Mariner Backtesting - TA-LIB Vector Log Natural

LN

 

 real = LN(close)

Plot

Vector Log Natural

Working Example

 
from cloudquant.interfaces import Strategy from collections import OrderedDict import ktgfunc import talib class WE_LN(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.LN(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 = 'LN: ' + self.symbol print ktgfunc.talib_table(symbol, 1, dict)

Console

MSFT:  2017-02-09 09:30:00.000000
LN: MSFT
Input Output
date close real
2016-09-16 56.87 4.04
2016-09-19 56.55 4.04
2016-09-20 56.43 4.03
2016-09-21 57.37 4.05
2016-09-22 57.43 4.05
2016-09-23 57.04 4.04
2016-09-26 56.52 4.03
2016-09-27 57.56 4.05
2016-09-28 57.64 4.05
2016-09-29 57.01 4.04
2016-09-30 57.21 4.05
2016-10-03 57.03 4.04
2016-10-04 56.86 4.04
2016-10-05 57.25 4.05
2016-10-06 57.35 4.05
2016-10-07 57.41 4.05
2016-10-10 57.65 4.05
2016-10-11 56.81 4.04
2016-10-12 56.73 4.04
2016-10-13 56.54 4.03
2016-10-14 57.03 4.04
2016-10-17 56.84 4.04
2016-10-18 57.27 4.05
2016-10-19 57.14 4.05
2016-10-20 56.87 4.04
2016-10-21 59.26 4.08
2016-10-24 60.59 4.10
2016-10-25 60.58 4.10
2016-10-26 60.22 4.10
2016-10-27 59.70 4.09
2016-10-28 59.47 4.09
2016-10-31 59.52 4.09
2016-11-01 59.40 4.08
2016-11-02 59.03 4.08
2016-11-03 58.81 4.07
2016-11-04 58.32 4.07
2016-11-07 60.01 4.09
2016-11-08 60.06 4.10
2016-11-09 59.77 4.09
2016-11-10 58.31 4.07
2016-11-11 58.62 4.07
2016-11-14 57.73 4.06
2016-11-15 58.87 4.08
2016-11-16 59.65 4.09
2016-11-17 60.64 4.10
2016-11-18 60.35 4.10
2016-11-21 60.86 4.11
2016-11-22 61.12 4.11
2016-11-23 60.40 4.10
2016-11-25 60.53 4.10
2016-11-28 60.61 4.10
2016-11-29 61.09 4.11
2016-11-30 60.26 4.10
2016-12-01 59.20 4.08
2016-12-02 59.25 4.08
2016-12-05 60.22 4.10
2016-12-06 59.95 4.09
2016-12-07 61.37 4.12
2016-12-08 61.01 4.11
2016-12-09 61.97 4.13
2016-12-12 62.17 4.13
2016-12-13 62.98 4.14
2016-12-14 62.68 4.14
2016-12-15 62.58 4.14
2016-12-16 62.30 4.13
2016-12-19 63.62 4.15
2016-12-20 63.54 4.15
2016-12-21 63.54 4.15
2016-12-22 63.55 4.15
2016-12-23 63.24 4.15
2016-12-27 63.28 4.15
2016-12-28 62.99 4.14
2016-12-29 62.90 4.14
2016-12-30 62.14 4.13
2017-01-03 62.58 4.14
2017-01-04 62.30 4.13
2017-01-05 62.30 4.13
2017-01-06 62.84 4.14
2017-01-09 62.64 4.14
2017-01-10 62.62 4.14
2017-01-11 63.19 4.15
2017-01-12 62.61 4.14
2017-01-13 62.70 4.14
2017-01-17 62.53 4.14
2017-01-18 62.50 4.14
2017-01-19 62.30 4.13
2017-01-20 62.74 4.14
2017-01-23 62.96 4.14
2017-01-24 63.52 4.15
2017-01-25 63.68 4.15
2017-01-26 64.27 4.16
2017-01-27 65.78 4.19
2017-01-30 65.13 4.18
2017-01-31 64.65 4.17
2017-02-01 63.58 4.15
2017-02-02 63.17 4.15
2017-02-03 63.68 4.15
2017-02-06 63.64 4.15
2017-02-07 63.43 4.15
2017-02-08 63.34 4.15