官方示例可以参考：https://gallery.pyecharts.org/#/Candlestick/kline_datazoom_slider
文章目录
绘制K线图绘制有辅助线的K线图
绘制K线图
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Kline
def get_file_df(start_date="2020-01-01", end_date="2021-01-01"):
"""获取K线数据"""
file_df = pd.read_pickle("000001.pkl")
file_df.index = pd.to_datetime(file_df['日期'])
file_df = file_df.iloc[:, :7]
file_df.columns = ['date', 'open', 'close', 'high', 'low', 'volume', 'turnover']
file_df = file_df.drop(["date"], axis=1)
file_df = file_df.sort_index().truncate(before=start_date, after=end_date)
return file_df
def draw_kline(k_dataframe: pd.DataFrame):
kline_chart = Kline(init_opts=opts.InitOpts(width="1700px", height="750px"))
kline_chart.add_xaxis(xaxis_data=k_dataframe.index.strftime("%Y-%m-%d").tolist())
kline_chart.add_yaxis("K线", y_axis=k_dataframe[['open', "close", "low", "high"]].values.tolist())
kline_chart.set_global_opts(
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)
),
),
datazoom_opts=[opts.DataZoomOpts()],
title_opts=opts.TitleOpts(title="k线图"),
)
kline_chart.render("save_kline.html")
def main():
file_df: pd.DataFrame = get_file_df() # 数据源
draw_kline(k_dataframe=file_df)
if __name__ == '__main__':
main()
其中K线数据样例：
最终保存下来的效果图如下：
绘制有辅助线的K线图
代码如下：
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Kline, Line
def get_file_df(start_date="2020-01-01", end_date="2021-01-01"):
file_df = pd.read_pickle("000001.pkl")
file_df.index = pd.to_datetime(file_df['日期'])
file_df = file_df.iloc[:, :7]
file_df.columns = ['date', 'open', 'close', 'high', 'low', 'volume', 'turnover']
file_df = file_df.drop(["date"], axis=1)
file_df = file_df.sort_index().truncate(before=start_date, after=end_date)
return file_df
def draw_kline(k_dataframe: pd.DataFrame, same_axis_feature: list = None):
x_value = k_dataframe.index.strftime("%Y-%m-%d").tolist()
kline_chart = Kline(init_opts=opts.InitOpts(width="1700px", height="750px"))
kline_chart.add_xaxis(xaxis_data=x_value)
kline_chart.add_yaxis("K线", y_axis=k_dataframe[['open', "close", "low", "high"]].values.tolist())
kline_chart.set_global_opts(
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)
),
),
datazoom_opts=[opts.DataZoomOpts()],
title_opts=opts.TitleOpts(title="k线图"),
)
# 添加同价格的辅助线
for _index, _feature_name in enumerate(same_axis_feature):
this_line = Line().add_xaxis(x_value).add_yaxis(_feature_name, k_dataframe[_feature_name], yaxis_index=0)
this_line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=4),
)
kline_chart.overlap(this_line)
kline_chart.render("save_kline.html")
def main():
file_df: pd.DataFrame = get_file_df() # 数据源
file_df['mean_high_close'] = (file_df['high'] + file_df['low']) / 2
draw_kline(file_df, ["mean_high_close"])
if __name__ == '__main__':
main()
如图，同样的价格因素（最高价与最低价的均值）被传递到图中，辅助我们看图，其他折线同理所示
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