winq python基本面选股例子
一、选股的需求
- 上市日期在给定之日之后
- 最近N个交易日成交额大于给定值
- 大股东质押率不大于给定值
- 归母利润达到指定值
- 上一年的年报审计意见为正常
- 最近没有停牌
二、选股常见方式
- 一键式选股：各种金融终端里面的自定义条件
- 自编公式选股：tongdaxin里面自编公式，引用专业财务数据
- winq选股：
- 量化API选股：
三、如何选择
- 研究阶段的选择：用一键式，自编公式选股
- 模型稳定后，用winq来综合实现（后期可以直接移植到wind量化接口下）
- 其他量化接口选股
四、winq选股的实现例子
连接服务器
#连接服务器
from WindPy import *
w.start(); #命令超时时间为 120 秒
print('连接状态：' ,w.isconnected())
w.start(); #命令超时时间设置成 60
获取A股列表
import datetime
#获取当前的日期
today_date = datetime.date.today().isoformat() #str类型，iso格式：2020-01-01
print("当前时间为:",today_date)
#读取A股股票列表
a_stocks = w.wset("sectorconstituent", f"date={today_date};sectorId=a001010100000000", usedf=True)
#错误代码处理
err_code = a_stocks[0]
#print(f"err_code = {err_code}")
if err_code != 0:
raise ValueError(f"读取A股股票列表，出错，错误代码:{err_code}")
a_stock_df = a_stocks[1]
"""
格式：
===========a_stock_df：dataFrame=============
date wind_code sec_name
-------------------------------------------------
1 2020-02-25 00:00:00.005 000001.SZ 平安银行
2 2020-02-25 00:00:00.005 000002.SZ 万科A
"""
a_stock_list = a_stock_df.wind_code.values.tolist()
print(f"读取的股票代码数量为：{len(a_stock_list)}")
#print(f"type of a_stock_list is {type(a_stock_list)}")
print("代码的格式如下：",a_stock_list[-1:])
today = '20210914' #日期 年【----】月【--】日【--】 总计8个数字
sjnb_date = '20201231' #审计年报的读入日期 '####1231'-该年年报读取日期，目前尚无2019年的年报审计意见，遂，读取上一年的年报审计意见
函数定义
import pandas as pd
#获取大股东累计质押数占持股数比例
def get_dagudong_zhiya_zhanbi(stock_code:str,deal_date:str):
"""
功能：获取大股东累计质押数占持股数比例
参数：
stock_code:股票代码,包含市场后缀，可以是一只或者多只股票 eg. 一只 '002157.SZ' 多只 ['002157.SZ','600797.SH']
deal_date:交易日期 eg. '20191106'
return:df
==========================
大股东质押占比
002157.SZ 80.841848
600797.SH 76.293443
==========================
"""
dd = w.wss(stock_code, "share_pledgeda_pct_largestholder", f"tradeDate={deal_date}", usedf=True)
df = dd[1]
df.columns = ['大股东质押占比']
df['大股东质押占比'] = df.apply(lambda r: 0 if r['大股东质押占比']==None else r['大股东质押占比'],axis = 1) #单独查询一只股，占比为0时，返回None的处理
df = df.fillna(0) #查询多只股，占比为0时，返回NaN
return df
"""
#=========test code=============
stock_code = ['002157.SZ','600797.SH']
deal_date = '20191106'
mydf =get_dagudong_zhiya_zhanbi(stock_code,deal_date)
mydf
"""
#获取财报的 [归属母公司的净利润]
def get_guishu_mugongsi_jinglirun(stock_code:str,deal_date:str):
"""
功能：归属母公司的净利润
参数：
stock_code:股票代码,包含市场后缀，可以是一只或者多只股票 eg. 一只 '002157.SZ' 多只 ['002157.SZ','600797.SH']
deal_date:交易日期 eg. '20191106'
**万矿是取每个季度最后一天作为报告期，如取2017年的四个定期报告数据，那报告期设置分别为 ：一季报：2017-03-31，半年报：2017-06-30，三季报：2017-09-30，年报:2017-12-31
return:df
=============================
归属母公司的净利润
600352.SH 4.814994e+09
==============================
"""
dd = w.wss(stock_code, "netprofit_ttm", f"tradeDate={deal_date}", usedf=True)
df = dd[1]
df.columns = ['归属母公司的净利润']
