Mariner Backtesting - TA-LIB Pearson's Correlation Coefficient (r)

CORREL

 

 real = CORREL(high, low, timeperiod=30)

Plot

Pearson's Correlation Coefficient (r)

Working Example

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

class WE_CORREL(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)
        high = daily_bars.high
        low = daily_bars.low
        real = talib.CORREL(high, low, timeperiod=30)

        # 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['high'] = high
        dict['low'] = low
        dict['real'] = real
        symbol = 'CORREL: ' + self.symbol
        print ktgfunc.talib_table(symbol, 1, dict)

Console

MSFT:  2017-02-09 09:30:00.000000
CORREL: MSFT
Input Output
date high low real
2016-09-16 57.24 56.37 nan
2016-09-19 57.36 56.47 nan
2016-09-20 56.97 56.37 nan
2016-09-21 57.46 56.70 nan
2016-09-22 57.61 57.24 nan
2016-09-23 57.52 56.99 nan
2016-09-26 56.76 56.45 nan
2016-09-27 57.67 56.30 nan
2016-09-28 57.67 57.28 nan
2016-09-29 57.78 56.83 nan
2016-09-30 57.38 56.96 nan
2016-10-03 57.16 56.68 nan
2016-10-04 57.21 56.59 nan
2016-10-05 57.57 56.88 nan
2016-10-06 57.47 56.90 nan
2016-10-07 57.59 57.03 nan
2016-10-10 58.00 57.48 nan
2016-10-11 57.63 56.51 nan
2016-10-12 56.89 56.02 nan
2016-10-13 56.92 55.94 nan
2016-10-14 57.35 56.74 nan
2016-10-17 57.07 56.49 nan
2016-10-18 57.56 57.02 nan
2016-10-19 57.45 57.01 nan
2016-10-20 57.13 56.28 nan
2016-10-21 60.04 59.09 nan
2016-10-24 60.59 59.53 nan
2016-10-25 60.96 60.39 nan
2016-10-26 60.79 60.06 nan
2016-10-27 60.42 59.69 0.98
2016-10-28 60.11 59.18 0.98
2016-10-31 60.01 59.52 0.98
2016-11-01 59.62 58.85 0.98
2016-11-02 59.53 58.90 0.98
2016-11-03 59.24 58.71 0.98
2016-11-04 58.88 58.13 0.98
2016-11-07 60.11 59.38 0.99
2016-11-08 60.37 59.75 0.99
2016-11-09 60.18 58.80 0.99
2016-11-10 60.08 57.24 0.95
2016-11-11 58.72 57.62 0.95
2016-11-14 58.68 56.90 0.94
2016-11-15 59.49 58.31 0.94
2016-11-16 59.66 54.27 0.78
2016-11-17 60.95 59.97 0.79
2016-11-18 61.14 60.30 0.80
2016-11-21 60.97 60.42 0.81
2016-11-22 61.26 60.81 0.82
2016-11-23 61.10 60.25 0.81
2016-11-25 60.53 60.13 0.79
2016-11-28 61.02 60.21 0.79
2016-11-29 61.41 60.52 0.78
2016-11-30 61.18 60.22 0.78
2016-12-01 60.15 58.94 0.77
2016-12-02 59.47 58.80 0.74
2016-12-05 60.58 59.56 0.74
2016-12-06 60.46 59.80 0.74
2016-12-07 61.38 59.80 0.72
2016-12-08 61.58 60.84 0.74
2016-12-09 61.99 61.12 0.76
2016-12-12 62.30 61.72 0.78
2016-12-13 63.42 62.24 0.81
2016-12-14 63.45 62.53 0.83
2016-12-15 63.15 62.30 0.85
2016-12-16 62.95 62.12 0.86
2016-12-19 63.77 62.42 0.87
2016-12-20 63.80 63.03 0.88
2016-12-21 63.70 63.12 0.90
2016-12-22 64.10 63.40 0.90
2016-12-23 63.54 62.80 0.91
2016-12-27 64.07 63.21 0.91
2016-12-28 63.40 62.83 0.90
2016-12-29 63.20 62.73 0.90
2016-12-30 62.99 62.03 0.98
2017-01-03 62.84 62.12 0.98
2017-01-04 62.75 62.12 0.98
2017-01-05 62.66 62.03 0.98
2017-01-06 63.15 62.04 0.98
2017-01-09 63.08 62.54 0.98
2017-01-10 63.07 62.28 0.98
2017-01-11 63.23 62.43 0.98
2017-01-12 63.40 61.95 0.98
2017-01-13 62.87 62.35 0.97
2017-01-17 62.70 62.03 0.97
2017-01-18 62.70 62.12 0.96
2017-01-19 62.98 62.19 0.94
2017-01-20 62.82 62.37 0.92
2017-01-23 63.12 62.57 0.90
2017-01-24 63.74 62.94 0.87
2017-01-25 64.10 63.45 0.86
2017-01-26 64.54 63.55 0.87
2017-01-27 65.91 64.89 0.94
2017-01-30 65.79 64.80 0.96
2017-01-31 65.15 64.26 0.96
2017-02-01 64.62 63.47 0.96
2017-02-02 63.41 62.75 0.97
2017-02-03 63.70 63.07 0.97
2017-02-06 63.65 63.14 0.97
2017-02-07 63.78 63.23 0.97
2017-02-08 63.81 63.22 0.96