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UniversalRotation.py
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import os, random, time, datetime, schedule, webbrowser
import xlwings
import pandas
import requests
import pysnowball
import browser_cookie3
from chinese_calendar import is_workday
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
@xlwings.func
def get_fund_net_asset_value_history(fund_code: str, pz: int = 200) -> pandas.DataFrame:
'''
根据基金代码和要获取的页码抓取基金净值信息
Parameters
----------
fund_code : 6位基金代码
page : 页码 1 为最新页数据
Return
------
DataFrame : 包含基金历史k线数据
'''
# 请求头
EastmoneyFundHeaders = {
'Accept': '*/*',
'Accept-Encoding': 'gzip, deflate',
'Accept-Language': 'zh-CN,zh;q=0.9',
# 'Connection': 'keep-alive',
'Host': 'api.fund.eastmoney.com',
'Referer': 'http://fundf10.eastmoney.com/',
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36',
}
# 请求参数
Eastmoneyparams = {
'fundCode': f'{fund_code}',
'pageIndex': '1',
'pageSize': f'{pz}',
'startDate': '',
'endDate': '',
}
url = 'http://api.fund.eastmoney.com/f10/lsjz'
#设置重连次数
requests.adapters.DEFAULT_RETRIES = 10
session1 = requests.session()
# 设置连接活跃状态为False
session1.keep_alive = False
response1 = session1.get(url, headers=EastmoneyFundHeaders, params=Eastmoneyparams, verify=False, stream=False, timeout=10)
json_response = response1.json()
response1.close()
del(response1)
rows = []
columns = ['日期', '单位净值', '累计净值', '涨跌幅']
if json_response is None:
return pandas.DataFrame(rows, columns=columns)
datas = json_response['Data']['LSJZList']
if len(datas) == 0:
return pandas.DataFrame(rows, columns=columns)
for stock in datas:
rows.append({
'日期': stock['FSRQ'],
'单位净值': stock['DWJZ'],
'累计净值': stock['LJJZ'],
'涨跌幅': stock['JZZZL']
})
df = pandas.DataFrame(rows)
df['日期'] = pandas.to_datetime(df['日期'], errors='coerce')
df['单位净值'] = pandas.to_numeric(df['单位净值'], errors='coerce')
df['累计净值'] = pandas.to_numeric(df['累计净值'], errors='coerce')
df['涨跌幅'] = pandas.to_numeric(df['涨跌幅'], errors='coerce')
return df
@xlwings.func
# 获取基金的市价、溢价率、成交量
def get_fund_premium_rate_and_amount(fund_code: str):
detail = pandas.DataFrame(pysnowball.quote_detail(fund_code))
row1=detail.loc["quote"].iloc[0]
premium_rate1 = row1["premium_rate"]
current_price = row1["current"]
amount1 = row1["amount"]
amount1 = amount1/10000 if amount1 else 0
return current_price, premium_rate1, amount1
@xlwings.func
# 获取xq_a_token
def get_xq_a_token():
str_xq_a_token = ';'
while True:
cj = browser_cookie3.load()
for item in cj:
if item.name == "xq_a_token" :
print('get token, %s = %s' % (item.name, item.value))
str_xq_a_token = 'xq_a_token=' + item.value + ';'
return str_xq_a_token
if str_xq_a_token == ";" :
print('get token, retrying ......')