df['归属母公司的净利润'] = df['归属母公司的净利润'].astype('float')
return df
"""
#===========test code=============
stock_code = ['600352.SH','000001.SZ']
deal_date = '20181231'
my_df = get_guishu_mugongsi_jinglirun(stock_code,deal_date)
display(my_df)
"""
#获取股票的上市日期
def get_shangshi_date(stockCodes):
"""
功能：获取股票的上市日期
参数：
stockCodes:股票列表 ['000001.SZ','600000.SH']
return：df,
===========================================
上市日期
000001.SZ 1991-04-03 00:00:00.005
600000.SH 1999-11-10 00:00:00.005
===========================================
"""
dd = w.wss(stockCodes, "ipo_date", "", usedf=True)
df = dd[1]
df.columns = ['上市日期']
return df
"""
#==============test code===============
stockCodes = ['000001.SZ','600000.SH']
df = get_shangshi_date(stockCodes)
df
"""
#得到某些股票的N日成交额
def get_amount_nDays(stockCodes:list,days:int,tradeDate:str):
"""
功能：得到某些股票的N日成交额
参数：
stockCodes:要查询的股票列表
days:N日成交额中的N，往前推的交易天数，days = 1 ，表示往前推一天，连同当天（tradeDate），则有两个交易日
tradeDate:查询的日期
return df
===========================
N日成交额
000001.SZ 3.669349e+10
600000.SH 1.619291e+10
===========================
"""
dd = w.wss(stockCodes, "amt_nd", f"days=-{days};tradeDate={tradeDate}", usedf=True)
df = dd[1]
df.columns = ["N日成交额"]
return df
"""
#========test code=====================
stockCodes = ['000001.SZ','600000.SH']
days = 20
tradeDate = '20191111'
df = get_amount_nDays(stockCodes,days,tradeDate)
df
"""
#日期格式转换：timestamp_str '2019-01-08 00:00:00.005' to str '20190108'
def timestamp_2_str(timestamp_str_date):
"""
功能：日期格式转换：timestamp_str '2019-01-08 00:00:00.005' to str '20190108'
参数：
timestamp_str_date：'2019-01-08 00:00:00.005'
return：string，20190108
"""
day1 = timestamp_str_date.split(' ')[0].split('-') #'2019','01','08'
day2 = f"{day1[0]}{day1[1]}{day1[2]}" #'20190108'
return day2
#日期用年数进行相减操作
import datetime
from dateutil.relativedelta import relativedelta
#给定日期减去指定年数后，返回一个新日期
def substract_date_years(mydate:str,years_to_substract:int):
"""
功能：给定日期减去指定年数后，返回一个新日期
参数：
mydate:str日期 eg. '20190101' '%Y%m%d'
years_to_substract:int要减去的年数
return：'%Y%m%d' '20180101'
备注：pandas矢量化操作有日期加减函数
"""
d = datetime.datetime.strptime(f"{mydate}", '%Y%m%d')
#print(d) # 2018-01-31 00:00:00
new_date = (d - relativedelta(years=years_to_substract)).strftime('%Y%m%d')
#print(new_date) # 20170131
return new_date
#获取一个大盘交易日列表,作为交易日时间轴
def get_dapan_tradeday(dapan:str,begin_dapan_date:str,end_dapan_date:str):
"""
功能：获取一个大盘交易日列表,作为交易日时间轴
参数：
dapan:大盘代码 eg. '000001.SH'#上证指数
begin_dapan_date:起始日期
end_dapan_date：结束日期
return:days , 交易日系列，string
"""
dd =w.wsd(dapan, "close", begin_dapan_date, end_dapan_date, "Fill=Previous", usedf=True)
df = dd[1]
df['日期'] = df.index
days = df['日期'].astype('str').values.tolist()
return days
"""
#=========test code=============
