Mariner Backtesting - TA-LIB Linear Regression

LINEARREG

 real = LINEARREG(close, timeperiod=14)

Plot

Linear Regression

Working Example

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

class WE_LINEARREG(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(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: ' + self.symbol
        print ktgfunc.talib_table(symbol, 1, dict)

Console

MSFT:  2017-02-09 09:30:00.000000
LINEARREG: 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 57.25
2016-10-06 57.35 57.30
2016-10-07 57.41 57.30
2016-10-10 57.65 57.33
2016-10-11 56.81 57.24
2016-10-12 56.73 57.15
2016-10-13 56.54 56.98
2016-10-14 57.03 56.87
2016-10-17 56.84 56.85
2016-10-18 57.27 56.97
2016-10-19 57.14 56.98
2016-10-20 56.87 56.95
2016-10-21 59.26 57.51
2016-10-24 60.59 58.33
2016-10-25 60.58 59.09
2016-10-26 60.22 59.68
2016-10-27 59.70 60.06
2016-10-28 59.47 60.36
2016-10-31 59.52 60.49
2016-11-01 59.40 60.50
2016-11-02 59.03 60.32
2016-11-03 58.81 60.08
2016-11-04 58.32 59.63
2016-11-07 60.01 59.64
2016-11-08 60.06 59.56
2016-11-09 59.77 59.29
2016-11-10 58.31 58.90
2016-11-11 58.62 58.81
2016-11-14 57.73 58.54
2016-11-15 58.87 58.59
2016-11-16 59.65 58.82
2016-11-17 60.64 59.26
2016-11-18 60.35 59.60
2016-11-21 60.86 60.04
2016-11-22 61.12 60.44
2016-11-23 60.40 60.57
2016-11-25 60.53 60.62
2016-11-28 60.61 60.86
2016-11-29 61.09 61.22
2016-11-30 60.26 61.30
2016-12-01 59.20 60.88
2016-12-02 59.25 60.49
2016-12-05 60.22 60.21
2016-12-06 59.95 59.95
2016-12-07 61.37 60.13
2016-12-08 61.01 60.31
2016-12-09 61.97 60.69
2016-12-12 62.17 61.15
2016-12-13 62.98 61.82
2016-12-14 62.68 62.25
2016-12-15 62.58 62.61
2016-12-16 62.30 62.85
2016-12-19 63.62 63.45
2016-12-20 63.54 63.84
2016-12-21 63.54 63.98
2016-12-22 63.55 64.01
2016-12-23 63.24 63.97
2016-12-27 63.28 63.82
2016-12-28 62.99 63.71
2016-12-29 62.90 63.47
2016-12-30 62.14 63.12
2017-01-03 62.58 62.91
2017-01-04 62.30 62.73
2017-01-05 62.30 62.52
2017-01-06 62.84 62.46
2017-01-09 62.64 62.29
2017-01-10 62.62 62.29
2017-01-11 63.19 62.46
2017-01-12 62.61 62.49
2017-01-13 62.70 62.58
2017-01-17 62.53 62.60
2017-01-18 62.50 62.63
2017-01-19 62.30 62.60
2017-01-20 62.74 62.68
2017-01-23 62.96 62.72
2017-01-24 63.52 62.94
2017-01-25 63.68 63.14
2017-01-26 64.27 63.45
2017-01-27 65.78 64.17
2017-01-30 65.13 64.60
2017-01-31 64.65 64.85
2017-02-01 63.58 64.83
2017-02-02 63.17 64.62
2017-02-03 63.68 64.54
2017-02-06 63.64 64.40
2017-02-07 63.43 64.16
2017-02-08 63.34 63.85