def filter_stock_by_average_pe(min, max):
path=os.path.join(current_folder,'3年平均利润.csv')
if not os.path.exists(path): #没有就生成3年平均利润列表
calcu_all_stocks_3year_average_profit(calcu_average_profit_end_year)
gplb=pd.read_csv(path,encoding = "gbk",index_col=0)
now=datetime.now()
today=str(now)[:11]
current_sec=str(now)[:18].replace('-','_').replace(':','_')
price_path=os.path.join(current_folder,today+'股票价格.csv')
if not os.path.exists(price_path):
ts.get_today_all().set_index('code').to_csv(price_path)
current_price=pd.read_csv(price_path,encoding = "gbk",index_col=0)
current_price= current_price[['trade']]
current_price.columns=['价格']
gplb=gplb[['名字','行业','地区','流通股本','总股本(万)','总资产(万)','流动资产','固定资产','每股净资','市净率','上市日期','平均利润']]
data=pd.merge(gplb,current_price,left_index=True, right_index=True)
data['平均市盈率']=data['总股本(万)']*data['价格']/data['平均利润']
data = data[data['平均市盈率'] < max]
data = data[data['平均市盈率'] > min]
data['平均市盈率']=data['平均市盈率'].round()
data['平均利润']=data['平均利润'].round()
data['市净率']=data['市净率'].round(1)
data['固定资产']=data['固定资产'].round()
data['流动资产']=data['流动资产'].round()
data['总股本(万)']=data['总股本(万)'].round()
data['流通股本']=data['流通股本'].round()
average_pe_file = os.path.join(current_folder, today + '3年平均市盈率在%s和%s之间的公司.xlsx' % (min, max))
data.to_excel(average_pe_file)
def today_df_filter0(today_df):
#"""
today_df = ts.get_today_all()
today_df = today_df[today_df.amount>0]
today_df_high_open = today_df[today_df.open>today_df.settlement*1.005]
all_trade_code = today_df['code'].values.tolist()
all_a_code = ps.get_all_code(hist_dir="C:/中国银河证券海王星/T0002/export/")
all_stop_code = list(set(all_a_code).difference(set(all_trade_code)))
print('\n')
print('all_stop_code=%s' % all_stop_code)
print(len(all_stop_code))
high_open_code_str = today_df_high_open['code'].values.tolist()
print('all_trade_code = %s'%all_trade_code)
print(len(all_trade_code))
print('today_df_high_open = %s'%high_open_code_str)
today_df['star'] = ((today_df['trade']-today_df['open'])/(today_df['high']-today_df['low'])).round(3)
today_df['star_h'] = np.where(today_df['star']>=0, ((today_df['high']-today_df['trade'])/(today_df['high']-today_df['low'])).round(3),
((today_df['high']-today_df['open'])/(today_df['high']-today_df['low'])).round(3))
today_df['atr'] = np.where((today_df['high']-today_df['low'])<(today_df['high']-today_df['settlement']),
today_df['high']-today_df['settlement'],today_df['high']-today_df['low']) #temp_df['close'].shift(1)-temp_df['low'])
today_df['atr'] = np.where(today_df['atr']<(today_df['settlement']-today_df['low']),
(today_df['settlement']-today_df['low']),today_df['atr'])
today_df['atr_r'] = ((today_df['atr']/today_df['settlement']).round(3))*100.0
today_df['star_chg'] = today_df['star'] * today_df['changepercent']
#del today_df['atr']
describe_df = today_df.describe().round(3)
#print(type(describe_df))
lt_describe = describe_df.loc['25%']#.iloc[3].values
mean_chg = describe_df.loc['mean','changepercent']
most_chg = describe_df.loc['75%','changepercent']
least_chg = describe_df.loc['25%','changepercent']
most_atr_r = describe_df.loc['75%','atr_r']
least_atr_r = describe_df.loc['25%','atr_r']
print(mean_chg,most_chg,least_chg,most_atr_r,least_atr_r)
print(describe_df)
great_rate = 2.0
gt_today_df = today_df[today_df.changepercent> great_rate]
great_atd_df = today_df[today_df['atr_r']>11]
stock_basic_df=ts.get_stock_basics()
#stock_basic_df['outstanding'] = stock_basic_df['outstanding'] * 0.0001
#stock_basic_df['totals'] = stock_basic_df['totals'] * 0.0001
lt_outstanding_df = stock_basic_df[stock_basic_df.outstanding<100000] #流通股本小于10亿
print(lt_outstanding_df)
today_df['real'] = 0.00000001 *today_df['amount']/today_df['turnoverratio']
min_atr_df = today_df[today_df.real<10.0]
min_atr_df = min_atr_df[min_atr_df.atr_r<least_atr_r]
lt_outstanding_df_list = lt_outstanding_df.index.values.tolist()
