【python与数据分析】Tushare库详解（1）

目录

前言

1.使用对象

2.使用前提

3.下载安装

4.版本升级

一、交易数据

1.历史行情

2.【案例应用_1】

（1）使用tushare包获取某股票的历史行情数据

 （2）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？

（3）输出该股票所有收盘比开盘上涨3%以上的日期

（4）输出该股票所有开盘比前日收盘跌幅超过2%的日期

（5）假如我从2010年1月1日开始，每月第一个交易日买入一手股票，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？

前言

        Tushare是一个免费的、开源的python财经数据接口包。主要实现对股票等金融数据从数据采集、清洗加工到数据存储的过程，能够为金融分析人员提供快速、整洁和多样的便于分析的数据，为他们在数据获取方面极大地减轻工作量，使他们更加专注于策略和模型的研究与实现上。考虑到python pandas包在金融量化分析中体现出的优势，Tushare返回的绝大部分的数据格式pandas DataFrame类型，非常便于用pandas/NumPy/Matplotlib进行数据分析和可视化。当然，如果习惯了用Excel或者关系型数据库做分析，也可以通过Tushare的数据存储功能，将数据全部保存到本地后进行分析。应一些用户的请求，从0.2.5版本开始，Tushare同时兼容Python 2.x和Python 3.x，对部分代码进行了重构，并优化了一些算法，确保数据获取的高效和稳定。

        Tushare从发布到现在，已经帮助很多用户在数据分析方面降低了工作压力，同时也得到很多用户的反馈。Tushare将一如既往的用免费和开源的形式分享出来，希望对有需求的人带来一些帮助。

1.使用对象

• 量化投资分析师（Quant）
• 对金融市场进行大数据分析的企业和个人
• 开发以证券为基础的金融类产品和解决方案的公司
• 正在学习利用python进行数据分析的人

【注】最近有人说Tushare不方便看行情，我想说的是，Tushare不是普通炒股者用的软件，而是为那些有兴趣做股票期货数据分析的人提供pandas矩阵数据的工具，至于能不能用来炒股以及效果如何，就看个人能力了。

2.使用前提

• 安装python
• 安装pandas
• lxml也是必须的，正常情况下安装了Anaconda后无法单独安装，如果没有可执行：pip install lxml
• 建议安装Anaconda（http://www.continuum.io/downloads），一次安装包括了Python环境和全部依赖包，减少问题出现的机率。

3.下载安装

• 方式一：pip install tushare
• 方式二：访问https://pypi.python.org/pypi/Tushare/下载安装

4.版本升级

pip install tushare--upgrade

查看当前版本的方法：

>>> import tushare
>>> print(tushare.__version__)
1.2.85

一、交易数据

        交易类数据提供股票的交易行情数据，通过简单的接口调用可获取相应的DataFrame格式数据，主要包括以下类别：

• 历史行情数据
• 复权历史数据
• 实时行情数据
• 历史分笔数据
• 实时报价数据
• 当日历史数据
• 大盘指数列表
• 大单交易数据

1.历史行情

        请转移到Tushare Pro新接口，本接口不再维护。

        在Pro版接口中，我们也增加了通用行情接口，可以方便获得各种资源各种频度的数据，欢迎使用。

        获取个股历史交易数据（包括均线数据），可以通过参数设置获取日k线，周k线，月k线，以及5分钟、15分钟、30分钟和60分钟k线数据。本接口只能获取近3年的日线数据，适合搭配均线数据进行选股和分析，如果需要全部历史数据，请调用下一个接口：

get_h_data()

get_k_data()

