/day_' + symbol + '.csv'
df = pd.read_csv(fn)
df1 = df.loc[:,['date','time','close']]
n = 30
close_list = df.apply(lambda record: float(record['close']), axis=1).tolist()
close_list = np.array(close_list)
ma_arr = talib.SMA(close_list, n)
df['ma'] = ma_arr
df['close'] = df['close'] - df['ma']
df2 = df.loc[:,['date','time','close']]
line1 = self.gen_line_one(df1, symbol)
line2 = self.gen_line_two(df2)
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1300px",
height="700px",
#animation_opts=opts.AnimationOpts(animation=False),
)
)
grid_chart.add(
line1,
grid_opts=opts.GridOpts(pos_left="5%", pos_right="3%", height="39%"),
)
grid_chart.add(
line2,
grid_opts=opts.GridOpts(
pos_left="5%", pos_right="3%", pos_top="53%", height="39%" ),
)
grid_chart.render(fn_render)
def index_bar_every_1_tp():
ted = pd.read_csv("./static/data/ted_main.csv")
ted['film_date'] = ted['film_date'].apply(
lambda x: datetime.datetime.fromtimestamp(int(x)).strftime('%d-%m-%Y'))
ted['published_date'] = ted['published_date'].apply(
lambda x: datetime.datetime.fromtimestamp(int(x)).strftime('%d-%m-%Y'))
views_ted = ted[[
'main_speaker', 'title', 'published_date', 'views', 'comments', 'tags',
'speaker_occupation', 'num_speaker'
]].sort_values(by='views', ascending=False)
views_ted_1 = ted[[
'main_speaker', 'title', 'published_date', 'views', 'comments', 'tags',
'speaker_occupation', 'num_speaker'
]].sort_values(by='comments', ascending=False)
bar = (Bar().add_xaxis(
views_ted.head(10).main_speaker.values.tolist()).add_yaxis(
"top10",
views_ted.head(10).views.values.tolist()).set_global_opts(
title_opts=opts.TitleOpts(
title="浏览量和评论数TOP10视频")).set_series_opts(
label_opts=opts.LabelOpts(is_show=True), ))
bar1 = (Bar().add_xaxis(
views_ted_1.head(10).main_speaker.values.tolist()).add_yaxis(
"top10",
views_ted_1.head(10).comments.values.tolist()).set_global_opts(
title_opts=opts.TitleOpts(
title="浏览量和评论数TOP10视频")).set_series_opts(
label_opts=opts.LabelOpts(is_show=True), ))
grid = (Grid().add(bar, grid_opts=opts.GridOpts(pos_bottom="60%")).add(
bar1, grid_opts=opts.GridOpts(pos_top="60%")))
return render_template('index.html',
myechart=grid.render_embed(),
select=list(res.keys()))
def grid_vertical():
bar = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(title_opts=opts.TitleOpts(title="Grid-Bar"))
)
line = (
Line()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
title_opts=opts.TitleOpts(title="Grid-Line", pos_top="48%"),
legend_opts=opts.LegendOpts(pos_top="48%"),
)
)
grid = (
Grid()
.add(bar, grid_opts=opts.GridOpts(pos_bottom="60%"))
.add(line, grid_opts=opts.GridOpts(pos_top="60%"))
)
return grid
def get_chart_of_date(date):
# 一个只标注出对应年份，其他是空字符串的list
date_mark = [0] * len(date_list)
date_mark[date_list.index(date)] = dayly_count_list[date_list.index(date)]
# print(date_mark)
line_chart = (Line().add_xaxis(date_list).add_yaxis(
"", dayly_count_list,
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
"",
date_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='2017年海口市5至10月正常天气滴滴每日出行总量', pos_top='50%')))
bar_chart = (Bar().add_xaxis(xaxis_data=hour_24_list).add_yaxis(
series_name='',
yaxis_data=hourly_count_list[date_list.index(date)],
label_opts=opts.LabelOpts(is_show=True,
position='top',