print(lt_outstanding_df_list)
min_atr_df = min_atr_df.set_index('code')
min_atr_df_list = min_atr_df.index.values.tolist()
print(min_atr_df_list)
inter_list = list(set(min_atr_df_list).intersection(set(lt_outstanding_df_list)))
print(len(inter_list))
filter_df = min_atr_df[min_atr_df.index.isin(inter_list)]
#print(filter_df)
#print(type(filter_df))
min_atr_df = filter_df.sort_values(axis=0, by='atr_r', ascending=True)
min_atr_df = filter_df.sort_values(axis=0, by='star', ascending=False)
print(min_atr_df)
def get_break_bvps():
base_info = ts.get_stock_basics()
current_prices = ts.get_today_all()
current_prices[current_prices['code'] == '000625']['trade'].values[0]
base_info.loc['000625']['bvps']
def daily_market():
df = ts.get_today_all()
try:
df.to_sql(SaveData.today,SaveData.daily_engine,if_exists='replace')
except Exception as e:
logger.info(e)
logger.info("Save {} data to MySQL".format(SaveData.today))
def get_stock_price(code, include_realtime_price):
"""
获取个股股价
:param code: 股票代码
:param include_realtime_price: 是否含实时股价
:return:
"""
# 获取历史股价
df = ts.get_hist_data(code)
df = df[['close']]
df['date'] = df.index
if include_realtime_price:
df_today = ts.get_today_all()
df_code = df_today[df_today['code']==code]
df_code = df_code[['trade']]
df_code['date'] = GetNowDate()
df_code.rename(columns={'trade': 'close'}, inplace=True)
df = pd.concat([df, df_code], ignore_index=True)
df.sort(columns='date', inplace=True)
df = df.drop_duplicates(['date'])
df.index = range(len(df))
print '\n'
# print df.head()
print df.tail()
return df
def fetch_classification(self): # 数据来源自新浪财经的行业分类/概念分类/地域分类 print( "Trying: get_today_all" ) today_all = ts.get_today_all() #一次性获取今日全部股价 set_today_all = set(today_all.T.values[0]) print( "Trying: get_industry_classified" ) industry_classified = ts.get_industry_classified() set_industry_classified = set(industry_classified.T.values[0]) print( "Trying: get_area_classified" ) area_classified = ts.get_area_classified() set_area_classified = set(area_classified.T.values[0]) print( "Trying: get_concept_classified" ) concept_classified = ts.get_concept_classified() set_concept_classified = set(concept_classified.T.values[0]) print( "Trying: get_sme_classified" ) sme_classified = ts.get_sme_classified() set_sme_classified = set(sme_classified.T.values[0]) return [ today_all , set_today_all , industry_classified , set_industry_classified , area_classified , set_area_classified , concept_classified , set_concept_classified , sme_classified , set_sme_classified ]
def GetAllTodayData(self):
#存储每天 涨幅排行 榜,避免每次读取耗时过长
filename=self.today+'_all_.xls'
#放在data文件夹下
filename=os.path.join(self.path,filename)
if not os.path.exists(filename):
re_try=5
while re_try>0:
try:
self.df_today_all=ts.get_today_all()
#过滤停牌的
# self.df_today_all.drop(self.df_today_all[self.df_today_all['turnoverratio']==0].index,inplace=True)
#实测可用，删除的方法
#n1=self.df_today_all[self.df_today_all['turnoverratio']==0]
#n2=self.df_today_all.drop(n1.index)
#print n2
# print self.df_today_all
break
except Exception,e:
re_try=re_try-1
time.sleep(5)
if len(self.df_today_all)!=0:
self.df_today_all.to_excel(filename,sheet_name='All')
else:
self.df_today_all=None
def FindGoodStock():
pdReportToday = ts.get_today_all()
# print(type(pdReportToday), pdReportToday)
for index,row in pdReportToday.iterrows():
delta = row["open"] - row["trade"]
stockId = row["code"]
# print(stockId, "--------------", delta)
try:
try:
retVal = PrintOpenLow([stockId])
if retVal == None:
print("Network data 1")
retVal = CheckLowDiffByTushare( stockId )
except:
print("Network data 2")
retVal = CheckLowDiffByTushare( stockId )
if delta > retVal[0]:
print(stockId, "delta:", delta, "now:", row["trade"], retVal)
if delta > retVal[0] + 0.5*retVal[1]:
print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++")
elif delta > retVal[0] + retVal[1]:
print("--------------------------------------------------------------------------------------------------")
# CheckLowDiffByTushare( stockId, bShow = False)
except:
print(stockId, "Exception")
def get_day_list():
print "tushare version:" + ts.__version__
'''
获取当天的涨幅排行榜
code
name
changepercent 涨跌幅
trade:现价
open:
high:
low:
settlement:昨日收盘价
volume:
turnoverratio:
'''
all_data = ts.get_today_all()
if all_data is None:
print 'None data fetched'
return
trade_date = datetime.datetime.today()
if int(trade_date.strftime("%w")) == 0:
# sunday
trade_date = trade_date + datetime.timedelta(days=-2)
elif int(trade_date.strftime("%w")) == 6:
# saturday
trade_date = trade_date + datetime.timedelta(days=-1)
all_data_file_name = "allData" + trade_date.strftime("%Y-%m-%d") + ".csv"
output_all_data_file_dir = "e:/data/" + all_data_file_name
all_data.to_csv(output_all_data_file_dir, encoding='gbk')
# output_all_data_file_dir = "e:/data/" + all_data_file_name
# up_data = pd.read_csv(output_all_data_file_dir, encoding='gbk', index_col=0, dtype={'code': str})
print all_data['code']
def get_url_data_(self):
# 从 tushare.org 获取股票市场的代码列表
code_list_ = pandas.DataFrame((tushare.get_today_all())['code'])
# 排序
code_list_ = code_list_.sort(columns='code', ascending=True)
# 增加一自然数列做为 index
code_list_['index'] = pandas.DataFrame([i for i in range(0, len(code_list_))], code_list_.index)
code_list_.reindex(index=code_list_['code'])
# 写库
code_list_.to_sql('code_list_', self.engine_, if_exists='replace', index=True, index_label='index')
# 把 index 设为主键
self.engine_.connect().execute('alter table testdb.code_list_ add primary key(`index`)')
# 根据上面股票列表逐个获取个股数据
for i in range(0, len(code_list_)):
# 取的是已经复权的数据
stock_data_ = tushare.get_h_data(code_list_['code'][i])
# 因为获取的数据以 date 为 index，但是写库时不能把 date 当 index 写入，所以复制该列
stock_data_['date'] = pandas.Series(stock_data_.index, stock_data_.index)
stock_data_ = stock_data_.sort_values(by='date', ascending=True)
stock_data_['index'] = pandas.DataFrame([i for i in range(0, len(stock_data_))], stock_data_.index)
stock_data_.to_sql(code_list_['code'][i], self.engine_, if_exists='replace', index=True,
index_label='index')
self.engine_.connect().execute('alter table testdb.' + code_list_['code'][i] + ' add primary key(`index`)')
def real_time_data_all_stock(self):
now_str = sutils.get_datetime_str()
now_data = ts.get_today_all()
now_data_len = len(now_data)
logger.debug('The length of real time data is: %d' % now_data_len)
now_data['time'] = pd.Series(np.array([now_str] * now_data_len))
mutils.to_sql(now_data, table='real_time_data', engine)
def save_excel():
df = ts.get_today_all()
df.to_excel('1.xls', sheet_name='all_stock')
df2 = ts.get_hist_data('300333')
df2.to_excel('1.xls', sheet_name='basic_info')
df.ExcelWriter
out = pd.ExcelWriter("2.xls")
df.to_excel()
def gsz():
hq = ts.get_today_all()
hq['trade'] = hq.apply(lambda x: x.settlement if x.trade == 0 else x.trade, axis=1)
basedata = stock_info[['outstanding', 'totals', 'reservedPerShare', 'esp']]
hqdata = hq[['code', 'name', 'trade', 'mktcap', 'nmc']]
hqdata = hqdata.set_index('code')
data = basedata.merge(hqdata, left_index=True, right_index=True)
print(data.head(10))
def get_day_all():
try:
#print('Get Day All...')
df = ts.get_today_all()
except:
print('ts.get_today_all failed.')
sys.exit(1)
return(df)
def get_all_top():
gold = {}
goldl = []
df = ts.get_today_all()
top = df[df['changepercent'] > 6]
print "top:", len(top['code'])
for code in top['code']:
ave=sl.get_today_tick_ave(code)
def get_profit_report():
df = ts.profit_data(top=4000)
df = df.sort_values('divi', ascending=False)
#获取最新股价
df_quots = ts.get_today_all()
df_quots['roe'] = df_quots['pb'] * 100 / df_quots['per']
#df_basic['peg'] = df_basic['pe']/df_basic['profit']
df_quots['close'] = map(swap_value, df_quots['trade'],
df_quots['settlement'])
df_quots.reset_index(inplace=True)
df_quots = df_quots.drop([