webbrowser.open("https://xueqiu.com/")
time.sleep(1)
@xlwings.func
# 根据排名计算排名分(精确到小数点后2位)
def get_rank_value(length: int, rank: int):
return round((length-rank)/length*100, 2)
@xlwings.func
def rotate_fund_by_premium_rate_and_20net_asset_value(source_sheets: str, dest_range: str, delay: int):
'''
根据20天净值增长率和溢价率来量化轮动基金
Parameters
----------
source_sheets : Excel里sheet名称
dest_range : 写回Excel的区域
delay : 延时
'''
xlwings.Book("UniversalRotation.xlsm").set_mock_caller()
wb = xlwings.Book.caller() # wb = xlwings.Book(r'UniversalRotation.xlsm')
pandas.options.display.max_columns = None
pandas.options.display.max_rows = None
pysnowball.set_token(get_xq_a_token())
sheet_fund = wb.sheets[source_sheets]
# 数据表范围请参考Excel里的"最新数据"区域
source_range = 'H2:Y' + str(sheet_fund.used_range.last_cell.row)
print('数据表范围：' + source_range)
data_fund = pandas.DataFrame(sheet_fund.range(source_range).value,
columns=['基金代码','基金名称','投资种类','溢价率','5天净值增长率','10天净值增长率',
'20天净值增长率','60天净值增长率','120天净值增长率','250天净值增长率',
'500天净值增长率','750天净值增长率','总排名分','20天净值增长率排名分',
'溢价率排名分','成交额(万元)','Mark','持有周期'])
refresh_time = str(time.strftime("%Y-%m-%d__%H.%M.%S", time.localtime()))
sheet_fund.range('AA6').value = '更新时间:' + refresh_time
log_file = open('log-' + source_sheets + refresh_time + '.txt', 'a+')
for i,fund_code in enumerate(data_fund['基金代码']):
# 延时
if (i % 10 == 0) :
time.sleep(random.randrange(delay))
netAssetValueRaw = get_fund_net_asset_value_history(fund_code[2:8])
# 最新净值以及5个交易日前、10个交易日前、20个交易日前、60个交易日前、120个交易日前、250个交易日前、500个交易日前、750个交易日前的累计净值
netAssetDate1 = str(netAssetValueRaw.loc[0]['日期'])[0:10]
netAssetLJValue1 = netAssetValueRaw.loc[0]['累计净值']
netAssetDate20 = str(netAssetValueRaw.loc[len(netAssetValueRaw)-1]['日期'])[0:10]
netAssetLJValue5 = netAssetLJValue10 = netAssetLJValue20 = netAssetLJValue60 = netAssetLJValue120 \
= netAssetLJValue250 = netAssetLJValue500 \
= netAssetLJValue750 = netAssetValueRaw.loc[len(netAssetValueRaw)-1]['累计净值']
if (len(netAssetValueRaw) > 20) :
netAssetLJValue5 = netAssetValueRaw.loc[5]['累计净值']
netAssetLJValue10 = netAssetValueRaw.loc[10]['累计净值']
netAssetDate20 = str(netAssetValueRaw.loc[20]['日期'])[0:10]
netAssetLJValue20 = netAssetValueRaw.loc[20]['累计净值']
if (len(netAssetValueRaw) > 60) :
netAssetLJValue60 = netAssetValueRaw.loc[60]['累计净值']
if (len(netAssetValueRaw) > 120) :
netAssetLJValue120 = netAssetValueRaw.loc[120]['累计净值']
if (len(netAssetValueRaw) > 250) :
netAssetLJValue250 = netAssetValueRaw.loc[250]['累计净值']
if (len(netAssetValueRaw) > 500) :
netAssetLJValue500 = netAssetValueRaw.loc[500]['累计净值']
if (len(netAssetValueRaw) > 750) :
netAssetLJValue750 = netAssetValueRaw.loc[750]['累计净值']
# 更新5天、10天、20天、60天、120天、250天、500天、750天的累计净值增长率
netAssetLJValue5Rate = round((netAssetLJValue1 - netAssetLJValue5) / netAssetLJValue5, 4)
data_fund.loc[i, '5天净值增长率'] = netAssetLJValue5Rate
netAssetLJValue10Rate = round((netAssetLJValue1 - netAssetLJValue10) / netAssetLJValue10, 4)
data_fund.loc[i, '10天净值增长率'] = netAssetLJValue10Rate
netAssetLJValue20Rate = round((netAssetLJValue1 - netAssetLJValue20) / netAssetLJValue20, 4)
data_fund.loc[i, '20天净值增长率'] = netAssetLJValue20Rate
