1.2 金融数据处理_qstock get_data

跳转到根目录：知行合一：投资篇

已完成：
1、投资&技术
  1.1.1 投资-编程基础-numpy
  1.1.2 投资-编程基础-pandas
  1.2 金融数据处理
  1.3 金融数据可视化
2、投资方法论
  2.1.1 预期年化收益率
3、投资实证
  [3.1 2023这一年] 被鸽

1. 股市数据获取

1.1. 使用qstock获取股票数据

目前看，使用qstock是最好的获取股票历史数据的包，因为：

tushare：基本都收费了，羊毛少了很多

akshare：貌似也可以的，等会儿研究

python爬虫：之前做过，用来爬雪球的历史数据，只要一开始写好了，后面也容易（第一次写就比较困难，要搞定登录cookie，下面也有源代码）

可以说，qstock是我目前用的最顺手最香的，谢谢大佬！

为了防止下车，把文档自己拷贝了一份，放在本地。《qstock基本使用》

import qstock as qs
# 如果没有安装qstock，执行：
# pip install qstock
# pip install pywencai

df=qs.get_data('512000')   # 获取的是512000的所有日线历史行情数据，自己保存，不用每次都去拉，做一些简单算法、回测是够用了。
df.to_csv('512000.csv')
df

						name	code	open	high	low	close	volume	turnover	turnover_rate
date									
2016-09-14	券商ETF	512000	0.980	0.985	0.977	0.980	1177241	115431402.0	0.46
2016-09-19	券商ETF	512000	0.979	0.985	0.979	0.982	234915	23072514.0	0.09
2016-09-20	券商ETF	512000	0.982	0.983	0.978	0.980	172012	16855033.0	0.07
2016-09-21	券商ETF	512000	0.979	0.994	0.979	0.989	520398	51417210.0	0.20
2016-09-22	券商ETF	512000	0.998	0.998	0.988	0.989	264471	26239881.0	0.10
...	...	...	...	...	...	...	...	...	...
2023-12-22	券商ETF	512000	0.854	0.860	0.848	0.855	5963605	509122717.0	2.34
2023-12-25	券商ETF	512000	0.850	0.853	0.845	0.847	5823702	493326830.0	2.29
2023-12-26	券商ETF	512000	0.847	0.848	0.836	0.838	5493080	461334707.0	2.16
2023-12-27	券商ETF	512000	0.838	0.851	0.830	0.844	6957843	584933791.0	2.73
2023-12-28	券商ETF	512000	0.841	0.868	0.840	0.863	7554094	647459067.0	2.97
1771 rows × 9 columns
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1.2. tushare

tushare官网：https://www.tushare.pro/

拿日线行情来看：

调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史

天意啊，我正好还有120积分，正好可以调用此接口。

import time
import tushare as ts
ts.set_token('你注册后拿到的token')
pro = ts.pro_api()

df = pro.daily(ts_code='000001.SZ', start_date='20230101', end_date='20231228')
# 将 日线行情 数据保存到csv
df.to_csv('000001.csv')
df

	ts_code	trade_date	open	high	low	close	pre_close	change	pct_chg	vol	amount
0	000001.SZ	20231228	9.11	9.47	9.08	9.45	9.12	0.33	3.6184	1661591.84	1550256.591
1	000001.SZ	20231227	9.10	9.13	9.02	9.12	9.10	0.02	0.2198	641534.35	582036.661
2	000001.SZ	20231226	9.19	9.20	9.07	9.10	9.19	-0.09	-0.9793	541896.33	493746.623
3	000001.SZ	20231225	9.18	9.20	9.14	9.19	9.20	-0.01	-0.1087	413970.88	379638.234
4	000001.SZ	20231222	9.19	9.28	9.11	9.20	9.17	0.03	0.3272	1005645.02	924998.769
...	...	...	...	...	...	...	...	...	...	...	...
236	000001.SZ	20230109	14.75	14.88	14.52	14.80	14.62	0.18	1.2312	1057659.11	1561368.487
237	000001.SZ	20230106	14.50	14.72	14.48	14.62	14.48	0.14	0.9669	1195744.71	1747915.169
238	000001.SZ	20230105	14.40	14.74	14.37	14.48	14.32	0.16	1.1173	1665425.18	2417272.356
239	000001.SZ	20230104	13.71	14.42	13.63	14.32	13.77	0.55	3.9942	2189682.53	3110729.449
240	000001.SZ	20230103	13.20	13.85	13.05	13.77	13.16	0.61	4.6353	2194127.94	2971546.989
241 rows × 11 columns
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1.3. python爬虫

