Mariner Backtesting - TA-LIB MESA Adaptive Moving Average

MAMA

 mama, fama = MAMA(close, fastlimit=0, slowlimit=0)

Plot

Working Example

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

class WE_MAMA(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
        mama, fama = talib.MAMA(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['mama'] = mama
        dict['fama'] = fama
        symbol = 'MAMA: ' + self.symbol
        print ktgfunc.talib_table(symbol, 2, dict)

Console

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

MAMA: MSFT
	Input	Output
date	close	mama	fama
2016-09-16	56.87	nan	nan
2016-09-19	56.55	nan	nan
2016-09-20	56.43	nan	nan
2016-09-21	57.37	nan	nan
2016-09-22	57.43	nan	nan
2016-09-23	57.04	nan	nan
2016-09-26	56.52	nan	nan
2016-09-27	57.56	nan	nan
2016-09-28	57.64	nan	nan
2016-09-29	57.01	nan	nan
2016-09-30	57.21	nan	nan
2016-10-03	57.03	nan	nan
2016-10-04	56.86	nan	nan
2016-10-05	57.25	nan	nan
2016-10-06	57.35	nan	nan
2016-10-07	57.41	nan	nan
2016-10-10	57.65	nan	nan
2016-10-11	56.81	nan	nan
2016-10-12	56.73	nan	nan
2016-10-13	56.54	nan	nan
2016-10-14	57.03	nan	nan
2016-10-17	56.84	nan	nan
2016-10-18	57.27	nan	nan
2016-10-19	57.14	nan	nan
2016-10-20	56.87	nan	nan
2016-10-21	59.26	nan	nan
2016-10-24	60.59	nan	nan
2016-10-25	60.58	nan	nan
2016-10-26	60.22	nan	nan
2016-10-27	59.70	nan	nan
2016-10-28	59.47	nan	nan
2016-10-31	59.52	nan	nan
2016-11-01	59.40	59.34	53.50
2016-11-02	59.03	59.33	53.65
2016-11-03	58.81	59.30	53.79
2016-11-04	58.32	58.81	55.04
2016-11-07	60.01	58.87	55.14
2016-11-08	60.06	58.93	55.23
2016-11-09	59.77	58.97	55.33
2016-11-10	58.31	58.94	55.42
2016-11-11	58.62	58.92	55.50
2016-11-14	57.73	58.33	56.21
2016-11-15	58.87	58.35	56.26
2016-11-16	59.65	58.42	56.32
2016-11-17	60.64	59.53	57.12
2016-11-18	60.35	59.57	57.18
2016-11-21	60.86	59.63	57.24
2016-11-22	61.12	59.71	57.30
2016-11-23	60.40	60.05	57.99
2016-11-25	60.53	60.08	58.04
2016-11-28	60.61	60.10	58.10
2016-11-29	61.09	60.15	58.15
2016-11-30	60.26	60.16	58.20
2016-12-01	59.20	60.11	58.25
2016-12-02	59.25	59.68	58.60
2016-12-05	60.22	59.71	58.63
2016-12-06	59.95	59.83	58.93
2016-12-07	61.37	59.94	58.97
2016-12-08	61.01	59.99	58.99
2016-12-09	61.97	60.09	59.02
2016-12-12	62.17	60.20	59.05
2016-12-13	62.98	61.59	59.68
2016-12-14	62.68	61.64	59.73
2016-12-15	62.58	61.69	59.78
2016-12-16	62.30	61.72	59.83
2016-12-19	63.62	61.81	59.88
2016-12-20	63.54	61.90	59.93
2016-12-21	63.54	61.98	59.98
2016-12-22	63.55	62.06	60.03
2016-12-23	63.24	62.65	60.69
2016-12-27	63.28	62.97	61.26
2016-12-28	62.99	62.97	61.30
2016-12-29	62.90	62.96	61.34
2016-12-30	62.14	62.92	61.38
2017-01-03	62.58	62.90	61.42
2017-01-04	62.30	62.60	61.72
2017-01-05	62.30	62.59	61.74
2017-01-06	62.84	62.61	61.77
2017-01-09	62.64	62.61	61.79
2017-01-10	62.62	62.61	61.81
2017-01-11	63.19	62.64	61.83
2017-01-12	62.61	62.64	61.85
2017-01-13	62.70	62.67	62.05
2017-01-17	62.53	62.66	62.07
2017-01-18	62.50	62.58	62.20
2017-01-19	62.30	62.57	62.21
2017-01-20	62.74	62.58	62.22
2017-01-23	62.96	62.77	62.35
2017-01-24	63.52	62.81	62.37
2017-01-25	63.68	62.85	62.38
2017-01-26	64.27	62.92	62.39
2017-01-27	65.78	64.35	62.88
2017-01-30	65.13	64.39	62.92
2017-01-31	64.65	64.40	62.96
2017-02-01	63.58	64.36	62.99
2017-02-02	63.17	64.16	63.09
2017-02-03	63.68	64.14	63.12
2017-02-06	63.64	64.11	63.14
2017-02-07	63.43	64.08	63.16
2017-02-08	63.34	63.71	63.30