Mariner Backtesting - TA-LIB Vector Square Root

SQRT

 real = SQRT(close)

Plot

$Vector Square Root$

Working Example

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

class WE_SQRT(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.SQRT(close)

        # 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 = 'SQRT: ' + self.symbol
        print ktgfunc.talib_table(symbol, 1, dict)

Console

MSFT:  2017-02-09 09:30:00.000000

SQRT: MSFT
	Input	Output
date	close	real
2016-09-16	56.87	7.54
2016-09-19	56.55	7.52
2016-09-20	56.43	7.51
2016-09-21	57.37	7.57
2016-09-22	57.43	7.58
2016-09-23	57.04	7.55
2016-09-26	56.52	7.52
2016-09-27	57.56	7.59
2016-09-28	57.64	7.59
2016-09-29	57.01	7.55
2016-09-30	57.21	7.56
2016-10-03	57.03	7.55
2016-10-04	56.86	7.54
2016-10-05	57.25	7.57
2016-10-06	57.35	7.57
2016-10-07	57.41	7.58
2016-10-10	57.65	7.59
2016-10-11	56.81	7.54
2016-10-12	56.73	7.53
2016-10-13	56.54	7.52
2016-10-14	57.03	7.55
2016-10-17	56.84	7.54
2016-10-18	57.27	7.57
2016-10-19	57.14	7.56
2016-10-20	56.87	7.54
2016-10-21	59.26	7.70
2016-10-24	60.59	7.78
2016-10-25	60.58	7.78
2016-10-26	60.22	7.76
2016-10-27	59.70	7.73
2016-10-28	59.47	7.71
2016-10-31	59.52	7.71
2016-11-01	59.40	7.71
2016-11-02	59.03	7.68
2016-11-03	58.81	7.67
2016-11-04	58.32	7.64
2016-11-07	60.01	7.75
2016-11-08	60.06	7.75
2016-11-09	59.77	7.73
2016-11-10	58.31	7.64
2016-11-11	58.62	7.66
2016-11-14	57.73	7.60
2016-11-15	58.87	7.67
2016-11-16	59.65	7.72
2016-11-17	60.64	7.79
2016-11-18	60.35	7.77
2016-11-21	60.86	7.80
2016-11-22	61.12	7.82
2016-11-23	60.40	7.77
2016-11-25	60.53	7.78
2016-11-28	60.61	7.79
2016-11-29	61.09	7.82
2016-11-30	60.26	7.76
2016-12-01	59.20	7.69
2016-12-02	59.25	7.70
2016-12-05	60.22	7.76
2016-12-06	59.95	7.74
2016-12-07	61.37	7.83
2016-12-08	61.01	7.81
2016-12-09	61.97	7.87
2016-12-12	62.17	7.88
2016-12-13	62.98	7.94
2016-12-14	62.68	7.92
2016-12-15	62.58	7.91
2016-12-16	62.30	7.89
2016-12-19	63.62	7.98
2016-12-20	63.54	7.97
2016-12-21	63.54	7.97
2016-12-22	63.55	7.97
2016-12-23	63.24	7.95
2016-12-27	63.28	7.95
2016-12-28	62.99	7.94
2016-12-29	62.90	7.93
2016-12-30	62.14	7.88
2017-01-03	62.58	7.91
2017-01-04	62.30	7.89
2017-01-05	62.30	7.89
2017-01-06	62.84	7.93
2017-01-09	62.64	7.91
2017-01-10	62.62	7.91
2017-01-11	63.19	7.95
2017-01-12	62.61	7.91
2017-01-13	62.70	7.92
2017-01-17	62.53	7.91
2017-01-18	62.50	7.91
2017-01-19	62.30	7.89
2017-01-20	62.74	7.92
2017-01-23	62.96	7.93
2017-01-24	63.52	7.97
2017-01-25	63.68	7.98
2017-01-26	64.27	8.02
2017-01-27	65.78	8.11
2017-01-30	65.13	8.07
2017-01-31	64.65	8.04
2017-02-01	63.58	7.97
2017-02-02	63.17	7.95
2017-02-03	63.68	7.98
2017-02-06	63.64	7.98
2017-02-07	63.43	7.96
2017-02-08	63.34	7.96