[Asm] 纯文本查看 复制代码
import requests
import re
import time
import csv
import pandas as pd
from concurrent.futures import ThreadPoolExecutor
from multiprocessing import Pool
today = time.strftime("%Y-%m-%d", time.localtime())
# 建立可转债类，这样做的好处就是拿到类就可以拿到这个可转债的所有的数据，便于后期分析，而不用总是去用列表加来加去
class ConvertibleBond(object):
def __init__(self, datas):
# 转债代码
self.bond_id = datas['bond_id']
# 转债名称
self.bond_nm = datas['bond_nm']
# 正股代码
self.stock_id = datas['stock_id']
# 正股名称
self.stock_nm = datas['stock_nm']
#
self.btype = datas['btype']
# 转股价
self.convert_price = datas['convert_price']
#
self.convert_price_valid_from = datas['convert_price_valid_from']
self.convert_dt = datas['convert_dt']
# 到期时间
self.maturity_dt = datas['maturity_dt']
# 正股价
self.sprice = datas['sprice']
# 现价
self.price = datas['price']
# 到期税前收益率
self.ytm_rt = datas['ytm_rt']
# 剩余年限
self.year_left = datas['year_left']
# 双低
self.dblow = datas['dblow']
# 强赎触发价
self.force_redeem_price = datas['force_redeem_price']
# 回售触发价
self.put_convert_price = datas['put_convert_price']
# 溢价率
self.premium_rt = datas['premium_rt']
# 到期税后收益
self.ytm_rt_tax = datas['ytm_rt_tax']
# 剩余规模
self.orig_iss_amt = datas['orig_iss_amt']
# 建仓线
self.build = None
# 加仓线
self.plus = None
# 重仓线
self.Multiple = None
# 评级
self.rating_cd = datas['rating_cd']
# 操作
self.cz = None
# 基准
self.jz_rate = None
self.sh_price = None
self.dq_price = None
self.sum_lixi = 0
# 从集思录获取可转债的详情页面里面的可转债到期价值
def getdarse(self):
darse_url = 'https://www.jisilu.cn/data/convert_bond_detail/' + str(self.bond_id)
darse_resp = requests.get(darse_url)
html = darse_resp.content.decode('utf-8')
lixi = re.findall(r'''
<td.+?cpn_desc".+?>(.+?)</td>
''', html, re.VERBOSE)
try:
lixi = re.findall('.+?(\d+\.\d+).*?', lixi[0])
for i in range(len(lixi) - 1):
self.sum_lixi = self.sum_lixi + float(lixi[i])
self.sh_price = float(re.findall('<td.+?redeem_price.+?\n(.+?)</td>', html)[0].strip())
self.dq_price = self.sh_price + self.sum_lixi * 0.8
self.build = float(self.dq_price) - (float(self.year_left) * 2.5)
self.plus = float(self.dq_price) - (float(self.year_left) * 4)
self.Multiple = float(self.dq_price) - (float(self.year_left) * 6)
self.jz_rate = (self.build - float(self.price)) / float(self.price)
except:
pass
try:
if float(self.price) <= self.build:
self.cz = '建仓'
if float(self.price) <= self.plus:
self.cz = '加仓'
if float(self.price) <= self.Multiple:
self.cz = '重仓'
except:
pass
def get_data():
UA = 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'
headers = {'User_Agent': UA}
url ='https://www.jisilu.cn/data/cbnew/cb_list/?___jsl=LST___t=1620267814804'
#最的简单的header字段，防止部分网址反爬虫机制
response=requests.get(url,headers=headers)
#当爬取的界面需要用户名密码登陆的时候，构件的请求需要包含auth字段
data=response.json()
rows=data['rows']
return rows
# 最后根据筛选好的目录写入csv文件
def wirte_csv(cbs, name):
f = open(f'{name}.csv', 'w', encoding='utf-8', newline='')
csv_writer = csv.writer(f)
csv_writer.writerow(["代 码", "转债名称", "现 价", "正股名称", "正股价格", "建仓线", "加仓线", "重仓线", "溢价率", "评级",
"回售触发价", "强赎触发价", "剩余年限", "双低", "操作"])
for cb in cbs:
csv_writer.writerow([cb.bond_id, cb.bond_nm, cb.price, cb.stock_nm, cb.sprice, cb.build, cb.plus, cb.Multiple,
cb.premium_rt, cb.rating_cd, cb.put_convert_price, cb.force_redeem_price, cb.year_left,
cb.dblow, cb.cz])
f.close()
def wirte_xls(cbs):
data_list = []
for cb in cbs:
data=[cb.bond_id, cb.bond_nm, cb.price, cb.stock_nm, cb.sprice, cb.build, cb.plus, cb.Multiple,cb.premium_rt, cb.rating_cd, cb.put_convert_price, cb.force_redeem_price, cb.year_left,cb.dblow, cb.cz]
data_list.append(data)
df = pd.DataFrame(data_list) #以数组方式写入
df.columns = ["代 码", "转债名称", "现 价", "正股名称", "正股价格", "建仓线", "加仓线", "重仓线", "溢价率", "评级","回售触发价", "强赎触发价", "剩余年限", "双低", "操作"]
# print(df)
df[["代 码", "现 价", "正股价格", "建仓线", "加仓线", "重仓线","强赎触发价", "剩余年限", "双低"]] = df[
["代 码", "现 价", "正股价格", "建仓线", "加仓线", "重仓线","强赎触发价", "剩余年限", "双低"]].astype(float)
#,dtype = {"代 码' : float,"现 价' : float,"正股价格" : float,"建仓线": float, "加仓线": float, "重仓线": float, "溢价率": float, "回售触发价": float, "强赎触发价": float, "剩余年限": float}
#df.apply(pd.to_numeric, errors='ignore')
#df = df.infer_objects()
df.to_excel('./筛选可转债.xlsx',sheet_name=today,index=False)
print("=====================================已全部导出!=====================================")
def data(datas):
data = datas['cell']
cb = ConvertibleBond(data)
cb.getdarse()
opt = ['AAA', 'AA+', 'AA']
if cb.build != None and cb.rating_cd in opt and float(cb.price) <= 110 and float(cb.year_left) < 5.5 and float(cb.dblow) < 125:
return cb
if __name__ == "__main__":
start = time.time()
cbs = []
bs = []
t = ThreadPoolExecutor(max_workers=10)
for datas in t.map(data, get_data()):
if datas!=None:
cbs.append(datas)
# 根据到期收益率筛选前50名可转债
print(list(cbs))
cbs.sort(key=lambda x: x.jz_rate, reverse=True)
cbs1 = cbs[:50]
# 根据溢价率筛选前30名可转债
cbs1.sort(key=lambda x: float(x.premium_rt[:-1]) * 0.01, reverse=False)
cbs2 = cbs1[:30]
cbs2.sort(key=lambda x: float(x.price), reverse=False)
# print(cbs2)
wirte_xls(cbs2)
# wirte_csv(cbs2)
# print("爬取完成")
end = time.time()
print(end - start)
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发表于 2021-5-4 15:26
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发表于 2021-5-4 15:33
