Mariner Backtesting - TA-LIB Kaufman Adaptive Moving Average

KAMA

 

 real = KAMA(close, timeperiod=30)

Plot

Kaufman Adaptive Moving Average

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