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
df.reset_index(inplace=True)
df
输出：
Date High Low Open Close Volume Adj Close Gain Loss Avg Gain Avg Loss RSI
0 2018-07-03 3.87 3.76 3.83 3.84 684899302.0 3.629538 NaN NaN NaN NaN NaN
1 2018-07-04 3.91 3.84 3.86 3.86 460325574.0 3.648442 0.02 0.00 NaN NaN NaN
2 2018-07-05 3.70 3.62 3.68 3.68 292810499.0 3.680000 0.00 -0.18 NaN NaN NaN
3 2018-07-06 3.72 3.61 3.69 3.67 343653088.0 3.670000 0.00 -0.01 NaN NaN NaN
4 2018-07-09 3.75 3.68 3.70 3.69 424596186.0 3.690000 0.02 0.00 NaN NaN NaN
5 2018-07-10 3.74 3.70 3.71 3.71 327048051.0 3.710000 0.02 0.00 NaN NaN NaN
6 2018-07-11 3.65 3.61 3.63 3.64 371355401.0 3.640000 0.00 -0.07 NaN NaN NaN
7 2018-07-12 3.69 3.63 3.66 3.66 309888328.0 3.660000 0.02 0.00 NaN NaN NaN
8 2018-07-13 3.69 3.62 3.69 3.63 261928758.0 3.630000 0.00 -0.03 NaN NaN NaN
9 2018-07-16 3.63 3.57 3.61 3.62 306970074.0 3.620000 0.00 -0.01 NaN NaN NaN
10 2018-07-17 3.62 3.56 3.62 3.58 310294921.0 3.580000 0.00 -0.04 NaN NaN NaN
11 2018-07-18 3.61 3.55 3.58 3.58 334592695.0 3.580000 0.00 0.00 NaN NaN NaN
12 2018-07-19 3.61 3.56 3.61 3.56 211984563.0 3.560000 0.00 -0.02 NaN NaN NaN
13 2018-07-20 3.64 3.52 3.57 3.61 347506394.0 3.610000 0.05 0.00 NaN NaN NaN
14 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
15 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
16 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
17 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
18 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
19 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
20 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
21 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
22 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
23 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
24 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
25 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
26 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
27 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
28 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
29 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
在这种情况下，我想创建一个新列“ 14个交易日内的最高收盘价”。 “ 14个交易日内的最高收盘价” =接下来14天内的最大“收盘价”。 例如， 在第0行中，数据范围应从第1行到第15行 “ 14个交易日内最高收盘价” = 3.86
答案 0 :(得分：1)
您可以执行以下操作：
# convert to date time
df['Date'] = pd.to_datetime(df['Date'])
# calculate max for 14 days
df['max_close_within_14_trade_days'] = df['Date'].map(df.groupby([pd.Grouper(key='Date', freq='14D')])['Close'].max())
# fill missing values by previous value
df['max_close_within_14_trade_days'].fillna(method='ffill', inplace=True)
Date High Low Open Close max_close_within_14_trade_days
0 0 2018-07-03 3.87 3.76 3.83 3.86
1 1 2018-07-04 3.91 3.84 3.86 3.86
2 2 2018-07-05 3.70 3.62 3.68 3.86
3 3 2018-07-06 3.72 3.61 3.69 3.86
4 4 2018-07-09 3.75 3.68 3.70 3.86
其他解决方案：
df['max_close_within_14_trade_days'] = [df.loc[x+1:x+14,'Close'].max() for x in range(0, df.shape[0])]