'index', 'name', 'changepercent', 'trade', 'open', 'high', 'low',
'settlement', 'volume', 'turnoverratio', 'amount', 'nmc'
],
axis=1)
df = pd.merge(df, df_quots, how='left', on=key)
df['rate'] = df['divi'] / 10 * 100 / df['close']
df['valueprice'] = df['roe'] * (df['close'] / df['pb']) / 15
df = df.sort_values('rate', ascending=False)
#df['value'] = df['esp']/0.08
#df['rate'] = (df['value']-df['close'])*100/df['close']
df = df[df['per'] > 0]
df = df.sort_values('report_date', ascending=False)
df = df.drop_duplicates(['name'])
# 按现金股息率排行
df = df.sort_values('rate', ascending=False)
export_report(df, title="stock_dividence")
# 按roe排行
df = df.sort_values('roe', ascending=False)
export_report(df, title="stock_roe")
# 按市盈率
df = df.sort_values('per', ascending=True)
export_report(df, title="stock_pe")
# 按市净率
df = df.sort_values('pb', ascending=True)
export_report(df, title="stock_pb")
# 不排序
df = df.sort_values('mktcap', ascending=True)
export_report(df, title="stock_value")
def get_all_trading_codes():
'''
获取A股市场全部的交易代码和交易名称
'''
all_data = ts.get_today_all()
current_dir = os.path.dirname(__file__)
ab_path = os.path.join( current_dir, "..\\", "..\\" )
meta_data_path =os.path.abspath( ab_path ) + "\\meta_data\\"
all_data[['code', 'name']].to_csv(meta_data_path+"meta_codes.csv", encoding='utf-8', index=False)
def get_stop_trade_symbol():
today_df = ts.get_today_all()
today_df = today_df[today_df.amount>0]
today_df_high_open = today_df[today_df.open>today_df.settlement*1.005]
all_trade_code = today_df['code'].values.tolist()
all_a_code = pds.get_all_code(hist_dir="C:/中国银河证券海王星/T0002/export/")
#all_a_code = pds.get_all_code(hist_dir="C:/hist/day/data/")
all_stop_codes = list(set(all_a_code).difference(set(all_trade_code)))
return all_stop_codes
def process_stock_exchange_rate():
# 代码，名称，涨跌幅，现价，开盘价，最高价，最低价，最日收盘价，成交量，换手率，成交额，市盈率，市净率，总市值，流通市值
# code name changepercent trade open high low settlement volume turnoverratio amount per pb mktcap nmc
while True:
basic = ts.get_today_all()
push = basic[(basic.turnoverratio > 0.3) & (basic.changepercent < 0.0)]
print(push.code)
break
time.sleep(60)
def update_stocks_data(state, all_stocks):
if not state:
try:
all_stocks = ts.get_today_all()
state = True
except Exception, e:
print e
all_stocks = None
state = False
def get_all_toplist():
# gold = {}
# goldl = []
df = ts.get_today_all()
top = df[df['changepercent'] > 6]
top = top[top['changepercent'] < 10]
list = top.index
print len(list)
return list
def daily_market():
df = ts.get_today_all()
try:
df.to_sql(SaveData.today,
SaveData.daily_engine,
if_exists='replace')
except Exception as e:
logger.info(e)
logger.info("Save {} data to MySQL".format(SaveData.today))
def GetCurrentData():
CurrentData = ts.get_today_all()
if CurrentData.index.name != 'code' and CurrentData.columns.contains('code'):
CurrentData.set_index(['code'], inplace=True)
else:
return False
if CurrentData.columns.contains('trade'):
CurrentData.rename(columns={'trade': 'close'}, inplace=True)
return CurrentData
def gsz():
hq = ts.get_today_all()
hq['trade'] = hq.apply(lambda x: x.settlement if x.trade == 0 else x.trade,
axis=1)
basedata = stock_info[['outstanding', 'totals', 'reservedPerShare', 'esp']]
hqdata = hq[['code', 'name', 'trade', 'mktcap', 'nmc']]
hqdata = hqdata.set_index('code')
data = basedata.merge(hqdata, left_index=True, right_index=True)
print(data.head(10))
def getOutStandingStock():
df_stk = ts.get_stock_basics().reset_index()
df_trans = ts.get_today_all()
df_result = pd.merge(df_stk,df_trans,on = 'code')
df_result = df_result.loc[:,['code','name_x','industry','outstanding','trade']]
df_result['outstanding_amt'] = df_result['outstanding'] * df_result['trade'] * 10000
df_result = df_result[df_result.outstanding_amt < 5000000000]
return df_result
def download_stock_D1(self):
"""
下载日线数据
"""
#print('downloading the stock daily info...')
