import tushare as ts
import pandas as pd
from pandas import DataFrame,Series
DataFrame
- 索引:
- df[col] df[[c1,c2]]:取列
- df.loc[index] : 取行
- df.loc[index,col] : 取元素
- 切片:
- df[a:b]:切行
- df.loc[:,a:b]:切列
- df运算:Series运算一致
- df级联:拼接In [8]:
df = pd.read_csv('maotai.csv',index_col='date',parse_dates=['date'])
df.drop(labels='Unnamed: 0',axis=1,inplace=True)
df
Out[8]:
| open | close | high | low | volume | code | |
|---|---|---|---|---|---|---|
| date | ||||||
| 2001-08-27 | 5.392 | 5.554 | 5.902 | 5.132 | 406318.00 | 600519 |
| 2001-08-28 | 5.467 | 5.759 | 5.781 | 5.407 | 129647.79 | 600519 |
| 2001-08-29 | 5.777 | 5.684 | 5.781 | 5.640 | 53252.75 | 600519 |
| 2001-08-30 | 5.668 | 5.796 | 5.860 | 5.624 | 48013.06 | 600519 |
| 2001-08-31 | 5.804 | 5.782 | 5.877 | 5.749 | 23231.48 | 600519 |
| 2001-09-03 | 5.812 | 5.779 | 5.870 | 5.757 | 22112.09 | 600519 |
| 2001-09-04 | 5.782 | 5.852 | 5.949 | 5.762 | 37006.77 | 600519 |
| 2001-09-05 | 5.876 | 5.849 | 5.924 | 5.813 | 26066.95 | 600519 |
| 2001-09-06 | 5.835 | 5.734 | 5.854 | 5.704 | 28997.03 | 600519 |
| 2001-09-07 | 5.702 | 5.574 | 5.773 | 5.570 | 31552.25 | 600519 |
| 2001-09-10 | 5.531 | 5.734 | 5.757 | 5.470 | 18878.89 | 600519 |
| 2001-09-11 | 5.749 | 5.670 | 5.773 | 5.656 | 11390.81 | 600519 |
| 2001-09-12 | 5.520 | 5.621 | 5.656 | 5.515 | 25045.19 | 600519 |
| 2001-09-13 | 5.626 | 5.607 | 5.671 | 5.577 | 10986.68 | 600519 |
| 2001-09-14 | 5.626 | 5.671 | 5.702 | 5.593 | 7672.83 | 600519 |
| 2001-09-17 | 5.637 | 5.599 | 5.670 | 5.546 | 8983.97 | 600519 |
| 2001-09-18 | 5.606 | 5.663 | 5.710 | 5.601 | 10773.26 | 600519 |
| 2001-09-19 | 5.671 | 5.768 | 5.768 | 5.634 | 8650.53 | 600519 |
| 2001-09-20 | 5.765 | 5.720 | 5.788 | 5.702 | 11173.35 | 600519 |
| 2001-09-21 | 5.668 | 5.634 | 5.718 | 5.624 | 7879.72 | 600519 |
| 2001-09-24 | 5.634 | 5.632 | 5.685 | 5.624 | 4068.60 | 600519 |
| 2001-09-25 | 5.668 | 5.660 | 5.709 | 5.632 | 3488.45 | 600519 |
| 2001-09-26 | 5.642 | 5.637 | 5.699 | 5.624 | 4956.26 | 600519 |
| 2001-09-27 | 5.637 | 5.734 | 5.777 | 5.624 | 8778.04 | 600519 |
| 2001-09-28 | 5.765 | 5.795 | 5.812 | 5.702 | 17088.47 | 600519 |
| 2001-10-08 | 5.781 | 5.715 | 5.809 | 5.663 | 6552.17 | 600519 |
| 2001-10-09 | 5.718 | 5.827 | 5.859 | 5.718 | 9558.52 | 600519 |
| 2001-10-10 | 5.827 | 5.640 | 5.848 | 5.629 | 17548.69 | 600519 |
| 2001-10-11 | 5.626 | 5.585 | 5.702 | 5.570 | 12306.84 | 600519 |
| 2001-10-12 | 5.609 | 5.624 | 5.726 | 5.320 | 20010.70 | 600519 |
| ... | ... | ... | ... | ... | ... | ... |
| 2019-01-16 | 658.000 | 661.100 | 665.650 | 653.600 | 42083.00 | 600519 |
| 2019-01-17 | 663.000 | 659.250 | 669.700 | 655.800 | 30284.00 | 600519 |
| 2019-01-18 | 665.000 | 683.610 | 690.200 | 661.500 | 68609.00 | 600519 |
| 2019-01-21 | 690.000 | 686.980 | 698.880 | 681.880 | 47051.00 | 600519 |
| 2019-01-22 | 686.000 | 671.000 | 686.000 | 665.220 | 42122.00 | 600519 |
| 2019-01-23 | 667.220 | 665.660 | 674.500 | 663.130 | 33624.00 | 600519 |
| 2019-01-24 | 670.300 | 672.500 | 677.830 | 664.680 | 29155.00 | 600519 |
| 2019-01-25 | 675.880 | 679.700 | 684.900 | 669.930 | 28986.00 | 600519 |
| 2019-01-28 | 684.000 | 677.000 | 693.980 | 676.030 | 36102.00 | 600519 |
| 2019-01-29 | 677.500 | 685.000 | 688.000 | 668.000 | 30342.00 | 600519 |
