Mariner Backtesting - TA-LIB Summation

SUM

 

 real = SUM(close, timeperiod=30)

Plot

Summation

Working Example

 from cloudquant.interfaces import Strategy
from collections import OrderedDict
import ktgfunc
import talib

class WE_SUM(Strategy):

    def on_start(self, md, order, service, account):
        # symbol and timestamp
        print(self.symbol + ":  " + service.time_to_string(service.system_time))
        daily_bars = md.bar.daily(start=-100)
        close = daily_bars.close
        real = talib.SUM(close, timeperiod=30)

        # get the date values
        dates = service._context.market._storage.market_hours.keys()
        dateList = []
        for date in dates:
           dateList.append(str(date.strftime('%Y-%m-%d')))
        dates = sorted(dateList, reverse=True)[1:101]
        dates.sort()

        dict = OrderedDict()
        dict['date'] = dates
        dict['close'] = close
        dict['real'] = real
        symbol = 'SUM: ' + self.symbol
        print ktgfunc.talib_table(symbol, 1, dict)

Console

MSFT:  2017-02-09 09:30:00.000000
SUM: MSFT
Input Output
date close real
2016-09-16 56.87 nan
2016-09-19 56.55 nan
2016-09-20 56.43 nan
2016-09-21 57.37 nan
2016-09-22 57.43 nan
2016-09-23 57.04 nan
2016-09-26 56.52 nan
2016-09-27 57.56 nan
2016-09-28 57.64 nan
2016-09-29 57.01 nan
2016-09-30 57.21 nan
2016-10-03 57.03 nan
2016-10-04 56.86 nan
2016-10-05 57.25 nan
2016-10-06 57.35 nan
2016-10-07 57.41 nan
2016-10-10 57.65 nan
2016-10-11 56.81 nan
2016-10-12 56.73 nan
2016-10-13 56.54 nan
2016-10-14 57.03 nan
2016-10-17 56.84 nan
2016-10-18 57.27 nan
2016-10-19 57.14 nan
2016-10-20 56.87 nan
2016-10-21 59.26 nan
2016-10-24 60.59 nan
2016-10-25 60.58 nan
2016-10-26 60.22 nan
2016-10-27 59.70 1726.77
2016-10-28 59.47 1729.38
2016-10-31 59.52 1732.35
2016-11-01 59.40 1735.32
2016-11-02 59.03 1736.98
2016-11-03 58.81 1738.36
2016-11-04 58.32 1739.63
2016-11-07 60.01 1743.12
2016-11-08 60.06 1745.63
2016-11-09 59.77 1747.75
2016-11-10 58.31 1749.04
2016-11-11 58.62 1750.45
2016-11-14 57.73 1751.15
2016-11-15 58.87 1753.16
2016-11-16 59.65 1755.56
2016-11-17 60.64 1758.85
2016-11-18 60.35 1761.79
2016-11-21 60.86 1765.00
2016-11-22 61.12 1769.31
2016-11-23 60.40 1772.98
2016-11-25 60.53 1776.97
2016-11-28 60.61 1780.55
2016-11-29 61.09 1784.80
2016-11-30 60.26 1787.79
2016-12-01 59.20 1789.85
2016-12-02 59.25 1792.23
2016-12-05 60.22 1793.19
2016-12-06 59.95 1792.55
2016-12-07 61.37 1793.34
2016-12-08 61.01 1794.13
2016-12-09 61.97 1796.40
2016-12-12 62.17 1799.10
2016-12-13 62.98 1802.56
2016-12-14 62.68 1805.84
2016-12-15 62.58 1809.39
2016-12-16 62.30 1812.88
2016-12-19 63.62 1818.19
2016-12-20 63.54 1821.71
2016-12-21 63.54 1825.19
2016-12-22 63.55 1828.97
2016-12-23 63.24 1833.90
2016-12-27 63.28 1838.56
2016-12-28 62.99 1843.82
2016-12-29 62.90 1847.85
2016-12-30 62.14 1850.34
2017-01-03 62.58 1852.28
2017-01-04 62.30 1854.23
2017-01-05 62.30 1855.67
2017-01-06 62.84 1857.39
2017-01-09 62.64 1859.63
2017-01-10 62.62 1861.72
2017-01-11 63.19 1864.30
2017-01-12 62.61 1865.82
2017-01-13 62.70 1868.26
2017-01-17 62.53 1871.59
2017-01-18 62.50 1874.84
2017-01-19 62.30 1876.92
2017-01-20 62.74 1879.71
2017-01-23 62.96 1881.30
2017-01-24 63.52 1883.81
2017-01-25 63.68 1885.52
2017-01-26 64.27 1887.62
2017-01-27 65.78 1890.42
2017-01-30 65.13 1892.87
2017-01-31 64.65 1894.94
2017-02-01 63.58 1896.22
2017-02-02 63.17 1895.77
2017-02-03 63.68 1895.91
2017-02-06 63.64 1896.01
2017-02-07 63.43 1895.89
2017-02-08 63.34 1895.99