Mariner Backtesting - TA-LIB Linear Regression Intercept

Technical Analysis Library

LINEARREG_INTERCEPT

 real = LINEARREG_INTERCEPT(close, timeperiod=14)

Plot

Plot

Working Example

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

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

Console

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

LINEARREG_INTERCEPT: 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	56.86
2016-10-06	57.35	56.88
2016-10-07	57.41	57.00
2016-10-10	57.65	57.15
2016-10-11	56.81	57.16
2016-10-12	56.73	57.15
2016-10-13	56.54	57.25
2016-10-14	57.03	57.43
2016-10-17	56.84	57.35
2016-10-18	57.27	57.17
2016-10-19	57.14	57.18
2016-10-20	56.87	57.17
2016-10-21	59.26	56.92
2016-10-24	60.59	56.64
2016-10-25	60.58	56.35
2016-10-26	60.22	56.17
2016-10-27	59.70	56.11
2016-10-28	59.47	56.08
2016-10-31	59.52	56.33
2016-11-01	59.40	56.70
2016-11-02	59.03	57.24
2016-11-03	58.81	57.74
2016-11-04	58.32	58.40
2016-11-07	60.01	58.78
2016-11-08	60.06	59.27
2016-11-09	59.77	59.96
2016-11-10	58.31	60.21
2016-11-11	58.62	60.03
2016-11-14	57.73	59.89
2016-11-15	58.87	59.64
2016-11-16	59.65	59.41
2016-11-17	60.64	59.13
2016-11-18	60.35	58.91
2016-11-21	60.86	58.68
2016-11-22	61.12	58.58
2016-11-23	60.40	58.67
2016-11-25	60.53	58.94
2016-11-28	60.61	58.78
2016-11-29	61.09	58.57
2016-11-30	60.26	58.56
2016-12-01	59.20	59.11
2016-12-02	59.25	59.59
2016-12-05	60.22	60.23
2016-12-06	59.95	60.64
2016-12-07	61.37	60.70
2016-12-08	61.01	60.57
2016-12-09	61.97	60.43
2016-12-12	62.17	60.16
2016-12-13	62.98	59.76
2016-12-14	62.68	59.65
2016-12-15	62.58	59.58
2016-12-16	62.30	59.59
2016-12-19	63.62	59.35
2016-12-20	63.54	59.43
2016-12-21	63.54	59.90
2016-12-22	63.55	60.49
2016-12-23	63.24	60.96
2016-12-27	63.28	61.58
2016-12-28	62.99	61.93
2016-12-29	62.90	62.44
2016-12-30	62.14	62.81
2017-01-03	62.58	63.08
2017-01-04	62.30	63.16
2017-01-05	62.30	63.31
2017-01-06	62.84	63.42
2017-01-09	62.64	63.64
2017-01-10	62.62	63.49
2017-01-11	63.19	63.27
2017-01-12	62.61	63.10
2017-01-13	62.70	62.90
2017-01-17	62.53	62.78
2017-01-18	62.50	62.63
2017-01-19	62.30	62.57
2017-01-20	62.74	62.46
2017-01-23	62.96	62.54
2017-01-24	63.52	62.45
2017-01-25	63.68	62.45
2017-01-26	64.27	62.42
2017-01-27	65.78	62.12
2017-01-30	65.13	62.04
2017-01-31	64.65	62.09
2017-02-01	63.58	62.16
2017-02-02	63.17	62.45
2017-02-03	63.68	62.67
2017-02-06	63.64	62.98
2017-02-07	63.43	63.34
2017-02-08	63.34	63.81