这个自从有了qstock之后，就不香了，不过还是记录下来，后面也许可以再用到。

注意：代码里面需要把cookies_str这个变量值替换成自己的雪球访问时的cookie字符串。

import json
import time
import requests as requests
import pandas as pd


def date_range(start, end, freq):
    """
    构造时间区间，使用pandas函数
    :param start: '2012-05-08'
    :param end: '2023-09-01'
    :param freq: '38D'
    :return: ['2012-05-28', '2012-07-05', '2012-08-12', '2012-09-19'....'2023-07-16', '2023-08-23']
    """
    # date_range = pd.date_range(start='2022-01-01', end='2022-06-17', freq='38D')
    date_range = pd.date_range(start=start, end=end, freq=freq)
    return date_range


class SecurityHistory:
    """
    cookies_str 是要注意可能会过期，如果过期了，就重新通过浏览器手机模拟器获取一下。
    """
    def __init__(self, symbol) -> None:
        super().__init__()
        self.symbol = symbol
        self.fetch_days = {}
        self.data_of_days = []

    def do_request(self, begin_unix_time_str):
        data = {
            "symbol": self.symbol,
            "begin": begin_unix_time_str,
            "period": "day",
            "type": "before",
            "count": "38",
            "indicator": "kline,pe,pb,ps,pcf,market_capital,agt,ggt,balance"
        }
        # 头部，伪装自己为浏览器
        headers = {
            'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 13_2_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.3 Mobile/15E148 Safari/604.1',
          }
        # cookie字符串，可以省去自己拼接k-v形式
        cookies_str = '这里是自己的cookie字符串，从雪球请求F12，看请求的cookie，直接全部拷贝过来就可以了。'
        cookies_dir = {cookie.split('=')[0]:cookie.split('=')[-1] for cookie in cookies_str.split('; ')}
        # 调用请求
        index_result = requests.get('https://stock.xueqiu.com/v5/stock/chart/kline.json',
                                    params=data,
                                    cookies=cookies_dir,
                                    headers=headers)
        # 打印结果
        print('获取到结果\n' + index_result.text)
        return index_result.text

    def result_save(self, result_text):
        # 请求结果字符串转换为json对象
        result_json = json.loads(result_text)
        last_record = {}
        for item in result_json['data']['item']:
            record = {}
            # 日期格式化
            date = time.strftime("%Y-%m-%d", time.localtime(item[0]/1000))
            # 自行构造一个uuid，是为了判断此日期的数据有没有存在，存在就不必再保存了，不存在的才保存下来
            record['uuid'] = result_json['data']['symbol'] + '|' + date
            record['date'] = date
            record['volume'] = item[1]
            record['open'] = item[2]
            record['high'] = item[3]
            record['low'] = item[4]
            record['close'] = item[5]
            record['chg'] = item[6]
            record['percent'] = item[7]
            record['turnoverrate'] = item[8]
            record['amount'] = item[9]
            print(record)
            if record['uuid'] in self.fetch_days:
                print('已有当日数据')
            else:
                # self.fetch_days记录获取过数据的日期、data_of_days记录日线数据。
                self.fetch_days[record['uuid']] = record['uuid']
                self.data_of_days.append(record)
            last_record = record
        return last_record


