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
def plot_kline(ka, bs=None, file_html="kline.html", width="1400px", height="680px"):
"""
:param ka: KlineAnalyze
:param bs: pd.DataFrame
买卖点，包含三个字段 ["操作提示", "交易时间", "交易价格"]
:param file_html: str
:param width: str
:param height: str
:return: None
"""
df = ka.to_df(use_macd=True, ma_params=(5, 20,))
x = df.dt.to_list()
title = "%s | %s 至 %s" % (ka.symbol, ka.start_dt, ka.end_dt)
kline = (
Kline()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="",
y_axis=df[['open', 'close', 'low', 'high']].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True)
)
.set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside")
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
),
opts.DataZoomOpts(
is_show=True, xaxis_index=[0, 1], pos_top="96%", range_end=100
),
opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
],
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
)
)
kline_line = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="笔",
y_axis=df.bi.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='diamond',
symbol_size=8,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='dotted', width=2),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="线段",
y_axis=df.xd.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='triangle',
symbol_size=12,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='solid', width=2),
label_opts=opts.LabelOpts(is_show=True, position='right'),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
)
)
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
c = (
Scatter()
.add_xaxis(bs['交易时间'].to_list())
.add_yaxis(
"买卖点",
bs['交易价格'].to_list(),
label_opts=opts.LabelOpts(
is_show=True,
position="left",
formatter=JsCode(
"function(params){return bsName[params.dataIndex][0];}"
)
),
))
overlap_kline_line = overlap_kline_line.overlap(c)
# draw volume
bar_1 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="Volumn",
yaxis_data=df.vol.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=True, position='inside')
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="MACD",
yaxis_data=df.macd.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
line_2 = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="DIF",
y_axis=df['diff'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="DEA",
y_axis=df['dea'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
)
# draw MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(init_opts=opts.InitOpts(width=width, height=height, page_title=title))
grid_chart.add_js_funcs("var barData = {}".format(df[['open', 'close', 'low', 'high']].values.tolist()))
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
grid_chart.add_js_funcs("var bsName = {}".format(bs[["操作提示", "交易价格"]].values.tolist()))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="71%", height="10%"),
)
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="82%", height="14%"),
)
grid_chart.render(path=file_html)
def draw_chart():
kline = (Kline().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143"),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(position="middle",
color="blue",
font_size=15),
data=split_data_part(),
symbol=["circle", "none"],
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True, data=split_data_part())).set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(is_show=True,
type_="inside",
xaxis_index=[0, 0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0, 1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0, 2],
range_end=100),
],
))
kline_line = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
))
overlap_kline_line = kline.overlap(kline_line)
bar_1 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="Volumn",
y_axis=data["vols"],
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params){
var colorList;
if(barData[params.dataIndex][1] > barData[params.dataIndex][0]){
colorList = '#ef232a';
}else{
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
bar_2 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MACD",
y_axis=data["macds"],
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params){
var colorList;
if(params.data >= 0){
colorList = '#ef232a';
}else{
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="DIF",
y_axis=data["difs"],
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
overlap_bar_line = bar_2.overlap(line_2)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px", height="800px", theme=ThemeType.ESSOS))
grid_chart.add_js_funcs("var barData={}".format(data["datas"]))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="71%",
height="10%"),
)
grid_chart.add(overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="82%",
height="14%"))
grid_chart.render("professional_kline_chart.html")
def draw_chart(cls, data):
kline = (
Kline().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(position="middle",
color="blue",
font_size=15),
data=cls.split_data_part(data),
symbol=["circle", "none"],
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True, data=cls.split_data_part(data))).
