def get_cdlspinningtop(ohlc):
cdlspinningtop = ta.CDLSPINNINGTOP(ohlc['1_open'], ohlc['2_high'],
ohlc['3_low'], ohlc['4_close'])
ohlc['cdlspinningtop'] = cdlspinningtop
return ohlc
def CDLSPINNINGTOP(open, high, low, close):
''' Spinning Top 纺锤
分组: Pattern Recognition 形态识别
简介: 一日K线，实体小。
integer = CDLSPINNINGTOP(open, high, low, close)
'''
return talib.CDLSPINNINGTOP(open, high, low, close)
def spinning_top(self):
"""
名称：Spinning Top 纺锤
简介：一日K线，实体小。
"""
result = talib.CDLSPINNINGTOP(open=np.array(self.dataframe['open']),
high=np.array(self.dataframe['high']),
low=np.array(self.dataframe['low']),
close=np.array(self.dataframe['close']))
self.dataframe['spinning_top'] = result
def spinning_top(self, sym, frequency):
if not self.kbars_ready(sym, frequency):
return []
opens = self.open(sym, frequency)
highs = self.high(sym, frequency)
lows = self.low(sym, frequency)
closes = self.close(sym, frequency)
cdl = ta.CDLSPINNINGTOP(opens, highs, lows, closes)
return cdl
def ta(name, price_h, price_l, price_c, price_v, price_o):
# function 'MAX'/'MAXINDEX'/'MIN'/'MININDEX'/'MINMAX'/'MINMAXINDEX'/'SUM' is missing
if name == 'AD':
return talib.AD(np.array(price_h), np.array(price_l),
np.array(price_c), np.asarray(price_v, dtype='float'))
if name == 'ADOSC':
return talib.ADOSC(np.array(price_h),
np.array(price_l),
np.array(price_c),
np.asarray(price_v, dtype='float'),
fastperiod=2,
slowperiod=10)
if name == 'ADX':
return talib.ADX(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'ADXR':
return talib.ADXR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'APO':
return talib.APO(np.asarray(price_c, dtype='float'),
fastperiod=12,
slowperiod=26,
matype=0)
if name == 'AROON':
AROON_DWON, AROON2_UP = talib.AROON(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=90)
return (AROON_DWON, AROON2_UP)
if name == 'AROONOSC':
return talib.AROONOSC(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=14)
if name == 'ATR':
return talib.ATR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'AVGPRICE':
return talib.AVGPRICE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'BBANDS':
BBANDS1, BBANDS2, BBANDS3 = talib.BBANDS(np.asarray(price_c,
dtype='float'),
matype=MA_Type.T3)
return BBANDS1
if name == 'BETA':
return talib.BETA(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=5)
if name == 'BOP':
return talib.BOP(np.array(price_o), np.array(price_h),
np.array(price_l), np.asarray(price_c, dtype='float'))
if name == 'CCI':
return talib.CCI(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'CDL2CROWS':
return talib.CDL2CROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3BLACKCROWS':
return talib.CDL3BLACKCROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3INSIDE':
return talib.CDL3INSIDE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3LINESTRIKE':
return talib.CDL3LINESTRIKE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3OUTSIDE':
return talib.CDL3OUTSIDE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3STARSINSOUTH':
return talib.CDL3STARSINSOUTH(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3WHITESOLDIERS':
return talib.CDL3WHITESOLDIERS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLABANDONEDBABY':
return talib.CDLABANDONEDBABY(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLADVANCEBLOCK':
return talib.CDLADVANCEBLOCK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLBELTHOLD':
return talib.CDLBELTHOLD(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLBREAKAWAY':
return talib.CDLBREAKAWAY(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLCLOSINGMARUBOZU':
return talib.CDLCLOSINGMARUBOZU(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLCONCEALBABYSWALL':
return talib.CDLCONCEALBABYSWALL(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLCOUNTERATTACK':
return talib.CDLCOUNTERATTACK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLDARKCLOUDCOVER':
return talib.CDLDARKCLOUDCOVER(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLDOJI':
return talib.CDLDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLDOJISTAR':
return talib.CDLDOJISTAR(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLDRAGONFLYDOJI':
return talib.CDLDRAGONFLYDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLENGULFING':
return talib.CDLENGULFING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLEVENINGDOJISTAR':
return talib.CDLEVENINGDOJISTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLEVENINGSTAR':
return talib.CDLEVENINGSTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLGAPSIDESIDEWHITE':
return talib.CDLGAPSIDESIDEWHITE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLGRAVESTONEDOJI':
return talib.CDLGRAVESTONEDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHAMMER':
return talib.CDLHAMMER(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHANGINGMAN':
return talib.CDLHANGINGMAN(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHARAMI':
return talib.CDLHARAMI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHARAMICROSS':
return talib.CDLHARAMICROSS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHIGHWAVE':
return talib.CDLHIGHWAVE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHIKKAKE':
return talib.CDLHIKKAKE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHIKKAKEMOD':
return talib.CDLHIKKAKEMOD(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHOMINGPIGEON':
return talib.CDLHOMINGPIGEON(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLIDENTICAL3CROWS':
return talib.CDLIDENTICAL3CROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLINNECK':
return talib.CDLINNECK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLINVERTEDHAMMER':
return talib.CDLINVERTEDHAMMER(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLKICKING':
return talib.CDLKICKING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLKICKINGBYLENGTH':
return talib.CDLKICKINGBYLENGTH(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLLADDERBOTTOM':
return talib.CDLLADDERBOTTOM(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLLONGLEGGEDDOJI':
return talib.CDLLONGLEGGEDDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLLONGLINE':
return talib.CDLLONGLINE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMARUBOZU':
return talib.CDLMARUBOZU(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMATCHINGLOW':
return talib.CDLMATCHINGLOW(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMATHOLD':
return talib.CDLMATHOLD(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMORNINGDOJISTAR':
return talib.CDLMORNINGDOJISTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLMORNINGSTAR':
return talib.CDLMORNINGSTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLONNECK':
return talib.CDLONNECK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLPIERCING':
return talib.CDLPIERCING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLRICKSHAWMAN':
return talib.CDLRICKSHAWMAN(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLRISEFALL3METHODS':
return talib.CDLRISEFALL3METHODS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSEPARATINGLINES':
return talib.CDLSEPARATINGLINES(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSHOOTINGSTAR':
return talib.CDLSHOOTINGSTAR(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSHORTLINE':
return talib.CDLSHORTLINE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSPINNINGTOP':
return talib.CDLSPINNINGTOP(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSTALLEDPATTERN':
return talib.CDLSTALLEDPATTERN(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSTICKSANDWICH':
return talib.CDLSTICKSANDWICH(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTAKURI':
return talib.CDLTAKURI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTASUKIGAP':
return talib.CDLTASUKIGAP(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTHRUSTING':
return talib.CDLTHRUSTING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTRISTAR':
return talib.CDLTRISTAR(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLUNIQUE3RIVER':
return talib.CDLUNIQUE3RIVER(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLUPSIDEGAP2CROWS':
return talib.CDLUPSIDEGAP2CROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLXSIDEGAP3METHODS':
return talib.CDLXSIDEGAP3METHODS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CMO':
return talib.CMO(np.asarray(price_c, dtype='float'), timeperiod=14)
if name == 'CORREL':
return talib.CORREL(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=30)
if name == 'DEMA':
return talib.DEMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'DX':
return talib.DX(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'EMA':
return talib.EMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'HT_DCPERIOD':
return talib.HT_DCPERIOD(np.asarray(price_c, dtype='float'))
if name == 'HT_DCPHASE':
return talib.HT_DCPHASE(np.asarray(price_c, dtype='float'))
if name == 'HT_PHASOR':
HT_PHASOR1, HT_PHASOR2 = talib.HT_PHASOR(
np.asarray(price_c, dtype='float')
) # use HT_PHASOR1 for the indication of up and down
return HT_PHASOR1
if name == 'HT_SINE':
HT_SINE1, HT_SINE2 = talib.HT_SINE(np.asarray(price_c, dtype='float'))
return HT_SINE1
if name == 'HT_TRENDLINE':
return talib.HT_TRENDLINE(np.asarray(price_c, dtype='float'))
if name == 'HT_TRENDMODE':
return talib.HT_TRENDMODE(np.asarray(price_c, dtype='float'))
if name == 'KAMA':
return talib.KAMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'LINEARREG':
return talib.LINEARREG(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'LINEARREG_ANGLE':
return talib.LINEARREG_ANGLE(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'LINEARREG_INTERCEPT':
return talib.LINEARREG_INTERCEPT(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'LINEARREG_SLOPE':
return talib.LINEARREG_SLOPE(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'MA':
return talib.MA(np.asarray(price_c, dtype='float'),
timeperiod=30,
matype=0)
if name == 'MACD':
MACD1, MACD2, MACD3 = talib.MACD(np.asarray(price_c, dtype='float'),
fastperiod=12,
slowperiod=26,
signalperiod=9)
return MACD1
if nam == 'MACDEXT':
return talib.MACDEXT(np.asarray(price_c, dtype='float'),
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
if name == 'MACDFIX':
MACDFIX1, MACDFIX2, MACDFIX3 = talib.MACDFIX(np.asarray(price_c,
dtype='float'),
signalperiod=9)
return MACDFIX1
if name == 'MAMA':
MAMA1, MAMA2 = talib.MAMA(np.asarray(price_c, dtype='float'),
fastlimit=0,
slowlimit=0)
return MAMA1
if name == 'MEDPRICE':
return talib.MEDPRICE(np.array(price_h),
np.asarray(price_l, dtype='float'))
if name == 'MINUS_DI':
return talib.MINUS_DI(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'MINUS_DM':
return talib.MINUS_DM(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=14)
if name == 'MOM':
return talib.MOM(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'NATR':
return talib.NATR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'OBV':
return talib.OBV(np.array(price_c), np.asarray(price_v, dtype='float'))
if name == 'PLUS_DI':
return talib.PLUS_DI(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'PLUS_DM':
return talib.PLUS_DM(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=14)
if name == 'PPO':
return talib.PPO(np.asarray(price_c, dtype='float'),
fastperiod=12,
slowperiod=26,
matype=0)
if name == 'ROC':
return talib.ROC(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'ROCP':