data_fund.loc[i, '60天净值增长率'] = round((netAssetLJValue1 - netAssetLJValue60) / netAssetLJValue60, 4)
data_fund.loc[i, '120天净值增长率'] = round((netAssetLJValue1 - netAssetLJValue120) / netAssetLJValue120, 4)
data_fund.loc[i, '250天净值增长率'] = round((netAssetLJValue1 - netAssetLJValue250) / netAssetLJValue250, 4)
data_fund.loc[i, '500天净值增长率'] = round((netAssetLJValue1 - netAssetLJValue500) / netAssetLJValue500, 4)
data_fund.loc[i, '750天净值增长率'] = round((netAssetLJValue1 - netAssetLJValue750) / netAssetLJValue750, 4)
# 更新基金的市价、溢价率、成交量
current_price,fundPremiumRateValue,fundAmount = get_fund_premium_rate_and_amount(fund_code)
netAssetDWValue1 = netAssetValueRaw.loc[0]['单位净值']
if (current_price) :
fundPremiumRateValue = round((current_price - netAssetDWValue1) / netAssetDWValue1, 4)
data_fund.loc[i, '溢价率'] = fundPremiumRateValue
data_fund.loc[i, '成交额(万元)'] = fundAmount
log_str = 'No.' + format(str(i), "<6") + fund_code + ':'+ format(data_fund.loc[i, '基金名称'], "<15") \
+ netAssetDate1 + ':净值:' + format(str(netAssetLJValue1), "<10") \
+ netAssetDate20 + ':净值:' + format(str(netAssetLJValue20), "<10") \
+ '二十个交易日净值增长率:' + format(str(netAssetLJValue20Rate), "<10") \
+ '溢价率:'+ format(str(fundPremiumRateValue), "<10")
print(log_str)
print(log_str, file=log_file)
# 更新'溢价率排名分'
data_fund = data_fund.sort_values(by='溢价率', ascending=True)
data_fund.reset_index(drop=True, inplace=True)
for i,fundPremiumRateValueItem in enumerate(data_fund['溢价率']):
data_fund.loc[i, '溢价率排名分'] = get_rank_value(len(data_fund), i)
# 更新'20天净值增长率排名分'
data_fund = data_fund.sort_values(by='20天净值增长率', ascending=False)
data_fund.reset_index(drop=True, inplace=True)
for i,netAssetLJValue20RateItem in enumerate(data_fund['20天净值增长率']):
data_fund.loc[i, '20天净值增长率排名分'] = get_rank_value(len(data_fund), i)
# 计算'总排名分'
for i,sumValue in enumerate(data_fund['总排名分']):
data_fund.loc[i, '总排名分'] = data_fund.loc[i, '溢价率排名分'] + data_fund.loc[i, '20天净值增长率排名分']
data_fund = data_fund.sort_values(by='总排名分', ascending=False)
data_fund.reset_index(drop=True, inplace=True)
# 根据排名更新'总排名分'
for i,fundPremiumRateValue in enumerate(data_fund['总排名分']):
data_fund.loc[i, '总排名分'] = get_rank_value(len(data_fund), i)
# 将数据起始下标从1开始计数
data_fund.index += 1
print(data_fund)
print(data_fund, file=log_file)
log_file.close()
# 更新数据到原Excel
sheet_fund.range(dest_range).value = data_fund
wb.save()
@xlwings.func
# 轮动20天净值增长和溢价率选LOF、ETF和封基
def rotate_LOF_ETF():
print("------------------------20天净值增长率和溢价率轮动LOF、ETF和封基-------------------------------------------------")
rotate_fund_by_premium_rate_and_20net_asset_value('20天净值增长率和溢价率轮动LOF、ETF和封基','G1', 10)
@xlwings.func
# 轮动20天净增和溢价率选债券和境外基金
def rotate_abroad_fund():
print("-----------------------20天净值增长率和溢价率轮动债券和境外基金---------------------------------------------------")
rotate_fund_by_premium_rate_and_20net_asset_value('20天净值增长率和溢价率轮动债券和境外基金', 'G1', 10)
source_range_convertible_bond = 'B8:T'
# 获取可转债实时数据列表上方的多因子策略的阈值上限和权重
def get_convertible_bond_factor(factor:str):
factor = factor.split(',', -1)
return float(factor[0]), float(factor[1])
@xlwings.func
# 更新可转债实时数据：价格、涨跌幅、转股价、转股价值、溢价率、双低值、到期时间、剩余年限、剩余规模、成交金额、换手率、税前收益、振幅等
def refresh_convertible_bond():