| 2019-01-30 | 684.990 | 676.000 | 686.220 | 676.000 | 22699.00 | 600519 |
| 2019-01-31 | 680.000 | 689.600 | 689.600 | 677.560 | 36696.00 | 600519 |
| 2019-02-01 | 697.040 | 692.670 | 699.000 | 689.610 | 30520.00 | 600519 |
| 2019-02-11 | 695.000 | 725.300 | 729.500 | 688.180 | 58140.00 | 600519 |
| 2019-02-12 | 723.000 | 717.920 | 725.490 | 715.200 | 35091.00 | 600519 |
| 2019-02-13 | 723.000 | 735.250 | 736.160 | 717.220 | 35677.00 | 600519 |
| 2019-02-14 | 731.200 | 737.160 | 743.660 | 730.140 | 32258.00 | 600519 |
| 2019-02-15 | 734.880 | 724.780 | 734.880 | 723.020 | 23237.00 | 600519 |
| 2019-02-18 | 731.000 | 740.000 | 744.950 | 731.000 | 37745.00 | 600519 |
| 2019-02-19 | 740.960 | 730.760 | 742.500 | 726.660 | 29501.00 | 600519 |
| 2019-02-20 | 735.000 | 722.000 | 735.000 | 716.060 | 37882.00 | 600519 |
| 2019-02-21 | 720.000 | 720.250 | 730.480 | 717.000 | 29850.00 | 600519 |
| 2019-02-22 | 717.000 | 726.010 | 728.490 | 714.100 | 25908.00 | 600519 |
| 2019-02-25 | 730.750 | 742.330 | 744.440 | 717.990 | 82622.00 | 600519 |
| 2019-02-26 | 747.800 | 727.350 | 749.180 | 726.000 | 57964.00 | 600519 |
| 2019-02-27 | 733.000 | 740.700 | 758.020 | 731.000 | 65942.00 | 600519 |
| 2019-02-28 | 737.900 | 755.010 | 763.420 | 736.950 | 56986.00 | 600519 |
| 2019-03-01 | 761.500 | 789.300 | 790.000 | 761.000 | 63840.00 | 600519 |
| 2019-03-04 | 795.000 | 781.860 | 798.330 | 777.220 | 84585.00 | 600519 |
| 2019-03-05 | 785.000 | 779.780 | 789.550 | 775.880 | 44830.00 | 600519 |
4174 rows × 6 columns
In [9]:
#假如我从2010年1月1日开始，每月第一个交易日买入1手股票，每年最后一个交易日卖出所有股票，到今天为止，我的收益如何？
price_last = df['open'][-1]
df = df['2010':'2019'] #剔除首尾无用的数据
#Pandas提供了resample函数用便捷的方式对时间序列进行重采样，根据时间粒度的变大或者变小分为降采样和升采样：
df_monthly = df.resample("M").first()
df_yearly = df.resample("Y").last()[:-1] #去除最后一年
cost_money = 0
hold = 0 #每年持有的股票
for year in range(2010, 2020):
cost_money -= df_monthly.loc[str(year)]['open'].sum()*100
hold += len(df_monthly[str(year)]['open']) * 100
if year != 2019:
cost_money += df_yearly[str(year)]['open'][0] * hold
hold = 0 #每年持有的股票
cost_money += hold * price_last
print(cost_money)
310250.69999999984
In [7]:
Out[7]:
| open | close | high | low | volume | code | |
|---|---|---|---|---|---|---|
| date | ||||||
| 2010-01-04 | 109.760 | 108.446 | 109.760 | 108.044 | 44304.88 | 600519 |
| 2010-01-05 | 109.116 | 108.127 | 109.441 | 107.846 | 31513.18 | 600519 |
| 2010-01-06 | 107.840 | 106.417 | 108.165 | 106.129 | 39889.03 | 600519 |
| 2010-01-07 | 106.417 | 104.477 | 106.691 | 103.302 | 48825.55 | 600519 |
| 2010-01-08 | 104.655 | 103.379 | 104.655 | 102.167 | 36702.09 | 600519 |
| 2010-01-11 | 104.400 | 102.926 | 105.230 | 102.422 | 24461.03 | 600519 |
| 2010-01-12 | 103.028 | 105.708 | 106.040 | 102.492 | 31063.40 | 600519 |
| 2010-01-13 | 104.649 | 103.022 | 105.389 | 102.741 | 37924.44 | 600519 |
| 2010-01-14 | 103.379 | 107.552 | 107.974 | 103.379 | 46454.64 | 600519 |
| 2010-01-15 | 107.533 | 108.401 | 110.641 | 107.533 | 45938.50 | 600519 |
| 2010-01-18 | 108.484 | 109.110 | 109.926 | 108.420 | 21461.53 | 600519 |
| 2010-01-19 | 109.116 | 108.337 | 109.441 | 108.165 | 17818.91 | 600519 |
| 2010-01-20 | 108.427 | 105.881 | 108.580 | 105.804 | 20972.95 | 600519 |
