python可转债问题救助

molinchz

# -*- coding:utf-8 -*-
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())

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)
    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:
    if cb.build != None and float(cb.price) <= 200:
        return cb

if __name__ == "__main__":
    start = time.time()
    cbs = []
    bs = []
    t = ThreadPoolExecutor(max_workers=10)
    # gedat = get_data()
    # print(gedat)
    for datas in t.map(data, get_data()):
        tmp = datas
        print(tmp)
        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)


代码 如上，运行到这里for datas in t.map(data, get_data()):   结果 是空，不知是怎么回事，调试数据 是有的，条件也用最简单的，但输出 <__main__.ConvertibleBond object at 0x000000000B374FD0>
<__main__.ConvertibleBond object at 0x000000000B3AA9D0>     ，还有一个问题是，我想单步调试多线程，但发现 进不了def data(datas):  这个里面，求调试的方法？谢谢

molinchz

那位大佬帮忙解一下

JetMei

我是为了弄可转债，开始自学的Python

puianger

我也是，我已经写出来了。

puianger

加我微信交流一下，pulistie