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