二.使用pandas.Resample函数转换日K为周K,日K转月K
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | from jqdatasdk import * import pandas as pd import time auth( '193340***' , '****' ) # 账号是申请时所填写的手机号;密码为聚宽官网登录密码 stocks = list (get_all_securities([ 'stock' ]).index) # for stock_code in stocks: # print("正在股票所有数据:"+stock_code) # df = get_price(stock_code, end_date="2022-05-17 14:00:00", count=5, frequency="1d", fields=['open', 'close', 'high', 'low', 'volume', 'money']) # print(df) # time.sleep(3) df = get_price( "000001.XSHG" , end_date = "2021-05-20 14:00:00" , count = 20 , frequency = "1d" , fields = [ 'open' , 'close' , 'high' , 'low' , 'volume' , 'money' ]) #添加一列数据。星期几,0-6,周一为0 2 df[ 'weekday' ] = df.index.weekday print (df) #转换周k df_week = pd.DataFrame() df_week[ 'open' ] = df[ 'open' ].resample( 'W' ).first() df_week[ 'close' ] = df[ 'close' ].resample( 'W' ).last() df_week[ 'high' ] = df[ 'high' ].resample( 'W' ). max () df_week[ 'low' ] = df[ 'low' ].resample( 'W' ). min () df_week[ 'volume(sum)' ] = df[ 'volume' ].resample( 'W' ). sum () #周汇总成交量 df_week[ 'money(sum)' ] = df[ 'money' ].resample( 'W' ). sum () #周汇总成交金额 print (df_week)<br><br><br><br><br> |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | df = get_price( "000001.XSHG" , end_date = "2021-05-20 14:00:00" , start_date = "2020-05-20 14:00:00" , frequency = "1d" , fields = [ 'open' , 'close' , 'high' , 'low' , 'volume' , 'money' ]) #添加一列数据。星期几,0-6,周一为0 #2021-04-20 3467.15 3472.94 3494.30 ... 2.853001e+10 3.589828e+11 1 #2021-04-21 3456.30 3472.93 3481.25 ... 2.537779e+10 3.239141e+11 2 df[ 'weekday' ] = df.index.weekday print (df) #转换周-W-k,月M-K df_week = pd.DataFrame() df_week[ 'open' ] = df[ 'open' ].resample( 'M' ).first() df_week[ 'close' ] = df[ 'close' ].resample( 'M' ).last() df_week[ 'high' ] = df[ 'high' ].resample( 'M' ). max () df_week[ 'low' ] = df[ 'low' ].resample( 'M' ). min () df_week[ 'volume(sum)' ] = df[ 'volume' ].resample( 'M' ). sum () #月汇总成交量 df_week[ 'money(sum)' ] = df[ 'money' ].resample( 'M' ). sum () #月汇总成交金额 print (df_week) open close high ... volume money weekday 2021 - 04 - 20 3467.15 3472.94 3494.30 ... 2.853001e + 10 3.589828e + 11 1 2021 - 04 - 21 3456.30 3472.93 3481.25 ... 2.537779e + 10 3.239141e + 11 2 2021 - 04 - 22 3482.83 3465.11 3485.36 ... 2.511145e + 10 3.170810e + 11 3 2021 - 04 - 23 3462.09 3474.17 3482.36 ... 2.493604e + 10 3.230159e + 11 4 2021 - 04 - 26 3484.11 3441.17 3497.12 ... 2.769708e + 10 3.881617e + 11 0 2021 - 04 - 27 3440.09 3442.61 3443.85 ... 2.530324e + 10 3.270460e + 11 1 2021 - 04 - 28 3432.16 3457.07 3457.07 ... 2.473811e + 10 3.496692e + 11 2 2021 - 04 - 29 3458.08 3474.90 3478.23 ... 2.766314e + 10 3.716116e + 11 3 2021 - 04 - 30 3468.30 3446.86 3469.09 ... 3.126604e + 10 4.014423e + 11 4 2021 - 05 - 06 3446.07 3441.28 3471.24 ... 3.104242e + 10 4.005252e + 11 3 2021 - 05 - 07 3446.41 3418.87 3457.89 ... 3.537854e + 10 4.110788e + 11 4 2021 - 05 - 10 3423.59 3427.99 3429.74 ... 3.741709e + 10 3.997175e + 11 0 2021 - 05 - 11 3406.60 3441.85 3448.10 ... 3.509341e + 10 3.903263e + 11 1 2021 - 05 - 12 3429.75 3462.75 3466.37 ... 3.114445e + 10 3.438604e + 11 2 2021 - 05 - 13 3432.14 3429.54 3448.02 ... 3.193253e + 10 3.563769e + 11 3 2021 - 05 - 14 3436.09 3490.38 3490.64 ... 3.369823e + 10 4.111169e + 11 4 2021 - 05 - 17 3490.41 3517.62 3530.51 ... 3.221360e + 10 4.244768e + 11 0 2021 - 05 - 18 3520.65 3529.01 3529.01 ... 2.713086e + 10 3.360288e + 11 1 2021 - 05 - 19 3521.11 3510.96 3521.11 ... 2.783211e + 10 3.499732e + 11 2 2021 - 05 - 20 3500.88 3506.94 3517.74 ... 3.260093e + 10 3.903553e + 11 3 |
open close high low volume(sum) money(sum)
2020-05-31 2896.47 2852.35 2896.47 2802.47 1.583052e+11 1.875487e+12
2020-06-30 2871.96 2984.67 2990.83 2871.96 4.699820e+11 5.733483e+12
2020-07-31 2991.18 3310.01 3458.79 2984.98 1.024500e+12 1.324089e+13
2020-08-31 3332.18 3395.68 3456.72 3263.27 7.359126e+11 9.621175e+12
2020-09-30 3389.74 3218.05 3425.63 3202.34 4.906229e+11 6.193725e+12
2020-10-31 3262.61 3224.53 3371.09 3219.42 3.036961e+11 4.268415e+12
2020-11-30 3228.72 3391.76 3456.74 3209.91 5.680577e+11 7.256434e+12
2020-12-31 3388.99 3473.07 3474.92 3325.17 6.445146e+11 8.351828e+12
2021-01-31 3474.68 3483.07 3637.10 3446.55 6.639624e+11 9.659809e+12
2021-02-28 3477.17 3509.08 3731.69 3465.77 4.840738e+11 6.510009e+12
2021-03-31 3531.48 3441.91 3577.62 3328.31 7.440631e+11 8.402503e+12
2021-04-30 3444.81 3446.86 3497.12 3373.09 5.695268e+11 6.871377e+12
2021-05-31 3446.07 3506.94 3530.51 3384.70 3.554842e+11 4.213836e+12
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