         参数说明：

code：股票代码，即6位数字代码，或者指数代码（sh=上证指数sz=深圳成指hs300=沪深300指数sz50=上证50zxb=中小板cyb=创业板）

start：开始日期，格式YYYY-MM-DD

end：结束日期，格式YYYY-MM-DD

ktype：数据类型，D=日 k线 W=周 M=月 5=5分钟 15=15分钟 30=30分钟 60=60分钟，默认为D 

retry_count：当网络异常后重试次数，默认为3

pause：重试时停顿秒数，默认为0 

        返回值说明：

date：日期

open：开盘价

high：最高价

close：收盘价

low：最低价

volume：成交量

price_change：价格变动

p_change：涨跌幅

ma5：5日均价

ma10：10日均价

ma20：20日均价

v_max5：5日均量

v_max10：10日均量

v_max20：20日均量

turnover：换手率[注：指数无此项]

        调用方法：

>>> import tushare as ts
>>> p1=ts.get_hist_data('600848')    #一次性获取全部日k线数据
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
>>> p1
             open   high  close    low  ...     v_ma5    v_ma10    v_ma20  turnover
date                                    ...                                        
2022-11-10  11.89  12.00  11.96  11.83  ...  26698.54  29148.51  30058.40      0.09
2022-11-09  12.04  12.15  11.95  11.93  ...  26244.56  31886.21  30317.23      0.12
2022-11-08  11.95  12.08  12.01  11.93  ...  26275.42  34077.42  30761.28      0.08
2022-11-07  11.95  12.04  11.98  11.84  ...  31547.13  35641.61  31334.16      0.13
2022-11-04  11.80  12.02  11.96  11.77  ...  29920.10  35862.92  31142.60      0.13
...           ...    ...    ...    ...  ...       ...       ...       ...       ...
2020-05-18  20.50  20.88  20.53  20.42  ...  50837.18  50837.18  50837.18      0.60
2020-05-15  20.26  20.78  20.61  20.22  ...  48396.27  48396.27  48396.27      0.82
2020-05-14  20.10  20.45  20.18  20.05  ...  36782.36  36782.36  36782.36      0.31
2020-05-13  20.05  20.47  20.29  19.92  ...  39611.55  39611.55  39611.55      0.36
2020-05-12  20.30  20.36  20.06  19.89  ...  42419.52  42419.52  42419.52      0.42
 
[609 rows x 14 columns]

        设定历史数据时间：

>>ts.get_hist_data('600848',start='2022-01-05',end='2022-01-09') 
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
             open   high  close    low  ...     v_ma5    v_ma10    v_ma20  turnover
date                                    ...                                        
2022-01-07  15.07  15.20  15.13  15.01  ...  59561.83  68826.06  66761.07      0.39
2022-01-06  15.07  15.20  15.05  14.99  ...  64292.86  70723.62  67634.21      0.36
2022-01-05  15.00  15.23  15.07  14.95  ...  88915.92  71704.66  67229.49      0.55
 
[3 rows x 14 columns]
>>ts.get_hist_data('600848',ktype='W')#获取周k线数据
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
             open   high  close  ...     v_ma10     v_ma20  turnover
date                             ...                                
2022-11-10  11.95  12.15  11.96  ...  167918.90  171783.42      0.43
2022-11-04  11.50  12.02  11.96  ...  179407.99  181925.79      0.63
2022-10-28  12.01  12.13  11.52  ...  179379.23  199834.01      0.89
2022-10-21  12.09  12.28  11.94  ...  180142.60  202218.20      0.47
2022-10-14  11.83  12.13  12.09  ...  186334.12  205772.96      0.66
...           ...    ...    ...  ...        ...        ...       ...
1994-04-22  10.91  11.23   9.68  ...  168624.60  168624.60     55.39
1994-04-15  12.20  12.60  10.90  ...  186410.50  186410.50     34.86
1994-04-08  14.58  14.60  12.15  ...  228098.00  228098.00     18.78
1994-04-01  14.99  16.75  14.20  ...  325617.00  325617.00    108.66
1994-03-25  20.50  22.48  15.40  ...  459992.00  459992.00    261.36
 