| 2010-01-21 | 105.842 | 106.397 | 107.450 | 105.613 | 17257.48 | 600519 |
| 2010-01-22 | 106.314 | 104.738 | 106.755 | 103.309 | 25432.94 | 600519 |
| 2010-01-25 | 104.560 | 105.957 | 106.761 | 103.704 | 23239.15 | 600519 |
| 2010-01-26 | 107.380 | 106.378 | 108.593 | 105.038 | 32889.16 | 600519 |
| 2010-01-27 | 105.951 | 104.643 | 107.068 | 104.400 | 19316.57 | 600519 |
| 2010-01-28 | 104.566 | 107.974 | 108.708 | 104.336 | 30267.52 | 600519 |
| 2010-01-29 | 108.452 | 107.552 | 108.612 | 107.004 | 37172.82 | 600519 |
| 2010-02-01 | 107.769 | 107.776 | 108.216 | 106.576 | 29655.94 | 600519 |
| 2010-02-02 | 107.208 | 106.263 | 108.484 | 106.117 | 15493.53 | 600519 |
| 2010-02-03 | 106.066 | 105.887 | 107.272 | 104.783 | 23034.65 | 600519 |
| 2010-02-04 | 105.868 | 107.591 | 108.006 | 105.376 | 22475.33 | 600519 |
| 2010-02-05 | 106.959 | 109.282 | 109.684 | 106.570 | 26234.30 | 600519 |
| 2010-02-08 | 109.282 | 109.269 | 112.058 | 108.816 | 31496.10 | 600519 |
| 2010-02-09 | 109.760 | 109.193 | 110.609 | 108.822 | 14151.24 | 600519 |
| 2010-02-10 | 109.760 | 109.652 | 110.137 | 108.931 | 6398.14 | 600519 |
| 2010-02-11 | 109.633 | 110.641 | 111.318 | 109.505 | 14945.05 | 600519 |
| 2010-02-12 | 111.018 | 110.456 | 111.164 | 109.888 | 9346.40 | 600519 |
| ... | ... | ... | ... | ... | ... | ... |
| 2019-01-16 | 658.000 | 661.100 | 665.650 | 653.600 | 42083.00 | 600519 |
| 2019-01-17 | 663.000 | 659.250 | 669.700 | 655.800 | 30284.00 | 600519 |
| 2019-01-18 | 665.000 | 683.610 | 690.200 | 661.500 | 68609.00 | 600519 |
| 2019-01-21 | 690.000 | 686.980 | 698.880 | 681.880 | 47051.00 | 600519 |
| 2019-01-22 | 686.000 | 671.000 | 686.000 | 665.220 | 42122.00 | 600519 |
| 2019-01-23 | 667.220 | 665.660 | 674.500 | 663.130 | 33624.00 | 600519 |
| 2019-01-24 | 670.300 | 672.500 | 677.830 | 664.680 | 29155.00 | 600519 |
| 2019-01-25 | 675.880 | 679.700 | 684.900 | 669.930 | 28986.00 | 600519 |
| 2019-01-28 | 684.000 | 677.000 | 693.980 | 676.030 | 36102.00 | 600519 |
| 2019-01-29 | 677.500 | 685.000 | 688.000 | 668.000 | 30342.00 | 600519 |
| 2019-01-30 | 684.990 | 676.000 | 686.220 | 676.000 | 22699.00 | 600519 |
| 2019-01-31 | 680.000 | 689.600 | 689.600 | 677.560 | 36696.00 | 600519 |
| 2019-02-01 | 697.040 | 692.670 | 699.000 | 689.610 | 30520.00 | 600519 |
| 2019-02-11 | 695.000 | 725.300 | 729.500 | 688.180 | 58140.00 | 600519 |
| 2019-02-12 | 723.000 | 717.920 | 725.490 | 715.200 | 35091.00 | 600519 |
| 2019-02-13 | 723.000 | 735.250 | 736.160 | 717.220 | 35677.00 | 600519 |
| 2019-02-14 | 731.200 | 737.160 | 743.660 | 730.140 | 32258.00 | 600519 |
| 2019-02-15 | 734.880 | 724.780 | 734.880 | 723.020 | 23237.00 | 600519 |
| 2019-02-18 | 731.000 | 740.000 | 744.950 | 731.000 | 37745.00 | 600519 |
| 2019-02-19 | 740.960 | 730.760 | 742.500 | 726.660 | 29501.00 | 600519 |
| 2019-02-20 | 735.000 | 722.000 | 735.000 | 716.060 | 37882.00 | 600519 |
| 2019-02-21 | 720.000 | 720.250 | 730.480 | 717.000 | 29850.00 | 600519 |
| 2019-02-22 | 717.000 | 726.010 | 728.490 | 714.100 | 25908.00 | 600519 |
| 2019-02-25 | 730.750 | 742.330 | 744.440 | 717.990 | 82622.00 | 600519 |
| 2019-02-26 | 747.800 | 727.350 | 749.180 | 726.000 | 57964.00 | 600519 |