return talib.ROCP(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'ROCR100':
return talib.ROCR100(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'RSI':
return talib.RSI(np.asarray(price_c, dtype='float'), timeperiod=14)
if name == 'SAR':
return talib.SAR(np.array(price_h),
np.asarray(price_l, dtype='float'),
acceleration=0,
maximum=0)
if name == 'SAREXT':
return talib.SAREXT(np.array(price_h),
np.asarray(price_l, dtype='float'),
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
if name == 'SMA':
return talib.SMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'STDDEV':
return talib.STDDEV(np.asarray(price_c, dtype='float'),
timeperiod=5,
nbdev=1)
if name == 'STOCH':
STOCH1, STOCH2 = talib.STOCH(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
return STOCH1
if name == 'STOCHF':
STOCHF1, STOCHF2 = talib.STOCHF(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
fastk_period=5,
fastd_period=3,
fastd_matype=0)
return STOCHF1
if name == 'STOCHRSI':
STOCHRSI1, STOCHRSI2 = talib.STOCHRSI(np.asarray(price_c,
dtype='float'),
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
return STOCHRSI1
if name == 'T3':
return talib.T3(np.asarray(price_c, dtype='float'),
timeperiod=5,
vfactor=0)
if name == 'TEMA':
return talib.TEMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'TRANGE':
return talib.TRANGE(np.array(price_h), np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'TRIMA':
return talib.TRIMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'TRIX':
return talib.TRIX(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'TSF':
return talib.TSF(np.asarray(price_c, dtype='float'), timeperiod=14)
if name == 'TYPPRICE':
return talib.TYPPRICE(np.array(price_h), np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'ULTOSC':
return talib.ULTOSC(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
if name == 'VAR':
return talib.VAR(np.asarray(price_c, dtype='float'),
timeperiod=5,
nbdev=1)
if name == 'WCLPRICE':
return talib.WCLPRICE(np.array(price_h), np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'WILLR':
return talib.WILLR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'WMA':
return talib.WMA(np.asarray(price_c, dtype='float'), timeperiod=30)
def technical(df):
open = df['open'].values
close = df['close'].values
high = df['high'].values
low = df['low'].values
volume = df['volume'].values
# define the technical analysis matrix
retn = np.array([
tb.MA(close, timeperiod=60), # 1
tb.MA(close, timeperiod=120), # 2
tb.ADX(high, low, close, timeperiod=14), # 3
tb.ADXR(high, low, close, timeperiod=14), # 4
tb.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)[0], # 5
tb.RSI(close, timeperiod=14), # 6
tb.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[0], # 7
tb.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[1], # 8
tb.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[2], # 9
tb.AD(high, low, close, volume), # 10
tb.ATR(high, low, close, timeperiod=14), # 11
tb.HT_DCPERIOD(close), # 12
tb.CDL2CROWS(open, high, low, close), # 13
tb.CDL3BLACKCROWS(open, high, low, close), # 14
tb.CDL3INSIDE(open, high, low, close), # 15
tb.CDL3LINESTRIKE(open, high, low, close), # 16
tb.CDL3OUTSIDE(open, high, low, close), # 17
tb.CDL3STARSINSOUTH(open, high, low, close), # 18
tb.CDL3WHITESOLDIERS(open, high, low, close), # 19
tb.CDLABANDONEDBABY(open, high, low, close, penetration=0), # 20
tb.CDLADVANCEBLOCK(open, high, low, close), # 21
tb.CDLBELTHOLD(open, high, low, close), # 22
tb.CDLBREAKAWAY(open, high, low, close), # 23
tb.CDLCLOSINGMARUBOZU(open, high, low, close), # 24
tb.CDLCONCEALBABYSWALL(open, high, low, close), # 25
tb.CDLCOUNTERATTACK(open, high, low, close), # 26
tb.CDLDARKCLOUDCOVER(open, high, low, close, penetration=0), # 27
tb.CDLDOJI(open, high, low, close), # 28
tb.CDLDOJISTAR(open, high, low, close), # 29
tb.CDLDRAGONFLYDOJI(open, high, low, close), # 30
tb.CDLENGULFING(open, high, low, close), # 31
tb.CDLEVENINGDOJISTAR(open, high, low, close, penetration=0), # 32
tb.CDLEVENINGSTAR(open, high, low, close, penetration=0), # 33
tb.CDLGAPSIDESIDEWHITE(open, high, low, close), # 34
tb.CDLGRAVESTONEDOJI(open, high, low, close), # 35
tb.CDLHAMMER(open, high, low, close), # 36
tb.CDLHANGINGMAN(open, high, low, close), # 37
tb.CDLHARAMI(open, high, low, close), # 38
tb.CDLHARAMICROSS(open, high, low, close), # 39
tb.CDLHIGHWAVE(open, high, low, close), # 40
tb.CDLHIKKAKE(open, high, low, close), # 41
tb.CDLHIKKAKEMOD(open, high, low, close), # 42
tb.CDLHOMINGPIGEON(open, high, low, close), # 43
tb.CDLIDENTICAL3CROWS(open, high, low, close), # 44
tb.CDLINNECK(open, high, low, close), # 45
tb.CDLINVERTEDHAMMER(open, high, low, close), # 46
tb.CDLKICKING(open, high, low, close), # 47
tb.CDLKICKINGBYLENGTH(open, high, low, close), # 48
tb.CDLLADDERBOTTOM(open, high, low, close), # 49
tb.CDLLONGLEGGEDDOJI(open, high, low, close), # 50
tb.CDLLONGLINE(open, high, low, close), # 51
tb.CDLMARUBOZU(open, high, low, close), # 52
tb.CDLMATCHINGLOW(open, high, low, close), # 53
tb.CDLMATHOLD(open, high, low, close, penetration=0), # 54
tb.CDLMORNINGDOJISTAR(open, high, low, close, penetration=0), # 55
tb.CDLMORNINGSTAR(open, high, low, close, penetration=0), # 56
tb.CDLONNECK(open, high, low, close), # 57
tb.CDLPIERCING(open, high, low, close), # 58
tb.CDLRICKSHAWMAN(open, high, low, close), # 59
tb.CDLRISEFALL3METHODS(open, high, low, close), # 60
tb.CDLSEPARATINGLINES(open, high, low, close), # 61
tb.CDLSHOOTINGSTAR(open, high, low, close), # 62
tb.CDLSHORTLINE(open, high, low, close), # 63
tb.CDLSPINNINGTOP(open, high, low, close), # 64
tb.CDLSTALLEDPATTERN(open, high, low, close), # 65
tb.CDLSTICKSANDWICH(open, high, low, close), # 66
tb.CDLTAKURI(open, high, low, close), # 67
tb.CDLTASUKIGAP(open, high, low, close), # 68
tb.CDLTHRUSTING(open, high, low, close), # 69
tb.CDLTRISTAR(open, high, low, close), # 70
tb.CDLUNIQUE3RIVER(open, high, low, close), # 71
tb.CDLUPSIDEGAP2CROWS(open, high, low, close), # 72
tb.CDLXSIDEGAP3METHODS(open, high, low, close) # 73
]).T
return retn
def CDLSPINNINGTOP(open, high, low, close):
return talib.CDLSPINNINGTOP(open, high, low, close)
cdlrickshawman = ta.CDLRICKSHAWMAN(openp, high, low, close)
#CDLRISEFALL3METHODS - Rising/Falling Three Methods
cdlrisefall3methods = ta.CDLRISEFALL3METHODS(openp, high, low, close)
#CDLSEPARATINGLINES - Separating Lines
cdlseperatinglines = ta.CDLSEPARATINGLINES(openp, high, low, close)
#CDLSHOOTINGSTAR - Shooting Star
cdlshootingstar = ta.CDLSHOOTINGSTAR(openp, high, low, close)
#CDLSHORTLINE - Short Line Candle
cdlshortline = ta.CDLSHORTLINE(openp, high, low, close)
#CDLSPINNINGTOP - Spinning Top
cdlspinningtop = ta.CDLSPINNINGTOP(openp, high, low, close)
#CDLSTALLEDPATTERN - Stalled Pattern
cdlstalledpattern = ta.CDLSTALLEDPATTERN(openp, high, low, close)
#CDLSTICKSANDWICH - Stick Sandwich
cdlsticksandwich = ta.CDLSTICKSANDWICH(openp, high, low, close)
#CDLTAKURI - Takuri (Dragonfly Doji with very long lower shadow)
cdltakuri = ta.CDLTAKURI(openp, high, low, close)
#CDLTASUKIGAP - Tasuki Gap
cdltasukigap = ta.CDLTASUKIGAP(openp, high, low, close)
#CDLTHRUSTING - Thrusting Pattern
cdlthrusting = ta.CDLTHRUSTING(openp, high, low, close)
def CDLSPINNINGTOP(data, **kwargs):
_check_talib_presence()
popen, phigh, plow, pclose, pvolume = _extract_ohlc(data)
return talib.CDLSPINNINGTOP(popen, phigh, plow, pclose, **kwargs)
ohlc_df['close'])
ohlc_df['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLSHORTLINE'] = ta.CDLSHORTLINE(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLTAKURI'] = ta.CDLTAKURI(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
resorted['close'])
CDLRISEFALL3METHODS_real = talib.CDLRISEFALL3METHODS(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLSEPARATINGLINES_real = talib.CDLSEPARATINGLINES(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLSHOOTINGSTAR_real = talib.CDLSHOOTINGSTAR(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLSHORTLINE_real = talib.CDLSHORTLINE(resorted['open'],
resorted['high'],
resorted['low'],
resorted['close'])
CDLSPINNINGTOP_real = talib.CDLSPINNINGTOP(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLSTALLEDPATTERN_real = talib.CDLSTALLEDPATTERN(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLSTICKSANDWICH_real = talib.CDLSTICKSANDWICH(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLTAKURI_real = talib.CDLTAKURI(resorted['open'],
resorted['high'],
resorted['low'],
resorted['close'])
CDLTASUKIGAP_real = talib.CDLTASUKIGAP(resorted['open'],
resorted['high'],
resorted['low'],
resorted['close'])
def create_signal_dataframe(df_):
o = np.array(df_['始値'])
c = np.array(df_['終値'])
l = np.array(df_['安値'])
h = np.array(df_['高値'])
df = df_.copy()
df['CDL2CROWS'] = ta.CDL2CROWS(o, h, l, c)
df['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(o, h, l, c)
df['CDL3INSIDE'] = ta.CDL3INSIDE(o, h, l, c)
df['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(o, h, l, c)
df['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(o, h, l, c)
df['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(o, h, l, c)
df['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(o, h, l, c)
df['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY(o, h, l, c)
df['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(o, h, l, c)
df['CDLBELTHOLD'] = ta.CDLBELTHOLD(o, h, l, c)
df['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(o, h, l, c)
df['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU(o, h, l, c)
df['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL(o, h, l, c)
df['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK(o, h, l, c)
df['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(o, h, l, c)
df['CDLDOJI'] = ta.CDLDOJI(o, h, l, c)
df['CDLDOJISTAR'] = ta.CDLDOJISTAR(o, h, l, c)
df['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(o, h, l, c)
df['CDLENGULFING'] = ta.CDLENGULFING(o, h, l, c)
df['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(o, h, l, c)
df['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(o, h, l, c)
df['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE(o, h, l, c)
df['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(o, h, l, c)
df['CDLHAMMER'] = ta.CDLHAMMER(o, h, l, c)
df['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(o, h, l, c)
df['CDLHARAMI'] = ta.CDLHARAMI(o, h, l, c)
df['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(o, h, l, c)
df['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(o, h, l, c)
df['CDLHIKKAKE'] = ta.CDLHIKKAKE(o, h, l, c)
df['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(o, h, l, c)
df['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(o, h, l, c)
df['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(o, h, l, c)
df['CDLINNECK'] = ta.CDLINNECK(o, h, l, c)
df['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(o, h, l, c)
df['CDLKICKING'] = ta.CDLKICKING(o, h, l, c)
df['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH(o, h, l, c)
df['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM(o, h, l, c)
df['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(o, h, l, c)
df['CDLLONGLINE'] = ta.CDLLONGLINE(o, h, l, c)
df['CDLMARUBOZU'] = ta.CDLMARUBOZU(o, h, l, c)
df['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(o, h, l, c)
df['CDLMATHOLD'] = ta.CDLMATHOLD(o, h, l, c)
df['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(o, h, l, c)