dapan = '000001.SH'#上证指数
begin_dapan_date = '2019-01-01'
end_dapan_date = '2019-11-10'
days = get_dapan_tradeday(dapan,begin_dapan_date,end_dapan_date)
"""
#审计意见类别
def get_shenjiyijian(stocks:list,date:str):
"""
功能：获取给定股票的某期财务审计意见
参数：
stocks:股票列表list
date:报告期 str
return：df
df的格式如下：
-----------------------
|审计意见类别
-----------------------
000001.SZ|标准无保留意见
600000.SH|标准无保留意见
-----------------------
"""
dd = w.wss(stocks, "stmnote_audit_category", f"rptDate={date};zoneType=1", usedf=True)
df = dd[1]
df.columns = ['审计意见类别']
return df
"""
#==========test code==============
stocks = ['600352.SH','000001.SZ']
date = '20181231'
DF = get_shenjiyijian(stocks,date)
DF
"""
ST股票处理
#st处理的函数
#获取ST字典
def get_st_dict(df_st,st_start_date,st_end_date)->dict:
#df_st = pd.read_excel(file_path,converters={'代码':str})
df_st['操作日期'] = pd.to_datetime(df_st['操作日期'])
#display(df_st)
#整理成字典
st_dict = {}
for name,df in df_st.groupby(['代码']):
#print(name)
df = df.sort_values(['操作日期'])
df.index = range(len(df))
#display(df)
df_tmp = get_st_info(df,st_start_date,st_end_date)
#display(df_tmp)
st_dict[name] = df_tmp
print("停牌字典处理完毕！")
return st_dict
#====测试代码====
file_path = 'C:/缓存数据/ST状态合并表.xls'
df_st_dict = get_st_dict(file_path)
df_st_dict['600163']
#获取st的信息
def get_st_info(df,st_start_date,st_end_date)->pd.DataFrame:
"""
注意事项：中间用到两个日期常量，需要设定
df:ST合并表：实施ST 和 撤销ST
st_start_date:2020-01-01
st_end_date:2020-12-31
"""
lenth_df = len(df)
status_list = []
for idx in range(len(df)):
#print(idx)
op = df.at[idx,'操作']
op_date = df.at[idx,'操作日期']
#1、实施及撤销操作前后的状态处理
if op == '实施':
#print('实施')
#----实施前----
status_before_op = "正常"
if '*ST'in df.at[idx,'操作前简称']:
#print(f"操作前为：*ST")
status_before_op = "*ST"
else:
if 'ST' in df.at[idx,'操作前简称']:
#print(f"操作前为：ST")
status_before_op = "ST"
#----实施后----
status_after_op = "正常"
if '*ST'in df.at[idx,'操作后简称']:
#print(f"操作后为：*ST")
status_after_op = "*ST"
else:
if 'ST' in df.at[idx,'操作后简称']:
#print(f"操作后为：ST")
status_after_op = "ST"
if '退' in df.at[idx,'操作后简称']:
#print(f"操作后为：退")
status_after_op = "退"
elif op == '撤销':
#print("撤销")
#----撤销前----
status_before_op = "正常"
if '*ST'in df.at[idx,'操作前简称']:
#print(f"操作前为：*ST")
status_before_op = "*ST"
else:
if 'ST' in df.at[idx,'操作前简称']:
#print(f"操作前为：ST")
status_before_op = "ST"
if '退' in df.at[idx,'操作前简称']:
#print(f"操作后为：退")
status_before_op = "退"
#----撤销后----
status_after_op = "正常"
if '*ST'in df.at[idx,'操作后简称']:
#print(f"操作后为：*ST")
status_after_op = "*ST"
else:
if 'ST' in df.at[idx,'操作后简称']:
#print(f"操作后为：ST")
status_after_op = "ST"
if '退' in df.at[idx,'操作后简称']:
#print(f"操作后为：退")
status_after_op = "退"
#2、状态对应的时间段处理
if idx == 0: #当前为第一条记录
#增加一段：起始日期到当前日期的状态
start_date_1 = pd.to_datetime(st_start_date)