formatter='{c}'),
).set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=True)),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=True)),
title_opts=opts.TitleOpts(title=date + '各时段出行量 天气：' +
weather_list[date_list.index(date)] + ', ' +
weekday_list[date_list.index(date)])))
grid = (Grid().add(bar_chart,
grid_opts=opts.GridOpts(pos_bottom="60%")).add(
line_chart, grid_opts=opts.GridOpts(pos_top="60%")))
return grid
def pie_base() -> 'html':
df = pd.read_csv('data3.csv', encoding='utf8')
bar = (
Bar()
.add_xaxis(['不限', '3-5年', '1-3年', '5-10年', '无经验', '一年以下', '10年以上'])
.add_yaxis("职位数量", [6183, 5164, 4842, 1516, 366, 111, 34])
.set_global_opts(title_opts=opts.TitleOpts(title="工作经验-职位分布数量"))
)
line = (
Line()
.add_xaxis(['本科', '大专', '不限', '硕士', '博士', '中专'])
.add_yaxis("职位数量", [9954, 3704, 3205, 1137, 88, 31])
.set_global_opts(
title_opts=opts.TitleOpts(title="最低要求学历-职位分布数量", pos_top="50%"),
legend_opts=opts.LegendOpts(pos_top="50%"),
)
)
grid = (
Grid()
.add(bar, grid_opts=opts.GridOpts(pos_bottom="60%", pos_right="0", height="30%"))
.add(line, grid_opts=opts.GridOpts(pos_top="60%", pos_right="0", height="30%"))
)
bar,line,grid.render("./templates/pie_base.html")
with open("./templates/pie_base.html", encoding="utf8", mode="r") as f:
pie_base = "".join(f.readlines())
return render_template('python_pie_base.html',
the_pie_base=pie_base,
)
def create_grid_bar_and_bar(title, x_list, data_list, data_list_2, data_unit_list):
# 1.先生成上方的柱状图
bar1 = Bar()
bar1.add_xaxis(x_list)
for key in data_list.keys():
bar1.add_yaxis(key, data_list[key])
bar1.set_global_opts(title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(pos_left="25%", pos_top="5%"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit_list[0]
, is_scale=True))
# 2.下方柱状图
bar2 = Bar()
bar2.add_xaxis(x_list)
for key in data_list_2.keys():
bar2.add_yaxis(key, data_list_2[key])
bar2.set_global_opts(title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(pos_left="25%", pos_top="55%"),
yaxis_opts=opts.AxisOpts(name="单位:" + data_unit_list[1],
is_scale=True))
# 3.生成最终的组合图
c = (
Grid(init_opts=opts.InitOpts(theme=THEME_TYPE,height="700px"))
.add(bar1, grid_opts=opts.GridOpts(pos_bottom="60%"))
.add(bar2, grid_opts=opts.GridOpts(pos_top="60%"))
)
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 grid_graph(self, grid_graph=None):
# 创建组合图表画布对象
self.grid_chart = Grid(
init_opts=opts.InitOpts(width="1900px", height="900px"))
bar_data = [[
row["open_price"], row["close_price"], row["low_price"],
row["high_price"]
] for ix, row in self.bar_data.iterrows()]
self.grid_chart.add_js_funcs("var barData = {}".format(bar_data))
self.grid_chart.add(
chart=self.kline_chart,
grid_index=0,
grid_opts=opts.GridOpts(pos_left="5%",
pos_right="5%",
pos_top="10%",
height="60%"),
)
self.grid_chart.add(
chart=grid_graph,
grid_index=1,
grid_opts=opts.GridOpts(pos_left="5%",
pos_right="5%",
pos_top="75%",
height="20%"),
)
return self.grid_chart
def create_result_chart(data):
instrulist = list(set(d.instrument for d in data))
# x = ["{} P{}".format(localtime(d.upload_time).strftime("%Y-%m-%d %X"), d.platenum) for d in data]
x = ["{} P{}".format(d.upload_time.strftime("%Y-%m-%d %X"), d.platenum) for d in data]
# 均值直方图
b = Bar(init_opts=opts.InitOpts(width='1900px'))
