Mariner Backtesting - TA-LIB Vector Trigonometric Tan

TAN

 

 real = TAN(close)

Plot

Vector Trigonometric Tan

Working Example

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

class WE_TAN(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.TAN(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['real'] = real
        symbol = 'TAN: ' + self.symbol
        print ktgfunc.talib_table(symbol, 1, dict)

Console

MSFT:  2017-02-09 09:30:00.000000
TAN: MSFT
Input Output
date close real
2016-09-16 56.87 0.33
2016-09-19 56.55 -0.00
2016-09-20 56.43 -0.12
2016-09-21 57.37 1.08
2016-09-22 57.43 1.22
2016-09-23 57.04 0.54
2016-09-26 56.52 -0.03
2016-09-27 57.56 1.60
2016-09-28 57.64 1.93
2016-09-29 57.01 0.50
2016-09-30 57.21 0.78
2016-10-03 57.03 0.53
2016-10-04 56.86 0.32
2016-10-05 57.25 0.85
2016-10-06 57.35 1.04
2016-10-07 57.41 1.17
2016-10-10 57.65 1.97
2016-10-11 56.81 0.26
2016-10-12 56.73 0.18
2016-10-13 56.54 -0.01
2016-10-14 57.03 0.53
2016-10-17 56.84 0.30
2016-10-18 57.27 0.88
2016-10-19 57.14 0.68
2016-10-20 56.87 0.33
2016-10-21 59.26 -0.46
2016-10-24 60.59 1.26
2016-10-25 60.58 1.24
2016-10-26 60.22 0.59
2016-10-27 59.70 0.01
2016-10-28 59.47 -0.23
2016-10-31 59.52 -0.17
2016-11-01 59.40 -0.30
2016-11-02 59.03 -0.77
2016-11-03 58.81 -1.20
2016-11-04 58.32 -5.02
2016-11-07 60.01 0.34
2016-11-08 60.06 0.39
2016-11-09 59.77 0.08
2016-11-10 58.31 -5.30
2016-11-11 58.62 -1.81
2016-11-14 57.73 2.44
2016-11-15 58.87 -1.07
2016-11-16 59.65 -0.04
2016-11-17 60.64 1.40
2016-11-18 60.35 0.78
2016-11-21 60.86 2.36
2016-11-22 61.12 7.04
2016-11-23 60.40 0.86
2016-11-25 60.53 1.12
2016-11-28 60.61 1.31
2016-11-29 61.09 5.79
2016-11-30 60.26 0.64
2016-12-01 59.20 -0.53
2016-12-02 59.25 -0.47
2016-12-05 60.22 0.59
2016-12-06 59.95 0.27
2016-12-07 61.37 -9.14
2016-12-08 61.01 3.90
2016-12-09 61.97 -1.17
2016-12-12 62.17 -0.78
2016-12-13 62.98 0.15
2016-12-14 62.68 -0.15
2016-12-15 62.58 -0.26
2016-12-16 62.30 -0.59
2016-12-19 63.62 1.01
2016-12-20 63.54 0.86
2016-12-21 63.54 0.86
2016-12-22 63.55 0.87
2016-12-23 63.24 0.43
2016-12-27 63.28 0.48
2016-12-28 62.99 0.16
2016-12-29 62.90 0.07
2016-12-30 62.14 -0.83
2017-01-03 62.58 -0.26
2017-01-04 62.30 -0.59
2017-01-05 62.30 -0.59
2017-01-06 62.84 0.01
2017-01-09 62.64 -0.19
2017-01-10 62.62 -0.22
2017-01-11 63.19 0.37
2017-01-12 62.61 -0.23
2017-01-13 62.70 -0.13
2017-01-17 62.53 -0.31
2017-01-18 62.50 -0.34
2017-01-19 62.30 -0.59
2017-01-20 62.74 -0.09
2017-01-23 62.96 0.13
2017-01-24 63.52 0.82
2017-01-25 63.68 1.13
2017-01-26 64.27 7.49
2017-01-27 65.78 -0.20
2017-01-30 65.13 -1.12
2017-01-31 64.65 -3.96
2017-02-01 63.58 0.93
2017-02-02 63.17 0.35
2017-02-03 63.68 1.13
2017-02-06 63.64 1.05
2017-02-07 63.43 0.68
2017-02-08 63.34 0.56