[1354 rows x 14 columns]
>>ts.get_hist_data('600848',ktype='M')#获取月k线数据
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
             open   high  close  ...      v_ma10      v_ma20  turnover
date                             ...                                  
2022-11-10  11.70  12.15  11.96  ...   833804.25  1149169.85      0.97
2022-10-31  11.83  12.28  11.48  ...   934957.98  1164152.89      2.11
2022-09-30  12.58  13.06  11.82  ...  1024421.91  1161109.39      3.42
2022-08-31  12.20  13.10  12.65  ...  1006450.81  1151565.36      3.83
2022-07-29  13.59  13.68  12.19  ...   971014.76  1137911.91      2.91
...           ...    ...    ...  ...         ...         ...       ...
1994-07-29   6.88   6.90   4.20  ...   229020.00   229020.00     45.21
1994-06-30   8.40   8.40   6.85  ...   266384.25   266384.25     43.60
1994-05-31   9.65  10.20   8.40  ...   329597.67   329597.67     42.88
1994-04-29  13.90  14.95   9.65  ...   456663.50   456663.50    168.97
1994-03-31  20.50  22.48  13.90  ...   615940.00   615940.00    349.97
 
[328 rows x 14 columns]
>>ts.get_hist_data('600848',ktype='5')#获取五分钟k线数据
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
                      open   high  close  ...  v_ma10  v_ma20  turnover
date                                      ...                          
2022-11-10 15:00:00  11.98  12.00  11.99  ...  408.36  333.36      0.01
2022-11-10 14:55:00  11.98  11.98  11.98  ...  410.56  321.41      0.01
2022-11-10 14:50:00  11.97  11.98  11.98  ...  369.22  314.01      0.00
2022-11-10 14:45:00  11.96  11.98  11.96  ...  350.62  320.11      0.01
2022-11-10 14:40:00  11.94  11.98  11.96  ...  315.52  330.76      0.00
...                    ...    ...    ...  ...     ...     ...       ...
2022-11-01 14:15:00  11.85  11.88  11.84  ...  628.32  796.86      0.01
2022-11-01 14:10:00  11.82  11.85  11.85  ...  568.72  757.16      0.02
2022-11-01 14:05:00  11.80  11.82  11.82  ...  689.50  683.80      0.00
2022-11-01 14:00:00  11.81  11.84  11.80  ...  800.50  684.60      0.01
2022-11-01 13:55:00  11.78  11.80  11.80  ...  946.60  704.50      0.01
 
[350 rows x 14 columns]
>>> ts.get_hist_data('600848',ktype='15')#获取15分钟k线数据
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
                      open   high  close  ...   v_ma10   v_ma20  turnover
date                                      ...                            
2022-11-10 15:00:00  11.97  12.00  11.99  ...  1181.38  1533.34      0.02
2022-11-10 14:45:00  11.97  11.98  11.96  ...  1064.62  1478.21      0.01
2022-11-10 14:30:00  11.99  11.99  11.97  ...  1006.74  1476.03      0.02
2022-11-10 14:15:00  11.95  12.00  11.99  ...  1102.07  1474.00      0.01
2022-11-10 14:00:00  11.90  11.96  11.95  ...  1269.45  1478.69      0.01
...                    ...    ...    ...  ...      ...      ...       ...
2022-10-12 11:15:00  11.47  11.53  11.48  ...  1671.77  1536.36      0.02
2022-10-12 11:00:00  11.52  11.57  11.48  ...  1561.94  1622.97      0.02
2022-10-12 10:45:00  11.54  11.57  11.52  ...  1546.47  1673.89      0.01
2022-10-12 10:30:00  11.49  11.55  11.51  ...  1552.77  1948.81      0.02
2022-10-12 10:15:00  11.54  11.56  11.48  ...  1434.37  1934.61      0.02
 