b.add_xaxis(x)
for instru in instrulist:
mean = get_mean(data, instru)
b.add_yaxis(instru, mean)
b.set_global_opts(title_opts=opts.TitleOpts(title="结果mean图"))
# sd折线图
l = Line(init_opts=opts.InitOpts(width='1900px'))
l.add_xaxis(x)
for instru in instrulist:
sd = get_sd(data, instru)
l.add_yaxis(instru, sd, is_connect_nones=True)
l.set_global_opts(title_opts=opts.TitleOpts(title="结果sd图", pos_top='48%'))
grid = Grid(init_opts=opts.InitOpts(width='1900px'))
grid.add(b, grid_opts=opts.GridOpts(pos_bottom='60%'))
grid.add(l, grid_opts=opts.GridOpts(pos_top='60%'))
return grid
def test_liquid_grid(fake_writer):
l1 = (
Liquid()
.add("lq", [0.6, 0.7], center=["60%", "50%"])
.set_global_opts(title_opts=opts.TitleOpts(title="多个 Liquid 显示"))
)
l2 = Liquid().add(
"lq",
[0.3254],
center=["25%", "50%"],
label_opts=opts.LabelOpts(
font_size=50,
formatter=JsCode(
"""function (param) {
return (Math.floor(param.value * 10000) / 100) + '%';
}"""
),
position="inside",
),
)
c = Grid().add(l1, grid_opts=opts.GridOpts()).add(l2, grid_opts=opts.GridOpts())
c.render()
_, content = fake_writer.call_args[0]
assert_in("center", content)
def grid_chart(dataframe) -> Grid:
grid = (
Grid(init_opts=opts.InitOpts(
width="1200px",
height="800px",
#设置动画
animation_opts=opts.AnimationOpts(animation_delay=1000,
animation_easing="elasticOut"),
)).add(
line_datazoom_CO2(dataframe), # 图表实例，仅 `Chart` 类或者其子类
# grid 组件离容器右侧的距离。
# right 的值可以是像 20 这样的具体像素值，可以是像 '20%' 这样相对于容器高宽的百分比。
grid_opts=opts.GridOpts(pos_top="4%", height="24%")).
add(
line_datazoom_temp(dataframe), # 图表实例，仅 `Chart` 类或者其子类
# grid 组件离容器右侧的距离。
# right 的值可以是像 20 这样的具体像素值，可以是像 '20%' 这样相对于容器高宽的百分比。
grid_opts=opts.GridOpts(pos_bottom="39%", height="24%")
).add(
bar_datazoom_rh(dataframe), # 图表实例，仅 `Chart` 类或者其子类
# grid 组件离容器右侧的距离。
# right 的值可以是像 20 这样的具体像素值，可以是像 '20%' 这样相对于容器高宽的百分比。
grid_opts=opts.GridOpts(pos_bottom="6%",
height="24%")).dump_options_with_quotes())
return grid
def index_bar_every_4():
df2 = pd.read_csv(r'./static/data/house_price.csv')
df = pd.read_csv(r'./static/data/divorce.csv')
bar = (Bar().add_xaxis(df.province.values.tolist()).add_yaxis(
"离婚年均增长率", df.dir.values.tolist()).set_global_opts(
title_opts=opts.TitleOpts(title="中国分省离婚年均增长率情况"),
xaxis_opts=opts.AxisOpts(name_rotate=60,
name="省份",
axislabel_opts={"rotate": 45})))
line = (Line().add_xaxis(df2.province.values.tolist()).add_yaxis(
"房价年均增长率", df2.increase.values.tolist()).set_global_opts(
title_opts=opts.TitleOpts(title="中国分省住宅商品房价格年均增长率情况",
pos_top="48%"),
legend_opts=opts.LegendOpts(pos_top="48%"),
xaxis_opts=opts.AxisOpts(name_rotate=60,
name="省份",
axislabel_opts={"rotate": 45})))
grid = (Grid().add(bar, grid_opts=opts.GridOpts(pos_bottom="60%")).add(
line, grid_opts=opts.GridOpts(pos_top="60%")))
return render_template('index.html',
myechart=grid.render_embed(),
text1='''离婚年均增长率相对较低的省份：北京、上海、新疆、黑龙江、吉林、内蒙古；
但其实，北京、上海的房价年均增长率是非常高的，且新疆、黑龙江这些省份的房价年均增长率也是处于中游水平；
可以很明显地从整个图中看出，离婚年均增长率和房价年均增长率之间的关联性不大。''')
def draw(self, symbol, fn_render):
fn = get_dss() + 'fut/bar/day_' + symbol + '.csv'
df1 = pd.read_csv(fn)
# print(df1.head())
price_min = int(df1.close.min() * 0.99)
price_max = df1.close.max()
kline = self.gen_kline(df1, symbol)