df['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(o, h, l, c)
df['CDLONNECK'] = ta.CDLONNECK(o, h, l, c)
df['CDLPIERCING'] = ta.CDLPIERCING(o, h, l, c)
df['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(o, h, l, c)
df['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(o, h, l, c)
df['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(o, h, l, c)
df['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(o, h, l, c)
df['CDLSHORTLINE'] = ta.CDLSHORTLINE(o, h, l, c)
df['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(o, h, l, c)
df['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(o, h, l, c)
df['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(o, h, l, c)
df['CDLTAKURI'] = ta.CDLTAKURI(o, h, l, c)
df['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(o, h, l, c)
df['CDLTHRUSTING'] = ta.CDLTHRUSTING(o, h, l, c)
df['CDLTRISTAR'] = ta.CDLTRISTAR(o, h, l, c)
df['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER(o, h, l, c)
df['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS(o, h, l, c)
df['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS(o, h, l, c)
return df
def collectDATA(self, start_dt, end_dt, para_min, threshold):
# 建立数据库连接,剔除已入库的部分
db = pymysql.connect(host='127.0.0.1',
user='******',
passwd='admin',
db='stock',
charset='utf8')
cursor = db.cursor()
if para_min == 'day':
sql_done_set = "SELECT * FROM btc_day a where state_dt >= '%s' and state_dt <= '%s' order by state_dt asc" % (
start_dt, end_dt)
else:
sql_done_set = "SELECT * FROM btc_%smin a where state_dt >= '%s' and state_dt <= '%s' order by state_dt asc" % (
str(para_min), start_dt, end_dt)
cursor.execute(sql_done_set)
done_set = cursor.fetchall()
if len(done_set) == 0:
raise Exception
self.date_seq = []
self.open_list = []
self.close_list = []
self.high_list = []
self.low_list = []
self.vol_list = []
self.amount_list = []
self.tor_list = []
self.vr_list = []
self.ma5_list = []
self.ma10_list = []
self.ma20_list = []
self.ma30_list = []
self.ma60_list = []
for i in range(len(done_set)):
self.date_seq.append(done_set[i][0])
self.open_list.append(float(done_set[i][1]))
self.close_list.append(float(done_set[i][2]))
self.high_list.append(float(done_set[i][3]))
self.low_list.append(float(done_set[i][4]))
self.vol_list.append(float(done_set[i][6]))
self.amount_list.append(float(done_set[i][5]))
db.close()
cdl_2crows = ta.CDL2CROWS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3blackcrows = ta.CDL3BLACKCROWS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3inside = ta.CDL3INSIDE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3linestrike = ta.CDL3LINESTRIKE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3outside = ta.CDL3OUTSIDE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3starsinsouth = ta.CDL3STARSINSOUTH(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3whitesoldiers = ta.CDL3WHITESOLDIERS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_abandonedbaby = ta.CDLABANDONEDBABY(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_advancedblock = ta.CDLADVANCEBLOCK(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_belthold = ta.CDLBELTHOLD(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_breakaway = ta.CDLBREAKAWAY(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_closing = ta.CDLCLOSINGMARUBOZU(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_conbaby = ta.CDLCONCEALBABYSWALL(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_counterattack = ta.CDLCOUNTERATTACK(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_darkcloud = ta.CDLDARKCLOUDCOVER(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_doji = ta.CDLDOJI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_dojistar = ta.CDLDOJISTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_dragondoji = ta.CDLDRAGONFLYDOJI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_eng = ta.CDLENGULFING(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_evedoji = ta.CDLEVENINGDOJISTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_evestar = ta.CDLEVENINGSTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_gapside = ta.CDLGAPSIDESIDEWHITE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_gravedoji = ta.CDLGRAVESTONEDOJI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_hammer = ta.CDLHAMMER(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_hanging = ta.CDLHANGINGMAN(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_hara = ta.CDLHARAMI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_haracross = ta.CDLHARAMICROSS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_highwave = ta.CDLHIGHWAVE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_hikk = ta.CDLHIKKAKE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_hikkmod = ta.CDLHIKKAKEMOD(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_homing = ta.CDLHOMINGPIGEON(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_i3crows = ta.CDLIDENTICAL3CROWS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_inneck = ta.CDLINNECK(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_inverhammer = ta.CDLINVERTEDHAMMER(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_kicking = ta.CDLKICKING(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_kicking2 = ta.CDLKICKINGBYLENGTH(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_ladder = ta.CDLLADDERBOTTOM(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_longdoji = ta.CDLLONGLEGGEDDOJI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_longline = ta.CDLLONGLINE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_marubo = ta.CDLMARUBOZU(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_matchinglow = ta.CDLMATCHINGLOW(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_mathold = ta.CDLMATHOLD(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_morningdoji = ta.CDLMORNINGDOJISTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_morningstar = ta.CDLMORNINGSTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_onneck = ta.CDLONNECK(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_pier = ta.CDLPIERCING(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_rick = ta.CDLRICKSHAWMAN(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3methords = ta.CDLRISEFALL3METHODS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_seprate = ta.CDLSEPARATINGLINES(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_shoot = ta.CDLSHOOTINGSTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_shortcandle = ta.CDLSHORTLINE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_spin = ta.CDLSPINNINGTOP(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_stalled = ta.CDLSTALLEDPATTERN(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_sandwich = ta.CDLSTICKSANDWICH(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_taku = ta.CDLTAKURI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_takugap = ta.CDLTASUKIGAP(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_thrust = ta.CDLTHRUSTING(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_tristar = ta.CDLTRISTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_uni = ta.CDLUNIQUE3RIVER(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_upgap = ta.CDLUPSIDEGAP2CROWS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_xside = ta.CDLXSIDEGAP3METHODS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
self.data_train = []
self.data_target = []
self.data_target_onehot = []
for i in range(len(self.close_list) - 5):
train = [
cdl_2crows[i], cdl_3blackcrows[i], cdl_3inside[i],
cdl_3linestrike[i], cdl_3outside[i], cdl_3starsinsouth[i],
cdl_3whitesoldiers[i], cdl_abandonedbaby[i],
cdl_advancedblock[i], cdl_belthold[i], cdl_breakaway[i],
cdl_closing[i], cdl_conbaby[i], cdl_counterattack[i],
cdl_darkcloud[i], cdl_doji[i], cdl_dojistar[i],
cdl_dragondoji[i], cdl_eng[i], cdl_evedoji[i], cdl_evestar[i],
cdl_gapside[i], cdl_gravedoji[i], cdl_hammer[i],
cdl_hanging[i], cdl_hara[i], cdl_haracross[i], cdl_highwave[i],
cdl_hikk[i], cdl_hikkmod[i], cdl_homing[i], cdl_i3crows[i],
cdl_inneck[i], cdl_inverhammer[i], cdl_kicking[i],
cdl_kicking2[i], cdl_ladder[i], cdl_longdoji[i],
cdl_longline[i], cdl_marubo[i], cdl_matchinglow[i],
cdl_mathold[i], cdl_morningdoji[i], cdl_morningstar[i],
cdl_onneck[i], cdl_pier[i], cdl_rick[i], cdl_3methords[i],
cdl_seprate[i], cdl_shoot[i], cdl_shortcandle[i], cdl_spin[i],
cdl_stalled[i], cdl_sandwich[i], cdl_taku[i], cdl_takugap[i],
cdl_thrust[i], cdl_tristar[i], cdl_uni[i], cdl_upgap[i],
cdl_xside[i]
]
self.data_train.append(np.array(train))
# after_max_price = max(self.close_list[i+1:i + 5])
# after_min_price = min(self.close_list[i+1:i+5])
# if after_max_price / self.close_list[i] >= 1.01:
# self.data_target.append(float(1.00))
# self.data_target_onehot.append([1,0,0])
# elif after_min_price / self.close_list[i] < 0.99:
# self.data_target.append(float(-1.00))
# self.data_target_onehot.append([0,1,0])
# else:
# self.data_target.append(float(0.00))
# self.data_target_onehot.append([0,0,1])
after_mean_price = np.array(self.close_list[i + 1:i + 5]).mean()
if after_mean_price / self.close_list[i] > threshold:
self.data_target.append(float(1.00))
self.data_target_onehot.append([1, 0, 0])
else:
self.data_target.append(float(-1.00))
self.data_target_onehot.append([0, 1, 0])
self.cnt_pos = 0
self.cnt_pos = len([x for x in self.data_target if x == 1.00])
self.test_case = []
self.test_case = np.array([
cdl_2crows[-1], cdl_3blackcrows[-1], cdl_3inside[-1],
cdl_3linestrike[-1], cdl_3outside[-1], cdl_3starsinsouth[-1],
cdl_3whitesoldiers[-1], cdl_abandonedbaby[-1],
cdl_advancedblock[-1], cdl_belthold[-1], cdl_breakaway[-1],
cdl_closing[-1], cdl_conbaby[-1], cdl_counterattack[-1],
cdl_darkcloud[-1], cdl_doji[-1], cdl_dojistar[-1],
cdl_dragondoji[-1], cdl_eng[-1], cdl_evedoji[-1], cdl_evestar[-1],
cdl_gapside[-1], cdl_gravedoji[-1], cdl_hammer[-1],
cdl_hanging[-1], cdl_hara[-1], cdl_haracross[-1], cdl_highwave[-1],
cdl_hikk[-1], cdl_hikkmod[-1], cdl_homing[-1], cdl_i3crows[-1],
cdl_inneck[-1], cdl_inverhammer[-1], cdl_kicking[-1],
cdl_kicking2[-1], cdl_ladder[-1], cdl_longdoji[-1],
cdl_longline[-1], cdl_marubo[-1], cdl_matchinglow[-1],
cdl_mathold[-1], cdl_morningdoji[-1], cdl_morningstar[-1],
cdl_onneck[-1], cdl_pier[-1], cdl_rick[-1], cdl_3methords[-1],
cdl_seprate[-1], cdl_shoot[-1], cdl_shortcandle[-1], cdl_spin[-1],
cdl_stalled[-1], cdl_sandwich[-1], cdl_taku[-1], cdl_takugap[-1],
cdl_thrust[-1], cdl_tristar[-1], cdl_uni[-1], cdl_upgap[-1],
cdl_xside[-1]
])
self.data_train = np.array(self.data_train)
self.data_target = np.array(self.data_target)
def add_indicator(data):
open = data.Open
high = data.High
low = data.Low
close = data.Close
volume = data.Volume
data['CDL2CROWS'] = talib.CDL2CROWS(open, high, low, close)
data['CDL3BLACKCROWS'] = talib.CDL3BLACKCROWS(open, high, low, close)
data['CDL3INSIDE'] = talib.CDL3INSIDE(open, high, low, close)
data['CDL3LINESTRIKE'] = talib.CDL3LINESTRIKE(open, high, low, close)
data['CDL3OUTSIDE'] = talib.CDL3OUTSIDE(open, high, low, close)
data['CDL3STARSINSOUTH'] = talib.CDL3STARSINSOUTH(open, high, low, close)
data['CDL3WHITESOLDIERS'] = talib.CDL3WHITESOLDIERS(open, high, low, close)
data['CDLABANDONEDBABY'] = talib.CDLABANDONEDBABY(open,
high,
low,
close,
penetration=0)
data['CDLADVANCEBLOCK'] = talib.CDLADVANCEBLOCK(open, high, low, close)
