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历年G20大会前一个交易日涨跌幅

你在说什么呢发表于：6 月 25 日 08：44回复(1) 
import numpy as np
import pandas as pd
from jqdata import *

#G20大会前一交易日股票涨幅情况
df = get_price('000001.XSHG',start_date='2008-11-01',end_date='2019-01-31',frequency='daily')

date = ['2008-11-15','2009-04-02','2009-09-24','2010-06-26','2010-11-11','2011-11-03','2012-06-18',
        '2013-09-05','2014-11-15','2015-11-15','2016-09-04','2017-07-07','2018-11-30']
import datetime

def delta(j):
    return datetime.timedelta(days=j)
def strtodate(str1):
    return datetime.datetime.strptime(str1, '%Y-%m-%d').date()
def datetostr(date):
    return datetime.datetime.strftime(date,'%Y-%m-%d')

date_list =[str(i)[:10] for i in list(df.index)]
df1 = pd.DataFrame(columns=df.columns)
for i in date:
    
    j = 1
    
    while datetostr(strtodate(i)-delta(j)) not in date_list:
        
        j = j+1
#     delta = datetime.timedelta(days=j)

#     print(df.loc[datetostr(strtodate(i)-delta(j)),:])
    df1.loc[datetostr(strtodate(i)-delta(j))]=df.loc[datetostr(strtodate(i)-delta(j))]

df1
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
open close high low volume money
2008-11-14 1943.96 1986.44 1987.26 1921.84 1.304395e+10 8.784996e+10
2009-04-01 2380.98 2408.02 2422.63 2380.98 1.798576e+10 1.665421e+11
2009-09-23 2897.81 2842.72 2924.20 2825.86 1.076264e+10 1.167867e+11
2010-06-25 2556.54 2552.82 2572.83 2534.27 5.007885e+09 5.168800e+10
2010-11-10 3120.44 3115.36 3124.26 3093.88 1.850524e+10 2.489743e+11
2011-11-02 2437.87 2504.11 2505.12 2433.72 1.054682e+10 1.020828e+11
2012-06-15 2299.78 2306.85 2314.24 2283.40 6.868758e+09 6.785591e+10
2013-09-04 2121.68 2127.62 2133.00 2115.75 1.420004e+10 1.164574e+11
2014-11-14 2477.97 2478.82 2481.03 2457.05 2.206076e+10 1.919250e+11
2015-11-13 3600.76 3580.84 3632.56 3564.81 3.458709e+10 4.686687e+11
2016-09-02 3057.49 3067.35 3072.53 3050.49 1.504469e+10 1.705921e+11
2017-07-06 3203.86 3212.44 3215.95 3188.77 1.758093e+10 2.022096e+11
2018-11-29 2613.78 2567.44 2617.55 2567.44 1.571687e+10 1.294724e+11 
df1['change'] = (df['close']-df['open'])/df['open']

df1
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
open close high low volume money change
2008-11-14 1943.96 1986.44 1987.26 1921.84 1.304395e+10 8.784996e+10 NaN
2009-04-01 2380.98 2408.02 2422.63 2380.98 1.798576e+10 1.665421e+11 NaN
2009-09-23 2897.81 2842.72 2924.20 2825.86 1.076264e+10 1.167867e+11 NaN
2010-06-25 2556.54 2552.82 2572.83 2534.27 5.007885e+09 5.168800e+10 NaN
2010-11-10 3120.44 3115.36 3124.26 3093.88 1.850524e+10 2.489743e+11 NaN
2011-11-02 2437.87 2504.11 2505.12 2433.72 1.054682e+10 1.020828e+11 NaN
2012-06-15 2299.78 2306.85 2314.24 2283.40 6.868758e+09 6.785591e+10 NaN
2013-09-04 2121.68 2127.62 2133.00 2115.75 1.420004e+10 1.164574e+11 NaN
2014-11-14 2477.97 2478.82 2481.03 2457.05 2.206076e+10 1.919250e+11 NaN
2015-11-13 3600.76 3580.84 3632.56 3564.81 3.458709e+10 4.686687e+11 NaN
2016-09-02 3057.49 3067.35 3072.53 3050.49 1.504469e+10 1.705921e+11 NaN
2017-07-06 3203.86 3212.44 3215.95 3188.77 1.758093e+10 2.022096e+11 NaN
2018-11-29 2613.78 2567.44 2617.55 2567.44 1.571687e+10 1.294724e+11 NaN

       

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