def _sort(self, user_code_list: list = None, lines: int = None):
# df.sort_index(by='pb', axis=0, ascending=[True])
ndf = pd.DataFrame()
df = self._filter(user_ts_code_list=user_code_list)
if df.empty is False:
if lines is None:
max_lines = int(config.max_lines)
else:
max_lines = lines
"""排序"""
sort_title = config.sort_title.split('|')
sort_type = config.sort_type.split('|')
sort_title.append('Risk_level')
sort_type.append('False')
df1 = df[df['Risk_level'] == '0'].sort_values(
by=sort_title, ascending=sort_type).head(max_lines)
df2 = df[df['Risk_level'] == '1'].sort_values(
by=sort_title, ascending=sort_type).head(max_lines)
ndf = pd.concat([df1, df2], ignore_index=True)
stock_fi = Util_tools.bytes_to_dataFrame(
RedisBase().redis().get('stock_fi'))
if stock_fi.empty: # 在线取每股净资产
for idx, row in ndf.iterrows():
sg_data = get_skData().get_stock_fi(row['ts_code'])
ndf.loc[idx, 'bps'] = float(sg_data['bps'])
return ndf
def init_stock_data():
stock_data = get_skData().stock_b()
# 排除劣质股
sdf = stock_data[stock_data['name'].str.contains(r'.*ST.*')]
stock_data.drop(sdf.index, inplace=True)
stock_data.fillna(value=0) # 当数据中存在NaN时候用 0 替换
df_bytes = t_util.dataFrame_to_bytes(stock_data)
flag = RedisBase().redis().set('stock_base', df_bytes)
return flag
def __init__(self):
self.pro = tushare.pro_api(config.token)
yesterday = (datetime.today() + timedelta(-1))
usr_date = Util_tools.dateTime_content('17:00')
"""17:00"""
if datetime.today() > usr_date:
self.default_day = usr_date
else:
self.default_day = yesterday
def _init_stock_daily_udata():
new_df = pd.DataFrame()
# 股票基本分类数据
stock_base = t_util.bytes_to_dataFrame(
RedisBase().redis().get('stock_base'))
stock_daily = t_util.bytes_to_dataFrame(
RedisBase().redis().get('stock_daily'))
stock_fi = t_util.bytes_to_dataFrame(RedisBase().redis().get('stock_fi'))
for index, row in stock_base.iterrows():
df = stock_daily[stock_daily['ts_code'] == row['ts_code']]
if stock_fi.empty is False:
bvps = stock_fi.loc[row['symbol']]['bvps']
if df.empty is False:
daily_b.ts_code = str(row['ts_code']) # 股票名称
daily_b.name = str(row['name']) # 股票名称
daily_b.symbol = str(row['symbol']) # 股票名称
daily_b.industry = str(row['industry']) # 股票名称
daily_b.trade_date = str(df.loc[df.index,
'trade_date'].values[0]) # 交易日期
daily_b.close = float(df['close']) # 当日收盘价
daily_b.ts_code = str(row['ts_code']) # 股票代码
daily_b.pe = float(df['pe']) # 市盈率（总市值/净利润）
daily_b.pe_ttm = float(df['pe_ttm']) # 市盈率（TTM）
daily_b.pb = float(df['pb']) # 市净率（总市值/净资产）
if bvps is None:
daily_b.bps = float(0) # 每股净资产
else:
daily_b.bps = float(bvps) # 每股净资产
data = {
'ts_code': daily_b.ts_code,
'symbol': daily_b.symbol,
'name': daily_b.name,
'industry': daily_b.industry,
'trade_date': daily_b.trade_date,
'pe': daily_b.pe,
'pe_ttm': daily_b.pe_ttm,
'pb': daily_b.pb,
'bps': daily_b.bps
}
add_data = pd.Series(data)
"""ignore_index=True,表示不按原来的索引，从0开始自动递增"""
new_df = new_df.append(add_data, ignore_index=True)
return new_df
def get_stock_fi(self, ts_code=None, tr_date=None):
"""公司每日基本情况"""
df_data = pd.DataFrame()
if ts_code is None:
# if tr_date is None:
# tr_date = self.default_day.strftime('%Y%m%d')
try:
df_data = tushare.get_stock_basics(tr_date)
except OSError:
return df_data
except HTTPError:
return df_data
else:
start_data = Util_tools.data_util().get_last_year().get('start').strftime('%Y%m%d')