[350 rows x 14 columns]
>>> ts.get_hist_data('600848',ktype='30')#获取30分钟k线数据
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
                      open   high  close  ...   v_ma10   v_ma20  turnover
date                                      ...                            
2022-11-10 15:00:00  11.97  12.00  11.99  ...  3066.68  2857.67      0.03
2022-11-10 14:30:00  11.95  12.00  11.97  ...  2952.07  2762.90      0.03
2022-11-10 14:00:00  11.90  11.96  11.95  ...  2957.39  2709.08      0.01
2022-11-10 13:30:00  11.93  11.93  11.90  ...  3070.15  2764.78      0.04
2022-11-10 11:30:00  11.94  11.95  11.93  ...  2989.99  2979.70      0.04
...                    ...    ...    ...  ...      ...      ...       ...
2022-09-02 14:30:00  12.61  12.63  12.59  ...  5233.64  5498.00      0.07
2022-09-02 14:00:00  12.62  12.65  12.63  ...  5485.40  5424.79      0.03
2022-09-02 13:30:00  12.64  12.67  12.62  ...  5944.22  5668.56      0.04
2022-09-02 11:30:00  12.65  12.67  12.65  ...  6084.60  5942.25      0.04
2022-09-02 11:00:00  12.66  12.68  12.65  ...  6234.58  6127.09      0.04
 
[350 rows x 14 columns]

>>>sh1=ts.get_hist_data('sh',ktype='D')
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
sh1["price_change"]
date
2022-11-10   -12.04
2022-11-09   -16.32
2022-11-08   -13.33
2022-11-07     7.02
2022-11-04    72.99
              ...  
2020-05-18     6.96
2020-05-15    -1.88
2020-05-14   -27.71
2020-05-13     6.49
2020-05-12    -3.24
Name: price_change, Length: 609, dtype: float64
>>>ts.get_hist_data('sz')
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
                open      high  ...        v_ma10        v_ma20
date                            ...                            
2022-11-10  10964.94  10979.71  ...  4.268287e+08  4.089080e+08
2022-11-09  11149.37  11185.51  ...  4.268461e+08  4.052112e+08
2022-11-08  11202.44  11212.54  ...  4.313180e+08  4.028833e+08
2022-11-07  11184.16  11272.80  ...  4.305600e+08  3.967721e+08
2022-11-04  10848.30  11226.21  ...  4.257688e+08  3.889228e+08
...              ...       ...  ...           ...           ...
2020-05-18  10969.08  11017.50  ...  3.224774e+08  3.224774e+08
2020-05-15  11013.16  11039.68  ...  3.125770e+08  3.125770e+08
2020-05-14  11026.71  11047.38  ...  3.110545e+08  3.110545e+08
2020-05-13  10978.31  11096.62  ...  3.040613e+08  3.040613e+08
2020-05-12  10972.05  11018.93  ...  3.092858e+08  3.092858e+08
 
[610 rows x 13 columns]
>>>ts.get_hist_data('hs300')
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
               open     high    close  ...       v_ma5      v_ma10      v_ma20
date                                   ...                                    
2022-11-10  3685.83  3701.53  3685.69  ...  1143043.20  1230205.08  1144956.52
2022-11-09  3750.78  3760.41  3714.27  ...  1157156.68  1244694.45  1142711.33
2022-11-08  3773.66  3779.12  3749.33  ...  1261473.48  1276561.48  1152038.73
2022-11-07  3754.52  3792.79  3775.30  ...  1362013.98  1288498.91  1144502.81
2022-11-04  3646.77  3782.88  3767.17  ...  1379292.53  1288729.40  1130913.42
...             ...      ...      ...  ...         ...         ...         ...
2020-05-18  3914.66  3946.43  3922.91  ...   960782.75   960782.75   960782.75
2020-05-15  3941.13  3945.20  3912.82  ...   918974.03   918974.03   918974.03
2020-05-14  3952.20  3952.20  3925.22  ...   916570.77   916570.77   916570.77
2020-05-13  3946.64  3972.53  3968.25  ...   932206.13   932206.13   932206.13
2020-05-12  3961.34  3970.11  3960.24  ...   982505.56   982505.56   982505.56
 