stock_basics = ts.get_today_all()
stock_list = sorted(list(stock_basics.code.values))
total = 0
file_path = self.working_folder
for stock in stock_list:
filename = stock + '.csv'
stock_data = pd.DataFrame([])
try:
stock_data = pd.read_csv(file_path + filename)
stock_data.sort_values(by='date',
ascending=False,
inplace=True)
start_date = stock_data.head(1).iloc[0, 0]
end_date = strftime("%Y-%m-%d", localtime())
try:
stock_data_latest = ts.get_k_data(stock,
autype=None,
start=start_date,
end=end_date)
stock_data = stock_data.append(stock_data_latest)
except:
pass
except:
year = 1990
while year <= self.today_year:
start_date = str(year) + '-01-01'
end_date = str(year) + '-12-31'
try:
stock_data_latest = ts.get_k_data(stock,
autype=None,
start=start_date,
end=end_date)
stock_data = stock_data.append(stock_data_latest)
except:
pass
year = year + 1
if len(stock_data) > 0:
stock_data.drop_duplicates(subset=['date'],
keep='first',
inplace=True)
stock_data.sort_values(by='date',
ascending=False,
inplace=True)
stock_data.set_index(keys=['date'],
drop=True,
inplace=True,
append=False)
stock_data.to_csv(file_path + filename)
#print('download_stock_D1 successful ' + str(total))
return
def zqAmoMin(self, stime, etime):
# amosql="select * from stocksystem.bigcashflow where datetime>='"+stime+ "'and datetime <='"+etime+"'"
# print amosql
# amodata=pd.read_sql(amosql,con=self.engine)
#
# print '资金流数据读取完成'
#
# s2=pd.DataFrame()
# sindex=['vbigB','vbigS','lbigB','lbigS']
# tmpgroup=amodata.groupby('hq_code')
# for code,tmpdata in tmpgroup:
# tmpdata=tmpdata.groupby('type').agg({'amo':'sum','amoper':'sum'})
# tmpdata=tmpdata.reindex(sindex)
# tmpdata=tmpdata.unstack().unstack()
# tmpdata['hq_code']=code
# s2=s2.append(tmpdata)
#
# s2.fillna(0,inplace=True)
# s2['bigD']=s2['vbigB']+s2['lbigB']-s2['vbigS']-s2['lbigS']
# amodata=s2.loc['amo']
# amoperdata=s2.loc['amoper']
# amodata.set_index('hq_code',inplace=True)
# amoperdata.set_index('hq_code',inplace=True)
# amodata['bigper']=amoperdata['bigD']
# amodata.sort_values('bigper',inplace=True,ascending=False)
amosql = "select hq_code,amo from stocksystem.bigcashflow where datetime>='" + stime + "'and datetime <='" + etime + "'"
print amosql
amodata = pd.read_sql(amosql, con=self.engine, index_col='hq_code')
amonet = amodata.groupby(level=0).sum()
amodata['amo'] = np.abs(amodata['amo'])
amodata = amodata.groupby(level=0).sum()
stockdata = ts.get_today_all()
stockdata['code'] = stockdata['code'].astype(int)
stockdata.set_index('code', inplace=True)
#得到流通股本
amodata['nmc'] = stockdata['nmc']
amodata['amoper'] = amodata['amo'] / np.abs(amodata['amo']).sum()
amodata['nmcper'] = stockdata['nmc'] / (stockdata['nmc'].sum())
amodata['amopower'] = amodata['amoper'] / amodata['nmcper']
amodata = amodata.sort_values('amopower', ascending=False)
amonet['nmc'] = stockdata['nmc']
amonet['amoper'] = amonet['amo'] / np.abs(amonet['amo']).sum()
amonet['nmcper'] = stockdata['nmc'] / (stockdata['nmc'].sum())
amonet['amopower'] = amonet['amoper'] / amonet['nmcper']
amonet = amonet.sort_values('amopower', ascending=False)
return amodata.loc[:, [
'amo', 'amoper', 'amopower', 'nmcper'
]], amonet.loc[:, ['amo', 'amoper', 'amopower', 'nmcper']]
def __init__(self):
self.today_stock=ts.get_today_all()
now=datetime.datetime.now()
self.today=now.strftime("%Y-%m-%d")
#weekday=now+datetime.timedelta(days=-2)
#weekday=weekday.strftime("%Y-%m-%d")
#print weekday
#today=now.strftime('%Y-%m-%d')
self.path=os.path.join(os.getcwd(),'data')
self.filename=os.path.join(self.path,'recordMyChoice.xls')
def update_stocks_data(state, all_stocks):
if not state:
try:
print datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
all_stocks = ts.get_today_all()
state = True
except Exception, e:
print e
all_stocks = None
state = False
def gettodaymarket(self, re_try=5):
while re_try > 0:
try:
df = ts.get_today_all()
if len(df) != 0:
return df
except Exception, e:
print e
re_try = re_try - 1
time.sleep(self.TIMEOUT)
def get_day_rise(self):
if not os.path.isfile(self.appPath + '/' + self.lastTradeDate + '_Datas.csv'):
try:
codeList = ts.get_today_all()
except Exception:
return []
