Python代码：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas_datareader import data as wb
stock='3988.HK'
df = wb.DataReader(stock,data_source='yahoo',start='2018-07-01')
rsi_period = 14
chg = df['Close'].diff(1)
gain = chg.mask(chg<0,0)
df['Gain'] = gain
loss = chg.mask(chg>0,0)
df['Loss'] = loss
avg_gain = gain.ewm(com = rsi_period-1,min_periods=rsi_period).mean()
avg_loss = loss.ewm(com = rsi_period-1,min_periods=rsi_period).mean()
df['Avg Gain'] = avg_gain
df['Avg Loss'] = avg_loss
rs = abs(avg_gain/avg_loss)
rsi = 100-(100/(1+rs))
df['RSI'] = rsi
部分结果：
High Low Open Close Volume Adj Close Gain Loss Avg Gain Avg Loss RSI
Date
2018-07-03 3.87 3.76 3.83 3.84 684899302.0 3.629538 NaN NaN NaN NaN NaN
2018-07-04 3.91 3.84 3.86 3.86 460325574.0 3.648442 0.02 0.00 NaN NaN NaN
2018-07-05 3.70 3.62 3.68 3.68 292810499.0 3.680000 0.00 -0.18 NaN NaN NaN
2018-07-06 3.72 3.61 3.69 3.67 343653088.0 3.670000 0.00 -0.01 NaN NaN NaN
2018-07-09 3.75 3.68 3.70 3.69 424596186.0 3.690000 0.02 0.00 NaN NaN NaN
2018-07-10 3.74 3.70 3.71 3.71 327048051.0 3.710000 0.02 0.00 NaN NaN NaN
2018-07-11 3.65 3.61 3.63 3.64 371355401.0 3.640000 0.00 -0.07 NaN NaN NaN
2018-07-12 3.69 3.63 3.66 3.66 309888328.0 3.660000 0.02 0.00 NaN NaN NaN
2018-07-13 3.69 3.62 3.69 3.63 261928758.0 3.630000 0.00 -0.03 NaN NaN NaN
2018-07-16 3.63 3.57 3.61 3.62 306970074.0 3.620000 0.00 -0.01 NaN NaN NaN
2018-07-17 3.62 3.56 3.62 3.58 310294921.0 3.580000 0.00 -0.04 NaN NaN NaN
2018-07-18 3.61 3.55 3.58 3.58 334592695.0 3.580000 0.00 0.00 NaN NaN NaN
2018-07-19 3.61 3.56 3.61 3.56 211984563.0 3.560000 0.00 -0.02 NaN NaN NaN
2018-07-20 3.64 3.52 3.57 3.61 347506394.0 3.610000 0.05 0.00 NaN NaN NaN
2018-07-23 3.65 3.57 3.59 3.62 313125328.0 3.620000 0.01 0.00 0.010594 -0.021042 33.487100
2018-07-24 3.71 3.60 3.60 3.68 367627204.0 3.680000 0.06 0.00 0.015854 -0.018802 45.745967
2018-07-25 3.73 3.68 3.72 3.69 270460990.0 3.690000 0.01 0.00 0.015252 -0.016868 47.483263
2018-07-26 3.73 3.66 3.72 3.69 234388072.0 3.690000 0.00 0.00 0.013731 -0.015186 47.483263
2018-07-27 3.70 3.66 3.68 3.69 190039532.0 3.690000 0.00 0.00 0.012399 -0.013713 47.483263
2018-07-30 3.72 3.67 3.68 3.70 163971848.0 3.700000 0.01 0.00 0.012172 -0.012417 49.502851
2018-07-31 3.70 3.66 3.67 3.68 168486023.0 3.680000 0.00 -0.02 0.011047 -0.013118 45.716244
2018-08-01 3.72 3.66 3.71 3.68 199801191.0 3.680000 0.00 0.00 0.010047 -0.011930 45.716244
2018-08-02 3.68 3.59 3.66 3.61 307920738.0 3.610000 0.00 -0.07 0.009155 -0.017088 34.884632
2018-08-03 3.62 3.57 3.59 3.61 184816985.0 3.610000 0.00 0.00 0.008356 -0.015596 34.884632
2018-08-06 3.66 3.60 3.62 3.61 189696153.0 3.610000 0.00 0.00 0.007637 -0.014256 34.884632
2018-08-07 3.66 3.61 3.63 3.65 216157642.0 3.650000 0.04 0.00 0.010379 -0.013048 44.302922
2018-08-08 3.66 3.61 3.65 3.63 215365540.0 3.630000 0.00 -0.02 0.009511 -0.013629 41.101805
2018-08-09 3.66 3.59 3.59 3.65 230275455.0 3.650000 0.02 0.00 0.010378 -0.012504 45.353992