end_date_1 = op_date - pd.DateOffset(days = 1)
status_cur_1 = status_before_op
status_list.append([start_date_1,end_date_1,status_cur_1])
#当前日期及之后的状态处理
start_date_2 = op_date
if idx < lenth_df - 1: #不是最后一条记录，日期取下一次操作的日期
end_date_2 = op_date = df.at[idx + 1,'操作日期']
end_date_2 = end_date_2 - pd.DateOffset(days = 1)
elif idx == lenth_df - 1: #确定是最后一条记录，日期取2019-12-31
end_date_2 = pd.to_datetime(st_end_date)
status_cur_2 = status_after_op
status_list.append([start_date_2,end_date_2,status_cur_2])
#print(f"op = {op},op_date = {op_date} status_before_op = {status_before_op} status_after_op = {status_after_op} ")
elif idx > 0: #当前为第一条记录
#当前日期及之后的状态处理
start_date_2 = op_date
if idx < lenth_df - 1: #不是最后一条记录，日期取下一次操作的日期
end_date_2 = df.at[idx + 1,'操作日期']
end_date_2 = end_date_2 - pd.DateOffset(days = 1)
elif idx == lenth_df - 1: #确定是最后一条记录，日期取2019-12-31
end_date_2 = pd.to_datetime(st_start_date)
status_cur_2 = status_after_op
status_list.append([start_date_2,end_date_2,status_cur_2])
#print(f"op = {op},op_date = {op_date} status_before_op = {status_before_op} status_after_op = {status_after_op} ")
df_tmp = pd.DataFrame(status_list)
df_tmp.columns = ['起始日期','结束日期','状态']
df_tmp
return df_tmp
# .1、设置【起始日期】和【终止日期】
def get_st(last_3years_date,today_date):
"""
"""
# 生成ST信息
# .2、读取【实施ST】
dd = w.wset("carryoutspecialtreatment", f"startdate={last_3years_date};enddate={today_date}", usedf=True)
df = dd[1]
do_st_df = df[["wind_code","implementation_date","name_before","name_after"]]
do_st_df.columns=['代码','操作日期','操作前简称','操作后简称']
do_st_df['操作'] = '实施'
do_st_df.index = do_st_df.index + do_st_df['操作']
#print(do_st_df.head(2))
# .3、读取【撤销ST】
dd = w.wset("cancelspecialtreatment",f"startdate={last_3years_date};enddate={today_date}", usedf=True)
df = dd[1]
undo_st_df = df[["wind_code","implementation_date","name_before","name_after"]]
undo_st_df.columns=['代码','操作日期','操作前简称','操作后简称']
undo_st_df['操作'] = '撤销'
undo_st_df.index = undo_st_df.index + undo_st_df['操作']
#print(undo_st_df.head(2))
# .4、双表合并
st_df = pd.DataFrame()
st_df = st_df.append(do_st_df).append(undo_st_df)
#print(f"{len(do_st_df)},{len(undo_st_df)}")
#print(len(st_df))
# .5、计算st信息
st_dict = get_st_dict(st_df,last_3years_date,today_date) #{last_3years_date};enddate={today_date}"
#print(len(st_dict))
#print(list(st_dict.keys())[0])
#display(st_dict["000010.SZ"])
return st_dict
#判断筛选出来的股票集合，在指定的日期，它们是否在停牌字典里，并且当前处于停牌状态
def get_st_stocks(st_dict:dict,select_stocks:set,check_date:str)->set:
keys_set = set(st_dict.keys())
#print(len(keys_set))
common_set = select_stocks & keys_set
#print(len(common_set))
common_set
res_set = set()
for item in common_set:
#display(st_dict[item])
df = st_dict[item]
df1 = df.query("(起始日期 <= @check_date <= 结束日期) & (状态 != '正常')")