data['CDLBELTHOLD'] = talib.CDLBELTHOLD(open, high, low, close)
data['CDLBREAKAWAY'] = talib.CDLBREAKAWAY(open, high, low, close)
data['CDLCLOSINGMARUBOZU'] = talib.CDLCLOSINGMARUBOZU(
open, high, low, close)
data['CDLCONCEALBABYSWALL'] = talib.CDLCONCEALBABYSWALL(
open, high, low, close)
data['CDLCOUNTERATTACK'] = talib.CDLCOUNTERATTACK(open, high, low, close)
data['CDLDARKCLOUDCOVER'] = talib.CDLDARKCLOUDCOVER(open,
high,
low,
close,
penetration=0)
data['CDLDOJI'] = talib.CDLDOJI(open, high, low, close)
data['CDLDOJISTAR'] = talib.CDLDOJISTAR(open, high, low, close)
data['CDLDRAGONFLYDOJI'] = talib.CDLDRAGONFLYDOJI(open, high, low, close)
data['CDLENGULFING'] = talib.CDLENGULFING(open, high, low, close)
data['CDLEVENINGDOJISTAR'] = talib.CDLEVENINGDOJISTAR(open,
high,
low,
close,
penetration=0)
data['CDLEVENINGSTAR'] = talib.CDLEVENINGSTAR(open,
high,
low,
close,
penetration=0)
data['CDLGAPSIDESIDEWHITE'] = talib.CDLGAPSIDESIDEWHITE(
open, high, low, close)
data['CDLGRAVESTONEDOJI'] = talib.CDLGRAVESTONEDOJI(open, high, low, close)
data['CDLHAMMER'] = talib.CDLHAMMER(open, high, low, close)
data['CDLHANGINGMAN'] = talib.CDLHANGINGMAN(open, high, low, close)
data['CDLHARAMI'] = talib.CDLHARAMI(open, high, low, close)
data['CDLHARAMICROSS'] = talib.CDLHARAMICROSS(open, high, low, close)
data['CDLHIGHWAVE'] = talib.CDLHIGHWAVE(open, high, low, close)
data['CDLHIKKAKE'] = talib.CDLHIKKAKE(open, high, low, close)
data['CDLHIKKAKEMOD'] = talib.CDLHIKKAKEMOD(open, high, low, close)
data['CDLHOMINGPIGEON'] = talib.CDLHOMINGPIGEON(open, high, low, close)
data['CDLIDENTICAL3CROWS'] = talib.CDLIDENTICAL3CROWS(
open, high, low, close)
data['CDLINNECK'] = talib.CDLINNECK(open, high, low, close)
data['CDLINVERTEDHAMMER'] = talib.CDLINVERTEDHAMMER(open, high, low, close)
data['CDLKICKING'] = talib.CDLKICKING(open, high, low, close)
data['CDLKICKINGBYLENGTH'] = talib.CDLKICKINGBYLENGTH(
open, high, low, close)
data['CDLLADDERBOTTOM'] = talib.CDLLADDERBOTTOM(open, high, low, close)
data['CDLLONGLEGGEDDOJI'] = talib.CDLLONGLEGGEDDOJI(open, high, low, close)
data['CDLLONGLINE'] = talib.CDLLONGLINE(open, high, low, close)
data['CDLMARUBOZU'] = talib.CDLMARUBOZU(open, high, low, close)
data['CDLMATCHINGLOW'] = talib.CDLMATCHINGLOW(open, high, low, close)
data['CDLMATHOLD'] = talib.CDLMATHOLD(open,
high,
low,
close,
penetration=0)
data['CDLMORNINGDOJISTAR'] = talib.CDLMORNINGDOJISTAR(open,
high,
low,
close,
penetration=0)
data['CDLMORNINGSTAR'] = talib.CDLMORNINGSTAR(open,
high,
low,
close,
penetration=0)
data['CDLONNECK'] = talib.CDLONNECK(open, high, low, close)
data['CDLPIERCING'] = talib.CDLPIERCING(open, high, low, close)
data['CDLRICKSHAWMAN'] = talib.CDLRICKSHAWMAN(open, high, low, close)
data['CDLRISEFALL3METHODS'] = talib.CDLRISEFALL3METHODS(
open, high, low, close)
data['CDLSEPARATINGLINES'] = talib.CDLSEPARATINGLINES(
open, high, low, close)
data['CDLSHOOTINGSTAR'] = talib.CDLSHOOTINGSTAR(open, high, low, close)
data['CDLSHORTLINE'] = talib.CDLSHORTLINE(open, high, low, close)
data['CDLSPINNINGTOP'] = talib.CDLSPINNINGTOP(open, high, low, close)
data['CDLSTALLEDPATTERN'] = talib.CDLSTALLEDPATTERN(open, high, low, close)
data['CDLSTICKSANDWICH'] = talib.CDLSTICKSANDWICH(open, high, low, close)
data['CDLTAKURI'] = talib.CDLTAKURI(open, high, low, close)
data['CDLTASUKIGAP'] = talib.CDLTASUKIGAP(open, high, low, close)
data['CDLTHRUSTING'] = talib.CDLTHRUSTING(open, high, low, close)
data['CDLTRISTAR'] = talib.CDLTRISTAR(open, high, low, close)
data['CDLUNIQUE3RIVER'] = talib.CDLUNIQUE3RIVER(open, high, low, close)
data['CDLUPSIDEGAP2CROWS'] = talib.CDLUPSIDEGAP2CROWS(
open, high, low, close)
data['CDLXSIDEGAP3METHODS'] = talib.CDLXSIDEGAP3METHODS(
open, high, low, close)
# data['ADX'] = talib.ADX(high, low, close, timeperiod=14)
data['MACDFAS'], data['MACDSLO'], data['MACDSIGNA'] = talib.MACD(
close, fastperiod=12, slowperiod=26, signalperiod=9)
data['3day MA'] = close.shift(1).rolling(window=3).mean()
data['10day MA'] = close.shift(1).rolling(window=10).mean()
data['30day MA'] = close.shift(1).rolling(window=30).mean()
data['RSI_9'] = talib.RSI(close.values, timeperiod=9)
data['S_10'] = close.rolling(window=10).mean()
data['Corr'] = close.rolling(window=10).corr(data['S_10'])
data['Williams %R'] = talib.WILLR(data['High'].values, data['Low'].values,
data['Close'].values, 7)
return data
def calcind(df):
# Calculate indicators and interpret them into buy or sell signals
# Note 1: If the dataframe is not sorted in timeframe order, the results will be worthless
# Note 2: The dataframe must have the following OHLCV attributes at a minimum:
# {
# "o": 100 <-- open value
# "h": 150 <-- high value
# "l": 90 <-- low value
# "c": 110 <-- close value
# "v": 3000 <-- volume value
# }
if not df.empty:
# calculate the technical indicators if there is data to do so
# Ref: https://mrjbq7.github.io/ta-lib/func_groups/momentum_indicators.html
# Momentum indicators
df['ADX14'] = ta.ADX(df['h'], df['l'], df['c'])
df['ADXR14'] = ta.ADXR(df['h'], df['l'], df['c'])
df['APO12'] = ta.APO(df['c'], fastperiod=12, slowperiod=26, matype=0)
df['AROONUP'], df['AROONDN'] = ta.AROON(df['h'],
df['l'],
timeperiod=14)
df['BOP'] = ta.BOP(df['o'], df['h'], df['l'], df['c'])
df['CCI14'] = ta.CCI(df['h'], df['l'], df['c'], timeperiod=14)
df['CMO14'] = ta.CMO(df['c'], timeperiod=14)
df['DX14'] = ta.DX(df['h'], df['l'], df['c'], timeperiod=14)
df['MACD'], df['MACDSIG'], df['MACDHIST'] = ta.MACD(df['c'],
fastperiod=12,
slowperiod=26,
signalperiod=9)
df['MFI4'] = ta.MFI(df['h'], df['l'], df['c'], df['v'], timeperiod=14)
df['MOM10'] = ta.MOM(df['c'], timeperiod=10)
df['PPO12'] = ta.PPO(df['c'], fastperiod=12, slowperiod=26, matype=0)
df['ROC10'] = ta.MOM(df['c'], timeperiod=10)
df['RSI14'] = ta.RSI(df['c'], timeperiod=14)
df['STOCHK'], df['STOCHD'] = ta.STOCH(df['h'],
df['l'],
df['c'],
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['STOCHRSIK'], df['STOCHRSID'] = ta.STOCHRSI(df['c'],
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['TRIX30'] = ta.TRIX(df['c'], timeperiod=30)
df['ULTOSC'] = ta.ULTOSC(df['h'],
df['l'],
df['c'],
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
# Moving Average or Overlap functions
df['BBUPPER'], df['BBMID'], df['BBLOWER'] = ta.BBANDS(df['c'],
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
df['EMA14'] = ta.EMA(df['c'], timeperiod=14)
df['SMA14'] = ta.SMA(df['c'], timeperiod=14)
# Volume Indicators
df['AD'] = ta.AD(df['h'], df['l'], df['c'], df['v'])
df['ADOSC'] = ta.ADOSC(df['h'],
df['l'],
df['c'],
df['v'],
fastperiod=3,
slowperiod=10)
df['OBV'] = ta.OBV(df['c'], df['v'])
# Candlestick Patterns
df['DJI'] = ta.CDLDOJI(df['o'], df['h'], df['l'], df['c'])
df['ENG'] = ta.CDLENGULFING(df['o'], df['h'], df['l'], df['c'])
df['HMR'] = ta.CDLHAMMER(df['o'], df['h'], df['l'], df['c'])
df['HGM'] = ta.CDLHANGINGMAN(df['o'], df['h'], df['l'], df['c'])
df['PRC'] = ta.CDLPIERCING(df['o'], df['h'], df['l'], df['c'])
df['DCC'] = ta.CDLDARKCLOUDCOVER(df['o'],
df['h'],
df['l'],
df['c'],
penetration=0)
df['MSR'] = ta.CDLMORNINGSTAR(df['o'],
df['h'],
df['l'],
df['c'],
penetration=0)
df['ESR'] = ta.CDLEVENINGSTAR(df['o'],
df['h'],
df['l'],
df['c'],
penetration=0)
df['KKR'] = ta.CDLKICKING(df['o'], df['h'], df['l'], df['c'])
df['SSR'] = ta.CDLSHOOTINGSTAR(df['o'], df['h'], df['l'], df['c'])
df['IHM'] = ta.CDLINVERTEDHAMMER(df['o'], df['h'], df['l'], df['c'])
df['TWS'] = ta.CDL3WHITESOLDIERS(df['o'], df['h'], df['l'], df['c'])
df['TBC'] = ta.CDL3BLACKCROWS(df['o'], df['h'], df['l'], df['c'])
df['STP'] = ta.CDLSPINNINGTOP(df['o'], df['h'], df['l'], df['c'])
# ADX Trend Strength
df['ADXTREND'] = 'Weak'
df.loc[df['ADX14'] >= 25, 'ADXTREND'] = 'Changing'
df.loc[df['ADX14'] >= 50, 'ADXTREND'] = 'Strong'
df.loc[df['ADX14'] >= 75, 'ADXTREND'] = 'Very Strong'
# ADXR Trend Strength
df['ADXRTREND'] = 'Weak'
df.loc[df['ADXR14'] >= 25, 'ADXRTREND'] = 'Changing'
df.loc[df['ADXR14'] >= 50, 'ADXRTREND'] = 'Strong'
df.loc[df['ADXR14'] >= 75, 'ADXRTREND'] = 'Very Strong'
# AROON Oscillator
df['AROONOSC'] = df['AROONDN'] - df['AROONUP']
df['AROONVOTE'] = 0
df.loc[df['AROONOSC'] >= 25,
'AROONVOTE'] = 1 # This threshold is a guess
df.loc[df['AROONOSC'] <= -25,
'AROONVOTE'] = -1 # This threshold is a guess
# BOP Signal
df['BOPVOTE'] = 0
df.loc[(df['BOP'] > 0), 'BOPVOTE'] = 1
df.loc[(df['BOP'] < 0), 'BOPVOTE'] = -1
# CCI Vote
df['CCIVOTE'] = 0
df.loc[df['CCI14'] >= 100, 'CCIVOTE'] = 1
df.loc[df['CCI14'] <= -100, 'CCIVOTE'] = -1
# CMO Votes
df['CMOVOTE'] = 0
df.loc[df['CMO14'] < -50, 'CMOVOTE'] = 1
df.loc[df['CMO14'] > 50, 'CMOVOTE'] = -1
# MACD Vote; based on when the histogram crosses the zero line
df['MACDVOTE'] = 0
df.loc[(df['MACDHIST'] > 0) &
(df['MACDHIST'].shift(periods=-1) < df['MACDHIST']),
'MACDVOTE'] = 1
df.loc[(df['MACDHIST'] < 0) &
(df['MACDHIST'].shift(periods=-1) > df['MACDHIST']),
'MACDVOTE'] = -1
df.loc[df['MACDHIST'] == 0, 'MACDVOTE'] = 0
# MFI Votes
# Skipping interpretting MFI because it correlates to the direction of price
# MOM Votes
# Skipping basic momentum because it's not a good signal for buy or sell
# PPO Votes; cousin of MACD
df['PPOVOTE'] = 0
#df.loc[df['PPO12'] >= 0, 'RSIVOTE'] = 1
#df.loc[df['PPO12'] <= 0, 'RSIVOTE'] = -1
# ROC Votes
# Not using ROC because it's prone to whipsaws near the 0 line; and, this isn't used to trade
# RSI Votes
df['RSIVOTE'] = 0
df.loc[df['RSI14'] >= 70, 'RSIVOTE'] = -1
df.loc[df['RSI14'] <= 30, 'RSIVOTE'] = 1
# STOCH Votes
df['STOCHVOTE'] = 0
df.loc[(df['STOCHK'] >= 80) & (df['STOCHD'] >= 80), 'STOCHVOTE'] = -1
df.loc[(df['STOCHK'] <= 20) & (df['STOCHD'] <= 20), 'STOCHVOTE'] = 1
# STOCHRSI Votes
df['STOCHRSIVOTE'] = 0
df.loc[(df['STOCHRSIK'] >= 80) & (df['STOCHRSID'] >= 80),
'STOCHRSIVOTE'] = -1
df.loc[(df['STOCHRSIK'] <= 20) & (df['STOCHRSID'] <= 20),
'STOCHRSIVOTE'] = 1
# TRIX Votes
df['TRIXVOTE'] = 0
df.loc[df['TRIX30'] > 0, 'TRIXVOTE'] = 1
df.loc[df['TRIX30'] < 0, 'TRIXVOTE'] = -1
# ULTOSC Votes
# I'm skipping this oscillator because the buy/sell conditions are three-pronged and not clear
# ADOSC Votes
df['ADOSCVOTE'] = 0
df.loc[df['ADOSC'] > 0, 'ADOSCVOTE'] = 1
df.loc[df['ADOSC'] < 0, 'ADOSCVOTE'] = -1
# Drop rows where there isn't enough information to vote
# Note 1: TRIX30 should be cleaned up, but the period is too long and it removes too much data.
# Note 2: MACD has a long period as well and will essentially eliminate trading before 10:00 AM
df.dropna(subset=[
'AROONUP', 'AROONDN', 'BOP', 'CCI14', 'CMO14', 'MACDHIST', 'PPO12',
'RSI14', 'STOCHK', 'STOCHD', 'STOCHRSIK', 'STOCHRSID', 'ADOSC'
],
inplace=True)
for x in df.index:
#print (df.loc[x, 'STRATEGY_ID'][0])
a = chr(66 + df.loc[x, 'AROONVOTE'])
b = chr(66 + df.loc[x, 'BOPVOTE'])
c = chr(66 + df.loc[x, 'CCIVOTE'])
d = chr(66 + df.loc[x, 'CMOVOTE'])
e = chr(66 + df.loc[x, 'MACDVOTE'])
f = chr(66 + df.loc[x, 'PPOVOTE'])
g = chr(66 + df.loc[x, 'RSIVOTE'])
h = chr(66 + df.loc[x, 'STOCHVOTE'])
i = chr(66 + df.loc[x, 'STOCHRSIVOTE'])
j = chr(66 + df.loc[x, 'TRIXVOTE'])
k = chr(66 + df.loc[x, 'ADOSCVOTE'])
df.loc[x,
'STRATEGY_ID'] = a + b + c + d + e + f + g + h + i + j + k
return df
def CDLSPINNINGTOP(self):
integer = talib.CDLSPINNINGTOP(self.open, self.high, self.low, self.close)
return integer
def data_construct(DataFrame, lookUp, predictionWindow, pairName):
'''function to construct the features from the inspection window and to create the supervised x,y pairs for training.