if __name__ == '__main__':
    security = 'SH510300'
    start_date = '2012-05-28'
    end_date = time.strftime("%Y-%m-%d", time.localtime())
    # 构造请求时间区间
    datetime_range = date_range(start=start_date, end=end_date, freq='38D')
    this_class = SecurityHistory(security)
    for item in datetime_range:
        date_unix_time = str(int(time.mktime(item.timetuple()) * 1000))  # item是Timestamp类型 2012-05-28 00:00:00
        # 调用请求接口获取数据
        result_text = this_class.do_request(date_unix_time)  # date_unix_time='1338134400000'
        # 保存每日数据，这里存放在内存，并去重
        this_class.result_save(result_text)
        # 休眠，防止请求过于频繁被封
        time.sleep(3)
    # 数据存储到csv
    df = pd.DataFrame(this_class.data_of_days)
    # 默认自然序号的index会保存到csv文件
    df.to_csv("{}-{}-{}.csv".format(security, start_date, end_date))


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1.4. akshare

akshare的文档还是非常全面的，而且用起来也很ok，更而且，免费！

注意：股票数据和公募基金数据是不同的接口，这个是要注意的。

股票历史行情数据：https://www.akshare.xyz/data/stock/stock.html#id20

ETF基金历史行情-东财：https://www.akshare.xyz/data/fund/fund_public.html#id8

官网：https://www.akshare.xyz/index.html

安装akshare：https://www.akshare.xyz/installation.html

A股历史行情数据：https://www.akshare.xyz/data/stock/stock.html#id20

# pip install akshare --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
import akshare as ak

stock_zh_a_hist_df = ak.stock_zh_a_hist(symbol="600036", period="daily")
stock_zh_a_hist_df


              日期     开盘     收盘     最高     最低      成交量           成交额    振幅  \
0     2002-04-09  10.51  10.66  10.88  10.51  4141088  4.418822e+09  5.07   
1     2002-04-10  10.66  10.60  10.70  10.39   679455  7.166843e+08  2.91   
2     2002-04-11  10.60  10.52  10.68  10.49   227883  2.409635e+08  1.79   
3     2002-04-12  10.50  10.57  10.64  10.48   212565  2.240599e+08  1.52   
4     2002-04-15  10.57  10.39  10.60  10.35   185311  1.933069e+08  2.37   
...          ...    ...    ...    ...    ...      ...           ...   ...   
5200  2023-12-22  27.78  27.61  27.85  27.41   623644  1.721377e+09  1.59   
5201  2023-12-25  27.60  27.53  27.72  27.42   419034  1.154001e+09  1.09   
5202  2023-12-26  27.49  27.27  27.49  27.16   365257  9.971776e+08  1.20   
5203  2023-12-27  27.33  27.30  27.37  27.05   410637  1.117439e+09  1.17   
5204  2023-12-28  27.25  27.99  28.13  27.25  1061063  2.953201e+09  3.22   

        涨跌幅   涨跌额    换手率  
0     46.03  3.36  69.02  
1     -0.56 -0.06  11.32  
2     -0.75 -0.08   3.80  
3      0.48  0.05   3.54  
4     -1.70 -0.18   3.09  
...     ...   ...    ...  
5200  -0.36 -0.10   0.30  
5201  -0.29 -0.08   0.20  
5202  -0.94 -0.26   0.18  
5203   0.11  0.03   0.20  
5204   2.53  0.69   0.51  

[5205 rows x 11 columns]
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如果是要查ETF的数据，文章在这里：https://www.akshare.xyz/data/fund/fund_public.html#id8

import akshare as ak

fund_etf_hist_em_df = ak.fund_etf_hist_em(symbol="512000", period="daily")
print(fund_etf_hist_em_df)


