Python 滑动累积和

开盘时间开盘高位低位收盘价成交量
2019-12-30-22H 7265.247281.337256.587269.045643397.6053299
2019-12-30-23H 7269.04 7288.88 7266.02 7276.47 3496291.56664438
2019-12-31-00H 7277.57 7285.49 7239.0 7246.0 5480337.35603218
2019-12-31-01H 7246.0 7255.0 7200.0 7251.0 12944037.55061038
2019-12-31-02H 7251.0 7269.0 7245.0 7265.4 6574092.80269061
2019-12-31-03H 7264.96 7266.74 7225.34 7246.99 5331202.45221019
2019-12-31-04H 7247.01 7250.0 7221.0 7236.39 4508747.90607631
2019-12-31-05H 7236.67263.157228.47240.375609366.20613776
2019-12-31-06H 7240.21 7268.48 7240.09 7264.06 4719385.57010436
2019-12-31-07H 7264.05 7267.0 7243.94 7244.98 4840785.79801116
2019-12-31-08H 7244.72 7255.02 7236.01 7250.37 4149434.68258942

开盘时间开盘高低点收盘价成交量
2019-12-30-22H 7265.247281.337256.587269.045643397.605329119994.4289267424
2019-12-30-23H 7269.04 7288.88 7266.02 7276.47 3496291.56664438 119993.58885296652
2019-12-31-00H 7277.57 7285.49 7239.0 7246.0 5480337.35603218 119992.77877982403
2019-12-31-01H 7246.0 7255.0 7200.0 7251.0 12944037.55061038 119991.99352517813
2019-12-31-02H 7251.0 7269.0 7245.0 7265.4 6574092.80269061 119991.22161482804
2019-12-31-03H 7264.96 7266.74 7225.34 7246.99 5331202.45221019 119990.46819418059
2019-12-31-04H 7247.01 7250.0 7221.0 7236.39 4508747.90607631 119989.777319988
2019-12-31-05H 7236.67263.157228.47240.375609366.20613776119989.10188507008
2019-12-31-06H 7240.21 7268.48 7240.09 7264.06 4719385.57010436 119988.51559211954
2019-12-31-07H 7264.05 7267.0 7243.94 7244.98 4840785.79801186119988.00414524872
2019-12-31-08H 7244.72 7255.02 7236.01 7250.37 4149434.68258942 119987.5160859423
2019-12-31-09H 7250.37250.54 7223.36 7229.18 6587934.91085111 119987.02424826368
2019-12-31-10H 7229.84 7244.0 7219.07 7229.2 12442827.1189019 119986.48999096375
2019-12-31-11H 7229.2 7255.15 7217.5 7245.01 8102199.20236629 119985.95738604155
2019-12-31-12H 7244.08 7256.94 7236.02 7243.39 5415351.79554907 119985.4254179181
2019-12-31-13H 7243.64 7248.0 7222.14 7247.99 6286547.0197346 119984.87618273347
2019-12-31-14H 7247.997252.07235.637239.437230857.2360276119984.31644376695
2019-12-31-15H 7239.147320.07230.637237.6822666262.73763661119983.69886205425
2019-12-31-16H 7237.447261.027188.887195.9621634828.472953581119982.98965081113
2019-12-31-17H 7195.07225.627186.777211.497153008.84544945119982.26831325727
2019-12-31-18H 7212.45 7213.56 7151.0 7168.12 11591594.57384476 119981.5021412825
2019-12-31-19H 7167.72 7174.04 7145.01 7168.86 9220725.5526175 119980.71148398018
2019-12-31-20H 7169.327185.07156.857173.325476270.84769673119979.91252483618
2019-12-31-21H 7173.75 7187.89 7165.1 7176.41 3646460.18756114 119979.1179446736
2019-12-31-22H 7176.51 7188.93 7171.01 7186.19 2946479.05108401 119978.3280287224
2019-12-31-23H 7185.927208.417181.787200.484684235.22473528119977.54853467083
2020-01-01-00H 7200.52 7206.29 7185.76 7195.23 3755763.59960018 119976.82049999568
2020-01-01-01H 7195.247196.257175.467177.023675856.57948543119976.11691738569
2020-01-01-02H 7176.47 7230.0 7175.71 7216.27 6365952.54111276 119975.40658628644
2020-01-01-03H 7215.52 7244.87 7211.41 7242.85 4736719.38819138 119974.70558206929
2020-01-01-04H 7242.66 7245.0 7220.0 7225.01 5667367.29300603 119974.0136567676238
2020-01-01-05H 7225.0 7230.0 7215.03 7217.27 3379093.84979077 119973.43902784
2020-01-01-06H 7217.26 7229.76 7216.65 7224.21 2489507.24663728 119972.98221449331
2020-01-01-07H 7224.247236.277221.517225.624493048.24570801119972.54199323399
2020-01-01-08H 7225.88 7232.94 7199.11 7209.83 4528532.93295699 119972.14576063966
2020-01-01-09H 7209.83 7210.0 7180.0 7200.64 6584766.36902302 119971.76622711321
2020-01-01-10H 7200.29 7210.51 7188.0 7188.77 4580279.61372286 119971.39245794505
2020-01-01-11H 7189.07 7210.0 7185.2 7202.0 3872434.2394114 119971.01783386005

Python没有数据帧。我猜你在用熊猫？请在询问熊猫时始终包括熊猫标签（与NumPy、Scipy和其他产品相同），如标签wiki中所述。您正在向我们展示一段代码片段。我猜它不能像你想的那样工作？以什么方式？请阅读。是的，我使用的是Pandasical，一开始我认为这会有用。shift（14）#df['volume']=df['volume\u usdt'].shift（14）#df['price']=df['R']=（（volume*price）.cumsum（）/volume.cumsum（））.ffill（）与

rolling（14）.sum（）

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