| 2019-02-27 | 733.000 | 740.700 | 758.020 | 731.000 | 65942.00 | 600519 |
| 2019-02-28 | 737.900 | 755.010 | 763.420 | 736.950 | 56986.00 | 600519 |
| 2019-03-01 | 761.500 | 789.300 | 790.000 | 761.000 | 63840.00 | 600519 |
| 2019-03-04 | 795.000 | 781.860 | 798.330 | 777.220 | 84585.00 | 600519 |
| 2019-03-05 | 785.000 | 779.780 | 789.550 | 775.880 | 44830.00 | 600519 |
2221 rows × 6 columns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tushare as ts
使用tushare包获取某股票的历史行情数据
In [14]:
df = ts.get_k_data("600519",start="1988-01-01")
df.to_csv("600519.csv")
df
Out[14]:
| date | open | close | high | low | volume | code | |
|---|---|---|---|---|---|---|---|
| 0 | 2001-08-27 | 5.392 | 5.554 | 5.902 | 5.132 | 406318.00 | 600519 |
| 1 | 2001-08-28 | 5.467 | 5.759 | 5.781 | 5.407 | 129647.79 | 600519 |
| 2 | 2001-08-29 | 5.777 | 5.684 | 5.781 | 5.640 | 53252.75 | 600519 |
| 3 | 2001-08-30 | 5.668 | 5.796 | 5.860 | 5.624 | 48013.06 | 600519 |
| 4 | 2001-08-31 | 5.804 | 5.782 | 5.877 | 5.749 | 23231.48 | 600519 |
| 5 | 2001-09-03 | 5.812 | 5.779 | 5.870 | 5.757 | 22112.09 | 600519 |
| 6 | 2001-09-04 | 5.782 | 5.852 | 5.949 | 5.762 | 37006.77 | 600519 |
| 7 | 2001-09-05 | 5.876 | 5.849 | 5.924 | 5.813 | 26066.95 | 600519 |
| 8 | 2001-09-06 | 5.835 | 5.734 | 5.854 | 5.704 | 28997.03 | 600519 |
| 9 | 2001-09-07 | 5.702 | 5.574 | 5.773 | 5.570 | 31552.25 | 600519 |
| 10 | 2001-09-10 | 5.531 | 5.734 | 5.757 | 5.470 | 18878.89 | 600519 |
| 11 | 2001-09-11 | 5.749 | 5.670 | 5.773 | 5.656 | 11390.81 | 600519 |
| 12 | 2001-09-12 | 5.520 | 5.621 | 5.656 | 5.515 | 25045.19 | 600519 |
| 13 | 2001-09-13 | 5.626 | 5.607 | 5.671 | 5.577 | 10986.68 | 600519 |
| 14 | 2001-09-14 | 5.626 | 5.671 | 5.702 | 5.593 | 7672.83 | 600519 |
| 15 | 2001-09-17 | 5.637 | 5.599 | 5.670 | 5.546 | 8983.97 | 600519 |
| 16 | 2001-09-18 | 5.606 | 5.663 | 5.710 | 5.601 | 10773.26 | 600519 |
| 17 | 2001-09-19 | 5.671 | 5.768 | 5.768 | 5.634 | 8650.53 | 600519 |
| 18 | 2001-09-20 | 5.765 | 5.720 | 5.788 | 5.702 | 11173.35 | 600519 |
| 19 | 2001-09-21 | 5.668 | 5.634 | 5.718 | 5.624 | 7879.72 | 600519 |
| 20 | 2001-09-24 | 5.634 | 5.632 | 5.685 | 5.624 | 4068.60 | 600519 |
| 21 | 2001-09-25 | 5.668 | 5.660 | 5.709 | 5.632 | 3488.45 | 600519 |
| 22 | 2001-09-26 | 5.642 | 5.637 | 5.699 | 5.624 | 4956.26 | 600519 |
| 23 | 2001-09-27 | 5.637 | 5.734 | 5.777 | 5.624 | 8778.04 | 600519 |
| 24 | 2001-09-28 | 5.765 | 5.795 | 5.812 | 5.702 | 17088.47 | 600519 |
| 25 | 2001-10-08 | 5.781 | 5.715 | 5.809 | 5.663 | 6552.17 | 600519 |
| 26 | 2001-10-09 | 5.718 | 5.827 | 5.859 | 5.718 | 9558.52 | 600519 |
| 27 | 2001-10-10 | 5.827 | 5.640 | 5.848 | 5.629 | 17548.69 | 600519 |
| 28 | 2001-10-11 | 5.626 | 5.585 | 5.702 | 5.570 | 12306.84 | 600519 |
| 29 | 2001-10-12 | 5.609 | 5.624 | 5.726 | 5.320 | 20010.70 | 600519 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 4118 | 2018-12-07 | 595.600 | 587.000 | 598.860 | 584.700 | 26841.00 | 600519 |
| 4119 | 2018-12-10 | 576.000 | 577.230 | 587.500 | 574.180 | 24640.00 | 600519 |
| 4120 | 2018-12-11 | 577.000 | 582.990 | 586.550 | 577.000 | 16316.00 | 600519 |
| 4121 | 2018-12-12 | 588.790 | 589.000 | 589.200 | 582.800 | 19921.00 | 600519 |
| 4122 | 2018-12-13 | 590.000 | 601.750 | 606.880 | 589.600 | 40399.00 | 600519 |