              日期     开盘     收盘     最高     最低      成交量          成交额    振幅  \
0     2016-09-14  0.980  0.980  0.985  0.977  1177241  115431402.0  0.00   
1     2016-09-19  0.979  0.982  0.985  0.979   234915   23072514.0  0.61   
2     2016-09-20  0.982  0.980  0.983  0.978   172012   16855033.0  0.51   
3     2016-09-21  0.979  0.989  0.994  0.979   520398   51417210.0  1.53   
4     2016-09-22  0.998  0.989  0.998  0.988   264471   26239881.0  1.01   
...          ...    ...    ...    ...    ...      ...          ...   ...   
1766  2023-12-22  0.854  0.855  0.860  0.848  5963605  509122717.0  1.41   
1767  2023-12-25  0.850  0.847  0.853  0.845  5823702  493326830.0  0.94   
1768  2023-12-26  0.847  0.838  0.848  0.836  5493080  461334707.0  1.42   
1769  2023-12-27  0.838  0.844  0.851  0.830  6957843  584933791.0  2.51   
1770  2023-12-28  0.841  0.863  0.868  0.840  7554094  647459067.0  3.32   

       涨跌幅    涨跌额   换手率  
0     0.00  0.000  0.46  
1     0.20  0.002  0.09  
2    -0.20 -0.002  0.07  
3     0.92  0.009  0.20  
4     0.00  0.000  0.10  
...    ...    ...   ...  
1766  0.12  0.001  2.34  
1767 -0.94 -0.008  2.29  
1768 -1.06 -0.009  2.16  
1769  0.72  0.006  2.73  
1770  2.25  0.019  2.97  

[1771 rows x 11 columns]
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1.5. 沪深300加权市盈率

• 获取沪深300指标信息

import qstock as qs
#上证50成份股
df=qs.index_member('hs300')
#查看前几行数据
code_list = df['股票代码'].values.tolist()
# code_list=['300139','中国平安','西部建设','贵州茅台','丰元股份','002432']
hs300_stock_basics=qs.stock_basics(code_list)
hs300_stock_basics.to_csv('hs300_stock_basics.csv') # 获取沪深300成分股的基础信息
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• 计算 沪深300加权市盈率

import pandas as pd
import datetime
today = datetime.date.today().strftime("%Y-%m-%d")
hs300_basic_df = pd.read_csv('hs300_stock_basics.csv')

hs300_basic_df['总市值占比'] = hs300_basic_df['总市值']/hs300_basic_df['总市值'].sum()
# print(hs300_basic_df.loc[0])
hs300_basic_df.rename(columns={'市盈率(动)': '市盈率_动'}, inplace=True)
avg_pe = 0
for row in hs300_basic_df.itertuples():
#     print(row)
    avg_pe += getattr(row, '总市值占比') * getattr(row, '市盈率_动')
print(today, '平均：市盈率(动) = ', avg_pe)

2023-12-20 平均：市盈率(动) =  22.872546209060392
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2. 数据清洗、计算

2.1. dataframe整合历史数据

import qstock as qs
import pandas as pd

#默认日频率、前复权所有历史数据
#open：开盘价，high：最高价，low：最低价，close：收盘价 vol：成交量，turnover：成交金额，turnover_rate:换手率
# 沪深300， 中证500， 医药ETF, 券商ETF， 新能源ETF， 红利ETF, 黄金ETF, 房地产ETF
stocks_info = [
    {'code': '510300', 'name': '沪深300'},
    {'code': '510500', 'name': '中证500'},
    {'code': '512010', 'name': '医药ETF'},
    {'code': '512000', 'name': '券商ETF'},
    {'code': '516160', 'name': '新能源ETF'},
    {'code': '510800', 'name': '红利ETF'},
    {'code': '518880', 'name': '黄金ETF'},
    {'code': '512200', 'name': '房地产ETF'}
]
for stock in stocks_info:
    df = qs.get_data(stock['code'])  # 从qstock获取对应的股票历史数据
    stock['history_df'] = df         # 将其存在 history_df 这个key里面。

