【量化小工具】交易点标记

PlotCandlestick

PlotCandlestick(price_df,title,bar_name,begin_date,ma,ma_func,trade_data)

K线图是投资者常用的分析工具。在K线图中，除了画出K线和均线之外，还可以自己定义想画的曲线(如一些技术指标)。而且在K线图中也可以标记买卖点。

• 参数说明

• trade_data参数详情说明 传入的DataFrame中dated的数据类型必须为datetime.datetime

返回结果为：封面图

以下依赖聚宽数据

from jqdata import *
# 引入常用库
import talib  # 技术分析
import pandas as pd
import datetime as dt

import plotly.graph_objs as go
import plotly.offline as offline
offline.init_notebook_mode(connected=True)

# 显示K先图
## 适用plotly 版本为3.4.2
def PlotCandlestick(price_df: pd.DataFrame, title: str, bar_name: str,
                    begin_date: str, ma: list, ma_func,
                    trade_data: pd.DataFrame):
    
    '''
    prace_df:含有完整数据的表列名必须包含OHLC index-date
    title:图表名称
    begin_date:从什么时候开始,因为计算均线有前序期
    ma:均线计算的周期列表，最大显示6根均线
    ma_func:计算均线的方法函数
    trade_data:交易点 列名必须包含date,side,price
    ===============================================
    return 可视化图表
    '''
    trade_data = trade_data.copy()
    trade_data['date'] = trade_data['date'].dt.strftime('%Y-%m-%d')
    
    begin = get_trade_days(end_date=begin_date, count=10)[0]
    slice_df = price_df.loc[begin:]
    fig = go.Figure(data=[
        go.Candlestick(
            x=slice_df.index,
            open=slice_df['open'],
            high=slice_df['high'],
            low=slice_df['low'],
            close=slice_df['close'],
            name=bar_name)
    ])

    # 计算均线
    ma_df = pd.DataFrame({i: ma_func(price_df['close'], i) for i in ma[:6]},
                         index=price_df.index)

    ma_df.columns = ma

    # 添加均线
    for i in ma:
        fig.add_trace(
            go.Scatter(
                x=ma_df.reindex(slice_df.index).index,
                y=ma_df[i].reindex(slice_df.index),
                name='MA' + str(i),
                line=dict(width=1.5),
            ))

    # 买点
    fig.add_trace(
        go.Scatter(
            mode='markers',
            x=trade_data.query('side=="Buy"')['date'].values.tolist(),
            y=trade_data.query('side=="Buy"')['price'].values.tolist(),
            marker=dict(symbol=5, size=10, color="Crimson", line=dict(width=2)),
            name='Buy'))

    # 买点
    fig.add_trace(
        go.Scatter(
            mode='markers',
            x=trade_data.query('side=="Sell"')['date'].values.tolist(),
            y=trade_data.query('side=="Sell"')['price'].values.tolist(),
            marker=dict(
                symbol=6, size=10, color="ForestGreen", line=dict(width=2)),
            name='Sell'))

    fig.layout.update(
        title=title,
        legend=dict(x=0.1, y=1.1, orientation="h"),
        xaxis=dict(rangeslider=dict(visible=False), tickformat='%Y-%m-%d'))

    offline.iplot(fig)

symbol = '000651.XSHE'
start = '2014-04-14'
end = '2015-09-02'

dis_name = get_security_info(symbol).display_name
# 获取数据
begin_date = get_trade_days(end_date=start,count=250)[0]
price_df = get_price(symbol,start_date=begin_date,end_date=end,fields=['close','open','high','low'],panel=False)


# 交易点数据 (我这里是举例自己构建的交易点)
trade_data = pd.DataFrame(
    [[dt.datetime(2014, 4, 14), 'Buy', 11.89
     ], [dt.datetime(2014, 5, 9), 'Sell', '11.28'],
     [dt.datetime(2014, 6, 6), 'Buy', 11.83], [dt.date(2014, 11, 3), 'Sell', 11.39],
     [dt.datetime(2014, 11, 14), 'Buy', 11.62], [dt.date(2015, 9, 2), 'Sell', 15.2]
    ],
    columns=['date', 'side', 'price'])
    
# 可视化
PlotCandlestick(price_df,dis_name+'行情',dis_name,start,[5,10,20,60,120,250],talib.SMA,trade_data)