print("更新可转债实时数据：价格、涨跌幅、转股价、转股价值、溢价率、双低值、到期时间、剩余年限、剩余规模、成交金额、换手率、税前收益、振幅等")
xlwings.Book("UniversalRotation.xlsm").set_mock_caller()
wb = xlwings.Book.caller()
pandas.options.display.max_columns = None
pandas.options.display.max_rows = None
pysnowball.set_token(get_xq_a_token())
source_sheets = '可转债实时数据'
sheet_fund = wb.sheets[source_sheets]
source_range = source_range_convertible_bond + str(sheet_fund.used_range.last_cell.row)
print('数据表范围：' + source_range)
data_fund = pandas.DataFrame(sheet_fund.range(source_range).value,
columns=['转债代码','转债名称','当前价','涨跌幅','转股价','转股价值','溢价率','双低值',
'到期时间','剩余年限','剩余规模','成交金额','换手率','税前收益','最高价','最低价',
'振幅','多因子1排名','多因子2排名'])
refresh_time = str(time.strftime("%Y-%m-%d__%H.%M.%S", time.localtime()))
sheet_fund.range('W12').value = '更新时间:' + refresh_time
log_file = open('log-' + source_sheets + refresh_time + '.txt', 'a+')
for i,fund_code in enumerate(data_fund['转债代码']):
if str(fund_code).startswith('11') or str(fund_code).startswith('13'):
fund_code_str = ('SH' + str(fund_code))[0:8]
elif str(fund_code).startswith('12'):
fund_code_str = ('SZ' + str(fund_code))[0:8]
detail = pandas.DataFrame(pysnowball.quote_detail(fund_code_str))
row1 = detail.loc["quote"].iloc[0]
data_fund.loc[i, '当前价'] = row1["current"]
data_fund.loc[i, '涨跌幅'] = row1["percent"] / 100 if row1["percent"] != None else '停牌'
data_fund.loc[i, '转股价'] = row1["conversion_price"]
data_fund.loc[i, '转股价值'] = row1["conversion_value"]
data_fund.loc[i, '溢价率'] = row1["premium_rate"] / 100 if row1["premium_rate"] != None else '停牌'
data_fund.loc[i, '双低值'] = row1["current"] + row1["premium_rate"]
data_fund.loc[i, '到期时间'] = str(time.strftime("%Y-%m-%d", time.localtime(row1["maturity_date"]/1000)))
data_fund.loc[i, '剩余年限'] = row1["remain_year"]
data_fund.loc[i, '剩余规模'] = row1["outstanding_amt"] / 100000000 if row1["outstanding_amt"] != None else 1
data_fund.loc[i, '成交金额'] = row1["amount"] / 10000 if row1["amount"] != None else 0
data_fund.loc[i, '换手率'] = (data_fund.loc[i, '成交金额'] / 10000 / row1["current"]) / (data_fund.loc[i, '剩余规模'] / 100)
data_fund.loc[i, '税前收益'] = row1["benefit_before_tax"] / 100 if row1["benefit_before_tax"] != None else '停牌'
data_fund.loc[i, '最高价'] = row1["high"]
data_fund.loc[i, '最低价'] = row1["low"]
if row1["high"] and row1["low"]:
data_fund.loc[i, '振幅'] = (row1["high"] - row1["low"]) / row1["low"]
else:
data_fund.loc[i, '振幅'] = '停牌'
log_str = 'No.' + format(str(i), "<6") + format(str(fund_code_str), "<10") \
+ format(data_fund.loc[i, '转债名称'], "<15") \
+ '当前价:' + format(str(row1["current"]), "<10") \
+ '溢价率:' + format(str(row1["premium_rate"]), "<10") \
+ '涨跌幅:'+ format(str(row1["percent"]), "<10")
print(log_str)
print(log_str, file=log_file)
data_fund = data_fund.sort_values(by='溢价率')
data_fund.reset_index(drop=True, inplace=True)
data_fund.index += 1
print(data_fund)
print(data_fund, file=log_file)
log_file.close()
# 更新原Excel
sheet_fund.range('A7').value = data_fund
wb.save()
@xlwings.func
# 更新低溢价可转债数据
def refresh_premium_rate_convertible_bond():
print("--------------------------------------更新低溢价可转债数据----------------------------------------------------")