[609 rows x 13 columns]
>>>ts.get_hist_data('sz50')
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
               open     high    close  ...      v_ma5     v_ma10     v_ma20
date                                   ...                                 
2022-11-10  2422.01  2451.47  2440.32  ...  343385.46  366714.09  330489.80
2022-11-09  2460.24  2470.41  2440.35  ...  324282.93  364027.32  325701.40
2022-11-08  2476.65  2482.58  2459.09  ...  358614.06  375482.11  327456.54
2022-11-07  2452.01  2490.32  2477.77  ...  404213.09  376622.16  325915.11
2022-11-04  2383.96  2479.23  2468.28  ...  406370.41  375982.12  322553.31
...             ...      ...      ...  ...        ...        ...        ...
2020-05-18  2821.73  2849.85  2837.17  ...  203143.18  203143.18  203143.18
2020-05-15  2845.91  2849.17  2819.69  ...  197361.82  197361.82  197361.82
2020-05-14  2854.78  2854.78  2834.53  ...  198247.15  198247.15  198247.15
2020-05-13  2860.82  2869.70  2867.71  ...  198279.01  198279.01  198279.01
2020-05-12  2871.31  2880.50  2869.60  ...  213301.50  213301.50  213301.50
 
[609 rows x 13 columns]
>>>ts.get_hist_data('zxb')
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
               open     high    close  ...       v_ma5      v_ma10      v_ma20
date                                   ...                                    
2022-11-10  7443.46  7447.11  7385.15  ...  27761948.8  29729345.2  27252668.3
2022-11-09  7625.30  7631.52  7521.39  ...  28837266.0  30204169.8  27307972.3
2022-11-08  7642.50  7653.29  7605.50  ...  31841529.2  30875491.2  27441360.4
2022-11-07  7648.39  7703.12  7645.53  ...  33458323.6  30875514.0  27216572.1
2022-11-04  7397.68  7676.36  7648.52  ...  33507745.2  30552967.0  26710824.8
...             ...      ...      ...  ...         ...         ...         ...
2020-05-18  7149.74  7170.26  7097.87  ...  30687283.2  30687283.2  30687283.2
2020-05-15  7195.64  7227.61  7164.75  ...  29300007.0  29300007.0  29300007.0
2020-05-14  7218.82  7231.74  7163.63  ...  29257854.0  29257854.0  29257854.0
2020-05-13  7178.54  7275.78  7259.07  ...  29198595.0  29198595.0  29198595.0
2020-05-12  7174.98  7204.59  7202.65  ...  29226862.0  29226862.0  29226862.0
 
[610 rows x 13 columns]
>>> ts.get_hist_data('cyb')
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
               open     high    close  ...        v_ma5       v_ma10       v_ma20
date                                   ...                                       
2022-11-10  2380.23  2393.61  2357.13  ...  15429622.60  16807431.50  15938125.75
2022-11-09  2422.17  2432.29  2399.34  ...  15424331.00  17304259.30  15754517.40
2022-11-08  2452.86  2460.99  2432.40  ...  17622242.00  18123539.40  15812627.75
2022-11-07  2447.13  2470.64  2454.69  ...  18872023.20  18551280.60  15616770.90
2022-11-04  2378.42  2464.51  2451.22  ...  18837383.20  18385820.90  15325575.65
...             ...      ...      ...  ...          ...          ...          ...
2020-05-18  2122.31  2137.15  2114.86  ...  21334267.60  21334267.60  21334267.60
2020-05-15  2128.88  2138.97  2124.31  ...  20632134.50  20632134.50  20632134.50
2020-05-14  2131.46  2137.14  2117.65  ...  20503188.67  20503188.67  20503188.67
2020-05-13  2120.01  2145.02  2140.68  ...  19869736.00  19869736.00  19869736.00
2020-05-12  2106.22  2125.32  2124.15  ...  19653746.00  19653746.00  19653746.00
 
[610 rows x 13 columns]

