Mariner Backtesting - TA-LIB Rate of change ratio 100 scale: (real/prevPrice)*100

ROCR100

Rate of change ratio 100 scale: (price/prevPrice)*100

ROCR100

 real = ROCR100(close, timeperiod=10)

Plot

Rate of change ratio 100 scale: (price/prevPrice)*100

Working Example

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

class WE_ROCR100(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.ROCR100(close, timeperiod=10)

        # 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 = 'ROCR100: ' + self.symbol
        print ktgfunc.talib_table(symbol, 1, dict)

Console

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

ROCR100: 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	100.61
2016-10-03	57.03	100.86
2016-10-04	56.86	100.76
2016-10-05	57.25	99.79
2016-10-06	57.35	99.86
2016-10-07	57.41	100.64
2016-10-10	57.65	102.00
2016-10-11	56.81	98.69
2016-10-12	56.73	98.41
2016-10-13	56.54	99.16
2016-10-14	57.03	99.69
2016-10-17	56.84	99.65
2016-10-18	57.27	100.73
2016-10-19	57.14	99.81
2016-10-20	56.87	99.15
2016-10-21	59.26	103.22
2016-10-24	60.59	105.10
2016-10-25	60.58	106.64
2016-10-26	60.22	106.16
2016-10-27	59.70	105.59
2016-10-28	59.47	104.27
2016-10-31	59.52	104.72
2016-11-01	59.40	103.71
2016-11-02	59.03	103.30
2016-11-03	58.81	103.42
2016-11-04	58.32	98.41
2016-11-07	60.01	99.05
2016-11-08	60.06	99.15
2016-11-09	59.77	99.24
2016-11-10	58.31	97.67
2016-11-11	58.62	98.58
2016-11-14	57.73	97.00
2016-11-15	58.87	99.11
2016-11-16	59.65	101.05
2016-11-17	60.64	103.11
2016-11-18	60.35	103.49
2016-11-21	60.86	101.41
2016-11-22	61.12	101.76
2016-11-23	60.40	101.06
2016-11-25	60.53	103.81
2016-11-28	60.61	103.39
2016-11-29	61.09	105.82
2016-11-30	60.26	102.36
2016-12-01	59.20	99.25
2016-12-02	59.25	97.71
2016-12-05	60.22	99.78
2016-12-06	59.95	98.50
2016-12-07	61.37	100.41
2016-12-08	61.01	101.01
2016-12-09	61.97	102.38
2016-12-12	62.17	102.57
2016-12-13	62.98	103.09
2016-12-14	62.68	104.02
2016-12-15	62.58	105.71
2016-12-16	62.30	105.15
2016-12-19	63.62	105.65
2016-12-20	63.54	105.99
2016-12-21	63.54	103.54
2016-12-22	63.55	104.16
2016-12-23	63.24	102.05
2016-12-27	63.28	101.79
2016-12-28	62.99	100.02
2016-12-29	62.90	100.35
2016-12-30	62.14	99.30
2017-01-03	62.58	100.45
2017-01-04	62.30	97.93
2017-01-05	62.30	98.05
2017-01-06	62.84	98.90
2017-01-09	62.64	98.57
2017-01-10	62.62	99.02
2017-01-11	63.19	99.86
2017-01-12	62.61	99.40
2017-01-13	62.70	99.68
2017-01-17	62.53	100.63
2017-01-18	62.50	99.87
2017-01-19	62.30	100.00
2017-01-20	62.74	100.71
2017-01-23	62.96	100.19
2017-01-24	63.52	101.40
2017-01-25	63.68	101.69
2017-01-26	64.27	101.71
2017-01-27	65.78	105.06
2017-01-30	65.13	103.88
2017-01-31	64.65	103.39
2017-02-01	63.58	101.73
2017-02-02	63.17	101.40
2017-02-03	63.68	101.50
2017-02-06	63.64	101.08
2017-02-07	63.43	99.86
2017-02-08	63.34	99.47