xlwings.Book("UniversalRotation.xlsm").set_mock_caller()
wb = xlwings.Book.caller()
pandas.options.display.max_columns = None
pandas.options.display.max_rows = None
sheet_src = wb.sheets['可转债实时数据']
source_range = source_range_convertible_bond + str(sheet_src.used_range.last_cell.row)
data_fund_source = pandas.DataFrame(sheet_src.range(source_range).value,
columns=['转债代码','转债名称','当前价','涨跌幅','转股价','转股价值','溢价率','双低值',
'到期时间','剩余年限','剩余规模','成交金额','换手率','税前收益','最高价','最低价',
'振幅','多因子1排名','多因子2排名'])
data_fund_destination = data_fund_source[['转债代码','转债名称','当前价','溢价率','剩余规模']]
data_fund_destination = data_fund_destination[(data_fund_destination['当前价'] < sheet_src.range('D2').value) &
(data_fund_destination['溢价率'] < sheet_src.range('H2').value) &
(data_fund_destination['剩余规模'] < sheet_src.range('L2').value)]
data_fund_destination = data_fund_destination.sort_values(by='溢价率')
data_fund_destination.reset_index(drop=True, inplace=True)
data_fund_destination.index += 1
print(data_fund_destination)
# 更新低溢价可转债轮动sheet
sheet_dest = wb.sheets['低溢价可转债轮动']
sheet_dest.range('H2').value = data_fund_destination[:50]
wb.save()
@xlwings.func
# 更新双低可转债数据
def refresh_price_and_premium_rate_convertible_bond():
print("----------------------------------------更新双低可转债数据----------------------------------------------------")
xlwings.Book("UniversalRotation.xlsm").set_mock_caller()
wb = xlwings.Book.caller()
pandas.options.display.max_columns = None
pandas.options.display.max_rows = None
sheet_src = wb.sheets['可转债实时数据']
source_range = source_range_convertible_bond + str(sheet_src.used_range.last_cell.row)
data_fund_source = pandas.DataFrame(sheet_src.range(source_range).value,
columns=['转债代码','转债名称','当前价','涨跌幅','转股价','转股价值','溢价率','双低值',
'到期时间','剩余年限','剩余规模','成交金额','换手率','税前收益','最高价','最低价',
'振幅','多因子1排名','多因子2排名'])
data_fund_destination = data_fund_source[['转债代码','转债名称','当前价','溢价率','双低值','剩余规模']]
data_fund_destination = data_fund_destination[(data_fund_destination['当前价'] < sheet_src.range('D3').value) &
(data_fund_destination['溢价率'] < sheet_src.range('H3').value) &
(data_fund_destination['剩余规模'] < sheet_src.range('L3').value)]
data_fund_destination = data_fund_destination.sort_values(by='双低值')
data_fund_destination.reset_index(drop=True, inplace=True)
data_fund_destination.index +=1
print(data_fund_destination)
# 更新双低可转债轮动sheet
sheet_dest = wb.sheets['双低可转债轮动']
sheet_dest.range('H2').value = data_fund_destination[:50]
wb.save()
@xlwings.func
# 更新多因子1可转债数据
def refresh_multifactor1_convertible_bond():
print("--------------------------------------更新多因子1可转债数据----------------------------------------------------")
xlwings.Book("UniversalRotation.xlsm").set_mock_caller()
wb = xlwings.Book.caller()
pandas.options.display.max_columns = None
pandas.options.display.max_rows = None
sheet_src = wb.sheets['可转债实时数据']
source_range = source_range_convertible_bond + str(sheet_src.used_range.last_cell.row)
data_fund_source = pandas.DataFrame(sheet_src.range(source_range).value,
columns=['转债代码','转债名称','当前价','涨跌幅','转股价','转股价值','溢价率','双低值',
'到期时间','剩余年限','剩余规模','成交金额','换手率','税前收益','最高价','最低价',
'振幅','多因子1排名','多因子2排名'])
