Mariner Backtesting - TA-LIB Vector Log10

LOG10

 

 real = LOG10(close)

Plot

Vector Log10

Working Example

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

class WE_LOG10(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.LOG10(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 = 'LOG10: ' + self.symbol
        print ktgfunc.talib_table(symbol, 1, dict)

Console

MSFT:  2017-02-09 09:30:00.000000
LOG10: MSFT
Input Output
date close real
2016-09-16 56.87 1.75
2016-09-19 56.55 1.75
2016-09-20 56.43 1.75
2016-09-21 57.37 1.76
2016-09-22 57.43 1.76
2016-09-23 57.04 1.76
2016-09-26 56.52 1.75
2016-09-27 57.56 1.76
2016-09-28 57.64 1.76
2016-09-29 57.01 1.76
2016-09-30 57.21 1.76
2016-10-03 57.03 1.76
2016-10-04 56.86 1.75
2016-10-05 57.25 1.76
2016-10-06 57.35 1.76
2016-10-07 57.41 1.76
2016-10-10 57.65 1.76
2016-10-11 56.81 1.75
2016-10-12 56.73 1.75
2016-10-13 56.54 1.75
2016-10-14 57.03 1.76
2016-10-17 56.84 1.75
2016-10-18 57.27 1.76
2016-10-19 57.14 1.76
2016-10-20 56.87 1.75
2016-10-21 59.26 1.77
2016-10-24 60.59 1.78
2016-10-25 60.58 1.78
2016-10-26 60.22 1.78
2016-10-27 59.70 1.78
2016-10-28 59.47 1.77
2016-10-31 59.52 1.77
2016-11-01 59.40 1.77
2016-11-02 59.03 1.77
2016-11-03 58.81 1.77
2016-11-04 58.32 1.77
2016-11-07 60.01 1.78
2016-11-08 60.06 1.78
2016-11-09 59.77 1.78
2016-11-10 58.31 1.77
2016-11-11 58.62 1.77
2016-11-14 57.73 1.76
2016-11-15 58.87 1.77
2016-11-16 59.65 1.78
2016-11-17 60.64 1.78
2016-11-18 60.35 1.78
2016-11-21 60.86 1.78
2016-11-22 61.12 1.79
2016-11-23 60.40 1.78
2016-11-25 60.53 1.78
2016-11-28 60.61 1.78
2016-11-29 61.09 1.79
2016-11-30 60.26 1.78
2016-12-01 59.20 1.77
2016-12-02 59.25 1.77
2016-12-05 60.22 1.78
2016-12-06 59.95 1.78
2016-12-07 61.37 1.79
2016-12-08 61.01 1.79
2016-12-09 61.97 1.79
2016-12-12 62.17 1.79
2016-12-13 62.98 1.80
2016-12-14 62.68 1.80
2016-12-15 62.58 1.80
2016-12-16 62.30 1.79
2016-12-19 63.62 1.80
2016-12-20 63.54 1.80
2016-12-21 63.54 1.80
2016-12-22 63.55 1.80
2016-12-23 63.24 1.80
2016-12-27 63.28 1.80
2016-12-28 62.99 1.80
2016-12-29 62.90 1.80
2016-12-30 62.14 1.79
2017-01-03 62.58 1.80
2017-01-04 62.30 1.79
2017-01-05 62.30 1.79
2017-01-06 62.84 1.80
2017-01-09 62.64 1.80
2017-01-10 62.62 1.80
2017-01-11 63.19 1.80
2017-01-12 62.61 1.80
2017-01-13 62.70 1.80
2017-01-17 62.53 1.80
2017-01-18 62.50 1.80
2017-01-19 62.30 1.79
2017-01-20 62.74 1.80
2017-01-23 62.96 1.80
2017-01-24 63.52 1.80
2017-01-25 63.68 1.80
2017-01-26 64.27 1.81
2017-01-27 65.78 1.82
2017-01-30 65.13 1.81
2017-01-31 64.65 1.81
2017-02-01 63.58 1.80
2017-02-02 63.17 1.80
2017-02-03 63.68 1.80
2017-02-06 63.64 1.80
2017-02-07 63.43 1.80
2017-02-08 63.34 1.80