量化策略多因子选股之抓取不同时段的历史价格，及数据库操作

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#获取20170831，20180323的沪深三百成分及中证500的成分股当日收盘价

# coding=utf-8
import pandas as pd
import tushare as ts
import sys
from sqlalchemy import create_engine
import numpy as np

reload(sys)
sys.setdefaultencoding("utf-8")
hs300=ts.get_hs300s()

code=[]
price=[]

hs300_code= hs300['code']
for stock in hs300_code:
    try:
        df=ts.get_k_data(stock,ktype='M',start='2017-08-01', end='2018-03-24')

        #print df


        df1= df.iloc[0,6]


        df2= df.iloc[0,2]
        #print df1
        #print df2
        code.append(df1)
        price.append(df2)
    except:
        pass

print code
print len(code)
print price
print len(price)

price_201709=pd.DataFrame({'price':price},index=code
                          )
print price_201709


engine = create_engine('mysql://root:********@127.0.0.1/stock_report?charset=utf8')
price_201709.to_sql('price_201709',engine,if_exists='append')

然后在MYSQL进行表连接，将需要的财务数据（2017年第二季度）、历史股价连接

计算市值 （总市值和流通市值）

计算PE

CREATE TABLE final

SELECT p.index, b.name, p.price AS 20170901price, b.`outstanding`, b.`totals`
, esp, p.price * outstanding AS marketvalue, totals * p.price AS totalvalue
, p.price / esp AS pe, industry, g1.roe
, g1.`net_profits`, g1.`profits_yoy`, g.`net_profit_ratio`, g.`gross_profit_rate`, g.business_income
, `mbrg`, `nprg`, p2.price AS 20180323price
FROM `price_201709` p, `get_report_basic_nochange` b, `get_report2_201706` g, `get_report_201706` g1, `get_report3_201706` g3, `price_20180328` p2
WHERE p.index = b.code
AND g.code = p.index
AND g1.code = p.index
AND g3.code = p.index
AND p2.index = p.index

浏览结果：

接下来就是对部分财务数据进行取对数，检查数据的缺失值。。。