def parse(code, parseIfeng=True, parseCap=True, parseSina=True):
from ifengparser import parseFinanceData
deli = '*************************************'
print deli
if (parseIfeng):
s = parseFinanceData(code)
else:
s = Stock(code)
if parseSina is False:
pass
else:
from sinaparser import getStock
s1 = getStock('sh' + code)
s.current = s1.current
s.percent = s1.percent
if parseCap:
from sseparser import parseCap
s2 = parseCap(code)
s.totalCap = s2.totalCap
s.floatingCap = s2.floatingCap
s.compute()
print s
return s
def parse(code,parseIfeng= True,parseCap = True,parseSina = True):
from ifengparser import parseFinanceData
deli = '*************************************'
print deli
if (parseIfeng):
s = parseFinanceData(code)
else:
s = Stock(code)
if parseSina is False:
pass
else:
from sinaparser import getStock
s1 = getStock('sh'+code)
s.current = s1.current
s.percent = s1.percent
if parseCap:
from sseparser import parseCap
s2 = parseCap(code)
s.totalCap = s2.totalCap
s.floatingCap = s2.floatingCap
s.compute()
print s
return s
def find_percentage(code_list, from_date):
# history = find_history_by_date(code,from_date)
history = find_all_history_by_date(from_date)
# size = history.count()
# logger.debug(size)
#logger.debug(history[0])
#logger.debug(history[size-1])
#for item in history:
# logger.debug(item)
df = pd.DataFrame(list(history))
# logger.debug(df)
result = []
for code in code_list:
df_by_code = df.loc[df['code'] == code]
# logger.debug(df_by_code)
# logger.debug('df shape:{}'.format(df_by_code.shape))
logger.debug('code:{} df length:{}'.format(code,
len(df_by_code.index)))
# size = len(df_by_code)
# logger.debug('df length:{}'.format(size))
# high_week52_index = 0
# low_week52_index = size-1
# end_close = df_by_code[['date','close']][high_week52_index:high_week52_index+1]
# begin_close = df_by_code[['date','close']][low_week52_index:low_week52_index+1]
# logger.debug(begin_close)
# logger.debug(end_close)
# logger.debug(begin_close['close'])
# logger.debug(end_close['close'])
# logger.debug(type(begin_close['close']))
if len(df_by_code.index) == 0:
continue
end_close = df_by_code.iloc[0]['close']
begin_close = df_by_code.iloc[-1]['close']
# logger.debug(begin_close)
# logger.debug(end_close)
code_percentage = (float(end_close) -
float(begin_close)) / float(begin_close)
logger.debug('code:{}:{}'.format(code, code_percentage))
stock = Stock(code)
stock.percent = code_percentage
result.append(stock)
result = sorted(result, key=lambda s: s.percent)
logger.debug(result)
return result
def find_percentage(code_list,from_date):
# history = find_history_by_date(code,from_date)
history = find_all_history_by_date(from_date)
# size = history.count()
# logger.debug(size)
#logger.debug(history[0])
#logger.debug(history[size-1])
#for item in history:
# logger.debug(item)
df = pd.DataFrame(list(history))
# logger.debug(df)
result = []
for code in code_list:
df_by_code = df.loc[df['code'] == code]
# logger.debug(df_by_code)
# logger.debug('df shape:{}'.format(df_by_code.shape))
logger.debug('code:{} df length:{}'.format(code,len(df_by_code.index)))
# size = len(df_by_code)
# logger.debug('df length:{}'.format(size))
# high_week52_index = 0
# low_week52_index = size-1
# end_close = df_by_code[['date','close']][high_week52_index:high_week52_index+1]
# begin_close = df_by_code[['date','close']][low_week52_index:low_week52_index+1]
# logger.debug(begin_close)
# logger.debug(end_close)
# logger.debug(begin_close['close'])
# logger.debug(end_close['close'])
# logger.debug(type(begin_close['close']))
if len(df_by_code.index) == 0:
continue
end_close = df_by_code.iloc[0]['close']
begin_close = df_by_code.iloc[-1]['close']
# logger.debug(begin_close)
# logger.debug(end_close)
code_percentage = (float(end_close)-float(begin_close))/float(begin_close)
logger.debug('code:{}:{}'.format(code,code_percentage))
stock = Stock(code)
stock.percent = code_percentage
result.append(stock)
result = sorted(result,key=lambda s: s.percent)
logger.debug(result)
return result