data_fund_destination = data_fund_source[['转债代码','转债名称','当前价','溢价率','剩余规模']]
threshold_current_price, weight_current_price = get_convertible_bond_factor(sheet_src.range('D5').value)
threshold_premium_rate, weight_premium_rate = get_convertible_bond_factor(sheet_src.range('H5').value)
threshold_outstanding_amt, weight_outstanding_amt = get_convertible_bond_factor(sheet_src.range('L5').value)
# for i, fund_code in enumerate(data_fund_source['转债代码']):
# data_fund_source.loc[i, '多因子1排名'] = data_fund_source.loc[i, '当前价'] * weight_current_price
# + data_fund_source.loc[i, '溢价率'] * weight_premium_rate
# + data_fund_source.loc[i, '剩余规模'] * weight_outstanding_amt
data_fund_destination = data_fund_destination[(data_fund_destination['当前价'] < threshold_current_price) &
(data_fund_destination['溢价率'] < threshold_premium_rate) &
(data_fund_destination['剩余规模'] < threshold_outstanding_amt)]
data_fund_destination = data_fund_destination.sort_values(by='溢价率')
data_fund_destination.reset_index(drop=True, inplace=True)
data_fund_destination.index += 1
print(data_fund_destination)
# 更新低溢价可转债轮动sheet
sheet_dest = wb.sheets['低溢价可转债轮动']
sheet_dest.range('Q2').value = data_fund_destination[:50]
wb.save()
@xlwings.func
# 更新多因子2可转债数据
def refresh_multifactor2_convertible_bond():
print("----------------------------------------更新多因子2可转债数据----------------------------------------------------")
xlwings.Book("UniversalRotation.xlsm").set_mock_caller()
wb = xlwings.Book.caller()
pandas.options.display.max_columns = None
pandas.options.display.max_rows = None
sheet_src = wb.sheets['可转债实时数据']
source_range = source_range_convertible_bond + str(sheet_src.used_range.last_cell.row)
data_fund_source = pandas.DataFrame(sheet_src.range(source_range).value,
columns=['转债代码','转债名称','当前价','涨跌幅','转股价','转股价值','溢价率','双低值',
'到期时间','剩余年限','剩余规模','成交金额','换手率','税前收益','最高价','最低价',
'振幅','多因子1排名','多因子2排名'])
data_fund_destination = data_fund_source[['转债代码','转债名称','当前价','溢价率','剩余规模']]
threshold_current_price, weight_current_price = get_convertible_bond_factor(sheet_src.range('D6').value)
threshold_premium_rate, weight_premium_rate = get_convertible_bond_factor(sheet_src.range('H6').value)
threshold_outstanding_amt, weight_outstanding_amt = get_convertible_bond_factor(sheet_src.range('L6').value)
data_fund_destination = data_fund_destination[(data_fund_destination['当前价'] < threshold_current_price) &
(data_fund_destination['溢价率'] < threshold_premium_rate) &
(data_fund_destination['剩余规模'] < threshold_outstanding_amt)]
data_fund_destination = data_fund_destination.sort_values(by='溢价率')
data_fund_destination.reset_index(drop=True, inplace=True)
data_fund_destination.index += 1
print(data_fund_destination)
# 更新双低可转债轮动sheet
sheet_dest = wb.sheets['双低可转债轮动']
sheet_dest.range('R2').value = data_fund_destination[:50]
wb.save()
@xlwings.func
# 更新股票实时数据：价格、涨跌幅等
def refresh_stock():
print("更新股票实时数据：价格、涨跌幅等")
xlwings.Book("UniversalRotation.xlsm").set_mock_caller()
wb = xlwings.Book.caller()
pandas.options.display.max_columns = None
pandas.options.display.max_rows = None
pysnowball.set_token(get_xq_a_token())
source_sheets = '股票实时数据'