| 4123 | 2018-12-14 | 598.010 | 592.960 | 599.800 | 592.000 | 24554.00 | 600519 |
| 4124 | 2018-12-17 | 592.570 | 590.520 | 595.970 | 585.010 | 24895.00 | 600519 |
| 4125 | 2018-12-18 | 585.880 | 590.940 | 591.540 | 585.000 | 19222.00 | 600519 |
| 4126 | 2018-12-19 | 589.000 | 577.800 | 590.990 | 576.030 | 25715.00 | 600519 |
| 4127 | 2018-12-20 | 576.220 | 574.500 | 581.500 | 568.890 | 26940.00 | 600519 |
| 4128 | 2018-12-21 | 571.000 | 567.410 | 573.450 | 562.210 | 30640.00 | 600519 |
| 4129 | 2018-12-24 | 563.500 | 568.000 | 570.910 | 558.000 | 19109.00 | 600519 |
| 4130 | 2018-12-25 | 562.220 | 565.790 | 567.500 | 553.610 | 26592.00 | 600519 |
| 4131 | 2018-12-26 | 563.810 | 560.080 | 568.400 | 558.000 | 17866.00 | 600519 |
| 4132 | 2018-12-27 | 570.000 | 563.000 | 571.850 | 562.220 | 29493.00 | 600519 |
| 4133 | 2018-12-28 | 563.300 | 590.010 | 596.400 | 560.000 | 63678.00 | 600519 |
| 4134 | 2019-01-02 | 609.980 | 598.980 | 612.000 | 595.010 | 62286.00 | 600519 |
| 4135 | 2019-01-03 | 599.970 | 590.000 | 601.660 | 585.800 | 30977.00 | 600519 |
| 4136 | 2019-01-04 | 587.280 | 602.000 | 607.670 | 582.020 | 37683.00 | 600519 |
| 4137 | 2019-01-07 | 608.000 | 605.490 | 612.000 | 602.220 | 34750.00 | 600519 |
| 4138 | 2019-01-08 | 605.500 | 604.790 | 612.000 | 600.280 | 28838.00 | 600519 |
| 4139 | 2019-01-09 | 609.990 | 616.120 | 626.160 | 609.040 | 49892.00 | 600519 |
| 4140 | 2019-01-10 | 617.130 | 618.770 | 624.880 | 610.250 | 29043.00 | 600519 |
| 4141 | 2019-01-11 | 625.000 | 635.880 | 637.000 | 618.750 | 38884.00 | 600519 |
| 4142 | 2019-01-14 | 633.000 | 624.600 | 635.890 | 621.550 | 25648.00 | 600519 |
| 4143 | 2019-01-15 | 627.860 | 659.980 | 661.500 | 625.000 | 55764.00 | 600519 |
| 4144 | 2019-01-16 | 658.000 | 661.100 | 665.650 | 653.600 | 42083.00 | 600519 |
| 4145 | 2019-01-17 | 663.000 | 659.250 | 669.700 | 655.800 | 30284.00 | 600519 |
| 4146 | 2019-01-18 | 665.000 | 683.610 | 690.200 | 661.500 | 68609.00 | 600519 |
| 4147 | 2019-01-21 | 690.000 | 686.980 | 698.880 | 681.880 | 47051.00 | 600519 |
4148 rows × 7 columns
In [15]:
df.head()
Out[15]:
| date | open | close | high | low | volume | code | |
|---|---|---|---|---|---|---|---|
| 0 | 2001-08-27 | 5.392 | 5.554 | 5.902 | 5.132 | 406318.00 | 600519 |
| 1 | 2001-08-28 | 5.467 | 5.759 | 5.781 | 5.407 | 129647.79 | 600519 |
| 2 | 2001-08-29 | 5.777 | 5.684 | 5.781 | 5.640 | 53252.75 | 600519 |
| 3 | 2001-08-30 | 5.668 | 5.796 | 5.860 | 5.624 | 48013.06 | 600519 |
| 4 | 2001-08-31 | 5.804 | 5.782 | 5.877 | 5.749 | 23231.48 | 600519 |
In [20]:
df = pd.read_csv("600519.csv", index_col='date',parse_dates=['date'])[['open','close','high','low']]
df.head()
Out[20]:
| open | close | high | low | |
|---|---|---|---|---|
| date | ||||
| 2001-08-27 | 5.392 | 5.554 | 5.902 | 5.132 |
| 2001-08-28 | 5.467 | 5.759 | 5.781 | 5.407 |
| 2001-08-29 | 5.777 | 5.684 | 5.781 | 5.640 |
| 2001-08-30 | 5.668 | 5.796 | 5.860 | 5.624 |
| 2001-08-31 | 5.804 | 5.782 | 5.877 | 5.749 |
输出该股票所有收盘比开盘上涨3%以上的日期
In [19]:
df[(df['close']-df['open'])/df['open']>=0.03].index
Out[19]:
DatetimeIndex(['2001-08-27', '2001-08-28', '2001-09-10', '2001-12-21',
'2002-01-18', '2002-01-31', '2003-01-14', '2003-10-29',
'2004-01-05', '2004-01-14',
...