>>> ts.get_hist_data('sz',ktype='M')#获取深圳成指k线数据
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
                open      high  ...        v_ma10        v_ma20
date                            ...                            
2022-11-10  10420.95  11272.80  ...  8.090958e+09  8.462892e+09
2022-10-31  10788.69  11270.48  ...  8.650721e+09  8.701965e+09
2022-09-30  11789.82  11966.17  ...  9.123413e+09  8.666101e+09
2022-08-31  12243.33  12611.49  ...  9.344029e+09  8.689353e+09
2022-07-29  12899.47  13121.39  ...  8.862613e+09  8.538497e+09
...              ...       ...  ...           ...           ...
1991-08-31    574.82    580.59  ...  9.599200e+03  9.599200e+03
1991-07-31    685.65    687.48  ...  7.688750e+03  7.688750e+03
1991-06-29    784.48    784.48  ...  3.866000e+03  3.866000e+03
1991-05-31    876.57    876.57  ...  2.624500e+03  2.624500e+03
1991-04-30    988.05    988.05  ...  1.520000e+02  1.520000e+02
 
[379 rows x 13 columns]

2.【案例应用_1】

（1）使用tushare包获取某股票的历史行情数据

#获取k线数据，加载至DateFrame中，这个是茅台的股票
>>>df=ts.get_k_data("600519",start="1999-01-01")
                                   
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
 
Warning (from warnings module):
  File "E:\python 3.7\lib\site-packages\tushare\stock\trading.py", line 706
    data = data.append(_get_k_data(url, dataflag,
FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
#将从Tushare中获取的数据存储至本地
>>> df.to_csv("600519.csv")
...         
#将从原数据中的时间作为行索引，并将字符串类型的时间序列化为时间对象类型，并且给显示索引                           
>>> import pandas as pd
>>> df=pd.read_csv('600519.csv',index_col='date',parse_dates=['date'])[['open','close','high','low']]
>>> df
                open     close      high       low
date                                              
2001-08-27  -113.034  -112.849  -112.453  -113.329
2001-08-28  -112.949  -112.616  -112.591  -113.016
2001-08-29  -112.595  -112.702  -112.591  -112.751
2001-08-30  -112.719  -112.574  -112.501  -112.769
2001-08-31  -112.565  -112.590  -112.481  -112.627
...              ...       ...       ...       ...
2022-11-04  1437.960  1516.570  1527.770  1437.960
2022-11-07  1494.050  1507.110  1518.480  1486.590
2022-11-08  1508.000  1484.880  1517.990  1468.960
2022-11-09  1483.950  1459.900  1488.880  1450.000
2022-11-10  1444.000  1475.000  1485.000  1435.150
 
[5071 rows x 4 columns]

 （2）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？

>>> df = ts.get_k_data("600519",start="1999-01-01")
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
>>> df.to_csv("D600519.csv")
>>>df=pd.read_csv('600519.csv',index_col='date',parse_dates=['date'])[['open','close','high','low']]
>>>price_last = df['open'][-1]
>>>df = df['2010-01':'2021-02'] #剔除首尾无用的数据
>>>print(df)
                open     close      high       low
date                                              
2010-01-04    13.919    12.371    13.919    11.898
2010-01-05    13.160    11.996    13.543    11.665
2010-01-06    11.658     9.982    12.041     9.644
2010-01-07     9.982     7.698    10.305     6.316
2010-01-08     7.909     6.406     7.909     4.978
...              ...       ...       ...       ...
2021-02-22  2414.032  2247.052  2414.032  2237.252
2021-02-23  2224.172  2266.032  2303.922  2224.032
2021-02-24  2267.022  2148.032  2277.032  2119.532
2021-02-25  2168.032  2109.032  2183.532  2080.242
2021-02-26  2059.032  2081.812  2138.982  2026.332
 