codeList.to_csv(self.appPath + '/' + self.lastTradeDate + '_Datas.csv', encoding='utf8')
else:
codeList = pd.read_csv(self.appPath + '/' + self.lastTradeDate + '_Datas.csv', encoding='utf8',dtype={'code': str})
return codeList
def download__today_alldata():
ALL_datas=ts.get_today_all()# 读取去当天数据
files_path= '../report/Daily_database'
if os.path.exists(files_path) == False: # 判断文件是不是存在
os.mkdir(files_path) # 创建目录
ALL_datas.to_csv('../report/Daily_database/%s_full_stock_csv.csv'%todaytime,encoding='gbk') #存储到CSV
#存储到xlsx文件中
with pd.ExcelWriter('../report/Daily_database/%s_full_stock_xlx.xlsx'%todaytime) as writer:
ALL_datas.to_excel(writer, sheet_name='Sheet1')
print('today data base to save %s_database.csv'%todaytime)
def update_today_all():
'''
[更新当日实时数据]######
函数说明 乔晖 2018/4/23
一次性获取当前交易所有股票的行情数据（如果是节假日，即为上一交易日，结果显示速度取决于网速）
【行情不含基金和ETF】
结果保存在/data/today_all.csv
'''
df = ts.get_today_all()
df.to_csv('.\\data\\today_all.csv')
def get_today_and_putto_database(dbname):
tl = time.localtime()
if (tl.tm_hour < 15) and (tl.tm_hour > 8):
print("\ Now trading time , Do not update by This method")
return 0
df = ts.get_today_all()
neardate = get_shangHaiIndexAndReturnNearestTradeDate()
result = convertTodaydfTorows(df, neardate)
testsqlite(dbname, result, 0)
def downloadToday():
df = ts.get_today_all()
df.to_csv('today.csv', sep=',', encoding='utf-8')
code_list = df['code']
name_list = df['name']
name_dict = {}
for i in range(len(code_list)):
name_dict[code_list[i]] = name_list[i]
save_dict(name_dict, 'name_dict')
pass
def build_db(self):
import tushare as ts
df = ts.get_today_all()
rows, cols = df.shape
self.names = dict()
for i in range(rows):
self.names[df.ix[i, 'code']] = df.ix[i, 'name']
with open('names.json', 'w') as f:
json.dump(self.names, f)
def build_db(self):
import tushare as ts
df = ts.get_today_all()
rows, cols = df.shape
self.names = dict()
for i in range(rows):
self.names[df.ix[i,'code']] = df.ix[i,'name']
with open('names.json', 'w') as f:
json.dump(self.names, f)
def stock_info_html():
data = ts.get_today_all()
title = data.columns.values.tolist()
re_array = data[[
'code', 'name', 'changepercent', 'trade', 'open', 'high', 'low',
'settlement', 'volume', 'turnoverratio'
]]
data_li = list(row.tolist() for index, row in re_array.iterrows())
def update_stocks_data(state, all_stocks):
if not state:
try:
print datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
all_stocks = ts.get_today_all()
state = True
except Exception, e:
print e
all_stocks = None
state = False
def get_all_toplist():
# gold = {}
# goldl = []
df = ts.get_today_all()
top = df[df['changepercent'] > 6]
top = top[top['changepercent'] < 10]
# logging.info("top:", len(top['code']))
list = top['code']
print len(list)
return list
def __init__(self):
self.today_stock = ts.get_today_all()
now = datetime.datetime.now()
self.today = now.strftime("%Y-%m-%d")
#weekday=now+datetime.timedelta(days=-2)
#weekday=weekday.strftime("%Y-%m-%d")
#print weekday
#today=now.strftime('%Y-%m-%d')
self.path = os.path.join(os.getcwd(), 'data')
self.filename = os.path.join(self.path, 'recordMyChoice.xls')
def getCode():
try:
print('开始获取股票代码!!!')
#获取今日数据
pp = ts.get_today_all()
#获取所有的股票代码
codeList = np.array(pp.code).tolist()
return codeList
except Exception as err:
print(err)
def __init__(self, init=True):
"""
初始化，筛除688,300，ST，退市股，新股，低价股，下跌股
"""
if init:
self.data = ts.get_today_all() #今日复盘
self.updateNMC(self.data)
# data = ts.get_day_all(date='2021-02-18') #历史复盘
filt = self.data['code'].str.contains('^(?!688|605|300|301|8|43)')
self.data = self.data[filt]
filt = self.data['name'].str.contains('^(?!S|退市|\*ST|N)')
self.data = self.data[filt]
self.data = self.data.drop_duplicates()
data = self.data[self.data['trade'] >= 2]
data = data[data['changepercent'] > -3]
data = data.to_json(orient='records')
db.get_collection('today').remove()
base = db.get_collection('base').find()
industry = {i['code']: i['industry'] for i in base}
self.intersect = {}
for i in eval(data):
db.get_collection('today').insert(i)
res = db.get_collection('today').find()
for i in res:
try:
db.get_collection('today').update_many(
{'code': i['code']},
{'$set': {
'industry': industry[i['code']]
}})
except:
pass
# 剔除新股
try:
newStock = ts.new_stocks()
if not newStock.empty:
newStock = newStock[newStock.ipo_date > '2020-01-01']
for i in newStock['code'].tolist():
db.get_collection('today').remove({'code': i},
multi=True)
except Exception as e:
print('newStock Error', e)
# 剔除停牌
db.get_collection('today').remove({'open': 0})
# 龙虎榜
if not (10 < time.localtime().tm_hour < 18):
db.get_collection('topList').remove()
longhu = ts.top_list()
longhu = longhu.to_json(orient='index', )
longhu = json.loads(longhu)
for k, v in longhu.items():
db.get_collection('topList').insert(v)
def stoplossNotification(bot):
'''
消息推送：持仓股票提醒
:return: null
'''
print(strftime("%Y-%m-%d %H:%M:%S", localtime()) + ' - Start stoplossNotification thread ')
#generic variables initialization
working_folder = '/Users/yanghui/Documents/'
while True:
now = strftime("%H:%M", localtime())
today_ISO = datetime.today().date().isoformat()
## 2018 holiday calendar
holiday_calendar = ['2018-04-05', '2018-04-06', '2018-04-30', \
'2018-05-01', '2018-06-18', '2018-09-24', \
'2018-10-01', '2018-10-02', '2018-10-03', '2018-10-04', '2018-10-05']
if datetime.today().weekday() >= 5 or (today_ISO in holiday_calendar):
holiday = True
else:
holiday = False
#now = '14:30' # for testing purpose, need to remove before use
if (not holiday) and (now >= '09:30') and (now <= '15:05') :
filename = 'portfolio'
portfolio = pd.read_excel(io=working_folder + \
'quantest/risk_management/' + filename + '.xlsx')
portfolio.index = portfolio.index.map(lambda x: str(x).zfill(6))
portfolio_closed = portfolio[portfolio.price_close > 0]
portfolio_open = portfolio[portfolio.isnull().price_close]
try:
today_all = ts.get_today_all()
today_all['code'] = today_all['code'].map(lambda x: str(x).zfill(6))
for code in portfolio_open.index:
today_all_stock = today_all[(today_all.code == code)]
if (today_all_stock.iloc[0,3] <= portfolio_open.loc[code, 'price_stoploss']) and (portfolio_open.loc[code, 'notification_sent'] != 'Y'):
#消息推送
msg = '==========================' + '\n' + \
today_ISO + ' ' + now + ' - \n' + \
'==========================' + '\n' + \
'提醒：' + portfolio_open.loc[code, 'name'] + '-跌破止损价' + str(portfolio_open.loc[code, 'price_stoploss']) + '\n' + \
'=========================='
portfolio_open.loc[code, 'notification_sent'] = 'Y'
#发送消息
bot.friends().search('Yang Hui')[0].send(msg)
#bot.friends().search('欣')[0].send(msg)
#save file
portfolio = portfolio_closed.append(portfolio_open)
filename = 'portfolio'
portfolio.to_excel(working_folder + 'quantest/risk_management/' + filename + '.xlsx', encoding='GBK')
except:
pass
time.sleep(300)
print(strftime("%Y-%m-%d %H:%M:%S", localtime()) + ' - End stoplossNotification thread ')
return
def filter_stock_by_average_pe(min, max):
path = os.path.join(current_folder, '3年平均利润及其他财务指标%s.csv' % today)
if not os.path.exists(path): # 没有就生成3年平均利润列表
calcu_all_stocks_3year_roe_and_average_profit(
calcu_average_profit_end_year)
gplb = pd.read_csv(path, index_col=0, encoding='utf-8')
# 获取当前股票价格
price_path = os.path.join(current_folder, today + '股票价格.csv')
if not os.path.exists(price_path):
ts.get_today_all().set_index('code').to_csv(price_path,
encoding="utf-8")
current_price = pd.read_csv(price_path, encoding="utf-8", index_col=0)
current_price = current_price[['trade']]
current_price.columns = ['价格']
gplb = gplb[[
'名字', '行业', '地区', '流通股本', '总股本', '总资产(万)', '流动资产', '固定资产', '每股净资',
'市净率', '上市日期', '平均利润'
]]
data = pd.merge(gplb, current_price, left_index=True, right_index=True)
# 因为这里的平均利润单位是万元，而总股本单位是亿，价格单位是元
data['平均市盈率'] = data['总股本'] * data['价格'] * 10000 / data['平均利润']
print('\n%s:' % today)
print()
print('%d个公司' % data.shape[0])
print('3年市盈率中位数%.1f' % round(data['平均市盈率'].median(), 1))
print('市净率中位数%.1f' % round(data['市净率'].median(), 1))
data = data[data['平均市盈率'] < max]
data = data[data['平均市盈率'] > min]
data['平均市盈率'] = data['平均市盈率'].round(1)
data['平均利润'] = data['平均利润'].round()
data['市净率'] = data['市净率'].round(1)
data['固定资产'] = data['固定资产'].round()
data['流动资产'] = data['流动资产'].round()
data['总股本'] = data['总股本'].round()
data['流通股本'] = data['流通股本'].round()
average_pe_file = os.path.join(
current_folder, today + '-3年平均市盈率在%s和%s之间的公司.xlsx' % (min, max))
