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tshare.py
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import akshare as ak
import numpy as np
import pandas as pd
import tushare as ts
import requests as rq
import time
import json
from ConfigDB import MongoDB,RedisPool
db = MongoDB('Stock','Increate')
red_=RedisPool(db=1)
def stock_ak(code,):
# dataF=stock_financial_abstract_df = ak.stock_financial_abstract(stock="600004")
# dataF=stock_em_yjyg_df = ak.stock_em_yjyg(date="2019-03-31")
# for i in code:
data={}
data["code"]=code
data['id']=db.getID("stock")
try:
df=stock_financial_analysis_indicator_df = ak.stock_financial_analysis_indicator(stock=code)
for item,row in df.iteritems():
r=np.array(row).tolist()
data[item]=r
# print(r[:12])
if item=="净资产增长率(%)":
r=[i if i !="--" else "0" for i in r ]
num=np.sum(list(map(lambda x: float(x) >= 20, r[:15])))
if num > 12 :
data['High']=1
except Exception as e:
print(e)
# increate=np.array(df["净资产增长率(%)"]).tolist()
# print(data)
db.insertDict(data)
# write = pd.ExcelWriter("stock_ak_increate.xlsx")
# df=pd.read_excel("stock_ak.xlsx",index_col=0,)
# for i in df.index:
# print(df["净资产增长率(%)"])
# if df.
# dataF.to_excel(r"stock_ak.xlsx",)
# excel_header="financial"
# with pd.ExcelWriter('stock_ak.xlsx') as writer: # doctest: +SKIP
# df["净资产增长率(%)"].to_excel(write, sheet_name='Sheet_name_1',header=excel_header,index=False)
# df2.to_excel(writer, sheet_name='Sheet_name_2')
# print(df)
# print(stock_financial_abstract_df)
# write.save()
def read_code():
code=pd.read_excel("tshare.xlsx",index_col=0,)
dt=np.array(code.ts_code)
dt=dt.tolist()
res=[]
for i in dt:
i=i.split(".")[::-1]
i="".join(i)
res.append(i)
# print(i)
return res
def stock_tu():
pro = ts.pro_api("3bffe33c882b7cf4c90d49b32bd476515271003520a9b77232685195")
data = pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date')
# ts.set_token('your token here')
# with open("tushare.txt",'w',encoding='utf-8') as f:
# f.write(ts.get_industry_classified())
# print(type(data))
# print(data.ts_code)
data.to_excel(r"tshare.xlsx",)
dt=np.array(data.ts_code)
dt=dt.tolist()
def snowball():
user_agent = "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36"
cookie="device_id=24700f9f1986800ab4fcc880530dd0ed; s=cx11ywwoko; xq_a_token=083cb48f59c4e464135799326ae6b063cbe71b9a; xqat=083cb48f59c4e464135799326ae6b063cbe71b9a; xq_r_token=5ca3ac1644c5df08523173779a080773967ff6db; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOjU0OTY5NzYyOTQsImlzcyI6InVjIiwiZXhwIjoxNTk1MjkxMzU5LCJjdG0iOjE1OTM2ODk4OTYyMjksImNpZCI6ImQ5ZDBuNEFadXAifQ.fVSs6LDfY-wTqxk6Gs8bzkF3RolAyrPdCsCNp6lddScjseNUn9sQg6Y_tKQn81huBuFpgh7MmkzbGKUvdW8tfVzlmzhZ_GQ5kOQ-GbxZy311kf7ywWh-U8hyRpdT2Fnu1G5dt_9f2cdM5knllx_Un6I0GxR-7TiNIUPGayL3jf7sRu4vkONHU4lLtsFw2b7AFqPaylFtmcQDx_NPQdHT20l-i-TWo9oxpJ8XzHlbsO-py_t2kpvmjj2_sZmkioIVendhqIG3m7NlPYFv9oaoPwo-d9vptZ9kRGeORRDJx9MUU6ChiNQCUz2Wh3Tk4eImXSucbMT6eThJUtX6Y2xTZg; xq_is_login=1; u=5496976294; bid=f244c8f52388bb444c5461130c3ddc9a_kc4rhk37; Hm_lvt_1db88642e346389874251b5a1eded6e3=1593692705,1593693631,1593693945,1593758896; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1593764284"
headers = {
'cookie': cookie,
"user-agent": user_agent
}
# for i in dt:
# i=i.split(".")[::-1]
# i="".join(i)
# for i in range(1,18):
# # stock_list
# url=R"https://xueqiu.com/service/v5/stock/screener/quote/list?page={}&size=90&order=desc&order_by=amount&exchange=CN&market=CN&type=sha".format(str(i))
# # stock finance
# print(i)
i="SZ000858"
url=R"https://stock.xueqiu.com/v5/stock/finance/cn/income.json?symbol={}&type=all&is_detail=true".format(str(i))
res=rq.get(url, headers=headers).content.decode("utf8")
d_list=json.loads(res)["data"]["list"]
for i in d_list:
# s=i["net_profit_atsopc_yoy"]
for k in i.keys():
# k
print(k,"\r\n")
if __name__=="__main__":
# stock_tu()
# stock_ak()
code=read_code()
# # # print(code)
for i in code:
print(i)
if i:
red_.lpush("code",i)
# stock_ak(i[2:])
# while True:
# red_.get("code")
# stock_ak(str(red_.get("code"),encoding="utf-8")[2:])
# time.sleep(5)
# ts=db.getHigh()
# for t in ts:
# # print("*"*10,)
# try:
# data=t.pop("净资产报酬率(%)")
# r=[i if i !="--" else "0" for i in data]
# k=1
# inc_list=[]
# for i in range(1,5):
# start =(k-1)*1
# end=4*k
# r[start:end]
# temp=0
# for i in r[:4]:
# temp += float(i)
# inc=temp/4
# if inc>20:
# inc_list.append(inc)
# k+=1
# num=np.sum(list(map(lambda x: float(x) >= 20, inc_list)))
# if num == 4 :
# print(t["code"],inc_list)
# except:
# pass
# for i in ts:
# print(i)
# code="000858"
# stock_ak(code)
# snowball()