'2018-06-20', '2018-08-09', '2018-08-21', '2018-08-27',
'2018-09-18', '2018-09-26', '2018-10-19', '2018-10-31',
'2018-11-13', '2018-12-28'],
dtype='datetime64[ns]', name='date', length=291, freq=None)
输出该股票所有开盘比前日收盘跌幅超过2%的日期
In [20]:
df[(df['open']-df['close'].shift(1))/df['close'].shift(1)<=-0.02].index
Out[20]:
DatetimeIndex(['2001-09-12', '2002-06-26', '2002-12-13', '2004-07-01',
'2004-10-29', '2006-08-21', '2006-08-23', '2007-01-25',
'2007-02-01', '2007-02-06', '2007-03-19', '2007-05-21',
'2007-05-30', '2007-06-05', '2007-07-27', '2007-09-05',
'2007-09-10', '2008-03-13', '2008-03-17', '2008-03-25',
'2008-03-27', '2008-04-22', '2008-04-23', '2008-04-29',
'2008-05-13', '2008-06-10', '2008-06-13', '2008-06-24',
'2008-06-27', '2008-08-11', '2008-08-19', '2008-09-23',
'2008-10-10', '2008-10-15', '2008-10-16', '2008-10-20',
'2008-10-23', '2008-10-27', '2008-11-06', '2008-11-12',
'2008-11-20', '2008-11-21', '2008-12-02', '2009-02-27',
'2009-03-25', '2009-08-13', '2010-04-26', '2010-04-30',
'2011-08-05', '2012-03-27', '2012-08-10', '2012-11-22',
'2012-12-04', '2012-12-24', '2013-01-16', '2013-01-25',
'2013-09-02', '2014-04-25', '2015-01-19', '2015-05-25',
'2015-07-03', '2015-07-08', '2015-07-13', '2015-08-24',
'2015-09-02', '2015-09-15', '2017-11-17', '2018-02-06',
'2018-02-09', '2018-03-23', '2018-03-28', '2018-07-11',
'2018-10-11', '2018-10-24', '2018-10-25', '2018-10-29',
'2018-10-30'],
dtype='datetime64[ns]', name='date', freq=None)
假如我从2010年1月1日开始，每月第一个交易日买入1手股票，每年最后一个交易日卖出所有股票，到今天为止，我的收益如何？
In [21]:
df['open'][-1]
Out[21]:
690.0
In [22]:
price_last = df['open'][-1]
df = df['2010-01':'2019-01'] #剔除首尾无用的数据
df
Out[22]:
| open | close | high | low | |
|---|---|---|---|---|
| date | ||||
| 2010-01-04 | 109.760 | 108.446 | 109.760 | 108.044 |
| 2010-01-05 | 109.116 | 108.127 | 109.441 | 107.846 |
| 2010-01-06 | 107.840 | 106.417 | 108.165 | 106.129 |
| 2010-01-07 | 106.417 | 104.477 | 106.691 | 103.302 |
| 2010-01-08 | 104.655 | 103.379 | 104.655 | 102.167 |
| 2010-01-11 | 104.400 | 102.926 | 105.230 | 102.422 |
| 2010-01-12 | 103.028 | 105.708 | 106.040 | 102.492 |
| 2010-01-13 | 104.649 | 103.022 | 105.389 | 102.741 |
| 2010-01-14 | 103.379 | 107.552 | 107.974 | 103.379 |
| 2010-01-15 | 107.533 | 108.401 | 110.641 | 107.533 |
| 2010-01-18 | 108.484 | 109.110 | 109.926 | 108.420 |
| 2010-01-19 | 109.116 | 108.337 | 109.441 | 108.165 |
| 2010-01-20 | 108.427 | 105.881 | 108.580 | 105.804 |
| 2010-01-21 | 105.842 | 106.397 | 107.450 | 105.613 |
| 2010-01-22 | 106.314 | 104.738 | 106.755 | 103.309 |
| 2010-01-25 | 104.560 | 105.957 | 106.761 | 103.704 |
| 2010-01-26 | 107.380 | 106.378 | 108.593 | 105.038 |
| 2010-01-27 | 105.951 | 104.643 | 107.068 | 104.400 |
| 2010-01-28 | 104.566 | 107.974 | 108.708 | 104.336 |
| 2010-01-29 | 108.452 | 107.552 | 108.612 | 107.004 |
| 2010-02-01 | 107.769 | 107.776 | 108.216 | 106.576 |
| 2010-02-02 | 107.208 | 106.263 | 108.484 | 106.117 |
| 2010-02-03 | 106.066 | 105.887 | 107.272 | 104.783 |
| 2010-02-04 | 105.868 | 107.591 | 108.006 | 105.376 |
| 2010-02-05 | 106.959 | 109.282 | 109.684 | 106.570 |
| 2010-02-08 | 109.282 | 109.269 | 112.058 | 108.816 |
| 2010-02-09 | 109.760 | 109.193 | 110.609 | 108.822 |
| 2010-02-10 | 109.760 | 109.652 | 110.137 | 108.931 |
| 2010-02-11 | 109.633 | 110.641 | 111.318 | 109.505 |
| 2010-02-12 | 111.018 | 110.456 | 111.164 | 109.888 |
| ... | ... | ... | ... | ... |
| 2018-12-07 | 595.600 | 587.000 | 598.860 | 584.700 |
| 2018-12-10 | 576.000 | 577.230 | 587.500 | 574.180 |
| 2018-12-11 | 577.000 | 582.990 | 586.550 | 577.000 |