end_date = Util_tools.data_util().get_now_year().get('end').strftime('%Y%m%d')
df_data = self.pro.query('fina_indicator', ts_code=ts_code, start_date=start_data, end_date=end_date)
df_data = df_data[:1]
return df_data
def init_stock_fi_data():
# 股票每日基本数据
try:
daily_data = get_skData().get_stock_fi()
if daily_data.empty is False:
df_bytes = t_util.dataFrame_to_bytes(daily_data)
flag = RedisBase().redis().set('stock_fi', df_bytes)
else:
flag = False
except Exception as e:
print(e.args)
flag = False
return flag
def sale_stock(self, stock_df):
"""
为了止损 首先判断单只股票净资产，当净资产翻倍，可以提示出售该股票
当股票购买的时间超过1年的投资期时可以抛售该股票
"""
flag = ''
buy_bps = stock_df.iloc[0]['bps']
buy_data = stock_df.iloc[0]['trade_date']
# 取数当前股票净资产
df = self.stock_details_daily[self.stock_details_daily['ts_code'] ==
stock_df.iloc[0]['ts_code']]
now_bps = df.iloc[0]['bps']
now_date = df.iloc[0]['trade_date']
days = Util_tools.time_long(buy_data, now_date)
if now_bps >= (buy_bps * 2):
flag = 'X'
elif days > 365:
flag = 'X'
return flag
def init_stock_daily_user_details():
# 股票组装用户数据
stock_details_daily = _init_stock_daily_udata()
df_bytes = t_util.dataFrame_to_bytes(stock_details_daily)
flag = RedisBase().redis().set('stock_details_daily', df_bytes)
return flag
def init_stock_daily_data():
# 股票每日指标数据
daily_data = get_skData().daily_b()
df_bytes = t_util.dataFrame_to_bytes(daily_data)
flag = RedisBase().redis().set('stock_daily', df_bytes)
return flag
daily_b.bps = float(0) # 每股净资产
else:
daily_b.bps = float(bvps) # 每股净资产
data = {
'ts_code': daily_b.ts_code,
'symbol': daily_b.symbol,
'name': daily_b.name,
'industry': daily_b.industry,
'trade_date': daily_b.trade_date,
'pe': daily_b.pe,
'pe_ttm': daily_b.pe_ttm,
'pb': daily_b.pb,
'bps': daily_b.bps
}
add_data = pd.Series(data)
"""ignore_index=True,表示不按原来的索引，从0开始自动递增"""
new_df = new_df.append(add_data, ignore_index=True)
return new_df
if __name__ == '__main__':
# print(init_stock_data())
# print(init_stock_fi_data())
# print(init_stock_fi_data())
stock_base = t_util.bytes_to_dataFrame(
RedisBase().redis().get('stock_base'))
df = stock_base.drop_duplicates(['industry'])
for index, row in df.iterrows():
print(row['industry'])
# init_everyday()
# df = tuShare.get_realtime_quotes('000425')
# for index, cow in df.iterrows():
# print(cow)
# price = df.loc[0, 'price']
# print(price)
# print(df)
# stock_data = get_skData().stock_b()
# flag = RedisBase().redis().set('stock_base', df_bytes)
# df = t_util.bytes_to_dataFrame(RedisBase().redis().get('stock_base'))
# date:日期YYYY-MM-DD，默认为上一个交易日，目前只能提供2016-08-09之后的历史数据
stock_basics = tuShare.get_stock_basics('2020-02-28')
df = stock_basics.loc['000732']
start = datetime.datetime.now()
# daily_data = t_util.bytes_to_dataFrame(RedisBase().redis().get('stock_daily'))
# stock_data = t_util.bytes_to_dataFrame(RedisBase().redis().get('stock_base'))
stock_details_daily = t_util.bytes_to_dataFrame(
RedisBase().redis().get('stock_details_daily'))
end = datetime.datetime.now()
print('Running time: %s Seconds' % (end - start))
# print(daily_data)
print(tuShare.get_stock_basics())
# print(daily_data)
# r = 100 / 100
# print(round(r))
def __init__(self):
"""Redis 数据初始化，取股票基本数据"""
self.stock_details_daily = Util_tools.bytes_to_dataFrame(
RedisBase().redis().get('stock_details_daily'))
"""高危版块"""
self.list_industry = config.industry.split('|')