def plot_kline(df, rendered=True, col=""):
"""
针对 dataframe 直接画出标准看盘软件的上k线图下成交量图的形式
:param df:
:param rendered:
:param col:
:return:
"""
kline = (
Kline()
.add_xaxis(xaxis_data=list(df["date"]))
.add_yaxis(
series_name="",
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
y_axis=list(zip(df["open"], df["close"], df["high"], df["low"])),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
],
symbol="pin",
symbol_size=[56, 40],
# label_opts=opts.LabelOpts(color="#CCFFFF",position=["top", "bottom"])
),
)
.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=50,
range_end=100,
xaxis_index=[0, 1],
),
opts.DataZoomOpts(
is_show=False,
type_="inside",
range_start=50,
range_end=100,
xaxis_index=1,
),
],
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
)
)
if col:
line = (
Line()
.add_xaxis(xaxis_data=list(df["date"]))
.add_yaxis(
series_name="",
y_axis=list(df[col]),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
)
kline = kline.overlap(line)
if "volume" in df.columns:
vl = list(df["volume"])
elif "amount" in df.columns:
vl = list(df["amount"])
else:
vl = [0 for _ in range(len(df))]
bar = (
Bar()
.add_js_funcs("var barData = {}".format(list(df["close"] - df["open"])))
.add_xaxis(xaxis_data=list(df["date"]))
.add_yaxis(
series_name="",
yaxis_data=vl,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (barData[params.dataIndex]>0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
)
)
grid_chart = Grid()
grid_chart.add_js_funcs("var barData = {}".format(list(df["close"] - df["open"])))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(
pos_left="10%", pos_right="1%", pos_top="2%", height="65%"
),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10%", pos_right="1%", pos_top="71%", height="22%"
),
)
if rendered:
return grid_chart.render_notebook()
else:
return grid_chart
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Grid, Liquid
from pyecharts.commons.utils import JsCode
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",
),
)
grid = Grid().add(l1, grid_opts=opts.GridOpts()).add(l2, grid_opts=opts.GridOpts())
put_html(grid.render_notebook())
def draw_chart():
kline = (
Kline()
.add_xaxis(xaxis_data=data["times"])
.add_yaxis(
series_name="",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]
),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(
position="middle", color="blue", font_size=15
),
data=split_data_part(),
symbol=["circle", "none"],
),
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True, data=split_data_part())
)
.set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True)
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
),
opts.DataZoomOpts(
is_show=True, xaxis_index=[0, 1], pos_top="97%", range_end=100
),
opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
],
# 三个图的 axis 连在一块
# axispointer_opts=opts.AxisPointerOpts(
# is_show=True,
# link=[{"xAxisIndex": "all"}],
# label=opts.LabelOpts(background_color="#777"),
# ),
)
)
kline_line = (
Line()
.add_xaxis(xaxis_data=data["times"])
.add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
)
)
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
# Bar-1
bar_1 = (
Bar()
.add_xaxis(xaxis_data=data["times"])
.add_yaxis(
series_name="Volumn",
yaxis_data=data["vols"],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
# 根据 echarts demo 的原版是这么写的
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode("""
# function(params) {
# var colorList;
# if (data.datas[params.dataIndex][1]>data.datas[params.dataIndex][0]) {
# colorList = '#ef232a';
# } else {
# colorList = '#14b143';
# }
# return colorList;
# }
# """)
# )
# 改进后在 grid 中 add_js_funcs 后变成如下
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar()
.add_xaxis(xaxis_data=data["times"])
.add_yaxis(
series_name="MACD",
yaxis_data=data["macds"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
line_2 = (
Line()
.add_xaxis(xaxis_data=data["times"])
.add_yaxis(
series_name="DIF",
y_axis=data["difs"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="DIF",
y_axis=data["deas"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
)
# 最下面的柱状图和折线图
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid()
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))
# K线图和 MA5 的折线图
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
# Volumn 柱状图
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(
pos_left="3%", pos_right="1%", pos_top="71%", height="10%"
),
)
# MACD DIFS DEAS
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(
pos_left="3%", pos_right="1%", pos_top="82%", height="14%"