if len(df1) >=1:
#print("处于停牌状态")
res_set.add(item)
else:
#print("处于正常状态")
pass
return res_set
main流程
#获取A股列表 API--WSD日期序列 -- 直接拉清单
all_stocks = a_stock_list
print("指定的截止日期为today = ",today)
beginDate = substract_date_years(today,3) #得到今天往前推3年的日期，作为上市日期起始判断值
# 1、选取符合上市日期的股票列表
df = get_shangshi_date(all_stocks) #获取上市日期df
beginDate = pd.to_datetime(beginDate)
df = df.query("上市日期 <= @beginDate")
select_stocks = df.index.tolist()
print("上市日期筛选后的股票",len(select_stocks))
# 2、 近21个交易日成交额大于63亿
df = get_amount_nDays(select_stocks,20,today)
df = df.query("N日成交额>= 6300000000") #近21日成交额总计大于63亿
select_stocks = df.index.tolist() #更新股票列表
print("近21个交易日成交额大于63亿",len(select_stocks))
# 3、第一大股东质押率小于80%
df = get_dagudong_zhiya_zhanbi(select_stocks,today)
df = df.query("大股东质押占比 < 80")
#display(df)
select_stocks = df.index.tolist() #更新股票列表
print("第一大股东质押率小于80%",len(select_stocks))
# 4、归属母公司净利润判断
"""
在1月1日-8月30日之间的，选用上一年度报告净利润；
在9月1日-10月31日之间的，选用当年半年报数据*2；
在11月1日-12月31日的，选用本年度3季报数据*4/3
"""
month = pd.to_datetime(today).month
year = pd.to_datetime(today).year
if 1 <= month <= 8: #在1月1日-8月30日之间的，选用上一年度报告净利润；
last_year = year - 1
last_year_nianbao_date = f"{last_year}1231"
df = get_guishu_mugongsi_jinglirun(select_stocks,last_year_nianbao_date)
elif 9 <= month <= 10:
this_year_banNianBao_date = f"{year}0630"
df = get_guishu_mugongsi_jinglirun(select_stocks,this_year_banNianBao_date)
df['归属母公司的净利润'] = df.eval("归属母公司的净利润 * 2")
elif 11 <= month <= 12:
this_year_quarter3_date = f"{year}0930"
df = get_guishu_mugongsi_jinglirun(select_stocks,this_year_quarter3_date)
df['归属母公司的净利润'] = df.eval("归属母公司的净利润 * 4/3")
df = df.query("归属母公司的净利润 >= 50000000 ") #文档显示为1亿
select_stocks = df.index.tolist()
print("归属母公司的净利润：",len(select_stocks))
# 5、审核意见处理：排除“标准无保留意见”之外的其它股票
sjyj_df:pd.DataFrame = get_shenjiyijian(select_stocks,'20181231') #获取审计意见df
df2 = sjyj_df.query("审计意见类别 !='标准无保留意见' ") #筛选非标意见的股票
feibiao_stocks_set = set(df2.index) #非标准意见的股票集合
final_select_stocks = set(select_stocks) - feibiao_stocks_set #最终选股股票 = 排除了非标意见的股票
print(f"万矿选股结束,选出的股票数量为：{len(final_select_stocks)}")
# 6、ST信息获取
last_3years_date = substract_date_years(today_date.replace("-",""),3)
st_dict = get_st(last_3years_date,today_date)
print(f"ST信息获取，共获取停牌股票数量：{len(st_dict)}")
# 7、刨除ST的股票
st_set = get_st_stocks(st_dict,final_select_stocks,today)
final_select_stocks = final_select_stocks - st_set
print(f"ST停牌的股票：{len(st_set)}")
print(f"最终选出的股票只数：{len(final_select_stocks)}")
运行结果
指定的截止日期为today = 20210914
上市日期筛选后的股票 3496
近21个交易日成交额大于x亿 941
第一大股东质押率小于80% 900
归属母公司的净利润： 823
万矿选股结束,选出的股票数量为：817
停牌字典处理完毕！
ST信息获取，共获取停牌股票数量：836
ST停牌的股票：1
最终选出的股票只数：816
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'60......
