Mariner Backtesting - TA-LIB Kaufman Adaptive Moving Average

KAMA

 real = KAMA(close, timeperiod=30)

Plot

Working Example

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

class WE_KAMA(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.KAMA(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 = 'KAMA: ' + self.symbol
        print ktgfunc.talib_table(symbol, 1, dict)

Console

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

KAMA: 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	nan
2016-10-28	59.47	59.69
2016-10-31	59.52	59.68
2016-11-01	59.40	59.67
2016-11-02	59.03	59.66
2016-11-03	58.81	59.64
2016-11-04	58.32	59.62
2016-11-07	60.01	59.64
2016-11-08	60.06	59.65
2016-11-09	59.77	59.66
2016-11-10	58.31	59.64
2016-11-11	58.62	59.62
2016-11-14	57.73	59.60
2016-11-15	58.87	59.59
2016-11-16	59.65	59.59
2016-11-17	60.64	59.63
2016-11-18	60.35	59.65
2016-11-21	60.86	59.68
2016-11-22	61.12	59.75
2016-11-23	60.40	59.78
2016-11-25	60.53	59.81
2016-11-28	60.61	59.84
2016-11-29	61.09	59.90
2016-11-30	60.26	59.91
2016-12-01	59.20	59.90
2016-12-02	59.25	59.88
2016-12-05	60.22	59.89
2016-12-06	59.95	59.89
2016-12-07	61.37	59.90
2016-12-08	61.01	59.91
2016-12-09	61.97	59.95
2016-12-12	62.17	60.01
2016-12-13	62.98	60.10
2016-12-14	62.68	60.18
2016-12-15	62.58	60.26
2016-12-16	62.30	60.32
2016-12-19	63.62	60.50
2016-12-20	63.54	60.61
2016-12-21	63.54	60.71
2016-12-22	63.55	60.82
2016-12-23	63.24	60.97
2016-12-27	63.28	61.11
2016-12-28	62.99	61.25
2016-12-29	62.90	61.34
2016-12-30	62.14	61.37
2017-01-03	62.58	61.40
2017-01-04	62.30	61.42
2017-01-05	62.30	61.43
2017-01-06	62.84	61.46
2017-01-09	62.64	61.49
2017-01-10	62.62	61.53
2017-01-11	63.19	61.58
2017-01-12	62.61	61.60
2017-01-13	62.70	61.64
2017-01-17	62.53	61.69
2017-01-18	62.50	61.73
2017-01-19	62.30	61.75
2017-01-20	62.74	61.80
2017-01-23	62.96	61.83
2017-01-24	63.52	61.91
2017-01-25	63.68	61.97
2017-01-26	64.27	62.06
2017-01-27	65.78	62.25
2017-01-30	65.13	62.37
2017-01-31	64.65	62.45
2017-02-01	63.58	62.47
2017-02-02	63.17	62.47
2017-02-03	63.68	62.48
2017-02-06	63.64	62.48
2017-02-07	63.43	62.49
2017-02-08	63.34	62.49