),
)
put_html(grid_chart.render_notebook())
.set_global_opts(
title_opts=opts.TitleOpts(title="Grid - 多图合并"),
)
)
line = (
Line()
.add_xaxis(x)
.add_yaxis("蒸发量", [x + 50 for x in data_china]
)
)
bar.overlap(line)
grid = Grid()
grid.add(bar, opts.GridOpts(pos_left="5%", pos_right="5%"), is_control_axis_index=True)
grid.render_notebook()
# 词云
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"),]
wordCloud = (
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),
)
)
def draw_charts():
kline_data = [data[1:-1] for data in chart_data["values"]]
kline = (Kline().add_xaxis(
xaxis_data=chart_data["categoryData"]).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"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=98,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="85%",
range_start=98,
range_end=100,
),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
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"),
),
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=chart_data["categoryData"]).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA10",
y_axis=calculate_ma(day_count=10, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA20",
y_axis=calculate_ma(day_count=20, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA30",
y_axis=calculate_ma(day_count=30, data=chart_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=chart_data["categoryData"]).add_yaxis(
series_name="Volume",
yaxis_data=chart_data["volumes"],
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(init_opts=opts.InitOpts(
width="1000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
))
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="63%",
height="16%"),
)
put_html(grid_chart.render_notebook())
def plot_kline(
df,
rendered=True,
ucolor="#ef232a",
dcolor="#14b143",
ucolorborder=None,
dcolorborder=None,
ucolorvolume=None,
dcolorvolume=None,
col="",
):
"""
针对 dataframe 直接画出标准看盘软件的上k线图下成交量图的形式
:param df:
:param rendered:
:param ucolor: str for color when going up, default red in A stock as "#ef232a"
:param dcolor: str for color when going down, default green in A stock as "#14b143"
:param col:
:return:
"""
# TODO: color changing seems to make no effect, possible issue with pyecharts
if ucolorborder is None:
ucolorborder = ucolor
if dcolorborder is None:
dcolorborder = dcolor
if ucolorvolume is None:
if ucolor != "#ffffff":
ucolorvolume = ucolor
else:
ucolorvolume = ucolorborder
if dcolorvolume is None:
if dcolor != "#ffffff":
dcolorvolume = dcolor
else:
dcolorvolume = dcolorborder
kline = (
Kline().add_xaxis(xaxis_data=list(df["date"])).add_yaxis(
series_name="",
itemstyle_opts=opts.ItemStyleOpts(
color=ucolor,
color0=dcolor,
border_color=ucolorborder, # ucolor,
border_color0=dcolorborder, # dcolor,
),
y_axis=list(zip(df["open"], df["close"], df["low"], df["high"])),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
],
symbol="pin",
symbol_size=[56, 40],
# label_opts=opts.LabelOpts(color="#CCFFFF",position=["top", "bottom"])
),
).set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=50,
range_end=100,
xaxis_index=[0, 1],
),
opts.DataZoomOpts(
is_show=False,
type_="inside",
range_start=50,
range_end=100,
xaxis_index=1,
),
],
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
))
if col is not None:
for c in col:
line = (Line().add_xaxis(xaxis_data=list(df["date"])).add_yaxis(
series_name=c,
y_axis=list(df[c]),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
))
kline = kline.overlap(line)
if "volume" in df.columns:
vl = list(df["volume"])
elif "amount" in df.columns:
vl = list(df["amount"])
else:
vl = [0 for _ in range(len(df))]
bar = (
Bar().add_js_funcs("var barData = {}".format(
list(df["close"] - df["open"]))).add_xaxis(
xaxis_data=list(df["date"])).add_yaxis(
series_name="",
yaxis_data=vl,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode("""
function(params) {{
var colorList;
if (barData[params.dataIndex]>0) {{
colorList = '{ucolor}';
}} else {{
colorList = '{dcolor}';
}}
return colorList;
}}
""".format(ucolor=ucolorvolume,
dcolor=dcolorvolume)) # escape {} when using format
),
).set_global_opts(tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
), ))
grid_chart = Grid()
grid_chart.add_js_funcs("var barData = {}".format(
list(df["close"] - df["open"])))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="1%",
pos_top="2%",
height="65%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="1%",
pos_top="71%",
height="22%"),
)
if rendered:
return grid_chart.render_notebook()
else:
return grid_chart