Parameters
----------
DataFrame : dataFrame
LookUp : int
predictionWindow : int
pairName : str
Returns
-------
output : dict
a dict containing inputs matrix, targets matrix, raw inputs and mapping dict for features
'''
# fetch data for indicators calculations
openPrice = DataFrame.o.values.astype("double")
closePrice = DataFrame.c.values.astype("double")
highPrice = DataFrame.h.values.astype("double")
lowPrice = DataFrame.l.values.astype("double")
volume = DataFrame.volume.values.astype("double")
# calculate technical indicators values
simple_ma_slow = ta.SMA(closePrice, 30) # slow moving average
simple_ma_fast = ta.SMA(closePrice, 15) # fast moving average
exp_ma_slow = ta.EMA(closePrice, 20) # slow exp moving average
exp_ma_fast = ta.EMA(closePrice, 10) # fast exp moving average
bbands = ta.BBANDS(closePrice, timeperiod=15) # calculate bollinger bands
deltaBands = (bbands[0] - bbands[2]
) / bbands[2] # deltas between bands vector (bollinger)
macd_s1, macd_s2, macd_hist = ta.MACD(
closePrice) # MACD values calculation
sar = ta.SAR(highPrice, lowPrice) # prabolic SAR
stochK, stochD = ta.STOCH(highPrice, lowPrice,
closePrice) # stochastic calculations
rsi = ta.RSI(closePrice, timeperiod=15) # RSI indicator
adx = ta.ADX(highPrice, lowPrice, closePrice,
timeperiod=15) # ADX indicator
mfi = ta.MFI(highPrice, lowPrice, closePrice, volume,
timeperiod=15) # money flow index
# calculate statistical indicators values
beta = ta.BETA(highPrice, lowPrice, timeperiod=5) # beta from CAPM model
slope = ta.LINEARREG_ANGLE(
closePrice,
timeperiod=5) # slope for fitting linera reg. to the last x points
# calculate candle indicators values
spinTop = ta.CDLSPINNINGTOP(openPrice, highPrice, lowPrice, closePrice)
doji = ta.CDLDOJI(openPrice, highPrice, lowPrice, closePrice)
dojiStar = ta.CDLDOJISTAR(openPrice, highPrice, lowPrice, closePrice)
marubozu = ta.CDLMARUBOZU(openPrice, highPrice, lowPrice, closePrice)
hammer = ta.CDLHAMMER(openPrice, highPrice, lowPrice, closePrice)
invHammer = ta.CDLINVERTEDHAMMER(openPrice, highPrice, lowPrice,
closePrice)
hangingMan = ta.CDLHANGINGMAN(openPrice, highPrice, lowPrice, closePrice)
shootingStar = ta.CDLSHOOTINGSTAR(openPrice, highPrice, lowPrice,
closePrice)
engulfing = ta.CDLENGULFING(openPrice, highPrice, lowPrice, closePrice)
morningStar = ta.CDLMORNINGSTAR(openPrice, highPrice, lowPrice, closePrice)
eveningStar = ta.CDLEVENINGSTAR(openPrice, highPrice, lowPrice, closePrice)
whiteSoldier = ta.CDL3WHITESOLDIERS(openPrice, highPrice, lowPrice,
closePrice)
blackCrow = ta.CDL3BLACKCROWS(openPrice, highPrice, lowPrice, closePrice)
insideThree = ta.CDL3INSIDE(openPrice, highPrice, lowPrice, closePrice)
# prepare the final matrix
'''
matrix configurations ::> [o,c,h,l,ma_slow,ma_fast,exp_slow,exp_fast,
deltaBands,macd_s1,macd_s2,sar,stochK,
stochD,rsi,adx,mfi,beta,slope,spinTop,doji,dojiStar,
marubozu,hammer,invHammer,hangingMan,shootingStar,engulfing,
morningStar,eveningStar,whiteSoldier,blackCrow,insideThree]
a 33 features matrix in total
'''
DataMatrix = np.column_stack(
(openPrice, closePrice, highPrice, lowPrice, simple_ma_slow,
simple_ma_fast, exp_ma_slow, exp_ma_fast, deltaBands, macd_s1,
macd_s2, sar, stochK, stochD, rsi, adx, mfi, beta, slope, spinTop,
doji, dojiStar, marubozu, hammer, invHammer, hangingMan, shootingStar,
engulfing, morningStar, eveningStar, whiteSoldier, blackCrow,
insideThree))
# remove undifined values
DataMatrix = DataMatrix[~np.isnan(DataMatrix).any(
axis=1)] # remove all raws containing nan values
# define number of windows to analyze
framesCount = DataMatrix.shape[0] - (
lookUp +
predictionWindow) + 1 # 1D convolution outputsize = ceil[((n-f)/s)+1]
# define input/output arrays container
rawInputs = {}
inputsOpen = np.zeros((framesCount, lookUp))
inputsClose = np.zeros((framesCount, lookUp))
inputsHigh = np.zeros((framesCount, lookUp))
inputsLow = np.zeros((framesCount, lookUp))
inputs = np.zeros((framesCount, 62))
outputs = np.zeros((framesCount, 1))
# main loop and data
for i in range(framesCount):
mainFrame = DataMatrix[i:i + lookUp + predictionWindow, :]
window = np.array_split(mainFrame, [lookUp])[0]
windowForecast = np.array_split(mainFrame, [lookUp])[1]
'''
window configurations ::>
[0:o,1:c,2:h,3:l,4:ma_slow,5:ma_fast,6:exp_slow,7:exp_fast,
8:deltaBands,9:macd_slow,10:macd_fast,11:sar,12:stochK,
13:stochD,14:rsi,15:adx,16:mfi,17:beta,18:slope,19:spinTop,20:doji,21:dojiStar,
22:marubozu,23:hammer,24:invHammer,25:hangingMan,26:shootingStar,27:engulfing,
28:morningStar,29:eveningStar,30:whiteSoldier,31:blackCrow,32:insideThree]
'''
#sma features detection
ma_slow = window[:, 4]
ma_fast = window[:, 5]
uptrend_cross = ma_fast > ma_slow
uptrend_cross = np.concatenate(
(np.array([False]),
(uptrend_cross[:-1] <
uptrend_cross[1:]))) # check the false->true transition
try:
uptrend_cross_location = np.where(uptrend_cross == True)[0][
-1] # latest uptrend cross_over location
except:
uptrend_cross_location = -1
downtrend_cross = ma_slow > ma_fast
downtrend_cross = np.concatenate(
(np.array([False]),
(downtrend_cross[:-1] <
downtrend_cross[1:]))) # check the false->true transition
try:
downtrend_cross_location = np.where(downtrend_cross == True)[0][
-1] # latest downtrend cross_over location
except:
downtrend_cross_location = -1
if (uptrend_cross_location >
downtrend_cross_location): # latest cross is an uptrend
sma_latest_crossover = 1 # uptrend sign
sma_location_of_latest_crossover = uptrend_cross_location
alpha_1 = (math.atan(ma_slow[uptrend_cross_location] -
ma_slow[uptrend_cross_location - 1])) * (
180 / math.pi)
alpha_2 = (math.atan(ma_fast[uptrend_cross_location] -
ma_fast[uptrend_cross_location - 1])) * (
180 / math.pi)
sma_latest_crossover_angle = alpha_1 + alpha_2
elif (downtrend_cross_location >
uptrend_cross_location): # latest cross is a downtrend
sma_latest_crossover = -1 # downtrend sign
sma_location_of_latest_crossover = downtrend_cross_location
alpha_1 = (math.atan(ma_slow[downtrend_cross_location] -
ma_slow[downtrend_cross_location - 1])) * (
180 / math.pi)
alpha_2 = (math.atan(ma_fast[downtrend_cross_location] -
ma_fast[downtrend_cross_location - 1])) * (
180 / math.pi)
sma_latest_crossover_angle = alpha_1 + alpha_2
else: # no cross in the given window
sma_latest_crossover = 0 # no sign
sma_location_of_latest_crossover = -1
sma_latest_crossover_angle = 0
up_count = np.sum(ma_fast > ma_slow)
down_count = np.sum(ma_slow > ma_fast)
if (up_count > down_count):
sma_dominant_type_fast_slow = 1
elif (down_count > up_count):
sma_dominant_type_fast_slow = -1
else:
sma_dominant_type_fast_slow = 0
#ema features detection
exp_slow = window[:, 6]
exp_fast = window[:, 7]
uptrend_cross = exp_fast > exp_slow
uptrend_cross = np.concatenate(
(np.array([False]),
(uptrend_cross[:-1] <
uptrend_cross[1:]))) # check the false->true transition
try:
uptrend_cross_location = np.where(uptrend_cross == True)[0][
-1] # latest uptrend cross_over location
except:
uptrend_cross_location = -1
downtrend_cross = exp_slow > exp_fast
downtrend_cross = np.concatenate(
(np.array([False]),
(downtrend_cross[:-1] <
downtrend_cross[1:]))) # check the false->true transition
try:
downtrend_cross_location = np.where(downtrend_cross == True)[0][
-1] # latest downtrend cross_over location
except:
downtrend_cross_location = -1
if (uptrend_cross_location >
downtrend_cross_location): # latest cross is an uptrend
ema_latest_crossover = 1 # uptrend sign
ema_location_of_latest_crossover = uptrend_cross_location
alpha_1 = (math.atan(exp_slow[uptrend_cross_location] -
exp_slow[uptrend_cross_location - 1])) * (
180 / math.pi)
alpha_2 = (math.atan(exp_fast[uptrend_cross_location] -
exp_fast[uptrend_cross_location - 1])) * (
180 / math.pi)
ema_latest_crossover_angle = alpha_1 + alpha_2
elif (downtrend_cross_location >
uptrend_cross_location): # latest cross is a downtrend
ema_latest_crossover = -1 # downtrend sign
ema_location_of_latest_crossover = downtrend_cross_location
alpha_1 = (math.atan(exp_slow[downtrend_cross_location] -
exp_slow[downtrend_cross_location - 1])) * (
180 / math.pi)
alpha_2 = (math.atan(exp_fast[downtrend_cross_location] -
exp_fast[downtrend_cross_location - 1])) * (
180 / math.pi)
ema_latest_crossover_angle = alpha_1 + alpha_2
else: # no cross in the given window
ema_latest_crossover = 0 # no sign
ema_location_of_latest_crossover = -1
ema_latest_crossover_angle = 0
up_count = np.sum(exp_fast > exp_slow)
down_count = np.sum(exp_slow > exp_fast)
if (up_count > down_count):
ema_dominant_type_fast_slow = 1
elif (down_count > up_count):
ema_dominant_type_fast_slow = -1
else:
ema_dominant_type_fast_slow = 0
# B.Bands features detection
deltaBands = window[:, 8]
deltaBands_mean = np.mean(deltaBands)
deltaBands_std = np.std(deltaBands)
deltaBands_maximum_mean = np.amax(deltaBands) / deltaBands_mean
deltaBands_maximum_location = np.where(
deltaBands == np.amax(deltaBands))[0][-1] # location of maximum
deltaBands_minimum_mean = np.amin(deltaBands) / deltaBands_mean
deltaBands_minimum_location = np.where(
deltaBands == np.amin(deltaBands))[0][-1] # location of maximum
# macd features detection
macd_slow = window[:, 9]
macd_fast = window[:, 10]
uptrend_cross = macd_fast > macd_slow
uptrend_cross = np.concatenate(
(np.array([False]),
(uptrend_cross[:-1] <
uptrend_cross[1:]))) # check the false->true transition
try:
uptrend_cross_location = np.where(uptrend_cross == True)[0][
-1] # latest uptrend cross_over location
except:
uptrend_cross_location = -1
downtrend_cross = macd_slow > macd_fast
downtrend_cross = np.concatenate(
(np.array([False]),
(downtrend_cross[:-1] <
downtrend_cross[1:]))) # check the false->true transition
try:
downtrend_cross_location = np.where(downtrend_cross == True)[0][
-1] # latest downtrend cross_over location
except:
downtrend_cross_location = -1
if (uptrend_cross_location >
downtrend_cross_location): # latest cross is an uptrend
macd_latest_crossover = 1 # uptrend sign
macd_location_of_latest_crossover = uptrend_cross_location
alpha_1 = (math.atan(macd_slow[uptrend_cross_location] -
macd_slow[uptrend_cross_location - 1])) * (
180 / math.pi)
alpha_2 = (math.atan(macd_fast[uptrend_cross_location] -
macd_fast[uptrend_cross_location - 1])) * (
180 / math.pi)
macd_latest_crossover_angle = alpha_1 + alpha_2
elif (downtrend_cross_location >
uptrend_cross_location): # latest cross is a downtrend
macd_latest_crossover = -1 # downtrend sign
macd_location_of_latest_crossover = downtrend_cross_location
alpha_1 = (math.atan(macd_slow[downtrend_cross_location] -
macd_slow[downtrend_cross_location - 1])) * (
180 / math.pi)
alpha_2 = (math.atan(macd_fast[downtrend_cross_location] -
macd_fast[downtrend_cross_location - 1])) * (
180 / math.pi)
macd_latest_crossover_angle = alpha_1 + alpha_2
else: # no cross in the given window
macd_latest_crossover = 0 # no sign
macd_location_of_latest_crossover = -1
macd_latest_crossover_angle = 0
up_count = np.sum(macd_fast > macd_slow)
down_count = np.sum(macd_slow > macd_fast)
if (up_count > down_count):
macd_dominant_type_fast_slow = 1
elif (down_count > up_count):
macd_dominant_type_fast_slow = -1
else:
macd_dominant_type_fast_slow = 0
# sar features detection
average_price = (window[:, 0] + window[:, 1] + window[:, 2] +
window[:, 3]) / 4
sar = window[:, 11]
uptrend = sar < average_price
uptrend = np.concatenate(
(np.array([False]),
(uptrend[:-1] < uptrend[1:]))) # check the false->true transition
try:
uptrend_location = np.where(
uptrend == True)[0][-1] # latest uptrend location
except:
uptrend_location = -1
downtrend = sar > average_price
downtrend = np.concatenate(
(np.array([False]),
(downtrend[:-1] <
downtrend[1:]))) # check the false->true transition
try:
downtrend_location = np.where(
downtrend == True)[0][-1] # latest downtrend location
except:
downtrend_location = -1
if (uptrend_location >