[2703 rows x 4 columns]
>>> #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()
>>> #print(df_monthly)
... df_yearly = df.resample("A").last()[:-1] #去除最后一年
>>> print(df_yearly)
                open     close      high       low
date                                              
2010-12-31    22.172    23.765    24.035    21.608
2011-12-31    46.568    47.064    48.370    44.337
2012-12-31    66.904    63.359    68.136    61.152
2013-12-31    -1.601     2.019     2.788    -2.840
2014-12-31    68.262    71.917    72.262    67.717
2015-12-31   121.731   121.701   123.011   121.331
2016-12-31   236.292   243.832   244.972   236.292
2017-12-31   634.469   613.959   642.969   608.069
2018-12-31   490.768   517.478   523.868   487.468
2019-12-31  1125.007  1125.007  1130.007  1118.517
2020-12-31  1900.032  1957.032  1958.012  1898.032
>>> cost_money = 0
>>> hold = 0 #每年持有的股票
>>> for year in range(2010, 2017):
...     
...     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
... 
 
Warning (from warnings module):
  File "", line 4
FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
 
Warning (from warnings module):
  File "", line 6
FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
>>> cost_money += hold * price_last
>>> print(cost_money)
122939.49999999994

>>> import matplotlib.pyplot as plt
>>> df['close'].plot()
'date'>
>>> plt.show()
Traceback (most recent call last):
  File "", line 1, in 
    plt.show()
  File "E:\python 3.7\lib\site-packages\matplotlib\pyplot.py", line 421, in show
    return _get_backend_mod().show(*args, **kwargs)
  File "E:\python 3.7\lib\site-packages\matplotlib\backend_bases.py", line 3546, in show
    cls.mainloop()
  File "E:\python 3.7\lib\site-packages\matplotlib\backends\_backend_tk.py", line 1040, in mainloop
    first_manager.window.mainloop()
  File "E:\python 3.7\lib\tkinter\__init__.py", line 1458, in mainloop
    self.tk.mainloop(n)
KeyboardInterrupt
>>> df.describe()
              open        close         high          low
count  2703.000000  2703.000000  2703.000000  2703.000000
mean    368.869292   369.726308   375.221292   363.552602
std     491.748814   492.813804   498.445099   486.208050
min     -20.641000   -20.439000   -19.515000   -21.017000
25%      41.771500    41.944500    43.649500    39.952500
50%     111.281000   111.251000   114.911000   107.511000
75%     597.259000   597.373500   605.633000   589.018000
max    2547.012000  2560.032000  2586.912000  2444.032000

>>> df1=ts.get_k_data("002075",start="1999-01-01")
本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2
>>> df1.to_csv("002075.csv")
...                                    
>>> import pandas as pd
>>> df1=pd.read_csv("002075.csv",index_col='date',parse_dates=['date'])[['open','close','high','low']]
>>> import matplotlib.pyplot as plt
>>> print(df)
                open     close      high       low
date                                              
2010-01-04    13.919    12.371    13.919    11.898
2010-01-05    13.160    11.996    13.543    11.665
2010-01-06    11.658     9.982    12.041     9.644
2010-01-07     9.982     7.698    10.305     6.316
2010-01-08     7.909     6.406     7.909     4.978
...              ...       ...       ...       ...
2021-02-22  2414.032  2247.052  2414.032  2237.252
2021-02-23  2224.172  2266.032  2303.922  2224.032
2021-02-24  2267.022  2148.032  2277.032  2119.532
2021-02-25  2168.032  2109.032  2183.532  2080.242
2021-02-26  2059.032  2081.812  2138.982  2026.332
 
[2703 rows x 4 columns]
>>> df1['close'].plot()
'date'>

>>> df1.describe()
              open        close         high          low
count  2947.000000  2947.000000  2947.000000  2947.000000
mean      6.061386     6.070188     6.226058     5.915490
std       3.803842     3.812494     3.929221     3.692869
min       0.697000     0.711000     0.747000     0.675000
25%       2.933000     2.940000     3.011000     2.861000
50%       4.861000     4.861000     4.997000     4.790000
75%       8.601000     8.625000     8.869500     8.283000
max      23.947000    23.883000    24.625000    22.933000