set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(is_show=False,
type_="inside",
xaxis_index=[0,
0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0,
1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0,
2],
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0,
3],
range_end=100),
],
# 三个图的 axis 连在一块
# axispointer_opts=opts.AxisPointerOpts(
# is_show=True,
# link=[{"xAxisIndex": "all"}],
# label=opts.LabelOpts(background_color="#777"),
# ),
))
kline_line = (
Line().add_xaxis(xaxis_data=data["times"])
# # EMA10
# .add_yaxis(
# series_name="EMA10",
# y_axis=data["ema10"],
# xaxis_index=2,
# yaxis_index=2,
# label_opts=opts.LabelOpts(is_show=False),
# )
# EMA144
# .add_yaxis(
# series_name="EMA144",
# y_axis=data["ema144"],
# xaxis_index=2,
# yaxis_index=2,
# label_opts=opts.LabelOpts(is_show=False),
# )
# boll_median
.add_yaxis(
series_name="median",
y_axis=data["boll_median"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
# boll_upper
.add_yaxis(
series_name="upper",
y_axis=data["boll_upper"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
# boll_lower
.add_yaxis(
series_name="lower",
y_axis=data["boll_lower"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
))
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
# Bar-1
bar_1 = (
Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="Volumn",
yaxis_data=data["vols"],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
# 根据 echarts demo 的原版是这么写的
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode("""
# function(params) {
# var colorList;
# if (data.datas[params.dataIndex][1]>data.datas[params.dataIndex][0]) {
# colorList = '#ef232a';
# } else {
# colorList = '#14b143';
# }
# return colorList;
# }
# """)
# )
# 改进后在 grid 中 add_js_funcs 后变成如下
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MACD",
yaxis_data=data["macds"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="DIF",
y_axis=data["difs"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=data["deas"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
rsi_line = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="rsi6",
y_axis=data["rsi6"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="rsi12",
y_axis=data["rsi12"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="rsi24",
y_axis=data["rsi24"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
# MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(
init_opts=opts.InitOpts(width="1400px", height="800px"))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))
# K线图和 MA5 的折线图
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
height="45%"),
)
# Volumn 柱状图
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
pos_top="56%",
height="10%"),
)
# MACD DIFS DEAS
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
pos_top="68%",
height="14%"),
)
# rsi6
grid_chart.add(
rsi_line,
grid_opts=opts.GridOpts(pos_left="4%",
pos_right="1%",
pos_top="84%",
height="10%"),
)
return grid_chart.render_embed()
def draw_kine(stock_data):
'''
pyecharts V1 版本开始支持链式调用
文档地址 https://pyecharts.org/#/zh-cn/
'''
stock_data.index = pd.to_datetime(stock_data['TIME'], format="%Y/%m/%d")
stock_data = stock_data[["TIME", "OPEN", "CLOSE", "LOW", "HIGH", "VOLUME"]]
# stock_data = stock_data.sort_index(ascending=True) # 倒序，看时间顺序是否正常
# k线图
kline = (
Kline().add_xaxis(stock_data[["TIME"]].values.tolist()).add_yaxis(
"K线图",
stock_data.iloc[:, 1:5].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True, is_show=False),
# axis_opts=opts.AxisOpts(is_scale=True,min_=0), #y轴起始坐标可以设为0
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
), # y轴起始坐标可自动调整
title_opts=opts.TitleOpts(title="价格",
subtitle=ts_code,
pos_top="20%"),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
datazoom_opts=[ # 设置zoom参数后即可缩放
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1], # 设置第0轴和第1轴同时缩放
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,
),
],
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
# 成交量柱形图
x = stock_data[["TIME"]].values.tolist()
y = stock_data[["VOLUME"]].values[:, 0].tolist()
bar = (Bar().add_xaxis(x).add_yaxis(
"成交量",
y,
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
title_opts=opts.TitleOpts(title="成交量", pos_top="70%"),
legend_opts=opts.LegendOpts(is_show=False),
))
# 使用网格将多张图标组合到一起显示
grid_chart = Grid(init_opts=opts.InitOpts(
width="1200px",
height="600px",
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(
stock_data.iloc[:, 1:5].values.tolist()))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="15%", pos_right="15%", height="55%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="15%",
pos_right="15%",