2018-08-10 3.66 3.60 3.65 3.62 219157328.0 3.620000 0.00 -0.03 0.009530 -0.013933 40.617049
2018-08-13 3.59 3.54 3.58 3.56 270620120.0 3.560000 0.00 -0.06 0.008759 -0.017658 33.158019
2018-08-14 3.58 3.53 3.58 3.56 243870383.0 3.560000 0.00 0.00 0.008058 -0.016243 33.158019
2018-08-15 3.56 3.51 3.54 3.53 282427652.0 3.530000 0.00 -0.03 0.007418 -0.017336 29.966819
2018-08-16 3.57 3.49 3.50 3.52 275769630.0 3.520000 0.00 -0.01 0.006833 -0.016758 28.966085
2018-08-17 3.56 3.52 3.54 3.54 188357023.0 3.540000 0.02 0.00 0.007863 -0.015447 33.732515
2018-08-20 3.57 3.51 3.55 3.56 226473564.0 3.560000 0.02 0.00 0.008806 -0.014247 38.198441
2018-08-21 3.58 3.55 3.57 3.56 238534835.0 3.560000 0.00 0.00 0.008126 -0.013147 38.198441
2018-08-22 3.58 3.53 3.55 3.58 269029064.0 3.580000 0.02 0.00 0.009038 -0.012138 42.678636
2018-08-23 3.59 3.54 3.57 3.55 247570379.0 3.550000 0.00 -0.03 0.008348 -0.013502 38.204696
2018-08-24 3.55 3.52 3.53 3.54 279465912.0 3.540000 0.00 -0.01 0.007713 -0.013236 36.819162
2018-08-27 3.61 3.54 3.55 3.60 462469493.0 3.600000 0.06 0.00 0.011668 -0.012235 48.813844
2018-08-28 3.65 3.60 3.61 3.63 248882773.0 3.630000 0.03 0.00 0.013049 -0.011313 53.561107
2018-08-29 3.61 3.57 3.60 3.61 237332367.0 3.610000 0.00 -0.02 0.012070 -0.011965 50.217314
2018-08-30 3.63 3.55 3.62 3.57 185660963.0 3.570000 0.00 -0.04 0.011167 -0.014061 44.265314
2018-08-31 3.55 3.52 3.55 3.53 359900115.0 3.530000 0.00 -0.04 0.010335 -0.015993 39.254752
2018-09-03 3.54 3.49 3.52 3.52 305346975.0 3.520000 0.00 -0.01 0.009568 -0.015548 38.093832
2018-09-04 3.54 3.51 3.52 3.53 208551797.0 3.530000 0.01 0.00 0.009600 -0.014397 40.004620
2018-09-05 3.54 3.47 3.52 3.47 363913474.0 3.470000 0.00 -0.06 0.008890 -0.017765 33.352720
2018-09-06 3.48 3.41 3.46 3.44 397574154.0 3.440000 0.00 -0.03 0.008235 -0.018667 30.611900
2018-09-07 3.46 3.40 3.44 3.43 567329432.0 3.430000 0.00 -0.01 0.007630 -0.018030 29.734744
2018-09-10 3.42 3.36 3.42 3.39 320927280.0 3.390000 0.00 -0.04 0.007070 -0.019642 26.467750
2018-09-11 3.40 3.36 3.36 3.36 231749076.0 3.360000 0.00 -0.03 0.006552 -0.020400 24.310377
2018-09-12 3.36 3.31 3.35 3.32 377546352.0 3.320000 0.00 -0.04 0.006073 -0.021833 21.763243
2018-09-13 3.41 3.37 3.37 3.40 270315440.0 3.400000 0.08 0.00 0.011468 -0.020240 36.168232
2018-09-14 3.44 3.39 3.42 3.41 142324690.0 3.410000 0.01 0.00 0.011361 -0.018765 37.712062
2018-09-17 3.42 3.36 3.40 3.39 181671619.0 3.390000 0.00 -0.02 0.010535 -0.018855 35.844807
2018-09-18 3.41 3.36 3.37 3.40 287170628.0 3.400000 0.01 0.00 0.010496 -0.017485 37.510837
2018-09-19 3.44 3.40 3.42 3.42 230539211.0 3.420000 0.02 0.00 0.011185 -0.016216 40.820882
2018-09-20 3.47 3.42 3.45 3.43 175746914.0 3.430000 0.01 0.00 0.011100 -0.015040 42.461996
2018-09-21 3.54 3.46 3.47 3.52 393921372.0 3.520000 0.09 0.00 0.016813 -0.013951 54.650933
2018-09-24 3.50 3.44 3.46 3.46 239296460.0 3.460000 0.00 -0.06 0.015597 -0.017283 47.436285
2018-09-26 3.54 3.45 3.47 3.49 367428100.0 3.490000 0.03 0.00 0.016638 -0.016033 50.924753