# 只保留收盘价，合并数据
df_all = pd.DataFrame()
for stock in stocks_info:
    df = stock['history_df']
    df = df[['close']]         # 只需要 date 和 close 2列就行了。
    df.rename(columns={'close': stock['name']}, inplace=True)  # 用股票的名字来重命名close列
    if df_all.size == 0:
        df_all = df
    else:
        df_all = df_all.join(df)  # join是按照index来连接的。

print(df_all)

            沪深300  中证500  医药ETF  券商ETF  新能源ETF  红利ETF  黄金ETF  房地产ETF
date                                                                
2012-05-28  2.004    NaN    NaN    NaN     NaN    NaN    NaN     NaN
2012-05-29  2.044    NaN    NaN    NaN     NaN    NaN    NaN     NaN
2012-05-30  2.036    NaN    NaN    NaN     NaN    NaN    NaN     NaN
2012-05-31  2.030    NaN    NaN    NaN     NaN    NaN    NaN     NaN
2012-06-01  2.030    NaN    NaN    NaN     NaN    NaN    NaN     NaN
...           ...    ...    ...    ...     ...    ...    ...     ...
2023-12-25  3.415  5.403  0.404  0.847   0.615  1.016  4.634   0.522
2023-12-26  3.395  5.356  0.401  0.838   0.613  1.012  4.647   0.513
2023-12-27  3.405  5.372  0.403  0.844   0.608  1.013  4.660   0.514
2023-12-28  3.489  5.475  0.410  0.863   0.650  1.035  4.662   0.527
2023-12-29  3.489  5.496  0.410  0.861   0.642  1.034  4.642   0.526

[2822 rows x 8 columns]
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2.2. 逐年收益率计算

import qstock as qs
import pandas as pd

#默认日频率、前复权所有历史数据
#open：开盘价，high：最高价，low：最低价，close：收盘价 vol：成交量，turnover：成交金额，turnover_rate:换手率
# 沪深300， 中证500， 医药ETF, 券商ETF， 新能源ETF， 红利ETF, 黄金ETF, 房地产ETF
stocks_info = [
    {'code': '510300', 'name': '沪深300'},
    {'code': '510500', 'name': '中证500'},
    {'code': '512010', 'name': '医药ETF'},
    {'code': '512000', 'name': '券商ETF'},
    {'code': '516160', 'name': '新能源ETF'},
    {'code': '510800', 'name': '红利ETF'},
#     {'code': '518880', 'name': '黄金ETF'},
#     {'code': '512200', 'name': '房地产ETF'}
]
for stock in stocks_info:
    df = qs.get_data(stock['code'])  # 从qstock获取对应的股票历史数据
    stock['history_df'] = df         # 将其存在 history_df 这个key里面。

# 准备计算数据
df_all = pd.DataFrame()
for stock in stocks_info:
    df = stock['history_df']
    df = df[['close']]         # 只需要 date 和 close 2列就行了。
    df.rename(columns={'close': stock['name']}, inplace=True)  # 用股票的名字来重命名close列
    if df_all.size == 0:
        df_all = df
    else:
        df_all = df_all.join(df)  # join是按照index来连接的。

print(df_all)

# 退化日期到年
yearly_pct_change = df_all.pct_change().to_period('A')
print('退化日期到年：', yearly_pct_change)  # 这里可以看到输出的结果，其实就是把index的年月日格式，都变成年份，以方便后面的groupby分组计算。
# 按年分组，滚动计算收益率
y_ret = (yearly_pct_change.groupby(yearly_pct_change.index).apply(lambda x: ((1+x).cumprod()-1).iloc[-1])).round(4)
print('年分组滚动收益率：', y_ret)


