程序猿怎么利用技术挣钱？——python量化实践

#导入库
import tushare  as ts
import pandas as pd
import numpy as np
import time,random
pro = ts.pro_api(token='你自己的token')

# L上市 D退市 P暂停上市
# ts_code 股票代码 exchange 交易所 SSE上交所 SZSE 深交所 
# list_date 上市日期 delist_date 退市日期
stock_L = pro.stock_basic(exchange='', list_status='L', fields='ts_code,exchange,list_date,delist_date')
stock_D = pro.stock_basic(exchange='', list_status='D', fields='ts_code,exchange,list_date,delist_date')
stock_P = pro.stock_basic(exchange='', list_status='P', fields='ts_code,exchange,list_date,delist_date')

#整合所有股票
data=pd.concat([stock_L,stock_D,stock_P],axis=0).reset_index(drop=True)

result = pd.DataFrame()
count=0
for ts_code in data.ts_code.tolist():
    start_time=time.time()
    df = pro.daily_basic(ts_code=ts_code, trade_date='', start_date='20060101',fields='ts_code,trade_date,pe')
    result=pd.concat([result,df],axis=0).reset_index(drop=True)
    count+=1
    if count%200:
        pass
    else:
        end_time=time.time()
        tm=end_time-start_time
        
        #限制60秒内最多请求200次
        while tm<=60 :
            time.sleep(60-tm+1)
            end_time=time.time()
            tm=end_time-start_time
        print(count,"耗时 %s 秒"%tm)
        
#获取到的数据保存到本地，也可以存到数据库里持续更新        
result.to_csv("PE监控.csv",index=False)

#导入库
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn')
%matplotlib inline

#导入之前获取的数据
result=pd.read_csv("PE监控.csv")
#等权重处理
data=pd.pivot_table(result,values='pe',index='trade_date',aggfunc='mean')
#绘图
data.plot(figsize=(15,6),subplots=True)