| 2018-12-12 | 588.790 | 589.000 | 589.200 | 582.800 |
| 2018-12-13 | 590.000 | 601.750 | 606.880 | 589.600 |
| 2018-12-14 | 598.010 | 592.960 | 599.800 | 592.000 |
| 2018-12-17 | 592.570 | 590.520 | 595.970 | 585.010 |
| 2018-12-18 | 585.880 | 590.940 | 591.540 | 585.000 |
| 2018-12-19 | 589.000 | 577.800 | 590.990 | 576.030 |
| 2018-12-20 | 576.220 | 574.500 | 581.500 | 568.890 |
| 2018-12-21 | 571.000 | 567.410 | 573.450 | 562.210 |
| 2018-12-24 | 563.500 | 568.000 | 570.910 | 558.000 |
| 2018-12-25 | 562.220 | 565.790 | 567.500 | 553.610 |
| 2018-12-26 | 563.810 | 560.080 | 568.400 | 558.000 |
| 2018-12-27 | 570.000 | 563.000 | 571.850 | 562.220 |
| 2018-12-28 | 563.300 | 590.010 | 596.400 | 560.000 |
| 2019-01-02 | 609.980 | 598.980 | 612.000 | 595.010 |
| 2019-01-03 | 599.970 | 590.000 | 601.660 | 585.800 |
| 2019-01-04 | 587.280 | 602.000 | 607.670 | 582.020 |
| 2019-01-07 | 608.000 | 605.490 | 612.000 | 602.220 |
| 2019-01-08 | 605.500 | 604.790 | 612.000 | 600.280 |
| 2019-01-09 | 609.990 | 616.120 | 626.160 | 609.040 |
| 2019-01-10 | 617.130 | 618.770 | 624.880 | 610.250 |
| 2019-01-11 | 625.000 | 635.880 | 637.000 | 618.750 |
| 2019-01-14 | 633.000 | 624.600 | 635.890 | 621.550 |
| 2019-01-15 | 627.860 | 659.980 | 661.500 | 625.000 |
| 2019-01-16 | 658.000 | 661.100 | 665.650 | 653.600 |
| 2019-01-17 | 663.000 | 659.250 | 669.700 | 655.800 |
| 2019-01-18 | 665.000 | 683.610 | 690.200 | 661.500 |
| 2019-01-21 | 690.000 | 686.980 | 698.880 | 681.880 |
2195 rows × 4 columns
In [28]:
df_monthly = df.resample("M").first()
df_monthly.head()
Out[28]:
| open | close | high | low | |
|---|---|---|---|---|
| date | ||||
| 2010-01-31 | 109.760 | 108.446 | 109.760 | 108.044 |
| 2010-02-28 | 107.769 | 107.776 | 108.216 | 106.576 |
| 2010-03-31 | 106.219 | 106.085 | 106.857 | 105.925 |
| 2010-04-30 | 101.324 | 102.141 | 102.422 | 101.311 |
| 2010-05-31 | 81.676 | 82.091 | 82.678 | 80.974 |
In [29]:
df_yearly = df.resample("A").last()[:-1] #去除最后一年
df_yearly
Out[29]:
| open | close | high | low | |
|---|---|---|---|---|
| date | ||||
| 2010-12-31 | 117.103 | 118.469 | 118.701 | 116.620 |
| 2011-12-31 | 138.039 | 138.468 | 139.600 | 136.105 |
| 2012-12-31 | 155.208 | 152.087 | 156.292 | 150.144 |
| 2013-12-31 | 93.188 | 96.480 | 97.179 | 92.061 |
| 2014-12-31 | 157.642 | 161.056 | 161.379 | 157.132 |
| 2015-12-31 | 207.487 | 207.458 | 208.704 | 207.106 |
| 2016-12-31 | 317.239 | 324.563 | 325.670 | 317.239 |
| 2017-12-31 | 707.948 | 687.725 | 716.329 | 681.918 |
| 2018-12-31 | 563.300 | 590.010 | 596.400 | 560.000 |
In [31]:
df_monthly.loc[str(year)]
Out[31]:
| open | close | high | low | |
|---|---|---|---|---|
| date | ||||
| 2018-01-31 | 690.200 | 693.996 | 700.218 | 680.232 |
| 2018-02-28 | 756.262 | 747.122 | 756.558 | 742.379 |
| 2018-03-31 | 717.808 | 731.582 | 736.394 | 713.637 |
| 2018-04-30 | 670.480 | 670.539 | 681.326 | 664.673 |
| 2018-05-31 | 650.760 | 658.480 | 659.624 | 636.029 |
| 2018-06-30 | 740.614 | 734.679 | 744.410 | 728.417 |
| 2018-07-31 | 734.520 | 711.550 | 739.330 | 703.000 |
| 2018-08-31 | 731.400 | 714.940 | 732.300 | 714.110 |
| 2018-09-30 | 652.000 | 666.210 | 667.670 | 650.800 |
| 2018-10-31 | 715.410 | 686.150 | 719.000 | 686.150 |
| 2018-11-30 | 555.000 | 563.000 | 585.500 | 551.250 |
| 2018-12-31 | 589.000 | 601.200 | 605.000 | 584.770 |
In [33]:
cost_money = 0
hold = 0 #每年持有的股票
for year in range(2010, 2020):
cost_money -= df_monthly.loc[str(year)]['open'].sum()*100
hold += len(df_monthly[str(year)]['open']) * 100
if year != 2019:
cost_money += df_yearly[str(year)]['open'][0] * hold
