Python 熊猫ewm不';与marketwatch不相上下

Python 熊猫ewm不';与marketwatch不相上下,python,pandas,Python,Pandas,我得到一个5分钟的提要并将其存储在数据帧中。我的EWM 200与Marketwatch EWM 200不匹配 我试过2018年2月13日发布的代码。我的数据已经按升序排序了日期,但由于某些原因,它并没有起到作用 df = df.drop(df.index[-1]) print(df[['date','low','close']]) df.sort_values(by='date') df = df.sort_index() print(df[['date'

我得到一个5分钟的提要并将其存储在数据帧中。我的EWM 200与Marketwatch EWM 200不匹配

我试过2018年2月13日发布的代码。我的数据已经按升序排序了日期,但由于某些原因,它并没有起到作用

    df = df.drop(df.index[-1])
    print(df[['date','low','close']])
    df.sort_values(by='date')
    df = df.sort_index()
    print(df[['date','low','close']])

#print(df)
    df['ewm_5'] = round(df['close'].ewm(span=5,min_periods=0,adjust=False,ignore_na=False).mean(),2)
    df['ewm_9'] = round(df['close'].ewm(span=9,min_periods=0,adjust=False,ignore_na=False).mean(),2)
    df['ewm_15'] = df['close'].ewm(span=15,min_periods=0,adjust=False,ignore_na=False).mean()
    df['ewm_65'] = df['close'].ewm(span=65,min_periods=0,adjust=False,ignore_na=False).mean()
    df['ewm_200'] = df['close'].ewm(span=200,min_periods=0,adjust=False,ignore_na=False).mean()
    print(df[['date','low','close','ewm_9','ewm_15','ewm_65', 'ewm_200','volume']])
早上630点的Marketwatch显示ewm 200为155.70,我使用该功能时显示为161.78

df['ewm_200'] = df['close'].ewm(span=200,min_periods=0,adjust=False,ignore_na=False).mean()
这是我更新的原始数据,截至上午630点,超过200个数据点,但我的数据和市场观察数据不一致,甚至不接近。我现在的读数是:159.036336,市场观察指数是155.70

               date     low   close   ewm_9      ewm_15      ewm_65     ewm_200  volume
0 20190214 04:15:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 153.510000 1 1 20190214 04:20:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 2 20190214 04:25:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 3 20190214 04:30:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 4 20190214 04:35:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 5 20190214 04:40:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 6 20190214 04:45:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 7 20190214 04:50:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 8 20190214 04:55:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 9 20190214 05:00:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 10 20190214 05:05:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 11 20190214 05:10:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 12 20190214 05:15:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 13 20190214 05:20:00 153.51 153.51 153.51 153.510000 153.510000 153.510000 0 14 20190214 05:25:00 153.50 153.50 153.51 153.508750 153.509697 153.509900 1 15 20190214 05:30:00 153.50 153.50 153.51 153.507656 153.509403 153.509802 0 16 20190214 05:35:00 153.50 153.50 153.51 153.506699 153.509118 153.509704 0 17 20190214 05:40:00 153.50 153.50 153.50 153.505862 153.508842 153.509608 0 18 20190214 05:45:00 153.50 153.50 153.50 153.505129 153.508574 153.509512 0 19 20190214 05:50:00 153.50 153.50 153.50 153.504488 153.508314 153.509418 0 20 20190214 05:55:00 153.50 153.50 153.50 153.503927 153.508062 153.509324 0 21 20190214 06:00:00 153.50 153.50 153.50 153.503436 153.507818 153.509231 0 22 20190214 06:05:00 153.50 153.50 153.50 153.503007 153.507581 153.509139 0 23 20190214 06:10:00 153.50 153.50 153.50 153.502631 153.507351 153.509048 0 24 20190214 06:15:00 153.50 153.50 153.50 153.502302 153.507128 153.508958 0 25 20190214 06:20:00 153.70 153.70 153.54 153.527014 153.512973 153.510859 1 26 20190214 06:25:00 153.70 153.70 153.57 153.548637 153.518641 153.512741 0 27 20190214 06:30:00 153.70 153.70 153.60 153.567558 153.524136 153.514605 0 28 20190214 06:35:00 153.70 153.70 153.62 153.584113 153.529465 153.516449 0 29 20190214 06:40:00 153.70 153.70 153.63 153.598599 153.534633 153.518276 0 30 20190214 06:45:00 153.70 153.70 153.65 153.611274 153.539644 153.520084 0 31 20190214 06:50:00 153.70 153.70 153.66 153.622365 153.544504 153.521874 0 32 20190214 06:55:00 153.70 153.70 153.67 153.632069 153.549216 153.523646 0 33 20190214 07:00:00 153.81 154.05 153.74 153.684311 153.564391 153.528884 31 34 20190214 07:05:00 154.00 154.00 153.79 153.723772 153.577591 153.533572 3 35 20190214 07:10:00 154.07 154.37 153.91 153.804550 153.601604 153.541894 19 36 20190214 07:15:00 154.16 154.20 153.97 153.853981 153.619737 153.548442 15 37 20190214 07:20:00 154.28 154.56 154.09 153.942234 153.648230 153.558508 17 38 20190214 07:25:00 154.47 154.48 154.16 154.009455 153.673435 153.567677 5 39 20190214 07:30:00 154.43 154.43 154.22 154.062023 153.696361 153.576257 2 40 20190214 07:35:00 154.50 154.50 154.27 154.116770 153.720714 153.585449 30 41 20190214 07:40:00 154.50 154.50 154.32 154.164674 153.744328 153.594549 10 42 20190214 07:45:00 154.37 154.45 154.35 154.200339 153.765712 153.603061 20 43 20190214 07:50:00 154.35 154.35 154.35 154.219047 153.783418 153.610493 13 44 20190214 07:55:00 154.26 154.30 154.34 154.229166 153.799072 153.617354 29 45 20190214 08:00:00 153.90 154.17 154.30 154.221770 153.810312 153.622853 122 46 20190214 08:05:00 154.24 154.40 154.32 154.244049 153.828182 153.630585 26 47 20190214 08:10:00 154.37 154.43 154.34 154.267293 153.846419 153.638540 29 48 20190214 08:15:00 154.49 154.50 154.38 154.296381 153.866224 153.647111 6 49 20190214 08:20:00 154.45 154.45 154.39 154.315584 153.883914 153.655100 16 50 20190214 08:25:00 154.25 154.26 154.36 154.308636 153.895311 153.661119 18 51 20190214 08:30:00 153.97 154.02 154.30 154.272556 153.899089 153.664690 168 52 20190214 08:35:00 153.71 153.97 154.23 154.234737 153.901238 153.667728 165 53 20190214 08:40:00 153.50 153.51 154.09 154.144145 153.889382 153.666159 114 54 20190214 08:45:00 153.33 153.34 153.94 154.043627 153.872734 153.662913 57 55 20190214 08:50:00 153.40 153.71 153.89 154.001923 153.867803 153.663382 43 56 20190214 08:55:00 153.04 153.19 153.75 153.900433 153.847264 153.658672 142 57 20190214 09:00:00 152.25 152.51 153.50 153.726629 153.806740 153.647242 48 58 20190214 09:05:00 152.50 152.93 15