downtrend_location): # latest signal is an uptrend
sar_latest_shiftPoint = 1
sar_latest_shiftPoint_location = uptrend_location
elif (downtrend_location >
uptrend_location): # latest signal is a downtrend
sar_latest_shiftPoint = -1
sar_latest_shiftPoint_location = downtrend_location
else: # same direction along the frame under question
sar_latest_shiftPoint = 0 # no sign
sar_latest_shiftPoint_location = -1
sar_total_number_shifts = np.where(
downtrend == True)[0].shape[0] + np.where(
uptrend == True)[0].shape[0]
# stochastic(K) features detection
stochK = window[:, 12]
stochK_mean = np.mean(stochK)
stochK_std = np.std(stochK)
uptrend = stochK <= 20
uptrend = np.concatenate(
(np.array([False]),
(uptrend[:-1] < uptrend[1:]))) # check the false->true transition
try:
uptrend_location = np.where(
uptrend == True)[0][-1] # latest uptrend location
except:
uptrend_location = -1
downtrend = stochK >= 80
downtrend = np.concatenate(
(np.array([False]),
(downtrend[:-1] <
downtrend[1:]))) # check the false->true transition
try:
downtrend_location = np.where(
downtrend == True)[0][-1] # latest downtrend location
except:
downtrend_location = -1
if (uptrend_location >
downtrend_location): # latest signal is an uptrend
stochK_latest_event = 1
stochK_event_location = uptrend_location
elif (downtrend_location >
uptrend_location): # latest signal is a downtrend
stochK_latest_event = -1
stochK_event_location = downtrend_location
else: # same direction along the frame under question
stochK_latest_event = 0 # no sign
stochK_event_location = -1
# stochastic(D) features detection
stochD = window[:, 13]
stochD_mean = np.mean(stochD)
stochD_std = np.std(stochD)
uptrend = stochD <= 20
uptrend = np.concatenate(
(np.array([False]),
(uptrend[:-1] < uptrend[1:]))) # check the false->true transition
try:
uptrend_location = np.where(
uptrend == True)[0][-1] # latest uptrend location
except:
uptrend_location = -1
downtrend = stochD >= 80
downtrend = np.concatenate(
(np.array([False]),
(downtrend[:-1] <
downtrend[1:]))) # check the false->true transition
try:
downtrend_location = np.where(
downtrend == True)[0][-1] # latest downtrend location
except:
downtrend_location = -1
if (uptrend_location >
downtrend_location): # latest signal is an uptrend
stochD_latest_event = 1
stochD_event_location = uptrend_location
elif (downtrend_location >
uptrend_location): # latest signal is a downtrend
stochD_latest_event = -1
stochD_event_location = downtrend_location
else: # same direction along the frame under question
stochD_latest_event = 0 # no sign
stochD_event_location = -1
# rsi features detection
rsi = window[:, 14]
rsi_mean = np.mean(rsi)
rsi_std = np.std(rsi)
uptrend = rsi <= 30
uptrend = np.concatenate(
(np.array([False]),
(uptrend[:-1] < uptrend[1:]))) # check the false->true transition
try:
uptrend_location = np.where(
uptrend == True)[0][-1] # latest uptrend location
except:
uptrend_location = -1
downtrend = rsi >= 70
downtrend = np.concatenate(
(np.array([False]),
(downtrend[:-1] <
downtrend[1:]))) # check the false->true transition
try:
downtrend_location = np.where(
downtrend == True)[0][-1] # latest downtrend location
except:
downtrend_location = -1
if (uptrend_location >
downtrend_location): # latest signal is an uptrend
rsi_latest_event = 1
rsi_event_location = uptrend_location
elif (downtrend_location >
uptrend_location): # latest signal is a downtrend
rsi_latest_event = -1
rsi_event_location = downtrend_location
else: # same direction along the frame under question
rsi_latest_event = 0 # no sign
rsi_event_location = -1
# adx features detection
adx = window[:, 15]
adx_mean = np.mean(adx)
adx_std = np.std(adx)
splitted_array = np.array_split(adx, 2)
m0 = np.mean(splitted_array[0])
m1 = np.mean(splitted_array[1])
adx_mean_delta_bet_first_second_half = (m1 - m0) / m0
# mfi features detection
mfi = window[:, 16]
mfi_mean = np.mean(mfi)
mfi_std = np.std(mfi)
splitted_array = np.array_split(mfi, 2)
m0 = np.mean(splitted_array[0])
m1 = np.mean(splitted_array[1])
mfi_mean_delta_bet_first_second_half = (m1 - m0) / m0
# resistance levels features detection
closePrice = window[:, 1]
resLevels = argrelextrema(closePrice, np.greater, order=4)[0]
if (resLevels.shape[0] == 0):
relation_r1_close = 0
relation_r2_close = 0
relation_r3_close = 0
elif (resLevels.shape[0] == 1):
relation_r1_close = (closePrice[-1] -
closePrice[resLevels[-1]]) / closePrice[-1]
relation_r2_close = 0
relation_r3_close = 0
elif (resLevels.shape[0] == 2):
relation_r1_close = (closePrice[-1] -
closePrice[resLevels[-1]]) / closePrice[-1]
relation_r2_close = (closePrice[-1] -
closePrice[resLevels[-2]]) / closePrice[-1]
relation_r3_close = 0
else:
relation_r1_close = (closePrice[-1] -
closePrice[resLevels[-1]]) / closePrice[-1]
relation_r2_close = (closePrice[-1] -
closePrice[resLevels[-2]]) / closePrice[-1]
relation_r3_close = (closePrice[-1] -
closePrice[resLevels[-3]]) / closePrice[-1]
# support levels features detection
closePrice = window[:, 1]
supLevels = argrelextrema(closePrice, np.less, order=4)[0]
if (supLevels.shape[0] == 0):
relation_s1_close = 0
relation_s2_close = 0
relation_s3_close = 0
elif (supLevels.shape[0] == 1):
relation_s1_close = (closePrice[-1] -
closePrice[supLevels[-1]]) / closePrice[-1]
relation_s2_close = 0
relation_s3_close = 0
elif (supLevels.shape[0] == 2):
relation_s1_close = (closePrice[-1] -
closePrice[supLevels[-1]]) / closePrice[-1]
relation_s2_close = (closePrice[-1] -
closePrice[supLevels[-2]]) / closePrice[-1]
relation_s3_close = 0
else:
relation_s1_close = (closePrice[-1] -
closePrice[supLevels[-1]]) / closePrice[-1]
relation_s2_close = (closePrice[-1] -
closePrice[supLevels[-2]]) / closePrice[-1]
relation_s3_close = (closePrice[-1] -
closePrice[supLevels[-3]]) / closePrice[-1]
# slope features detection
slope = window[:, 18]
slope_mean = np.mean(slope)
# beta features detection
beta = window[:, 17]
beta_mean = np.mean(beta)
beta_std = np.std(beta)
# spinTop features detection np.sum(np.where(a==1)[0])
count100plus = np.sum(np.where(window[:, 19] == 100)[0])
count100minus = (np.sum(np.where(window[:, 19] == -100)[0])) * -1
spinTop_number_occurrence = count100plus + count100minus
# doji features detection
count100plus = np.sum(np.where(window[:, 20] == 100)[0])
count100minus = (np.sum(np.where(window[:, 20] == -100)[0])) * -1
doji_number_occurrence = count100plus + count100minus
# dojiStar features detection
count100plus = np.sum(np.where(window[:, 21] == 100)[0])
count100minus = (np.sum(np.where(window[:, 21] == -100)[0])) * -1
dojiStar_number_occurrence = count100plus + count100minus
# marubozu features detection
count100plus = np.sum(np.where(window[:, 22] == 100)[0])
count100minus = (np.sum(np.where(window[:, 22] == -100)[0])) * -1
marubozu_number_occurrence = count100plus + count100minus
# hammer features detection
count100plus = np.sum(np.where(window[:, 23] == 100)[0])
count100minus = (np.sum(np.where(window[:, 23] == -100)[0])) * -1
hammer_number_occurrence = count100plus + count100minus
# invHammer features detection
count100plus = np.sum(np.where(window[:, 24] == 100)[0])
count100minus = (np.sum(np.where(window[:, 24] == -100)[0])) * -1
invHammer_number_occurrence = count100plus + count100minus
# hangingMan features detection
count100plus = np.sum(np.where(window[:, 25] == 100)[0])
count100minus = (np.sum(np.where(window[:, 25] == -100)[0])) * -1
hangingMan_number_occurrence = count100plus + count100minus
# shootingStar features detection
count100plus = np.sum(np.where(window[:, 26] == 100)[0])
count100minus = (np.sum(np.where(window[:, 26] == -100)[0])) * -1
shootingStar_number_occurrence = count100plus + count100minus
# engulfing features detection
count100plus = np.sum(np.where(window[:, 27] == 100)[0])
count100minus = (np.sum(np.where(window[:, 27] == -100)[0])) * -1
engulfing_number_occurrence = count100plus + count100minus
# morningStar features detection
count100plus = np.sum(np.where(window[:, 28] == 100)[0])
count100minus = (np.sum(np.where(window[:, 28] == -100)[0])) * -1
morningStar_number_occurrence = count100plus + count100minus
# eveningStar features detection
count100plus = np.sum(np.where(window[:, 29] == 100)[0])
count100minus = (np.sum(np.where(window[:, 29] == -100)[0])) * -1
eveningStar_number_occurrence = count100plus + count100minus
# whiteSoldier features detection
count100plus = np.sum(np.where(window[:, 30] == 100)[0])
count100minus = (np.sum(np.where(window[:, 30] == -100)[0])) * -1
whiteSoldier_number_occurrence = count100plus + count100minus
# blackCrow features detection
count100plus = np.sum(np.where(window[:, 31] == 100)[0])
count100minus = (np.sum(np.where(window[:, 31] == -100)[0])) * -1
blackCrow_number_occurrence = count100plus + count100minus
# insideThree features detection
count100plus = np.sum(np.where(window[:, 32] == 100)[0])
count100minus = (np.sum(np.where(window[:, 32] == -100)[0])) * -1
insideThree_number_occurrence = count100plus + count100minus
# fill the inputs matrix
inputs[i, 0] = sma_latest_crossover
inputs[i, 1] = sma_location_of_latest_crossover
inputs[i, 2] = sma_latest_crossover_angle
inputs[i, 3] = sma_dominant_type_fast_slow
inputs[i, 4] = ema_latest_crossover
inputs[i, 5] = ema_location_of_latest_crossover
inputs[i, 6] = ema_latest_crossover_angle
inputs[i, 7] = ema_dominant_type_fast_slow
inputs[i, 8] = deltaBands_mean
inputs[i, 9] = deltaBands_std
inputs[i, 10] = deltaBands_maximum_mean
inputs[i, 11] = deltaBands_maximum_location
inputs[i, 12] = deltaBands_minimum_mean
inputs[i, 13] = deltaBands_minimum_location
inputs[i, 14] = macd_latest_crossover
inputs[i, 15] = macd_location_of_latest_crossover
inputs[i, 16] = macd_latest_crossover_angle
inputs[i, 17] = macd_dominant_type_fast_slow
inputs[i, 18] = sar_latest_shiftPoint
inputs[i, 19] = sar_latest_shiftPoint_location
inputs[i, 20] = sar_total_number_shifts
inputs[i, 21] = stochK_mean
inputs[i, 22] = stochK_std
inputs[i, 23] = stochK_latest_event
inputs[i, 24] = stochK_event_location
inputs[i, 25] = stochD_mean
inputs[i, 26] = stochD_std
inputs[i, 27] = stochD_latest_event
inputs[i, 28] = stochD_event_location
inputs[i, 29] = rsi_mean
inputs[i, 30] = rsi_std
inputs[i, 31] = rsi_latest_event
inputs[i, 32] = rsi_event_location
inputs[i, 33] = adx_mean
inputs[i, 34] = adx_std
inputs[i, 35] = adx_mean_delta_bet_first_second_half
inputs[i, 36] = mfi_mean
inputs[i, 37] = mfi_std
inputs[i, 38] = mfi_mean_delta_bet_first_second_half
inputs[i, 39] = relation_r1_close
inputs[i, 40] = relation_r2_close
inputs[i, 41] = relation_r3_close
inputs[i, 42] = relation_s1_close
inputs[i, 43] = relation_s2_close
inputs[i, 44] = relation_s3_close
inputs[i, 45] = slope_mean
inputs[i, 46] = beta_mean
inputs[i, 47] = beta_std
inputs[i, 48] = spinTop_number_occurrence
inputs[i, 49] = doji_number_occurrence
inputs[i, 50] = dojiStar_number_occurrence
inputs[i, 51] = marubozu_number_occurrence
inputs[i, 52] = hammer_number_occurrence
inputs[i, 53] = invHammer_number_occurrence
inputs[i, 54] = hangingMan_number_occurrence
inputs[i, 55] = shootingStar_number_occurrence
inputs[i, 56] = engulfing_number_occurrence
inputs[i, 57] = morningStar_number_occurrence
inputs[i, 58] = eveningStar_number_occurrence
inputs[i, 59] = whiteSoldier_number_occurrence
inputs[i, 60] = blackCrow_number_occurrence
inputs[i, 61] = insideThree_number_occurrence
# fill raw inputs matrices
inputsOpen[i, :] = window[:, 0].reshape(1, lookUp)
inputsClose[i, :] = window[:, 1].reshape(1, lookUp)
inputsHigh[i, :] = window[:, 2].reshape(1, lookUp)
inputsLow[i, :] = window[:, 3].reshape(1, lookUp)
# fill the output matrix
futureClose = windowForecast[:, 1]
if (pairName == "USD_JPY"):
outputs[
i, 0] = (futureClose[-1] - futureClose[0]
) / 0.01 # one pip = 0.01 for any pair containing JPY
else:
outputs[i, 0] = (futureClose[-1] - futureClose[0]