sheet_fund = wb.sheets[source_sheets]
source_range = 'B2:E' + str(sheet_fund.used_range.last_cell.row)
print('数据表范围：' + source_range)
data_fund = pandas.DataFrame(sheet_fund.range(source_range).value,
columns=['股票代码','股票名称','当前价','涨跌幅'])
refresh_time = str(time.strftime("%Y-%m-%d__%H.%M.%S", time.localtime()))
sheet_fund.range('W3').value = '更新时间:' + refresh_time
log_file = open('log-' + source_sheets + refresh_time + '.txt', 'a+')
for i,fund_code in enumerate(data_fund['股票代码']):
detail = pandas.DataFrame(pysnowball.quote_detail(fund_code))
row1 = detail.loc["quote"].iloc[0]
print(str(row1))
# data_fund.loc[i, '当前价'] = row1["current"]
# data_fund.loc[i, '涨跌幅'] = row1["percent"] / 100 if row1["percent"] != None else '停牌'
# data_fund.loc[i, '转股价'] = row1["conversion_price"]
# data_fund.loc[i, '转股价值'] = row1["conversion_value"]
# data_fund.loc[i, '溢价率'] = row1["premium_rate"] / 100 if row1["premium_rate"] != None else '停牌'
# data_fund.loc[i, '双低值'] = row1["current"] + row1["premium_rate"]
# data_fund.loc[i, '到期时间'] = str(time.strftime("%Y-%m-%d", time.localtime(row1["maturity_date"]/1000)))
# data_fund.loc[i, '剩余年限'] = row1["remain_year"]
# data_fund.loc[i, '剩余规模'] = row1["outstanding_amt"] / 100000000 if row1["outstanding_amt"] != None else 1
# data_fund.loc[i, '成交金额'] = row1["amount"] / 10000 if row1["amount"] != None else 0
# data_fund.loc[i, '换手率'] = (data_fund.loc[i, '成交金额'] / 10000 / row1["current"]) / (data_fund.loc[i, '剩余规模'] / 100)
# data_fund.loc[i, '税前收益'] = row1["benefit_before_tax"] / 100 if row1["benefit_before_tax"] != None else '停牌'
# data_fund.loc[i, '最高价'] = row1["high"]
# data_fund.loc[i, '最低价'] = row1["low"]
# if row1["high"] and row1["low"]:
# data_fund.loc[i, '振幅'] = (row1["high"] - row1["low"]) / row1["low"]
# else:
# data_fund.loc[i, '振幅'] = '停牌'
# log_str = 'No.' + format(str(i), "<6") + format(str(fund_code_str), "<10") \
# + format(data_fund.loc[i, '转债名称'], "<15") \
# + '当前价:' + format(str(row1["current"]), "<10") \
# + '溢价率:' + format(str(row1["premium_rate"]), "<10") \
# + '涨跌幅:'+ format(str(row1["percent"]), "<10")
# print(log_str)
# print(log_str, file=log_file)
#
# data_fund = data_fund.sort_values(by='溢价率')
# data_fund.reset_index(drop=True, inplace=True)
# data_fund.index += 1
# print(data_fund)
# print(data_fund, file=log_file)
log_file.close()
# 更新原Excel
sheet_fund.range('A1').value = data_fund
wb.save()
def main_function():
# date = datetime.datetime.now().date()
# if not is_workday(date):
# return
webbrowser.open("https://xueqiu.com/")
#删除旧log
for eachfile in os.listdir('./'):
filename = os.path.join('./', eachfile)
if os.path.isfile(filename) and filename.startswith("./log") :
os.remove(filename)
rotate_LOF_ETF()
webbrowser.open("https://xueqiu.com/")
refresh_convertible_bond()
refresh_premium_rate_convertible_bond()
refresh_price_and_premium_rate_convertible_bond()
refresh_multifactor1_convertible_bond()
refresh_multifactor2_convertible_bond()
rotate_abroad_fund()
# 更新股票实时数据
refresh_stock()
def main():
main_function()
schedule.every().day.at("07:00").do(main_function) # 部署7：00执行更新数据任务
while True:
schedule.run_pending()
if __name__ == "__main__":
main()