pos_top="70%",
height="20%"),
)
grid_chart.render('stock_{}.html'.format(ts_code)) #保存成用股票代码命名的文档
def NPS(CenterName):
Items = CenterName
SubTitle = "各项目NPS"
NPS_Scores = [
25.01, 26.33, 27.21, 22.22, 21.11, 20.03, 23.00, 24.44, 26.36
]
BigTitle = '碧桂园集中式商业消费者满意度研究'
Color_Com = 'rgba(61,109,207,0.6)'
Color_Min = 'rgba(55,148,140,1)'
Color_Max = 'rgba(212,105,105,1)'
bar = Bar(init_opts=opts.InitOpts(bg_color={
"type": "pattern",
"image": JsCode("img"),
"repeat": "no-repeat"
}, ))
bar.add_xaxis(xaxis_data=Items)
bar.add_yaxis(series_name='NPS',
yaxis_data=NPS_Scores,
itemstyle_opts=opts.ItemStyleOpts(color=Color_Com),
label_opts=opts.LabelOpts(position='inside',
formatter="{c}%"))
bar.set_global_opts(title_opts=opts.TitleOpts(
title=BigTitle,
subtitle=SubTitle,
pos_right='center',
title_textstyle_opts=opts.TextStyleOpts(color='rgba(0,0,0,0.8)',
font_size=20),
subtitle_textstyle_opts=opts.TextStyleOpts(color='rgba(0,0,0,0.8)',
font_size=16)),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter="{value}%")))
bar.set_series_opts(
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(name='Average', type_='average')],
linestyle_opts=opts.LineStyleOpts(color=Color_Max,
width=3,
type_='dashed'),
label_opts=opts.LabelOpts(formatter="{c}%")),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(
name='Min_Score',
type_='min',
symbol_size=75,
itemstyle_opts=opts.ItemStyleOpts(color=Color_Min)),
opts.MarkPointItem(
name='Max_Score',
type_='max',
symbol_size=75,
itemstyle_opts=opts.ItemStyleOpts(color=Color_Max))
],
label_opts=opts.LabelOpts(
formatter="{c}%",
font_size=10,
position='inside')))
bar.add_js_funcs(
"var img = new Image(); img.src = 'https://s1.ax1x.com/2020/03/27/GPWPKI.jpg';"
)
grid = Grid(init_opts=opts.InitOpts(bg_color={
"type": "pattern",
"image": JsCode("img"),
"repeat": "no-repeat"
},
width='1200px',
height='650px'))
grid.add_js_funcs(
"var img = new Image(); img.src = 'https://s1.ax1x.com/2020/03/27/GPWPKI.jpg';"
)
grid.add(bar, grid_opts=opts.GridOpts(width=850, pos_top='90'))
return grid
def Satisfaction(CenterName, TabName, Scores_List):
Items = CenterName
SubTitle = TabName + "指标得分"
Scores = Scores_List
BigTitle = '碧桂园集中式商业消费者满意度研究'
Color_Com = 'rgba(61,109,207,0.6)'
Color_Min = 'rgba(55,148,140,1)'
Color_Max = 'rgba(212,105,105,1)'
bar = Bar(init_opts=opts.InitOpts(bg_color={
"type": "pattern",
"image": JsCode("img"),
"repeat": "no-repeat"
}, ))
bar.add_xaxis(xaxis_data=Items)
for i in range(len(Scores)):
if (i == 0):
bar.add_yaxis(
series_name=Scores[i][0],
yaxis_data=Scores[i][1:],
itemstyle_opts=opts.ItemStyleOpts(color=Color_Com),
label_opts=opts.LabelOpts(position='inside'),
)
else:
bar.add_yaxis(is_selected=False,
series_name=Scores[i][0],
yaxis_data=Scores[i][1:],
itemstyle_opts=opts.ItemStyleOpts(color=Color_Com),
label_opts=opts.LabelOpts(position='inside'))
bar.set_global_opts(title_opts=opts.TitleOpts(
title=BigTitle,
subtitle=SubTitle,
pos_right='center',
title_textstyle_opts=opts.TextStyleOpts(color='rgba(0,0,0,0.8)',
font_size=20),
subtitle_textstyle_opts=opts.TextStyleOpts(color='rgba(0,0,0,0.8)',
font_size=16)),
legend_opts=opts.LegendOpts(orient='vertical',
pos_left='85%',
pos_top='middle',
item_width=10,
item_height=11,
item_gap=20,
selected_mode='single'))
bar.set_series_opts(
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(name='Average', type_='average')],
linestyle_opts=opts.LineStyleOpts(color=Color_Max,
width=3,
type_='dashed')),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(name='Min_Score',
type_='min',
symbol_size=75,
itemstyle_opts=opts.ItemStyleOpts(
color=Color_Min)),
opts.MarkPointItem(name='Max_Score',
type_='max',
symbol_size=75,
itemstyle_opts=opts.ItemStyleOpts(
color=Color_Max))
]))
bar.add_js_funcs(
"var img = new Image(); img.src = 'https://s1.ax1x.com/2020/03/27/GPWPKI.jpg';"
)
grid = Grid(init_opts=opts.InitOpts(bg_color={
"type": "pattern",
"image": JsCode("img"),
"repeat": "no-repeat"
},
width='1200px',
height='650px'))
grid.add_js_funcs(
"var img = new Image(); img.src = 'https://s1.ax1x.com/2020/03/27/GPWPKI.jpg';"
)
grid.add(bar, grid_opts=opts.GridOpts(width=850, pos_top='90'))
return grid
data=markareadata,
itemstyle_opts=opts.ItemStyleOpts(opacity=0.5, )))
kline.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True),
),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(type_="inside", range_start=-100),
opts.DataZoomOpts(pos_bottom="0%"),
],
# title_opts=opts.TitleOpts(title=stock_code),
)
grid_chart = Grid(init_opts=opts.InitOpts(
width="100%",
height="950px",
theme=ThemeType.ESSOS,
page_title=stock_code,
))
