Backtrader 文档学习-Indicators混合时间框架

Backtrader 文档学习-Indicators混合时间周期

1.不同时间周期

如果数据源在Cerebro引擎中具有不同的时间范围和不同的长度，指示器将会终止。
比如：data0是日线，data1是月线 。

pivotpoint = btind.PivotPoint(self.data1)
sellsignal = self.data0.close < pivotpoint.s1

当收盘低于s1线（第一支撑位）时为卖出信号

PivotPoint可以在更大的时间范围内工作

在以前的版本报错：

return self.array[self.idx + ago]
IndexError: array index out of range

原因是：self.data.close提供第一个bar的值，但PivotPoint（以及s1行）只有在一个完整月过去后才会有值，相当于self.data0.close的22个值。在这22个close值，s1的Line还没有值，从底层数组获取它的尝试失败，报错超出范围。

Line对象支持（ago）运算符（Python中的__call__特殊方法）来传递自身的延迟版本：

close1 = self.data.close(-1)

In this example the object close1 (when accessed via [0]) always contains the previous value (-1) delivered by close. The syntax has been reused to accomodate adapting timeframes. Let’s rewrite the above pivotpoint snippet:

对象close1（通过[0]访问时）始终包含close提供的前一个值（-1）。语法将重写以适应时间框架。重写上面的pivotpoint 片段：

pivotpoint = btind.PivotPoint(self.data1)
sellsignal = self.data0.close < pivotpoint.s1()

看看（）是如何在没有参数的情况下执行的（在后台没有提供任何参数）。发生了以下情况:

• pivotpoint.s1（）返回内部LinesCoupler对象，该对象遵循较大范围周期，coupler用来自实际s1的最新值填充，从默认值NaN开始 。

在后面章节中的参数说明：

PivotPoint Formula:

• pivot = (h + l + c) / 3 # variants duplicate close or add open
• support1 = 2.0 * pivot - high
• support2 = pivot - (high - low)
• resistance1 = 2.0 * pivot - low
• resistance2 = pivot + (high - low)
  对应计算后的Line：
• p
• s1
• s2
• r1
• r2

运行结果：

0069,0069,0014,2005-04-11,3080.60,3043.16,0.00
0070,0070,0014,2005-04-12,3065.18,3043.16,0.00
0071,0071,0014,2005-04-13,3080.54,3043.16,0.00
0072,0072,0014,2005-04-14,3075.33,3043.16,0.00
0073,0073,0014,2005-04-15,3013.89,3043.16,1.00
0074,0074,0015,2005-04-18,2947.79,2988.96,1.00
0075,0075,0015,2005-04-19,2957.37,2988.96,1.00
0076,0076,0015,2005-04-20,2944.33,2988.96,1.00
0077,0077,0015,2005-04-21,2950.34,2988.96,1.00
0078,0078,0015,2005-04-22,2976.39,2988.96,1.00
0079,0079,0016,2005-04-25,2987.05,2935.07,0.00
0080,0080,0016,2005-04-26,2983.22,2935.07,0.00
0081,0081,0016,2005-04-27,2942.62,2935.07,0.00

在长度为74 的时候，close < s1 。出现signal 。

2.代码

from __future__ import (absolute_import, division, print_function,unicode_literals)import argparseimport backtrader as bt
import backtrader.feeds as btfeeds
import backtrader.indicators as btind
import backtrader.utils.flushfileclass St(bt.Strategy):params = dict(multi=True)def __init__(self):self.pp = pp = btind.PivotPoint(self.data1)#print(dir(pp))pp.plotinfo.plot = False  # deactivate plottingif self.p.multi:pp1 = pp()  # couple the entire indicatorsself.sellsignal = self.data0.close < pp1.s1()else:self.sellsignal = self.data0.close < pp.s1()def next(self):txt = ' , '.join(['%04d' % len(self),'%04d' % len(self.data0),'%04d' % len(self.data1),self.data.datetime.date(0).isoformat(),'%.2f' % self.data0.close[0],'%.2f' % self.pp.s1[0],'%.2f' % self.sellsignal[0]])print(txt)def runstrat():args = parse_args()cerebro = bt.Cerebro()data = btfeeds.BacktraderCSVData(dataname=args.data)cerebro.adddata(data)cerebro.resampledata(data, timeframe=bt.TimeFrame.Weeks) # 增加周线cerebro.resampledata(data, timeframe=bt.TimeFrame.Months) # 增加月线cerebro.addstrategy(St, multi=args.multi)cerebro.run(stdstats=False, runonce=False)if args.plot:cerebro.plot(style='bar')def parse_args():parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter,description='Sample for pivot point and cross plotting')parser.add_argument('--data', required=False,default='./datas/2005-2006-day-001.txt',help='Data to be read in')parser.add_argument('--multi', required=False, action='store_true',help='Couple all lines of the indicator')parser.add_argument('--plot', required=False, action='store_true',help=('Plot the result'))return parser.parse_args()if __name__ == '__main__':runstrat()

允许参数说明：

python  ./mixing-timeframes.py --help
usage: mixing-timeframes.py [-h] [--data DATA] [--multi] [--plot]Sample for pivot point and cross plottingoptional arguments:-h, --help   show this help message and exit--data DATA  Data to be read in (default: ./datas/2005-2006-day-001.txt)--multi      Couple all lines of the indicator (default: False)--plot       Plot the result (default: False)

可以看到，日线、周线和月线，三个周期的数据，在cerebro 通过init中Indicator的初始化，在next中打印数据长度，数据和signal，执行结果：

3. 修改为不用args参数

在jupter中可以执行：

from __future__ import (absolute_import, division, print_function,unicode_literals)import backtrader as bt
import backtrader.feeds as btfeeds
import backtrader.indicators as btind
import backtrader.utils.flushfile%matplotlib inlineclass St(bt.Strategy):params = dict(multi=True)def __init__(self):self.pp = pp = btind.PivotPoint(self.data1)pp.plotinfo.plot = False  # deactivate plottingif self.p.multi:pp1 = pp()  # couple the entire indicatorsself.sellsignal = self.data0.close < pp1.s1else:self.sellsignal = self.data0.close < pp.s1()def next(self):txt = ','.join(['%04d' % len(self),'%04d' % len(self.data0),'%04d' % len(self.data1),self.data.datetime.date(0).isoformat(),'%.2f' % self.data0.close[0],'%.2f' % self.pp.s1[0],'%.2f' % self.sellsignal[0]])#print(txt)def runstrat(args_plot):#cerebro = bt.Cerebro()#data = btfeeds.BacktraderCSVData(dataname=args.data)cerebro = bt.Cerebro()stock_hfq_df = get_code('000858') start_date = datetime.datetime(2020, 1, 1)  # 回测开始时间end_date = datetime.datetime(2020, 12, 31)  # 回测结束时间data = bt.feeds.PandasData(dataname=stock_hfq_df, fromdate=start_date, todate=end_date)  # 加载数据# Add the Data Feed to Cerebrocerebro.adddata(data)cerebro.resampledata(data, timeframe=bt.TimeFrame.Weeks)cerebro.resampledata(data, timeframe=bt.TimeFrame.Months)#cerebro.addstrategy(St, multi=args.multi)cerebro.addstrategy(St, multi=True)cerebro.run(stdstats=False, runonce=False)if args_plot:cerebro.plot(iplot=False,style='bar')if __name__ == '__main__':args_plot = Truerunstrat(args_plot)

执行效果：

4.Indicator Reference

Indicator 参考说明，参数方法太多了，随用随学吧。