（3）输出该股票所有收盘比开盘上涨3%以上的日期

>>> condition=(df1['close']-df1['open'])/df1['open']>0.03
>>> df1.loc[condition].index
DatetimeIndex(['2006-10-25', '2006-10-30', '2006-11-01', '2006-11-08',
               '2006-11-09', '2006-11-13', '2006-11-27', '2006-12-01',
               '2006-12-11', '2007-01-05',
               ...
               '2022-02-07', '2022-02-18', '2022-02-21', '2022-04-06',
               '2022-04-29', '2022-05-12', '2022-07-05', '2022-07-18',
               '2022-08-05', '2022-10-17'],
              dtype='datetime64[ns]', name='date', length=427, freq=None)
>>> df1.loc[condition]
             open  close   high    low
date                                  
2006-10-25  2.325  2.583  2.611  2.325
2006-10-30  2.536  2.704  2.711  2.511
2006-11-01  2.665  2.797  2.965  2.650
2006-11-08  2.711  2.868  2.915  2.665
2006-11-09  2.858  2.954  3.058  2.808
...           ...    ...    ...    ...
2022-05-12  4.581  4.741  4.881  4.571
2022-07-05  4.820  4.970  5.100  4.810
2022-07-18  4.450  4.590  4.610  4.450
2022-08-05  4.250  4.450  4.650  4.250
2022-10-17  3.950  4.320  4.320  3.930
 
[427 rows x 4 columns]

（4）输出该股票所有开盘比前日收盘跌幅超过2%的日期

>>> #因为是与前日做对比
>>> #shift(1) 行索引不，值向下移动一位
>>> condition=(df1['open']-df1['close'].shift(1))/df1['close'].shift(1)<=-0.02
>>> condition
date
2006-10-25    False
2006-10-26    False
2006-10-27     True
2006-10-30    False
2006-10-31    False
              ...  
2022-11-04    False
2022-11-07    False
2022-11-08    False
2022-11-09    False
2022-11-10    False
Length: 2947, dtype: bool
>>> df1[condition].index
DatetimeIndex(['2006-10-27', '2006-12-08', '2007-01-25', '2007-02-05',
               '2007-03-19', '2007-05-14', '2007-05-16', '2007-05-21',
               '2007-06-04', '2007-06-05',
               ...
               '2021-07-08', '2021-07-09', '2021-07-12', '2021-07-13',
               '2021-07-14', '2021-09-22', '2021-11-03', '2022-02-22',
               '2022-02-24', '2022-05-06'],
              dtype='datetime64[ns]', name='date', length=194, freq=None)
>>> df1[condition]
             open  close   high    low
date                                  
2006-10-27  2.550  2.540  2.568  2.461
2006-12-08  3.218  3.040  3.254  3.033
2007-01-25  3.575  3.418  3.668  3.397
2007-02-05  3.075  3.161  3.197  3.075
2007-03-19  3.497  3.618  3.654  3.415
...           ...    ...    ...    ...
2021-09-22  7.161  7.301  7.351  7.151
2021-11-03  5.331  5.581  5.771  5.331
2022-02-22  6.921  6.701  7.061  6.691
2022-02-24  6.381  6.161  6.521  6.071
2022-05-06  4.621  4.581  4.661  4.561
 
[194 rows x 4 columns]

（5）假如我从2010年1月1日开始，每月第一个交易日买入一手股票，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？

>>> df1=pd.read_csv("002075.csv",index_col='date',parse_dates=['date'])[['open','close','high','low']]
>>> price_last=df1['open'][-1]#剔除首位无用的数据
>>> df1=df1['2010-01':'2021-02']
>>> #prinf(df)
>>> #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=df1.resample("M").first()
>>> #print(df_monthly)
>>> df_yearly=df1.resample("A").last()[:-1]    #去除最后一年
>>> #print(df_yearly)
>>> cost_money=0
>>> hold=0    #每年持有的股票
>>> for year in range(2010,2017):
...     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
... 
...         
 
Warning (from warnings module):
  File "", line 3
FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
 
Warning (from warnings module):
  File "", line 5
FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
>>> cost_money += hold*price_last
>>> print(cost_money)
31657.6