line_cci = self.gen_cci(df1, 100)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1390px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
height="60%"),
)
grid_chart.add(
line_cci,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="75%",
height="17%"),
)
grid_chart.render(fn_render)
def grid_horizontal():
scatter = (
Scatter()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
title_opts=opts.TitleOpts(title="Grid-Scatter"),
legend_opts=opts.LegendOpts(pos_left="20%"),
)
)
line = (
Line()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
toolbox_opts=opts.ToolboxOpts(is_show=True),
title_opts=opts.TitleOpts(title="Grid-Line", pos_right="5%"),
legend_opts=opts.LegendOpts(pos_right="20%"),
)
)
grid = (
Grid()
.add(scatter, grid_opts=opts.GridOpts(pos_left="55%"))
.add(line, grid_opts=opts.GridOpts(pos_right="55%")))
return grid
def draw_charts(fn1, fn2):
df1 = pd.read_csv(fn1)
df1['datetime'] = df1['date'] + ' ' + df1['time']
#print(df1.head())
kline1 = gen_kline_one(df1)
df2 = pd.read_csv(fn2)
df2['datetime'] = df2['date'] + ' ' + df2['time']
#print(df1.head())
kline2 = gen_kline_two(df2)
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1300px",
height="700px",
#animation_opts=opts.AnimationOpts(animation=False),
)
)
grid_chart.add(
kline1,
grid_opts=opts.GridOpts(pos_left="5%", pos_right="3%", height="39%"),
)
grid_chart.add(
kline2,
grid_opts=opts.GridOpts(
pos_left="3%", pos_right="5%", pos_top="53%", height="39%" ),
)
fn = get_dss( ) + 'backtest/render/bar2.html'
grid_chart.render(fn)
def overlap_line_scatter(day_data, week_data) -> Bar:
# 处理日数据
x_day = list(day_data[16:-7].index)
for i in range(0, len(x_day)):
x_day[i] = datetime.datetime.strftime(x_day[i], "%H:%M")
Y_day = list(day_data[16:-7])
for i in range(0, len(Y_day)):
Y_day[i] = round(Y_day[i])
# 处理周数据
print(week_data)
x_week = list(week_data.index)
Y_week = list(week_data['counts'])
print(Y_week)
bar = (Bar().add_xaxis(x_day).add_yaxis(
"未来一天",
Y_day).set_global_opts(title_opts=opts.TitleOpts(title="咨询量预测统计")))
line = (Line().add_xaxis(x_week).add_yaxis(
"未来一周", Y_week, symbol="triangle", symbol_size=20).set_global_opts(
legend_opts=opts.LegendOpts(pos_top="48%")))
grid = (Grid().add(bar, grid_opts=opts.GridOpts(pos_bottom="60%")).add(
line, grid_opts=opts.GridOpts(pos_top="60%")))
return grid
def grid(factor, factor2, instrument):
gridchart = Grid(init_opts=opts.InitOpts(width="100vw",
height="50vh",
animation_opts=opts.AnimationOpts(
animation=True)))
gridchart.add( #左边画最新数据
getplot(factor,
factor2,
kdata_part,
nnv,
new_p,
instrument,
xaxis_index=[0, 1]),
grid_opts=opts.GridOpts(pos_left="5%", pos_right="68%"))
gridchart.add( # 右边画全样本数据
getplot(factor,
factor2,
kdata_all,
nv,
new_a,
instrument,
xaxis_index=None),
grid_opts=opts.GridOpts(pos_left="40%", pos_right="5%"))
return gridchart
def grid_liquid_favorate_rate() -> Grid:
l1 = (Liquid().add(
"好评率",
[favorate_rate],
center=["60%", "50%"],
label_opts=opts.LabelOpts(
font_size=50,
formatter=JsCode("""function (param) {
return (Math.floor(param.value * 10000) / 100) + '%';
}"""),
position="inside",
),
).set_global_opts(title_opts=opts.TitleOpts(title="好评率和差评率")))
l2 = (Liquid().add(
"差评率",
[poor_rate],
center=["25%", "50%"],
label_opts=opts.LabelOpts(
font_size=50,
formatter=JsCode("""function (param) {