          沪深300  中证500  医药ETF  券商ETF  新能源ETF  红利ETF
date                                                 
2012-05-28  2.004    NaN    NaN    NaN     NaN    NaN
2012-05-29  2.044    NaN    NaN    NaN     NaN    NaN
2012-05-30  2.036    NaN    NaN    NaN     NaN    NaN
2012-05-31  2.030    NaN    NaN    NaN     NaN    NaN
2012-06-01  2.030    NaN    NaN    NaN     NaN    NaN
...           ...    ...    ...    ...     ...    ...
2023-12-25  3.415  5.403  0.404  0.847   0.615  1.016
2023-12-26  3.395  5.356  0.401  0.838   0.613  1.012
2023-12-27  3.405  5.372  0.403  0.844   0.608  1.013
2023-12-28  3.489  5.475  0.410  0.863   0.650  1.035
2023-12-29  3.498  5.518  0.413  0.864   0.648  1.036

[2822 rows x 6 columns]
退化日期到年：          
			沪深300     中证500     医药ETF     券商ETF    新能源ETF     红利ETF
date                                                            
2012       NaN       NaN       NaN       NaN       NaN       NaN
2012  0.019960       NaN       NaN       NaN       NaN       NaN
2012 -0.003914       NaN       NaN       NaN       NaN       NaN
2012 -0.002947       NaN       NaN       NaN       NaN       NaN
2012  0.000000       NaN       NaN       NaN       NaN       NaN
...        ...       ...       ...       ...       ...       ...
2023  0.002642 -0.001294  0.002481 -0.009357  0.004902  0.000985
2023 -0.005857 -0.008699 -0.007426 -0.010626 -0.003252 -0.003937
2023  0.002946  0.002987  0.004988  0.007160 -0.008157  0.000988
2023  0.024670  0.019173  0.017370  0.022512  0.069079  0.021718
2023  0.002580  0.007854  0.007317  0.001159 -0.003077  0.000966

[2822 rows x 6 columns]
年分组滚动收益率：        
		沪深300   中证500   医药ETF   券商ETF  新能源ETF   红利ETF
date                                                
2012 -0.0235     NaN     NaN     NaN     NaN     NaN
2013 -0.0756  0.1043 -0.0042     NaN     NaN     NaN
2014  0.6888  0.3900  0.0335     NaN     NaN     NaN
2015  0.0782  0.4757  0.3441     NaN     NaN     NaN
2016 -0.1120 -0.1640 -0.0090 -0.0112     NaN     NaN
2017  0.2643  0.0056  0.2523 -0.0671     NaN     NaN
2018 -0.2626 -0.3245 -0.1942 -0.2434     NaN -0.2092
2019  0.4246  0.2818  0.4277  0.4488     NaN  0.4099
2020  0.3066  0.2345  0.5844  0.1776     NaN  0.2925
2021 -0.0408  0.1725 -0.1571 -0.0343  0.4019 -0.0620
2022 -0.2044 -0.1835 -0.2449 -0.2618 -0.2727 -0.1679
2023 -0.0971 -0.0627 -0.1360  0.0385 -0.3435 -0.0952
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2.3. 逐月计算收益率

import qstock as qs
import pandas as pd

#默认日频率、前复权所有历史数据
#open：开盘价，high：最高价，low：最低价，close：收盘价 vol：成交量，turnover：成交金额，turnover_rate:换手率
# 沪深300， 中证500， 医药ETF, 券商ETF， 新能源ETF， 红利ETF, 黄金ETF, 房地产ETF
stocks_info = [
    {'code': '510300', 'name': '沪深300'},
    {'code': '510500', 'name': '中证500'},
    {'code': '512010', 'name': '医药ETF'},
    {'code': '512000', 'name': '券商ETF'},
    {'code': '516160', 'name': '新能源ETF'},
    {'code': '510800', 'name': '红利ETF'},
#     {'code': '518880', 'name': '黄金ETF'},
#     {'code': '512200', 'name': '房地产ETF'}
]
for stock in stocks_info:
    df = qs.get_data(stock['code'])  # 从qstock获取对应的股票历史数据
    stock['history_df'] = df         # 将其存在 history_df 这个key里面。