) / 0.0001 # one pip = 0.0001 for this pairs
# create mapping dict.
mappingDict = {
"sma_latest_crossover": 0,
"sma_location_of_latest_crossover": 1,
"sma_latest_crossover_angle": 2,
"sma_dominant_type_fast_slow": 3,
"ema_latest_crossover": 4,
"ema_location_of_latest_crossover": 5,
"ema_latest_crossover_angle": 6,
"ema_dominant_type_fast_slow": 7,
"deltaBands_mean": 8,
"deltaBands_std": 9,
"deltaBands_maximum_mean": 10,
"deltaBands_maximum_location": 11,
"deltaBands_minimum_mean": 12,
"deltaBands_minimum_location": 13,
"macd_latest_crossover": 14,
"macd_location_of_latest_crossover": 15,
"macd_latest_crossover_angle": 16,
"macd_dominant_type_fast_slow": 17,
"sar_latest_shiftPoint": 18,
"sar_latest_shiftPoint_location": 19,
"sar_total_number_shifts": 20,
"stochK_mean": 21,
"stochK_std": 22,
"stochK_latest_event": 23,
"stochK_event_location": 24,
"stochD_mean": 25,
"stochD_std": 26,
"stochD_latest_event": 27,
"stochD_event_location": 28,
"rsi_mean": 29,
"rsi_std": 30,
"rsi_latest_event": 31,
"rsi_event_location": 32,
"adx_mean": 33,
"adx_std": 34,
"adx_mean_delta_bet_first_second_half": 35,
"mfi_mean": 36,
"mfi_std": 37,
"mfi_mean_delta_bet_first_second_half": 38,
"relation_r1_close": 39,
"relation_r2_close": 40,
"relation_r3_close": 41,
"relation_s1_close": 42,
"relation_s2_close": 43,
"relation_s3_close": 44,
"slope_mean": 45,
"beta_mean": 46,
"beta_std": 47,
"spinTop_number_occurrence": 48,
"doji_number_occurrence": 49,
"dojiStar_number_occurrence": 50,
"marubozu_number_occurrence": 51,
"hammer_number_occurrence": 52,
"invHammer_number_occurrence": 53,
"hangingMan_number_occurrence": 54,
"shootingStar_number_occurrence": 55,
"engulfing_number_occurrence": 56,
"morningStar_number_occurrence": 57,
"eveningStar_number_occurrence": 58,
"whiteSoldier_number_occurrence": 59,
"blackCrow_number_occurrence": 60,
"insideThree_number_occurrence": 61
}
# remove undifined values from the output
refMatrix = inputs
inputs = inputs[~np.isnan(refMatrix).any(
axis=1)] # remove all raws containing nan values
outputs = outputs[~np.isnan(refMatrix).any(
axis=1)] # remove all raws containing nan values
inputsOpen = inputsOpen[~np.isnan(refMatrix).any(
axis=1)] # remove all raws containing nan values
inputsClose = inputsClose[~np.isnan(refMatrix).any(
axis=1)] # remove all raws containing nan values
inputsHigh = inputsHigh[~np.isnan(refMatrix).any(
axis=1)] # remove all raws containing nan values
inputsLow = inputsLow[~np.isnan(refMatrix).any(
axis=1)] # remove all raws containing nan values
# create raw inputs dict.
rawInputs["open"] = inputsOpen
rawInputs["close"] = inputsClose
rawInputs["high"] = inputsHigh
rawInputs["low"] = inputsLow
# return the function output
output = {
"mappingDict": mappingDict,
"rawInputs": rawInputs,
"inputFeatures": inputs,
"targets": outputs
}
return (output)
def patern(dataframe):
"""
Pattern Recognition:
CDL2CROWS Two Crows
CDL3BLACKCROWS Three Black Crows
CDL3INSIDE Three Inside Up/Down
CDL3LINESTRIKE Three-Line Strike
CDL3OUTSIDE Three Outside Up/Down
CDL3STARSINSOUTH Three Stars In The South
CDL3WHITESOLDIERS Three Advancing White Soldiers
CDLABANDONEDBABY Abandoned Baby
CDLADVANCEBLOCK Advance Block
CDLBELTHOLD Belt-hold
CDLBREAKAWAY Breakaway
CDLCLOSINGMARUBOZU Closing Marubozu
CDLCONCEALBABYSWALL Concealing Baby SwalLow
CDLCOUNTERATTACK Counterattack
CDLDARKCLOUDCOVER Dark Cloud Cover
CDLDOJI Doji
CDLDOJISTAR Doji Star
CDLDRAGONFLYDOJI Dragonfly Doji
CDLENGULFING Engulfing Pattern
CDLEVENINGDOJISTAR Evening Doji Star
CDLEVENINGSTAR Evening Star
CDLGAPSIDESIDEWHITE Up/Down-gap side-by-side white lines
CDLGRAVESTONEDOJI Gravestone Doji
CDLHAMMER Hammer
CDLHANGINGMAN Hanging Man
CDLHARAMI Harami Pattern
CDLHARAMICROSS Harami Cross Pattern
CDLHighWAVE High-Wave Candle
CDLHIKKAKE Hikkake Pattern
CDLHIKKAKEMOD Modified Hikkake Pattern
CDLHOMINGPIGEON Homing Pigeon
CDLIDENTICAL3CROWS Identical Three Crows
CDLINNECK In-Neck Pattern
CDLINVERTEDHAMMER Inverted Hammer
CDLKICKING Kicking
CDLKICKINGBYLENGTH Kicking - bull/bear determined by the longer marubozu
CDLLADDERBOTTOM Ladder Bottom
CDLLONGLEGGEDDOJI Long Legged Doji
CDLLONGLINE Long Line Candle
CDLMARUBOZU Marubozu
CDLMATCHINGLow Matching Low
CDLMATHOLD Mat Hold
CDLMORNINGDOJISTAR Morning Doji Star
CDLMORNINGSTAR Morning Star
CDLONNECK On-Neck Pattern
CDLPIERCING Piercing Pattern
CDLRICKSHAWMAN Rickshaw Man
CDLRISEFALL3METHODS Rising/Falling Three Methods
CDLSEPARATINGLINES Separating Lines
CDLSHOOTINGSTAR Shooting Star
CDLSHORTLINE Short Line Candle
CDLSPINNINGTOP Spinning Top
CDLSTALLEDPATTERN Stalled Pattern
CDLSTICKSANDWICH Stick Sandwich
CDLTAKURI Takuri (Dragonfly Doji with very long Lower shadow)
CDLTASUKIGAP Tasuki Gap
CDLTHRUSTING Thrusting Pattern
CDLTRISTAR Tristar Pattern
CDLUNIQUE3RIVER Unique 3 River
CDLUPSIDEGAP2CROWS Upside Gap Two Crows
CDLXSIDEGAP3METHODS Upside/Downside Gap Three Methods
"""
#CDL2CROWS - Two Crows
df[f'{ratio}_CDL2CROWS'] = talib.CDL2CROWS(Open,High, Low, Close)
#CDL2CROWS - Three Black Crows
df[f'{ratio}_CDL2CROWS'] = talib.CDL3BLACKCROWS(Open,High, Low, Close)
#CDL3INSIDE - Three Inside Up/Down
df[f'{ratio}_CDL3INSIDE'] = talib.CDL3INSIDE(Open,High, Low, Close)
#CDL3LINESTRIKE - Three-Line Strike
df[f'{ratio}_CDL3LINESTRIKE'] = talib.CDL3LINESTRIKE(Open,High, Low, Close)
#CDL3OUTSIDE - Three Outside Up/Down
df[f'{ratio}_CDL3OUTSIDE'] = talib.CDL3OUTSIDE(Open,High, Low, Close)
#CDL3STARSINSOUTH - Three Stars In The South
df[f'{ratio}_CDL3STARSINSOUTH'] = talib.CDL3STARSINSOUTH(Open,High, Low, Close)
#CDL3WHITESOLDIERS - Three Advancing White Soldiers
df[f'{ratio}_CDL3WHITESOLDIERS'] = talib.CDL3WHITESOLDIERS(Open,High, Low, Close)
#CDLABANDONEDBABY - Abandoned Baby
df[f'{ratio}_CDLABANDONEDBABY'] = talib.CDLABANDONEDBABY(Open,High, Low, Close, penetration=0)
#CDLADVANCEBLOCK - Advance Block
df[f'{ratio}_CDLADVANCEBLOCK'] = talib.CDLADVANCEBLOCK(Open,High, Low, Close)
#CDLBELTHOLD - Belt-hold
df[f'{ratio}_CDLBELTHOLD'] = talib.CDLBELTHOLD(Open,High, Low, Close)
#CDLBREAKAWAY - Breakaway
df[f'{ratio}_CDLBREAKAWAY'] = talib.CDLBREAKAWAY(Open,High, Low, Close)
#CDLCLOSINGMARUBOZU - Closing Marubozu
df[f'{ratio}_CDLCLOSINGMARUBOZU'] = talib.CDLCLOSINGMARUBOZU(Open,High, Low, Close)
#CDLCONCEALBABYSWALL - Concealing Baby SwalLow
df[f'{ratio}_CDLCLOSINGMARUBOZU'] = talib.CDLCONCEALBABYSWALL(Open,High, Low, Close)
#CDLCOUNTERATTACK - Counterattack
df[f'{ratio}_CDLCLOSINGMARUBOZU'] = talib.CDLCOUNTERATTACK(Open,High, Low, Close)
#CDLDARKCLOUDCOVER - Dark Cloud Cover
df[f'{ratio}_CDLCLOSINGMARUBOZU'] = talib.CDLDARKCLOUDCOVER(Open,High, Low, Close, penetration=0)
#CDLDOJI - Doji
df[f'{ratio}_CDLDOJI'] = talib.CDLDOJI(Open,High, Low, Close)
#CDLDOJISTAR - Doji Star
df[f'{ratio}_CDLDOJISTAR'] = talib.CDLDOJISTAR(Open,High, Low, Close)
#CDLDRAGONFLYDOJI - Dragonfly Doji
df[f'{ratio}_CDLDRAGONFLYDOJI'] = talib.CDLDRAGONFLYDOJI(Open,High, Low, Close)
#CDLENGULFING - Engulfing Pattern
df[f'{ratio}_CDLENGULFING'] = talib.CDLENGULFING(Open,High, Low, Close)
#CDLEVENINGDOJISTAR - Evening Doji Star
df[f'{ratio}_CDLEVENINGDOJISTAR'] = talib.CDLEVENINGDOJISTAR(Open,High, Low, Close, penetration=0)
#CDLEVENINGSTAR - Evening Star
df[f'{ratio}_CDLEVENINGSTAR'] = talib.CDLEVENINGSTAR(Open,High, Low, Close, penetration=0)
#CDLGAPSIDESIDEWHITE - Up/Down-gap side-by-side white lines
df[f'{ratio}_CDLEVENINGSTAR'] = talib.CDLGAPSIDESIDEWHITE(Open,High, Low, Close)
#CDLGRAVESTONEDOJI - Gravestone Doji
df[f'{ratio}_CDLGRAVESTONEDOJI'] = talib.CDLGRAVESTONEDOJI(Open,High, Low, Close)
#CDLHAMMER - Hammer
df[f'{ratio}_CDLGRAVESTONEDOJI'] = talib.CDLHAMMER(Open,High, Low, Close)
#CDLHANGINGMAN - Hanging Man
df[f'{ratio}_CDLGRAVESTONEDOJI'] = talib.CDLHANGINGMAN(Open,High, Low, Close)
#CDLHARAMI - Harami Pattern
df[f'{ratio}_CDLGRAVESTONEDOJI'] = talib.CDLHARAMI(Open,High, Low, Close)
#CDLHARAMICROSS - Harami Cross Pattern
df[f'{ratio}_CDLHARAMICROSS'] = talib.CDLHARAMICROSS(Open,High, Low, Close)
#CDLHighWAVE -High-Wave Candle
#df[f'{ratio}_CDLHighWAVE'] = talib.CDLHighWAVE(Open,High, Low, Close)
#CDLHIKKAKE - Hikkake Pattern
df[f'{ratio}_CDLHIKKAKE'] = talib.CDLHIKKAKE(Open,High, Low, Close)
#CDLHIKKAKEMOD - Modified Hikkake Pattern
df[f'{ratio}_CDLHIKKAKEMOD'] = talib.CDLHIKKAKEMOD(Open,High, Low, Close)
#CDLHOMINGPIGEON - Homing Pigeon
df[f'{ratio}_CDLHOMINGPIGEON'] = talib.CDLHOMINGPIGEON(Open,High, Low, Close)
#CDLIDENTICAL3CROWS - Identical Three Crows
df[f'{ratio}_CDLIDENTICAL3CROWS'] = talib.CDLIDENTICAL3CROWS(Open,High, Low, Close)
#CDLINNECK - In-Neck Pattern
df[f'{ratio}_CDLINNECK'] = talib.CDLINNECK(Open,High, Low, Close)
#CDLINVERTEDHAMMER - Inverted Hammer
df[f'{ratio}_CDLINVERTEDHAMMER'] = talib.CDLINVERTEDHAMMER(Open,High, Low, Close)
#CDLKICKING - Kicking
df[f'{ratio}_CDLKICKING'] = talib.CDLKICKING(Open,High, Low, Close)
#CDLKICKINGBYLENGTH - Kicking - bull/bear determined by the longer marubozu
df[f'{ratio}_CDLKICKINGBYLENGTH'] = talib.CDLKICKINGBYLENGTH(Open,High, Low, Close)
#CDLLADDERBOTTOM - Ladder Bottom
df[f'{ratio}_CDLLADDERBOTTOM'] = talib.CDLLADDERBOTTOM(Open,High, Low, Close)
#CDLLONGLEGGEDDOJI - Long Legged Doji
df[f'{ratio}_CDLLONGLEGGEDDOJI'] = talib.CDLLONGLEGGEDDOJI(Open,High, Low, Close)
#CDLLONGLINE - Long Line Candle
df[f'{ratio}_CDLLONGLINE'] = talib.CDLLONGLINE(Open,High, Low, Close)
#CDLMARUBOZU - Marubozu
df[f'{ratio}_DLMARUBOZU'] = talib.CDLMARUBOZU(Open,High, Low, Close)