return (Math.floor(param.value * 10000) / 100) + '%';
}"""),
position="inside",
),
))
grid = Grid().add(l2,
grid_opts=opts.GridOpts()).add(l1,
grid_opts=opts.GridOpts())
return grid
def get_year_chart(time,data):
map_chart=get_Map(time,data)
bar_chart=get_Bar(time,data)
g=(
Grid(init_opts=opts.InitOpts(width='1440px',height='600px'))
.add(bar_chart,grid_opts=opts.GridOpts(pos_left="10", pos_right="45%", pos_top="50%", pos_bottom="10"))
.add(map_chart,grid_opts=opts.GridOpts())
)
return g
def grid_like(data,user_id):
bar = draw_bar_like(data, user_id)
pie = draw_pie_like(data, user_id)
grid = (
Grid(init_opts=opts.InitOpts(width="1400px",height="500px"))
.add(bar, grid_opts=opts.GridOpts(pos_left="55%"))
.add(pie, grid_opts=opts.GridOpts(pos_right="55%"))
)
return grid
def draw():
# 饼图
pie = (Pie(init_opts=opts.InitOpts(theme=ThemeType.DARK)).add(
"疫情接口可视化", pageData).set_colors([
"blue", "green", "yellow", "red", "pink", "orange", "purple"
]).set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_show=True, min_=0, max_=2000))
)
map = (Map(init_opts=opts.InitOpts(theme=ThemeType.DARK)).add(
"疫情接口可视化", pageData, "china").set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_show=True, min_=0, max_=2000))
)
bar = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))
# 只取省份
.add_xaxis([item[0] for item in pageData])
# 只取人数
.add_yaxis(series_name="", y_axis=[item[1] for item in pageData])
# 设置全局配置项
.set_global_opts(
# 设置视觉配置项
visualmap_opts=opts.VisualMapOpts(
is_show=True,
min_=0,
max_=10000,
dimension=0,
# 翻转坐标之后，视觉映射不生效可以添加dimension=0
# pos_right=10, # pos_top=0
),
# x坐标轴默认标注去除
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
is_show=True)),
# y坐标轴默认标注去除
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
is_show=True)))
# 去除柱状图上的数字标注
.set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).reversal_axis() # x轴y轴
)
# grid = (
# # 初始化配置项，参考 `global_options.InitOpts`
# Grid(init_opts=opts.InitOpts(theme=ThemeType.DARK))
# .add(pie, grid_opts=opts.GridOpts())
# .add(map, grid_opts=opts.GridOpts())
# .add(bar, grid_opts=opts.GridOpts())
# .render("疫情接口数据组合图形.html")
# )
# 组合图形
grid = (
Grid(init_opts=opts.InitOpts(theme=ThemeType.DARK)).add(
bar, grid_opts=opts.GridOpts())
# 柱状图放在组合图形的第一个
.add(map, grid_opts=opts.GridOpts()).add(
pie, grid_opts=opts.GridOpts()).render("全国疫情数据可视化组合图形展示.html"))
def draw_charts():
pz = 'CF'
vtSymbol = 'CF'
fn = get_dss() + 'backtest/fut/' + pz + '/day_' + vtSymbol + '.csv'
df1 = pd.read_csv(fn)
# df1 = df1[df1.date >= '2019-01-20']
#df1['datetime'] = df1['date'] + ' ' + df1['time']
df1['datetime'] = df1['date']
price_min = int(df1.close.min() * 0.99)
price_max = df1.close.max()
#fn = get_dss( )+ 'fut/engine/aberration_raw/signal_aberration_raw_duo_deal_CF.csv'
#fn = get_dss( )+ 'fut/engine/aberration_raw/signal_aberration_raw_kong_deal_CF.csv'
fn = get_dss(
) + 'fut/engine/aberration_enhance/signal_aberration_enhance_duo_deal_CF.csv'
#fn = get_dss( )+ 'fut/engine/aberration_enhance/signal_aberration_enhance_kong_deal_CF.csv'
df2 = pd.read_csv(fn)
dt_list = df2['datetime'].tolist()
dt_list = [dt[:10] for dt in dt_list]
# print(dt_list)
df2['datetime'] = dt_list
line = gen_line(df1, vtSymbol, price_min, price_max)
line_atr = gen_atr(df1, 10)
