def VecCycleIndicators(stockcode = None,Cycle = 'D'):
dftotal = ts.get_k_data(stockcode, ktype=Cycle)
totalrows = len(dftotal)
if(totalrows <100):
print('Insufficiennt Data for Analyze'
)
return None
startIndex = 0
colNames = ['date',Cycle+'-smadir',Cycle+'-macddir',Cycle+'-light',Cycle+'-bandratio',
Cycle+'-upband',Cycle+'-lowband',Cycle+'-valuerel',Cycle+'-rsi']
dfIndicator = DataFrame([],columns=colNames)
while(startIndex + 100 <= totalrows):
print(startIndex)
df = dftotal[:startIndex + 100]
dates = df['date'].values
closes = df['close'].values
lows = df['low'].values
highs = df['high'].values
date = dates[-1]
smadir = ta.SMA_Direction(dates, closes)
macddir = ta.MACD_Direction(dates, closes)
lightcolor = trpscr.ImpluseLight(smadir,macddir)
rsi = ta.get_cur_rsi(dates,closes)
uppenetrationCounter, downpenetrationCounter, bandratio, upband, lowband \
= ta.envelop_ratio(dates, highs, lows, closes)
value_re = ta.SMAValueZone_GX(dates, closes)
s = Series([date,smadir,macddir,lightcolor,bandratio,upband,lowband,value_re,rsi],index = colNames)
dfIndicator = dfIndicator.append(s,ignore_index=True)
startIndex += 1
FileName = stockcode + Cycle +'.csv'
FilePath = os.path.join(DataRoot,FileName)
dfIndicator.to_csv(FilePath)
return dfIndicator
def sz50Daily():
codelist = DI.get_stockcode_list('sz50')
colNames = [
'stockcode', 'smadir', 'uppenetrationCounter',
'downpenetrationCounter', 'macddir', 'bandratio', 'upband', 'lowband',
'value_re'
]
filterdf = DataFrame([], columns=colNames)
filepath = os.path.join(os.getcwd(), 'data', 'list')
for stockcode in codelist['code']:
df = ts.get_k_data(stockcode, ktype='D')
dates = df['date'].values
closes = df['close'].values
lows = df['low'].values
highs = df['high'].values
smadir = ta.SMA_Direction(dates, closes)
macddir = ta.MACD_Direction(dates, closes)
uppenetrationCounter, downpenetrationCounter, bandratio, upband, lowband\
= ta.envelop_ratio(dates,highs,lows,closes)
#print(stockcode,uppenetrationCounter, downpenetrationCounter, bandratio, upband, lowband)
value_re = ta.SMAValueZone_GX(dates, closes)
s = Series([
stockcode, smadir, uppenetrationCounter, downpenetrationCounter,
macddir, bandratio, upband, lowband, value_re
],
index=colNames)
filterdf = filterdf.append(s, ignore_index=True)
filterdf.to_csv(filepath + '.csv')