hold = 0 #每年持有的股票
#print(cost_money)
cost_money += hold * price_last
print(cost_money)
# df_monthly['2001']
289604.69999999984
In [56]:
df['2017']
Out[56]:
| open | close | high | low | |
|---|---|---|---|---|
| date | ||||
| 2017-01-03 | 329.299 | 329.575 | 331.979 | 327.851 |
| 2017-01-04 | 329.634 | 346.667 | 346.923 | 329.615 |
| 2017-01-05 | 344.785 | 341.574 | 346.213 | 340.293 |
| 2017-01-06 | 341.475 | 345.534 | 354.419 | 340.943 |
| 2017-01-09 | 342.618 | 343.317 | 347.622 | 341.377 |
| 2017-01-10 | 343.258 | 343.800 | 346.755 | 341.436 |
| 2017-01-11 | 342.815 | 340.303 | 342.815 | 338.382 |
| 2017-01-12 | 341.386 | 341.879 | 342.224 | 339.377 |
| 2017-01-13 | 341.810 | 339.731 | 342.214 | 338.756 |
| 2017-01-16 | 339.003 | 336.382 | 339.663 | 333.752 |
| 2017-01-17 | 337.495 | 343.928 | 346.263 | 336.904 |
| 2017-01-18 | 343.682 | 349.789 | 351.454 | 342.037 |
| 2017-01-19 | 349.711 | 349.435 | 353.139 | 346.460 |
| 2017-01-20 | 349.612 | 349.701 | 352.075 | 347.770 |
| 2017-01-23 | 351.563 | 345.987 | 354.439 | 344.253 |
| 2017-01-24 | 346.036 | 345.090 | 347.543 | 341.583 |
| 2017-01-25 | 344.765 | 342.450 | 344.765 | 341.042 |
| 2017-01-26 | 342.953 | 339.288 | 343.603 | 336.215 |
| 2017-02-03 | 340.845 | 341.682 | 343.977 | 339.860 |
| 2017-02-06 | 343.298 | 341.682 | 343.692 | 339.820 |
| 2017-02-07 | 341.672 | 338.461 | 342.224 | 338.027 |
| 2017-02-08 | 338.638 | 339.081 | 340.352 | 337.564 |
| 2017-02-09 | 339.860 | 342.431 | 343.593 | 339.475 |
| 2017-02-10 | 343.426 | 339.938 | 344.608 | 339.860 |
| 2017-02-13 | 338.874 | 344.539 | 346.657 | 336.825 |
| 2017-02-14 | 344.332 | 344.992 | 347.346 | 343.898 |
| 2017-02-15 | 346.263 | 343.337 | 347.189 | 342.027 |
| 2017-02-16 | 343.307 | 342.904 | 343.426 | 340.362 |
| 2017-02-17 | 343.701 | 345.337 | 349.159 | 342.322 |
| 2017-02-20 | 345.189 | 355.907 | 356.921 | 344.795 |
| ... | ... | ... | ... | ... |
| 2017-10-20 | 581.000 | 574.330 | 581.000 | 571.760 |
| 2017-10-23 | 574.920 | 573.410 | 580.500 | 568.000 |
| 2017-10-24 | 572.000 | 567.330 | 573.800 | 563.610 |
| 2017-10-25 | 569.750 | 565.670 | 571.680 | 562.060 |
| 2017-10-26 | 605.000 | 605.090 | 613.000 | 591.910 |
| 2017-10-27 | 602.110 | 649.630 | 655.000 | 600.030 |
| 2017-10-30 | 643.050 | 622.080 | 644.000 | 621.000 |
| 2017-10-31 | 622.010 | 618.030 | 627.600 | 611.880 |
| 2017-11-01 | 620.880 | 623.010 | 631.540 | 619.220 |
| 2017-11-02 | 626.950 | 626.920 | 629.780 | 621.000 |
| 2017-11-03 | 628.650 | 639.170 | 643.440 | 628.650 |
| 2017-11-06 | 641.500 | 653.060 | 657.620 | 640.200 |
| 2017-11-07 | 650.580 | 642.070 | 657.150 | 638.180 |
| 2017-11-08 | 642.330 | 650.380 | 654.500 | 642.330 |
| 2017-11-09 | 648.000 | 650.070 | 654.150 | 644.610 |
| 2017-11-10 | 650.430 | 677.950 | 683.400 | 650.430 |
| 2017-11-13 | 680.000 | 687.880 | 692.780 | 675.800 |
| 2017-11-14 | 687.140 | 678.750 | 687.220 | 677.350 |
| 2017-11-15 | 678.750 | 688.080 | 689.990 | 674.000 |
| 2017-11-16 | 686.010 | 719.110 | 719.960 | 686.010 |
| 2017-11-17 | 696.000 | 690.250 | 709.000 | 677.770 |
| 2017-11-20 | 676.800 | 679.150 | 685.600 | 660.000 |
| 2017-11-21 | 669.000 | 677.250 | 688.800 | 662.020 |
| 2017-11-22 | 677.000 | 650.520 | 688.000 | 650.000 |
| 2017-11-23 | 642.000 | 633.730 | 646.000 | 630.220 |
| 2017-11-24 | 635.900 | 630.040 | 642.500 | 622.230 |
| 2017-11-27 | 634.000 | 621.290 | 636.330 | 613.010 |
| 2017-11-28 | 625.060 | 648.230 | 648.880 | 624.000 |
| 2017-11-29 | 649.940 | 638.120 | 651.970 | 634.010 |
| 2017-11-30 | 635.510 | 631.000 | 648.480 | 626.000 |
223 rows × 4 columns