CMO
real = CMO(close, timeperiod=14)
Plot
Working Example
from cloudquant.interfaces import Strategy
from collections import OrderedDict
import ktgfunc
import talib
class WE_CMO(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.CMO(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 = 'CMO: ' + self.symbol
print ktgfunc.talib_table(symbol, 1, dict)
Console
MSFT: 2017-02-09 09:30:00.000000
CMO: 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 | nan |
2016-10-06 | 57.35 | 9.44 |
2016-10-07 | 57.41 | 10.56 |
2016-10-10 | 57.65 | 15.05 |
2016-10-11 | 56.81 | -3.47 |
2016-10-12 | 56.73 | -5.02 |
2016-10-13 | 56.54 | -8.76 |
2016-10-14 | 57.03 | 2.17 |
2016-10-17 | 56.84 | -2.07 |
2016-10-18 | 57.27 | 7.07 |
2016-10-19 | 57.14 | 4.11 |
2016-10-20 | 56.87 | -2.18 |
2016-10-21 | 59.26 | 34.48 |
2016-10-24 | 60.59 | 46.07 |
2016-10-25 | 60.58 | 45.86 |
2016-10-26 | 60.22 | 38.26 |
2016-10-27 | 59.70 | 27.70 |
2016-10-28 | 59.47 | 23.30 |
2016-10-31 | 59.52 | 23.91 |
2016-11-01 | 59.40 | 21.40 |
2016-11-02 | 59.03 | 13.74 |
2016-11-03 | 58.81 | 9.33 |
2016-11-04 | 58.32 | -0.16 |
2016-11-07 | 60.01 | 24.10 |
2016-11-08 | 60.06 | 24.67 |
2016-11-09 | 59.77 | 18.86 |
2016-11-10 | 58.31 | -4.61 |
2016-11-11 | 58.62 | 0.02 |
2016-11-14 | 57.73 | -11.80 |
2016-11-15 | 58.87 | 3.81 |
2016-11-16 | 59.65 | 12.78 |
2016-11-17 | 60.64 | 22.64 |
2016-11-18 | 60.35 | 18.42 |
2016-11-21 | 60.86 | 23.42 |
2016-11-22 | 61.12 | 25.91 |
2016-11-23 | 60.40 | 14.77 |
2016-11-25 | 60.53 | 16.21 |
2016-11-28 | 60.61 | 17.14 |
2016-11-29 | 61.09 | 22.68 |
2016-11-30 | 60.26 | 9.11 |
2016-12-01 | 59.20 | -5.30 |
2016-12-02 | 59.25 | -4.60 |
2016-12-05 | 60.22 | 8.18 |
2016-12-06 | 59.95 | 4.36 |
2016-12-07 | 61.37 | 20.31 |
2016-12-08 | 61.01 | 15.07 |
2016-12-09 | 61.97 | 24.51 |
2016-12-12 | 62.17 | 26.35 |
2016-12-13 | 62.98 | 33.41 |
2016-12-14 | 62.68 | 28.50 |
2016-12-15 | 62.58 | 26.82 |
2016-12-16 | 62.30 | 22.01 |
2016-12-19 | 63.62 | 34.59 |
2016-12-20 | 63.54 | 33.19 |
2016-12-21 | 63.54 | 33.19 |
2016-12-22 | 63.55 | 33.29 |
2016-12-23 | 63.24 | 26.90 |
2016-12-27 | 63.28 | 27.39 |
2016-12-28 | 62.99 | 21.13 |
2016-12-29 | 62.90 | 19.17 |
2016-12-30 | 62.14 | 3.91 |
2017-01-03 | 62.58 | 11.01 |
2017-01-04 | 62.30 | 5.66 |
2017-01-05 | 62.30 | 5.66 |
2017-01-06 | 62.84 | 14.85 |
2017-01-09 | 62.64 | 10.55 |
2017-01-10 | 62.62 | 10.11 |
2017-01-11 | 63.19 | 19.96 |
2017-01-12 | 62.61 | 7.10 |
2017-01-13 | 62.70 | 8.73 |
2017-01-17 | 62.53 | 4.98 |
2017-01-18 | 62.50 | 4.29 |
2017-01-19 | 62.30 | -0.38 |
2017-01-20 | 62.74 | 9.25 |
2017-01-23 | 62.96 | 13.70 |
2017-01-24 | 63.52 | 23.94 |
2017-01-25 | 63.68 | 26.62 |
2017-01-26 | 64.27 | 35.62 |
2017-01-27 | 65.78 | 51.89 |
2017-01-30 | 65.13 | 35.96 |
2017-01-31 | 64.65 | 25.50 |
2017-02-01 | 63.58 | 5.92 |
2017-02-02 | 63.17 | -0.48 |
2017-02-03 | 63.68 | 7.05 |
2017-02-06 | 63.64 | 6.37 |
2017-02-07 | 63.43 | 2.72 |
2017-02-08 | 63.34 | 1.12 |