line_boll = gen_boll(df1, 10, 2)
line = line.overlap(line_boll)
scatter_open = gen_poit_open(df2)
scatter_close = gen_poit_close(df2)
line = line.overlap(scatter_open)
line = line.overlap(scatter_close)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1300px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="60%"),
)
grid_chart.add(
line_atr,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="76%",
height="17%"),
)
fn = get_dss() + 'backtest/render/k_deal_aberration_' + vtSymbol + '.html'
grid_chart.render(fn)
def get_line_charts():
line_chart_1 = (
Line(init_opts=opts.InitOpts(width='500px', height='250px'))
.add_xaxis(time_list)
.add_yaxis(series_name="迁出指数",
y_axis=hubei_out_data,
# symbol="emptyCircle",
is_symbol_show=True,
is_smooth=True, )
.add_yaxis(series_name="迁入指数",
y_axis=hubei_in_data,
# symbol="emptyCircle",
is_symbol_show=True,
is_smooth=True, )
.set_global_opts(
title_opts=opts.TitleOpts(title='01-01至03-10湖北省迁徙指数',
pos_left='72%', pos_top='15%'),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type='shadow'),
legend_opts=opts.LegendOpts(is_show=True ,orient='horizontal', pos_left='75%', pos_top='20%'),
yaxis_opts=opts.AxisOpts(
name='迁徙指数',
type_='value',
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
)
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False), splitline_opts=opts.SplitLineOpts(is_show=True))
)
wordcloud = (
WordCloud()
.add(series_name="城市", data_pair=get_out_cities(), word_size_range=[10, 66],
pos_left='-8%', pos_top='10%', shape='triangle-forward')
.set_global_opts(
title_opts=opts.TitleOpts(
title="1-21湖北省迁出热门城市", pos_left='20%', pos_top='15%'
),
tooltip_opts=opts.TooltipOpts(is_show=True),
)
)
grid_chart = (
Grid(init_opts=opts.InitOpts(width='1500px', height='700px'))
.add(
line_chart_1,
grid_opts=opts.GridOpts(
pos_left='65%', pos_right='80', pos_top='30%', pos_bottom='30%'
)
)
.add(wordcloud, grid_opts=opts.GridOpts(pos_left="20%", pos_top="10%"))
)
return grid_chart
def plot_grid():
"""
使用Grid来绘制组合图
子图的宽高是自适应画板(Grid)的宽高
子图的位置用grid_opts中pos选项来调节:
当两个子图为pos_left与pos_right时,此时为左右排列
当两个子图为pos_top与pos_bottom时,此时为上下排列
:return: html
"""
# 1.首先绘制一个柱状图
# 初始化数据来源
x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋"]
y1 = [5, 20, 36, 10, 75]
y2 = [10, 25, 8, 60, 20]
# 初始化柱状图
bar = Bar(init_opts=opts.InitOpts(width='200px', height='100px'))
# x轴数据
bar.add_xaxis(xaxis_data=x)
# y轴数据
bar.add_yaxis(series_name="商家A", y_axis=y1)
bar.add_yaxis(series_name="商家B", y_axis=y2)
# 设置配置
bar.set_global_opts(title_opts=opts.TitleOpts(title='柱状图:商家货物销量'))
# 2.绘制一个折线图
# 初始化数据来源
x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋"]
y1 = [5, 20, 36, 10, 75]
y2 = [10, 25, 8, 60, 20]
# 初始化折线图
line = Line(init_opts=opts.InitOpts(width="100px", height="100px"))
# x轴数据
line.add_xaxis(xaxis_data=x)
# y轴数据
line.add_yaxis(series_name='商家A', y_axis=y1)
line.add_yaxis(series_name='商家B', y_axis=y2)
# 设置配置
line.set_global_opts(
title_opts=opts.TitleOpts(title='折线图:商家货物销量', pos_top='50%'))
# 初始化组合图:Grid
grid = Grid(init_opts=opts.InitOpts(width='600px', height='400px'))
# 利用grid_bottom,grid_top,grid_left,grid_right四个参数控制子图的相对位置
grid.add(bar, grid_opts=opts.GridOpts(pos_right='60%'))
grid.add(line, grid_opts=opts.GridOpts(pos_left='60%'))
grid.render('组合图Grid示范图.html')