# 准备计算数据
df_all = pd.DataFrame()
for stock in stocks_info:
    df = stock['history_df']
    df = df[['close']]         # 只需要 date 和 close 2列就行了。
    df.rename(columns={'close': stock['name']}, inplace=True)  # 用股票的名字来重命名close列
    if df_all.size == 0:
        df_all = df
    else:
        df_all = df_all.join(df)  # join是按照index来连接的。

print(df_all)

# 退化日期到年
yearly_pct_change = df_all.pct_change().to_period('M')
print('退化日期到月：', yearly_pct_change)  # 这里可以看到输出的结果，其实就是把index的年月日格式，都变成月份，以方便后面的groupby分组计算。
# 按月分组，滚动计算收益率
y_ret = (yearly_pct_change.groupby(yearly_pct_change.index).apply(lambda x: ((1+x).cumprod()-1).iloc[-1])).round(4)
print('按月分组滚动收益率：', y_ret)



            沪深300  中证500  医药ETF  券商ETF  新能源ETF  红利ETF
date                                                 
2012-05-28  2.004    NaN    NaN    NaN     NaN    NaN
2012-05-29  2.044    NaN    NaN    NaN     NaN    NaN
2012-05-30  2.036    NaN    NaN    NaN     NaN    NaN
2012-05-31  2.030    NaN    NaN    NaN     NaN    NaN
2012-06-01  2.030    NaN    NaN    NaN     NaN    NaN
...           ...    ...    ...    ...     ...    ...
2023-12-28  3.489  5.475  0.410  0.863   0.650  1.035
2023-12-29  3.499  5.519  0.411  0.864   0.645  1.035
2024-01-02  3.453  5.497  0.407  0.854   0.631  1.024
2024-01-03  3.443  5.490  0.405  0.855   0.634  1.026
2024-01-04  3.413  5.446  0.401  0.847   0.621  1.018

[2825 rows x 6 columns]
退化日期到月：             沪深300     中证500     医药ETF     券商ETF    新能源ETF     红利ETF
date                                                               
2012-05       NaN       NaN       NaN       NaN       NaN       NaN
2012-05  0.019960       NaN       NaN       NaN       NaN       NaN
2012-05 -0.003914       NaN       NaN       NaN       NaN       NaN
2012-05 -0.002947       NaN       NaN       NaN       NaN       NaN
2012-06  0.000000       NaN       NaN       NaN       NaN       NaN
...           ...       ...       ...       ...       ...       ...
2023-12  0.024670  0.019173  0.017370  0.022512  0.069079  0.021718
2023-12  0.002866  0.008037  0.002439  0.001159 -0.007692  0.000000
2024-01 -0.013147 -0.003986 -0.009732 -0.011574 -0.021705 -0.010628
2024-01 -0.002896 -0.001273 -0.004914  0.001171  0.004754  0.001953
2024-01 -0.008713 -0.008015 -0.009877 -0.009357 -0.020505 -0.007797

[2825 rows x 6 columns]
按月分组滚动收益率：           沪深300   中证500   医药ETF   券商ETF  新能源ETF   红利ETF
date                                                   
2012-05  0.0130     NaN     NaN     NaN     NaN     NaN
2012-06 -0.0714     NaN     NaN     NaN     NaN     NaN
2012-07 -0.0605     NaN     NaN     NaN     NaN     NaN
2012-08 -0.0723     NaN     NaN     NaN     NaN     NaN
2012-09  0.0578     NaN     NaN     NaN     NaN     NaN
...         ...     ...     ...     ...     ...     ...
2023-09 -0.0195 -0.0092  0.0215 -0.0205 -0.0601  0.0000
2023-10 -0.0319 -0.0285  0.0234 -0.0055 -0.0542 -0.0385
2023-11 -0.0225  0.0018 -0.0046 -0.0055 -0.0691 -0.0177
2023-12 -0.0177 -0.0209 -0.0573 -0.0379  0.0190 -0.0190
2024-01 -0.0246 -0.0132 -0.0243 -0.0197 -0.0372 -0.0164