grid_chart.add_js_funcs("var areaData={}".format(markareadata))
grid_chart.add_js_funcs("console.log('hello world')")
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="85%"),
)
grid_chart.render('pyecharts.html')
def draw(df, file_png="test.png"):
x = df.dt.to_list()
kline = (
Kline()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="",
y_axis=df[['open', 'close', 'low', 'high']].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside")
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
),
opts.DataZoomOpts(
is_show=True, xaxis_index=[0, 1], pos_top="97%", range_end=100
),
opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
],
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
)
)
kline_line = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="笔",
y_axis=df.bi.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='diamond',
symbol_size=8,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='dotted', width=1.5),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="线段",
y_axis=df.xd.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='triangle',
symbol_size=12,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='solid', width=1.5),
label_opts=opts.LabelOpts(is_show=True, position='right'),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
)
)
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
df1 = df[df['基准价格'] > 0]
c = (
Scatter()
.add_xaxis(df1['出现时间'].to_list())
.add_yaxis(
"买卖点",
df1['基准价格'].to_list(),
label_opts=opts.LabelOpts(
is_show=True,
position="left",
formatter=JsCode(
"function(params){return bsName[params.dataIndex][0];}"
)
),
))
overlap_kline_line = overlap_kline_line.overlap(c)
# draw volume
bar_1 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="Volumn",
yaxis_data=df.vol.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=True, position='inside')
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="MACD",
yaxis_data=df.macd.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
line_2 = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="DIF",
y_axis=df['diff'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="DEA",
y_axis=df['dea'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
)
# draw MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(init_opts=opts.InitOpts(width="1400px", height="680px"))
grid_chart.add_js_funcs("var barData = {}".format(df[['open', 'close', 'low', 'high']].values.tolist()))
grid_chart.add_js_funcs("var bsName = {}".format(df1[["操作提示", "基准价格"]].values.tolist()))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="71%", height="10%"),
)
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="82%", height="14%"),
)
file_html = file_png.replace(".png", '.html')
make_snapshot(snapshot, grid_chart.render(path=file_html), file_png)
def draw_chart(stock_data):
'''
pyecharts V1 版本开始支持链式调用
文档地址 https://pyecharts.org/#/zh-cn/
'''
stock_data.index = pd.to_datetime(stock_data['TIME'], format="%Y/%m/%d")
x = stock_data["TIME"].values.tolist()
stock_basic_data = stock_data[[
"TIME", "OPEN", "CLOSE", "LOW", "HIGH", "VOLUME"
]]
# stock_data = stock_data.sort_index(ascending=True) # 倒序，看时间顺序是否正常决定是不是要用
# k线图
kline = (
Kline().add_xaxis(x).add_yaxis(
"K线图",
stock_basic_data.iloc[:, 1:5].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True, is_show=False),
# axis_opts=opts.AxisOpts(is_scale=True,min_=0), #y轴起始坐标可以设为0
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
), # y轴起始坐标可自动调整
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='20%'),
title_opts=opts.TitleOpts(title="股票价格",
subtitle=ts_code,
pos_left='22%',
pos_top="20%"),
#把所有的x轴连接在一起
# axispointer_opts=opts.AxisPointerOpts(is_show=True,
# link=[{"xAxisIndex": "all"}],
# label=opts.LabelOpts(background_color="#777"),
# ),
datazoom_opts=[ # 设置zoom参数后即可缩放
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1, 2, 3, 4], # 设置第0轴和第1轴同时缩放
range_start=0,
range_end=100,
),
],
))
# 成交量柱形图
bar_volumn = (Bar().add_xaxis(x).add_yaxis(
"成交量",
stock_basic_data["VOLUME"].values.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
title_opts=opts.TitleOpts(title="成交量", pos_left='22%', pos_top="48%"),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='48%'),
))
#绘制均线图
line_ma = (
Line().add_xaxis(x).add_yaxis(
series_name="MA5",
y_axis=stock_data["MA5"].values.tolist(),
is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="MA10",
y_axis=stock_data["MA10"].values.tolist(),
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="MA20",
y_axis=stock_data["MA20"].values.tolist(),
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
title_opts=opts.TitleOpts(title="MA",
pos_left='22%',
