Mariner Backtesting - TA-LIB Time Series Forecast

TSF

 real = TSF(close, timeperiod=14)

Plot

Working Example

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

class WE_TSF(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.TSF(close, timeperiod=14)

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

Console

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

TSF: 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	57.28
2016-10-06	57.35	57.33
2016-10-07	57.41	57.33
2016-10-10	57.65	57.34
2016-10-11	56.81	57.24
2016-10-12	56.73	57.15
2016-10-13	56.54	56.95
2016-10-14	57.03	56.83
2016-10-17	56.84	56.81
2016-10-18	57.27	56.96
2016-10-19	57.14	56.97
2016-10-20	56.87	56.93
2016-10-21	59.26	57.55
2016-10-24	60.59	58.46
2016-10-25	60.58	59.30
2016-10-26	60.22	59.95
2016-10-27	59.70	60.37
2016-10-28	59.47	60.69
2016-10-31	59.52	60.81
2016-11-01	59.40	60.79
2016-11-02	59.03	60.55
2016-11-03	58.81	60.26
2016-11-04	58.32	59.72
2016-11-07	60.01	59.70
2016-11-08	60.06	59.59
2016-11-09	59.77	59.24
2016-11-10	58.31	58.80
2016-11-11	58.62	58.71
2016-11-14	57.73	58.43
2016-11-15	58.87	58.51
2016-11-16	59.65	58.77
2016-11-17	60.64	59.27
2016-11-18	60.35	59.66
2016-11-21	60.86	60.14
2016-11-22	61.12	60.58
2016-11-23	60.40	60.72
2016-11-25	60.53	60.75
2016-11-28	60.61	61.02
2016-11-29	61.09	61.43
2016-11-30	60.26	61.51
2016-12-01	59.20	61.02
2016-12-02	59.25	60.56
2016-12-05	60.22	60.21
2016-12-06	59.95	59.89
2016-12-07	61.37	60.09
2016-12-08	61.01	60.29
2016-12-09	61.97	60.71
2016-12-12	62.17	61.23
2016-12-13	62.98	61.97
2016-12-14	62.68	62.45
2016-12-15	62.58	62.84
2016-12-16	62.30	63.10
2016-12-19	63.62	63.76
2016-12-20	63.54	64.17
2016-12-21	63.54	64.29
2016-12-22	63.55	64.28
2016-12-23	63.24	64.21
2016-12-27	63.28	64.00
2016-12-28	62.99	63.84
2016-12-29	62.90	63.55
2016-12-30	62.14	63.14
2017-01-03	62.58	62.90
2017-01-04	62.30	62.69
2017-01-05	62.30	62.46
2017-01-06	62.84	62.38
2017-01-09	62.64	62.18
2017-01-10	62.62	62.20
2017-01-11	63.19	62.40
2017-01-12	62.61	62.45
2017-01-13	62.70	62.55
2017-01-17	62.53	62.58
2017-01-18	62.50	62.63
2017-01-19	62.30	62.60
2017-01-20	62.74	62.70
2017-01-23	62.96	62.74
2017-01-24	63.52	62.98
2017-01-25	63.68	63.19
2017-01-26	64.27	63.53
2017-01-27	65.78	64.32
2017-01-30	65.13	64.80
2017-01-31	64.65	65.06
2017-02-01	63.58	65.04
2017-02-02	63.17	64.79
2017-02-03	63.68	64.68
2017-02-06	63.64	64.51
2017-02-07	63.43	64.22
2017-02-08	63.34	63.85