[141 rows x 6 columns]
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3. 数据存储

3.1. 案例demo数据

本系列的文章用到的csv，都存放在：https://gitee.com/kelvin11/public-resources，数据截止日期：2023-12-26

3.2. 数据库

一般还是选用mongodb来存储爬虫数据更配些。可以自己写一个连接，不过主要还是用pymongo的api多些。

mongodb菜鸟教程：https://www.runoob.com/mongodb/mongodb-tutorial.html

import pymongo
import time


class MongoUtil:

    def __init__(self) -> None:
        super().__init__()
        self.mongo_meta = {'host': 'my.tengxun', 'port': '27017', 'user_name': 'admin', 'password': '123456',
                           'db': 'my_db'}
        self.mongo_client = pymongo.MongoClient('mongodb://%s:%s@%s:%s/' % (
        self.mongo_meta['user_name'], self.mongo_meta['password'], self.mongo_meta['host'], self.mongo_meta['port']))
        # db默认配置为admin这个db
        self.db = self.mongo_client[self.mongo_meta['db']]

    def save_or_update_by_uuid(self, mongodb_collection, data_dict):
        """
        约定：data_dict 是一个字典，必须要包含 uuid 字段，用来后面据此做更新。如果记录中没有 uuid 字段，就自己新建一个，保证记录唯一即可。
        :param mongodb_collection: 集合名
        :param data_dict: 数据字典
        :return:
        """
        uuid = data_dict['uuid']
        detail_db_record = self.db[mongodb_collection].find_one({"uuid": uuid})
        if detail_db_record is None:
            # 主动给记录加一个 m_add_time、m_update_time 字段，设置为当前时间
            data_dict['m_add_time'] = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
            data_dict['m_update_time'] = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
            ret = self.db[mongodb_collection].insert_one(data_dict)
            return ret
        else:
            detail_db_record.update(data_dict)
            detail_db_record['m_update_time'] = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
            new_values = {"$set": detail_db_record}
            ret = self.db[mongodb_collection].update_one({"uuid": uuid}, new_values)
            return ret

    def load_all_data(self, mongodb_collection):
        """
        读取结束后，通过 for x in all_data 进行遍历
        :param mongodb_collection:
        :return:
        """
        return self.db[mongodb_collection].find()#.sort("date", 1)


if __name__ == '__main__':
    this = MongoUtil()
    # {family: {"husband":"文豪"}}  {house: {"$ne": null}}    {"house": 1}
    customer_dict = {"uuid": "234mjnlfjj", "name": "学英", "age": 18, "family": {"husband": "赵文豪"}}
    customer_dict = {"uuid": "234mjnlfjj22222", "name": "刘x坤", "salary": 2000000, "house": [{"address": "江苏"}, {"address": "上海"}]}
    this.save_or_update_by_uuid('test_storage', customer_dict)

    # all_data = this.db['SH510500'].find({"date": {"$gt": "2013-03-15"}}).sort("date", 1)
    # for x in all_data:
    #     print(x)

    # 读取所有数据
    # all_data = this.load_all_data('SH510500')
    # print('读取结束，通过for x in all_data进行遍历')
    # for x in all_data:
    #     print(x)

    # 新增或更新
    # mydict = {"uuid": "11111111", "name": "RUNOOB", "alexa": "100002222", "url": "https://www.runoob.com"}
    # this.save_or_update_by_uuid('sites', mydict)
    # print('插入或更新成功1')
    # time.sleep(3)
    # mydict['name'] = 'Kelvin'
    # this.save_or_update_by_uuid('sites', mydict)
    # print('插入或更新成功2')


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