2018-09-27 3.51 3.46 3.49 3.49 228112298.0 3.490000 0.00 0.00 0.015436 -0.014876 50.924753
2018-09-28 3.52 3.47 3.49 3.48 232063346.0 3.480000 0.00 -0.01 0.014322 -0.014524 49.650887
2018-10-02 3.47 3.38 3.45 3.40 286950375.0 3.400000 0.00 -0.08 0.013290 -0.019245 40.847746
2018-10-03 3.42 3.38 3.38 3.39 137098156.0 3.390000 0.00 -0.01 0.012332 -0.018579 39.895543
2018-10-04 3.38 3.33 3.38 3.33 286550800.0 3.330000 0.00 -0.06 0.011444 -0.021562 34.672910
2018-10-05 3.34 3.31 3.32 3.33 292804313.0 3.330000 0.00 0.00 0.010620 -0.020010 34.672910
2018-10-08 3.37 3.31 3.35 3.31 305245178.0 3.310000 0.00 -0.02 0.009856 -0.020009 33.002736
2018-10-09 3.35 3.30 3.30 3.33 173730676.0 3.330000 0.02 0.00 0.010586 -0.018571 36.306804
2018-10-10 3.38 3.33 3.36 3.34 215849513.0 3.340000 0.01 0.00 0.010544 -0.017236 37.954425
2018-10-11 3.28 3.22 3.24 3.27 595855447.0 3.270000 0.00 -0.07 0.009786 -0.021026 31.760877
2018-10-12 3.33 3.26 3.30 3.31 290519307.0 3.310000 0.04 0.00 0.011956 -0.019516 37.988157
2018-10-15 3.29 3.24 3.29 3.25 266829437.0 3.250000 0.00 -0.06 0.011098 -0.022422 33.107608
2018-10-16 3.28 3.23 3.26 3.25 272678220.0 3.250000 0.00 0.00 0.010301 -0.020813 33.107608
2018-10-18 3.27 3.22 3.27 3.23 241354726.0 3.230000 0.00 -0.02 0.009562 -0.020755 31.541056
2018-10-19 3.31 3.20 3.22 3.28 324848738.0 3.280000 0.05 0.00 0.012462 -0.019267 39.276709
2018-10-22 3.37 3.28 3.28 3.34 309029852.0 3.340000 0.06 0.00 0.015870 -0.017886 47.014077
2018-10-23 3.33 3.23 3.33 3.27 421680917.0 3.270000 0.00 -0.07 0.014732 -0.021620 40.526165
2018-10-24 3.32 3.25 3.28 3.27 286740826.0 3.270000 0.00 0.00 0.013677 -0.020071 40.526165
2018-10-25 3.27 3.22 3.22 3.27 228688814.0 3.270000 0.00 0.00 0.012697 -0.018634 40.526165
2018-10-26 3.28 3.23 3.25 3.26 195858287.0 3.260000 0.00 -0.01 0.011788 -0.018015 39.552287
2018-10-29 3.29 3.25 3.28 3.27 348004378.0 3.270000 0.01 0.00 0.011660 -0.016725 41.077173
2018-10-30 3.35 3.24 3.27 3.30 372317706.0 3.300000 0.03 0.00 0.012973 -0.015528 45.517552
2018-10-31 3.35 3.31 3.32 3.34 239423074.0 3.340000 0.04 0.00 0.014907 -0.014416 50.837351
2018-11-01 3.38 3.34 3.36 3.37 281115874.0 3.370000 0.03 0.00 0.015988 -0.013385 54.431140
问题： 如果“ RSI”低于25，我想为以下14行找到“关闭”的最大值。 例如， 我发现2018-09-12的RSI为21.763243，低于25。 然后，我想找到2018-09-13至2018-10-04之间的最大收盘价（14行）。 最终答案应该是3.52。 我可以在Python中执行此查找功能吗？
更新：
subdf = df.iloc[51:51+14]
它可以自动变为。 例如， 根据公式，我们知道第50和51行低于25。 但是在此阶段，我需要手动输入50和51。 python有什么办法可以帮到我，只需在下面的n中填写50和51：
subdf = df.iloc[n:n+14]
最后，结果将是3.52、3.52。
答案 0 :(得分：0)
首先，您需要添加新索引以获取连续的行号：
df = df.reset_index()
以下是带有RSI<25的行：
df[df['RSI']<25]
Date High Low Open Close Volume Adj Close Gain Loss Avg Gain Avg Loss RSI
50 2018-09-11 3.40 3.36 3.36 3.36 231749076 3.36 0.0 -0.03 0.006552 -0.020400 24.310377
51 2018-09-12 3.36 3.31 3.35 3.32 377546352 3.32 0.0 -0.04 0.006073 -0.021833 21.763243
选择所需行的索引并创建一个新的数据框：
subdf = df.iloc[51:51+14]
然后找到最大值：
subdf['Close'].max()
3.52
或一行：
df.iloc[51:51+14]['Close'].max()