pos_top="88%"),
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
is_scale=True),
datazoom_opts=[opts.DataZoomOpts(type_="inside")],
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='85%'),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
#绘制jdk线
line_kdj = (Line().add_xaxis(x).add_yaxis(
series_name="K",
y_axis=stock_data["K"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="D",
y_axis=stock_data["D"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="J",
y_axis=stock_data["J"].values.tolist(),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
title_opts=opts.TitleOpts(title="KDJ", pos_left='22%', pos_top="62%"),
datazoom_opts=[opts.DataZoomOpts(type_="inside", )],
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="7%",
pos_top='60%'),
xaxis_opts=opts.AxisOpts(
is_scale=True,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
#绘制macd柱状图
bar_macd = (Bar().add_xaxis(x).add_yaxis(
series_name='MACD',
yaxis_data=stock_data['MACD'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
title_opts=opts.TitleOpts(title="MACD", pos_left='22%', pos_top="75%"),
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=True,
orient='vertical',
pos_right="5%",
pos_top='70%'),
))
#绘制DIF和DEA
line_macd = (Line().add_xaxis(x).add_yaxis(
series_name="DIF",
y_axis=stock_data['DIF'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="DEA",
y_axis=stock_data['DEA'].values.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
#绘制布林线
line_boll = (Line().add_xaxis(x).add_yaxis(
series_name="MID",
y_axis=stock_data["MID"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="UPPER",
y_axis=stock_data["UPPER"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).add_yaxis(
series_name="LOWER",
y_axis=stock_data["LOWER"].values.tolist(),
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False,
).set_global_opts(
datazoom_opts=[opts.DataZoomOpts(type_="inside", )],
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
))
overlap_kline_linema = kline.overlap(line_boll)
overlap_macd = bar_macd.overlap(line_macd)
# 使用网格将多张图标组合到一起显示
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px",
height="700px",
animation_opts=opts.AnimationOpts(animation=False),
))
# 为了把 data.datas 的数据写入到html中作为全局变量,目前无法跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(
stock_basic_data.iloc[:, 1:5].values.tolist()))
grid_chart.add(
overlap_kline_linema,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="3%",
height="40%"),
)
grid_chart.add(
bar_volumn,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="45%",
height="10%"),
)
grid_chart.add(line_kdj,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="60%",
height="10%"))
grid_chart.add(overlap_macd,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="72%",
height="10%"))
grid_chart.add(line_ma,
grid_opts=opts.GridOpts(pos_left="35%",
pos_right="15%",
pos_top="85%",
height="10%"))
grid_chart.render('stock_{}/stock_{}.html'.format(
ts_code, ts_code)) # 保存成用股票代码命名的文档
return 0
def draw_charts(echarts_data):
kline_data = echarts_data["data"]
date = echarts_data["date"]
kline = (
Kline().add_xaxis(xaxis_data=date).add_yaxis(
series_name="Dow-Jones index",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
# 其中inside是支持鼠标缩放
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1, 2, 3, 4],
range_start=50,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1, 2, 3, 4],
type_="slider",
range_start=50,
range_end=100,
),
],
# # 不知道为什么要分开三个写，可能是想通过xaxis_index分别配置
# datazoom_opts=[
# opts.DataZoomOpts(
# is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
# ),
# opts.DataZoomOpts(
# is_show=True, xaxis_index=[0, 1], pos_top="97%", range_end=100
# ),
# opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
# ],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
title_opts=opts.TitleOpts(title="my_echart"),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#00da3c"
},
{
"value": -1,
"color": "#ec0000"
},
],
),
# 所有图的axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="MA5",
y_axis=echarts_data['ma5'],
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False, # 去掉折线上的小圆点
).add_yaxis(
series_name="MA20",
y_axis=echarts_data['ma20'],
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
is_symbol_show=False, # 去掉折线上的小圆点
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
# Kline And Line
overlap_kline_line = kline.overlap(line)
bar = (
Bar().add_xaxis(xaxis_data=date).add_yaxis(
series_name="Volume",
y_axis=echarts_data['volume'],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
#
# # 变色
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar().add_xaxis(xaxis_data=date).add_yaxis(