def draw_charts():
# #fn = get_dss() +'backtest/fut/m/' + 'm_01_05.csv'
# fn = get_dss() +'backtest/fut/m/' + 'day_m.csv'
# df1 = pd.read_csv(fn)
# df1['datetime'] = df1['date'] + ' ' + df1['time']
# #print(df1.head())
# kline1 = gen_kline_one(df1)
#
# fn = get_dss() +'backtest/fut/y/' + 'day_y.csv'
# df2 = pd.read_csv(fn)
# df2['datetime'] = df2['date'] + ' ' + df2['time']
# #print(df1.head())
# kline2 = gen_kline_two(df2)
#--------------------------------------------------------------------------
fn = 'bar_kamaresid_raw_duo_CF.csv'
fn = 'bar_kamaresid_raw_duo_m.csv'
df = pd.read_csv(fn)
df.columns = ['date','time','close','q1','q2','resid']
df1 = df.loc[:,['date','time','close']]
df2 = df.loc[:,['date','time','resid']]
df2['close'] = df2['resid']
print(df1.head(3))
print(df2.head(3))
s1 = 'close'
s2 = 'resid'
line1 = gen_line_one(df1)
line2 = gen_line_two(df2)
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1300px",
height="700px",
#animation_opts=opts.AnimationOpts(animation=False),
)
)
grid_chart.add(
line1,
grid_opts=opts.GridOpts(pos_left="5%", pos_right="3%", height="39%"),
)
grid_chart.add(
line2,
grid_opts=opts.GridOpts(
pos_left="5%", pos_right="3%", pos_top="53%", height="39%" ),
)
fn = get_dss( )+ 'backtest/render/k_k_' + s1 + '_' + s2 + '.html'
grid_chart.render(fn)
def ice_ga_shrinkage_chart(df):
"""ice ga shrinkage"""
os_ma = pyecharts_float_values_data(df, 'os_ma', 6)
# 信号信息
signal_infos = bolling_signals_data(df)
# k 线
ohlc_chart = ohlc_kline_chart(df,
x_axis_count=3,
signal_infos=signal_infos)
# os_ma
os_ma_line = Line().add_xaxis(
xaxis_data=pyecharts_time_data(df)).add_yaxis(
y_axis=os_ma,
series_name='os_ma',
is_symbol_show=False,
label_opts=None,
is_smooth=True,
)
# 资金曲线
equity_chart = equity_line_chart(df)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="600px",
bg_color="#ffffff",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
ohlc_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="5%",
height="30%"),
)
grid_chart.add(
os_ma_line,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="40%",
height="20%"),
)
grid_chart.add(
equity_chart,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="18%"),
)
return grid_chart
def draw_charts():
vtSymbol = 'CF005'
fn = get_dss() + 'fut/bar/min5_' + vtSymbol + '.csv'
df1 = pd.read_csv(fn)
df1 = df1[df1.date >= '2019-11-20']
df1['datetime'] = df1['date'] + ' ' + df1['time']
# print(df1.head())
fn = get_dss() + 'fut/engine/rsiboll/signal_rsiboll_duo_deal_CF.csv'
df2 = pd.read_csv(fn)
kline = gen_kline(df1)
line_rsi = gen_rsi(df1)
line_atr = gen_atr(df1)
scatter_deal_one = gen_poit_one(df2)
scatter_deal_two = gen_poit_two(df2)
scatter_deal_three = gen_poit_three(df2)
kline_scatter = kline.overlap(scatter_deal_one)
kline_scatter = kline_scatter.overlap(scatter_deal_two)
kline_scatter = kline_scatter.overlap(scatter_deal_three)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline_scatter,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="45%"),
)
grid_chart.add(
line_rsi,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="57%",
height="17%"),
)
grid_chart.add(
line_atr,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="76%",
height="17%"),
)
grid_chart.render("k_deal_rsi_atr.html")
