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三、示例：tushare股票分析

2021-05-13 22:36:41 作者：互联网

3.1 tushare模块

安装tushare包
```
pip3 install tushare
```
tushare官方文档

3.2 股票分析

demo

import tushare as ts
import pandas as pd

# 调用接口
df = ts.get_k_data(code='600519', start='2000-01-1')

# 数据持久化到本地
df.to_csv('./maotai.csv')

# 读取本地文件数据
df = pd.read_csv('./maotai.csv')
"""
Unnamed: 0	date	open	close	high	low	volume	code
0	0	2001-08-27	5.392	5.554	5.902	5.132	406318.00	600519
1	1	2001-08-28	5.467	5.759	5.781	5.407	129647.79	600519
2	2	2001-08-29	5.777	5.684	5.781	5.640	53252.75	600519
3	3	2001-08-30	5.668	5.796	5.860	5.624	48013.06	600519
4	4	2001-08-31	5.804	5.782	5.877	5.749	23231.48	600519
"""

# 删除第一列无效信息列
df.drop(labels='Unnamed: 0', axis=1, inplace=True)
"""
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
"""

# 查看数据基本信息
df.info()
"""
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4705 entries, 0 to 4704
Data columns (total 7 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   date    4705 non-null   object 
 1   open    4705 non-null   float64
 2   close   4705 non-null   float64
 3   high    4705 non-null   float64
 4   low     4705 non-null   float64
 5   volume  4705 non-null   float64
 6   code    4705 non-null   int64  
dtypes: float64(5), int64(1), object(1)
memory usage: 257.4+ KB
"""

# 将date列转为时间格式
df['date'] = pd.to_datetime(df['date'])

# 将date列做为行索引
df.set_index('date', inplace=True)

输出该股票收盘比开盘高3%以上的日期

# (收盘 - 开盘) / 开盘 > 0.03
(df['close'] - df['open']) / df['open'] > 0.03
"""
date
2001-08-27    False
2001-08-28    False
2001-08-29    False
2001-08-30    False
2001-08-31    False
              ...  
2021-05-06    False
2021-05-07     True
2021-05-10    False
2021-05-11    False
2021-05-12    False
Length: 4705, dtype: bool
"""

# 将上面的bool列做为loc索引，可直接得到True对应的行
df.loc[(df['open']-df['close'])/df['open']>0.03]
"""
open	close	high	low	volume	code
date						
2001-10-10	5.827	5.640	5.848	5.629	17548.69	600519
2001-11-07	5.468	5.288	5.468	5.281	9325.38	600519
2001-11-16	5.656	5.326	5.656	5.310	9954.70	600519
2001-12-20	5.609	5.431	5.609	5.413	11362.23	600519
2002-01-04	6.049	5.866	6.062	5.851	29109.62	600519
...	...	...	...	...	...	...
2021-02-24	2307.990	2189.000	2318.000	2160.500	82116.00	600519
2021-03-02	2180.000	2058.000	2180.000	2033.000	70765.00	600519
2021-03-08	2074.960	1960.000	2085.000	1960.000	63100.00	600519
2021-03-15	2050.000	1975.450	2069.800	1951.150	62485.00	600519
2021-05-07	1965.000	1903.000	1967.980	1901.000	52493.00	600519
"""

# 取出行索引，即为对应日期
df.loc[(df['open']-df['close'])/df['open']>0.03].index
"""
DatetimeIndex(['2001-10-10', '2001-11-07', '2001-11-16', '2001-12-20',
               '2002-01-04', '2002-01-17', '2002-01-28', '2002-04-17',
               '2002-11-08', '2003-01-02',
               ...
               '2020-07-16', '2020-07-24', '2021-01-19', '2021-02-18',
               '2021-02-22', '2021-02-24', '2021-03-02', '2021-03-08',
               '2021-03-15', '2021-05-07'],
              dtype='datetime64[ns]', name='date', length=177, freq=None)
"""

输出该股票开盘比前日收盘跌幅超过2%的日期

# (今日开盘 - 前日收盘) / 前日收盘 > 0.02
# close列下移一位：df['close'].shift(1)
(df['open'] - df['close'].shift(1)) / df['close'].shift(1) > 0.02

# 将上面的bool列做为loc索引，可直接得到True对应的行
df.loc[(df['open'] - df['close'].shift(1)) / df['close'].shift(1) > 0.02]

# 取出行索引，即为对应日期
df.loc[(df['open'] - df['close'].shift(1)) / df['close'].shift(1) > 0.02].index

如果一个人从2010年1月1日开始，每月第一个交易日买入100支股票，每年最后一个交易日卖出所有股票，到今天为止的收益如何？

"""
开盘买：1个完整年买1200支股票
收盘卖：1个完整年卖1200支股票
"""

# 对时间切片
new_df = df['2010-01-01': ]

# 每个月的第一个交易日：得到的行索引是最后一个，但是数据没问题
# resample，重新取样
df_monthly = new_df.resample('M').first()
"""
						open	close	high	low	volume	code
date						
2010-01-31	109.760	108.446	109.760	108.044	44304.88	600519
2010-02-28	107.769	107.776	108.216	106.576	29655.94	600519
2010-03-31	106.219	106.085	106.857	105.925	21734.74	600519
2010-04-30	101.324	102.141	102.422	101.311	23980.83	600519
2010-05-31	81.676	82.091	82.678	80.974	23975.16	600519
...	...	...	...	...	...	...
2021-01-31	1999.980	1997.000	2004.990	1983.810	43514.00	600519
2021-02-28	2130.000	2109.320	2160.000	2095.000	29341.00	600519
2021-03-31	2179.000	2158.000	2179.000	2120.000	44916.00	600519
2021-04-30	2021.000	2044.500	2046.800	2001.220	26588.00	600519
2021-05-31	2000.000	1959.000	2006.840	1953.000	53546.00	600519
"""

# 买股票花费总金额
cost = df_monthly['open'].sum() * 100

# 每年最后一个交易日，今年除外
df_yearly = new_df.resample('A').last()[:-1]

# 卖出股票赚的钱：今年的股票不卖
resv = df_yearly['open'].sum() * 1200

# 剩余股票
last_money = 500 * new_df['close'][-1]

# 收益
money = resv + last_money - cost

标签：分析,tushare,示例,df,08,600519,2021,close,2001
来源： https://www.cnblogs.com/xiangjianan/p/14766459.html