series_name="MACD",
y_axis=echarts_data['macd'],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
# axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度
# splitline_opts=opts.SplitLineOpts(is_show=False), # 不显示刻度
# axislabel_opts=opts.LabelOpts(is_show=False),
# split_number=20,
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
macd_line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="DIFF",
y_axis=echarts_data['macd_diff'],
# xaxis_index=2,
# yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=echarts_data["macd_dea"],
# xaxis_index=2,
# yaxis_index=2,
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
)
# .set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
# MACD BAR And MACD Line
bar_macd_line = bar_2.overlap(macd_line)
# KDJ
kdj_line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="K",
y_axis=echarts_data['kdj_k'],
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="D",
y_axis=echarts_data["kdj_d"],
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="J",
y_axis=echarts_data["kdj_j"],
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
# markline_opts=opts.MarkLineOpts(
# data=[100]
# ),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
# axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度
# splitline_opts=opts.SplitLineOpts(is_show=False), # 不显示刻度
# axislabel_opts=opts.LabelOpts(is_show=False),
# split_number=20,
), ))
######################
## kdj的macd
# Bar-2 (Overlap Bar + Line)
macd_kdj_bar = (
Bar().add_xaxis(xaxis_data=date).add_yaxis(
series_name="MACD_KDJ",
y_axis=echarts_data['kdj_macd'],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
# axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度
# splitline_opts=opts.SplitLineOpts(is_show=False), # 不显示刻度
# axislabel_opts=opts.LabelOpts(is_show=False),
# split_number=20,
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
macd_kdj_line = (
Line().add_xaxis(xaxis_data=date).add_yaxis(
series_name="KDJ_DIFF",
y_axis=echarts_data['kdj_macd_diff'],
# xaxis_index=2,
# yaxis_index=2,
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="KDJ_DEA",
y_axis=echarts_data["kdj_macd_dea"],
# xaxis_index=2,
# yaxis_index=2,
is_symbol_show=False, # 去掉折线上的小圆点
label_opts=opts.LabelOpts(is_show=False),
)
# .set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
# MACD BAR And MACD Line
bar_macd_kdj_line = macd_kdj_bar.overlap(macd_kdj_line)
######################
# Grid Overlap + Bar
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(kline_data))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="30%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="35%",
height="10%"),
)
# 加上MACD
grid_chart.add(
bar_macd_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top="45%",
height="10%",
),
)
# 加上KDJ
grid_chart.add(
kdj_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top="55%",
height="10%",
),
)
# 加上KDJ的MACD
grid_chart.add(
bar_macd_kdj_line,
grid_opts=opts.GridOpts(
pos_left="10%",
pos_right="8%",
pos_top="65%",
height="10%",
),
)
grid_chart.render("300368.html")
def kline(self):
data = self.calculate().to_dict('list')
kline = (
Kline().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="portfolio index",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
datazoom_opts=[
opts.DataZoomOpts(is_show=False,
type_="inside",
xaxis_index=[0, 0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0, 1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0, 2],
range_end=100),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
))
kline_line = (Line().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="MA5",
y_axis=self.calculate_ma(day_count=5, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA10",
y_axis=self.calculate_ma(day_count=10, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA20",
y_axis=self.calculate_ma(day_count=20, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA30",
y_axis=self.calculate_ma(day_count=30, data=data),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
))
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
# Bar-1
bar_1 = (Bar().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="Volumn",
y_axis=data["volume"],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Bar-2 (Overlap Bar + Line)
bar_2 = (Bar().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="MACD",
y_axis=data["MACD"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["date"]).add_yaxis(
series_name="DIFF",
y_axis=data["DIFF"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DEA",
y_axis=data["DEA"],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