data.to_excel(average_pe_file, encoding='utf-8')
def main(today):
path=os.path.join(os.path.dirname(__file__),'data')
filename=os.path.join(path,'each_day_profile.xls')
org_filename=os.path.join(path,'2016-09-30_all_.xls')
#df_filename=os.path.join(path,'each_day_profile.xls')
#df=pd.read_excel(org_filename)
df=ts.get_today_all()
new_df=join_dataframe(filename,today)
save_name=os.path.join(path,"each_day_profile.xls")
#这样会不会把原来的覆盖掉？
new_df.to_excel(save_name)
def getTodayAll(self):
otherStyleTime = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
fileName='%s/todayAll/%s.csv'%(dir,otherStyleTime)
print(fileName)
pp = ts.get_today_all()
if not os.path.exists('%s/todayAll'%(dir)):
os.makedirs('%s/todayAll'%(dir))
pp.to_csv(fileName,encoding='utf-8')
with codecs.open("log.txt", 'w', "utf-8") as fileObj:
fileObj.write("%s\n"%(fileName))
return pp
def main(today):
path = os.path.join(os.path.dirname(__file__), 'data')
filename = os.path.join(path, 'each_day_profile.xls')
org_filename = os.path.join(path, '2016-09-30_all_.xls')
#df_filename=os.path.join(path,'each_day_profile.xls')
#df=pd.read_excel(org_filename)
df = ts.get_today_all()
new_df = join_dataframe(filename, today)
save_name = os.path.join(path, "each_day_profile.xls")
#这样会不会把原来的覆盖掉？
new_df.to_excel(save_name)
def daily_market(self):
'''
保存每天收盘后的市场行情
:return:
'''
df = ts.get_today_all()
print df
try:
df.to_sql(self.today, daily_engine, if_exists='replace')
except Exception, e:
print e
def announcment_stastic_main(theday):
# date=mydate = time.strftime('%Y-%m-%d',time.localtime(time.time()))
# print date
#date=datelist(2015,1,1,2018,2,4)
date = theday
b = ts.get_today_all()
for j in range(len(b.index)):
stock = b.loc[b.index[j]]['code']
name = b.loc[b.index[j]]['name']
time1 = datetime2ts(date)
test(stock, time1, date, name)
def get_all_top():
gold = {}
goldl = []
df = ts.get_today_all()
top = df[df['changepercent'] > 6]
# top = df[df['changepercent'] < -2]
top = top[top['changepercent'] < 10]
# logging.info("top:", len(top['code']))
list = top['code'].tolist()
print len(list)
return list
def get_today_all():
try:
df = ts.get_today_all()
ret = {}
for idx, serie in df.iterrows():
ret[serie['code']] = serie['mktcap']
return ret
except Exception as e:
pass
finally:
session.close()
def daily_market(self):
'''
保存每天收盘后的市场行情
:return:
'''
df = ts.get_today_all()
print df
try:
df.to_sql(self.today, daily_engine, if_exists='replace')
except Exception, e:
print e
def GetTodayAllQuotesData():
fileName = 'D:/today_all_quotes.csv'
allData = ts.get_today_all() #一次性获取当前交易所有的股票行情数据（如果是节假日，即为上一交易日）
if os.path.exists(fileName):
allData.to_csv(fileName,
mode='a',
index=True,
header=None,
encoding='utf_8_sig')
else:
allData.to_csv(fileName, index=True, encoding='utf_8_sig')
def gettodaymarket(self, re_try=5):
while re_try > 0:
try:
df = ts.get_today_all()
if len(df) != 0:
return df
except Exception as e:
logger.error(e)
re_try = re_try - 1
time.sleep(self.TIMEOUT)
return None
def get_all():
try:
df = pd.DataFrame(ts.get_today_all())
j_datas = json.loads(df.to_json(orient='records'))
stocks = []
from models import Stock
for j_data in j_datas:
stocks.append(Stock(j_data))
return stocks
except IOError:
pass
def general_info():
t_all = ts.get_today_all()
result = []
t1 = t_all[t_all['changepercent'] <= -9.0].count()['changepercent']
result.append(t1)
for i in range(-9, 9, 1):
temp = t_all[(i * 1.00 < t_all['changepercent']) & (t_all['changepercent'] <= (i + 1) * 1.00)].count()[
'changepercent']
result.append(temp)
t2 = t_all[t_all['changepercent'] > 9.0].count()['changepercent']
result.append(t2)
return result
def daily_market(self):
'''
保存每天收盘后的市场行情
:return:
'''
df = ts.get_today_all()
print(df)
try:
df.to_sql(self.today, daily_engine, if_exists='replace')
except Exception as e:
print(e)
print("Save {} data to MySQL".format(self.today))
def get_codes_names(filename):
'''
Get the latest codes which includes stock and index
'''
zs = ts.get_index()
stock = ts.get_today_all()
codes = np.concatenate( (zs['code'].unique(), stock['code'].unique()))
names = np.concatenate( (zs['name'].unique(), stock['name'].unique()))
out = (codes, names)
if filename:
write_pickle(out, filename)
return out