# 最下面的柱状图和折线图
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(
init_opts=opts.InitOpts(width="1500px", height="750px"))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(data["datas"]))
# K线图和 MA5 的折线图
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
height="60%"),
)
# Volumn 柱状图
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="71%",
height="10%"),
)
# MACD DIFS DEAS
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="82%",
height="14%"),
)
makedir(self.path, 'kline')
grid_chart.render(
f"{self.path}\\kline\\{str(self.names)}_{time.strftime('%Y-%m-%d', time.localtime())}.html"
)
class MyPyecharts():
kline_chart = None
grid_chart = None
def __init__(self, bar_data):
self.bar_data = bar_data
self.bar_data_datetime = list(
self.bar_data.index.strftime("%Y-%m-%d %H:%M"))
def kline(self):
""""""
if "_" in self.bar_data.symbol[0]:
symbol_split = self.bar_data.symbol[0].split("_")
series_name = "_".join(
[symbol_split[0], symbol_split[1][-2:], symbol_split[2][-2:]])
else:
series_name = self.bar_data.symbol[0]
kline = Kline(init_opts=opts.InitOpts(width="1400px", height="800px"))
kline.add_xaxis(xaxis_data=self.bar_data_datetime)
kline.add_yaxis(
series_name=series_name,
yaxis_index=0,
y_axis=self.bar_data[[
"open_price", "close_price", "low_price", "high_price"
]].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(color="#ef232a",
color0="#14b143",
border_color="#8A0000",
border_color0="#008F28",
opacity=0.8),
)
kline.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 0],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
type_="slider",
xaxis_index=[0, 1],
pos_top="95%",
range_start=0,
range_end=100,
),
],
xaxis_opts=opts.AxisOpts(
is_scale=True,
type_="category",
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,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.8)),
),
brush_opts=opts.BrushOpts(
tool_box=[
"rect", "polygon", "keep", "lineX", "lineY", "clear"
],
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
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=12,
font_family="Arial",
font_weight="lighter",
),
),
toolbox_opts=opts.ToolboxOpts(orient="horizontal",
pos_left="right",
pos_top="0%"),
legend_opts=opts.LegendOpts(
is_show=True,
type_="scroll",
selected_mode="multiple",
pos_left="left",
pos_top="0%",
legend_icon="roundRect",
),
# 多图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
)
self.kline_chart = kline
def overlap_sma(self, sma_series: list = [5, 10, 20, 60]):
# 在K线图上绘制 SMA 均线
SMA_series = sma_series
SMA_line = (Line().add_xaxis(
xaxis_data=self.bar_data_datetime).set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category")))
for i in SMA_series:
sma_value = ta.SMA(np.array(self.bar_data["close_price"]), i)
SMA_line.add_yaxis(
series_name=f"SMA_{i}",
y_axis=sma_value.tolist(),
is_symbol_show=False,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.8),
label_opts=opts.LabelOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=False),
)
self.kline_chart.overlap(SMA_line)
def overlap_boll(self, timeperiod=14, nbdevup=2, nbdevdn=2, matype=0):
# 在K线图上绘制 布林带
boll_up, boll_mid, boll_low = ta.BBANDS(np.array(self.bar_data["close_price"]),\
timeperiod=timeperiod, nbdevup=nbdevup, nbdevdn=nbdevdn, matype=matype)
BOLL_line = (Line().add_xaxis(
xaxis_data=self.bar_data_datetime).add_yaxis(
series_name="布林上轨",
y_axis=boll_up.tolist(),
is_symbol_show=False,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.8),
label_opts=opts.LabelOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=False),
).add_yaxis(
series_name="布林中轨",
y_axis=boll_mid.tolist(),
is_symbol_show=False,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.8),
label_opts=opts.LabelOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=False),
).add_yaxis(
series_name="布林下轨",
y_axis=boll_low.tolist(),
is_symbol_show=False,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.8),
label_opts=opts.LabelOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=False),
))
self.kline_chart.overlap(BOLL_line)
def grid_volume(self, grid_index=1):
""""""
vol_bar = (Bar().add_xaxis(
xaxis_data=self.bar_data_datetime).add_yaxis(
series_name="成交量",
y_axis=self.bar_data["volume"].tolist(),
xaxis_index=grid_index,
yaxis_index=grid_index,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(opacity=0.8,
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=grid_index,
),
legend_opts=opts.LegendOpts(is_show=False),
))
return vol_bar
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 render(self, html_name="render.html"):
return self.grid_chart.render(html_name)
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