#CDLMATCHINGLow - Matching Low
#df[f'{ratio}_CDLMATCHINGLow'] = talib.CDLMATCHINGLow(Open,High, Low, Close)
#CDLMATHOLD - Mat Hold
df[f'{ratio}_CDLMATHOLD'] = talib.CDLMATHOLD(Open,High, Low, Close, penetration=0)
#CDLMORNINGDOJISTAR - Morning Doji Star
df[f'{ratio}_CDLMORNINGDOJISTAR'] = talib.CDLMORNINGDOJISTAR(Open,High, Low, Close, penetration=0)
#CDLMORNINGSTAR - Morning Star
df[f'{ratio}_CDLMORNINGSTAR'] = talib.CDLMORNINGSTAR(Open,High, Low, Close, penetration=0)
#CDLONNECK - On-Neck Pattern
df[f'{ratio}_CDLONNECK'] = talib.CDLONNECK(Open,High, Low, Close)
#CDLPIERCING - Piercing Pattern
df[f'{ratio}_CDLPIERCING'] = talib.CDLPIERCING(Open,High, Low, Close)
#CDLRICKSHAWMAN - Rickshaw Man
df[f'{ratio}_CDLRICKSHAWMAN'] = talib.CDLRICKSHAWMAN(Open,High, Low, Close)
#CDLRISEFALL3METHODS - Rising/Falling Three Methods
df[f'{ratio}_CDLRISEFALL3METHODS'] = talib.CDLRISEFALL3METHODS(Open,High, Low, Close)
#CDLSEPARATINGLINES - Separating Lines
df[f'{ratio}_CDLSEPARATINGLINES'] = talib.CDLSEPARATINGLINES(Open,High, Low, Close)
#CDLSHOOTINGSTAR - Shooting Star
df[f'{ratio}_CDLSHOOTINGSTAR'] = talib.CDLSHOOTINGSTAR(Open,High, Low, Close)
#CDLSHORTLINE - Short Line Candle
df[f'{ratio}_CDLSHORTLINE'] = talib.CDLSHORTLINE(Open,High, Low, Close)
#CDLSPINNINGTOP - Spinning Top
df[f'{ratio}_CDLSPINNINGTOP'] = talib.CDLSPINNINGTOP(Open,High, Low, Close)
#CDLSTALLEDPATTERN - Stalled Pattern
df[f'{ratio}_CDLSTALLEDPATTERN'] = talib.CDLSTALLEDPATTERN(Open,High, Low, Close)
#CDLSTICKSANDWICH - Stick Sandwich
df[f'{ratio}_CDLSTICKSANDWICH'] = talib.CDLSTICKSANDWICH(Open,High, Low, Close)
#CDLTAKURI - Takuri (Dragonfly Doji with very long Lower shadow)
df[f'{ratio}_CDLTAKURI'] = talib.CDLTAKURI(Open,High, Low, Close)
#CDLTASUKIGAP - Tasuki Gap
df[f'{ratio}_CDLTASUKIGAP'] = talib.CDLTASUKIGAP(Open,High, Low, Close)
#CDLTHRUSTING - Thrusting Pattern
df[f'{ratio}_CDLTHRUSTING'] = talib.CDLTHRUSTING(Open,High, Low, Close)
#CDLTRISTAR - Tristar Pattern
df[f'{ratio}_CDLTRISTAR'] = talib.CDLTRISTAR(Open,High, Low, Close)
#CDLUNIQUE3RIVER - Unique 3 River
df[f'{ratio}_CDLUNIQUE3RIVER'] = talib.CDLUNIQUE3RIVER(Open,High, Low, Close)
#CDLUPSIDEGAP2CROWS - Upside Gap Two Crows
df[f'{ratio}_CDLUPSIDEGAP2CROWS'] = talib.CDLUPSIDEGAP2CROWS(Open,High, Low, Close)
#CDLXSIDEGAP3METHODS - Upside/Downside Gap Three Methods
df[f'{ratio}_CDLXSIDEGAP3METHODS'] = talib.CDLXSIDEGAP3METHODS(Open,High, Low, Close)
return patern
def add_ta_features(df, ta_settings):
"""Add technial analysis features from typical financial dataset that
typically include columns such as "open", "high", "low", "price" and
"volume".
http://mrjbq7.github.io/ta-lib/
Args:
df(pandas.DataFrame): original DataFrame.
ta_settings(dict): configuration.
Returns:
pandas.DataFrame: DataFrame with new features included.
"""
open = df['open']
high = df['high']
low = df['low']
close = df['price']
volume = df['volume']
if ta_settings['overlap']:
df['ta_overlap_bbands_upper'], df['ta_overlap_bbands_middle'], df[
'ta_overlap_bbands_lower'] = ta.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
df['ta_overlap_dema'] = ta.DEMA(
close, timeperiod=15) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_ema'] = ta.EMA(close, timeperiod=30)
df['ta_overlap_kama'] = ta.KAMA(close, timeperiod=30)
df['ta_overlap_ma'] = ta.MA(close, timeperiod=30, matype=0)
df['ta_overlap_mama_mama'], df['ta_overlap_mama_fama'] = ta.MAMA(close)
period = np.random.randint(10, 20, size=len(close)).astype(float)
df['ta_overlap_mavp'] = ta.MAVP(close,
period,
minperiod=2,
maxperiod=30,
matype=0)
df['ta_overlap_midpoint'] = ta.MIDPOINT(close, timeperiod=14)
df['ta_overlap_midprice'] = ta.MIDPRICE(high, low, timeperiod=14)
df['ta_overlap_sar'] = ta.SAR(high, low, acceleration=0, maximum=0)
df['ta_overlap_sarext'] = ta.SAREXT(high,
low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
df['ta_overlap_sma'] = ta.SMA(close, timeperiod=30)
df['ta_overlap_t3'] = ta.T3(close, timeperiod=5, vfactor=0)
df['ta_overlap_tema'] = ta.TEMA(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_trima'] = ta.TRIMA(close, timeperiod=30)
df['ta_overlap_wma'] = ta.WMA(close, timeperiod=30)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_overlap_ht_trendline'] = ta.HT_TRENDLINE(close)
if ta_settings['momentum']:
df['ta_momentum_adx'] = ta.ADX(high, low, close, timeperiod=14)
df['ta_momentum_adxr'] = ta.ADXR(high, low, close, timeperiod=14)
df['ta_momentum_apo'] = ta.APO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_aroondown'], df['ta_momentum_aroonup'] = ta.AROON(
high, low, timeperiod=14)
df['ta_momentum_aroonosc'] = ta.AROONOSC(high, low, timeperiod=14)
df['ta_momentum_bop'] = ta.BOP(open, high, low, close)
df['ta_momentum_cci'] = ta.CCI(high, low, close, timeperiod=14)
df['ta_momentum_cmo'] = ta.CMO(close, timeperiod=14)
df['ta_momentum_dx'] = ta.DX(high, low, close, timeperiod=14)
df['ta_momentum_macd_macd'], df['ta_momentum_macd_signal'], df[
'ta_momentum_macd_hist'] = ta.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
df['ta_momentum_macdext_macd'], df['ta_momentum_macdext_signal'], df[
'ta_momentum_macdext_hist'] = ta.MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
df['ta_momentum_macdfix_macd'], df['ta_momentum_macdfix_signal'], df[
'ta_momentum_macdfix_hist'] = ta.MACDFIX(close, signalperiod=9)
df['ta_momentum_mfi'] = ta.MFI(high, low, close, volume, timeperiod=14)
df['ta_momentum_minus_di'] = ta.MINUS_DI(high,
low,
close,
timeperiod=14)
df['ta_momentum_minus_dm'] = ta.MINUS_DM(high, low, timeperiod=14)
df['ta_momentum_mom'] = ta.MOM(close, timeperiod=10)
df['ta_momentum_plus_di'] = ta.PLUS_DI(high, low, close, timeperiod=14)
df['ta_momentum_plus_dm'] = ta.PLUS_DM(high, low, timeperiod=14)
df['ta_momentum_ppo'] = ta.PPO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_roc'] = ta.ROC(close, timeperiod=10)
df['ta_momentum_rocp'] = ta.ROCP(close, timeperiod=10)
df['ta_momentum_rocr'] = ta.ROCR(close, timeperiod=10)
df['ta_momentum_rocr100'] = ta.ROCR100(close, timeperiod=10)
df['ta_momentum_rsi'] = ta.RSI(close, timeperiod=14)
df['ta_momentum_slowk'], df['ta_momentum_slowd'] = ta.STOCH(
high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHF(
high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHRSI(
close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['ta_momentum_trix'] = ta.TRIX(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_momentum_ultosc'] = ta.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df['ta_momentum_willr'] = ta.WILLR(high, low, close, timeperiod=14)
if ta_settings['volume']:
df['ta_volume_ad'] = ta.AD(high, low, close, volume)
df['ta_volume_adosc'] = ta.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)
df['ta_volume_obv'] = ta.OBV(close, volume)
if ta_settings['volatility']:
df['ta_volatility_atr'] = ta.ATR(high, low, close, timeperiod=14)
df['ta_volatility_natr'] = ta.NATR(high, low, close, timeperiod=14)
df['ta_volatility_trange'] = ta.TRANGE(high, low, close)
if ta_settings['price']:
df['ta_price_avgprice'] = ta.AVGPRICE(open, high, low, close)
df['ta_price_medprice'] = ta.MEDPRICE(high, low)
df['ta_price_typprice'] = ta.TYPPRICE(high, low, close)
df['ta_price_wclprice'] = ta.WCLPRICE(high, low, close)
if ta_settings['cycle']:
df['ta_cycle_ht_dcperiod'] = ta.HT_DCPERIOD(close)
df['ta_cycle_ht_phasor_inphase'], df[
'ta_cycle_ht_phasor_quadrature'] = ta.HT_PHASOR(close)
df['ta_cycle_ht_trendmode'] = ta.HT_TRENDMODE(close)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_cycle_ht_dcphase'] = ta.HT_DCPHASE(close)
# df['ta_cycle_ht_sine_sine'], df['ta_cycle_ht_sine_leadsine'] = ta.HT_SINE(close)
if ta_settings['pattern']:
df['ta_pattern_cdl2crows'] = ta.CDL2CROWS(open, high, low, close)
df['ta_pattern_cdl3blackrows'] = ta.CDL3BLACKCROWS(
open, high, low, close)
df['ta_pattern_cdl3inside'] = ta.CDL3INSIDE(open, high, low, close)
df['ta_pattern_cdl3linestrike'] = ta.CDL3LINESTRIKE(
open, high, low, close)
df['ta_pattern_cdl3outside'] = ta.CDL3OUTSIDE(open, high, low, close)
df['ta_pattern_cdl3starsinsouth'] = ta.CDL3STARSINSOUTH(
open, high, low, close)
df['ta_pattern_cdl3whitesoldiers'] = ta.CDL3WHITESOLDIERS(
open, high, low, close)
df['ta_pattern_cdlabandonedbaby'] = ta.CDLABANDONEDBABY(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdladvanceblock'] = ta.CDLADVANCEBLOCK(
open, high, low, close)
df['ta_pattern_cdlbelthold'] = ta.CDLBELTHOLD(open, high, low, close)
df['ta_pattern_cdlbreakaway'] = ta.CDLBREAKAWAY(open, high, low, close)
df['ta_pattern_cdlclosingmarubozu'] = ta.CDLCLOSINGMARUBOZU(
open, high, low, close)
df['ta_pattern_cdlconcealbabyswall'] = ta.CDLCONCEALBABYSWALL(
open, high, low, close)
df['ta_pattern_cdlcounterattack'] = ta.CDLCOUNTERATTACK(
open, high, low, close)
df['ta_pattern_cdldarkcloudcover'] = ta.CDLDARKCLOUDCOVER(
open, high, low, close, penetration=0)
df['ta_pattern_cdldoji'] = ta.CDLDOJI(open, high, low, close)
df['ta_pattern_cdldojistar'] = ta.CDLDOJISTAR(open, high, low, close)
df['ta_pattern_cdldragonflydoji'] = ta.CDLDRAGONFLYDOJI(
open, high, low, close)
df['ta_pattern_cdlengulfing'] = ta.CDLENGULFING(open, high, low, close)
df['ta_pattern_cdleveningdojistar'] = ta.CDLEVENINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdleveningstar'] = ta.CDLEVENINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlgapsidesidewhite'] = ta.CDLGAPSIDESIDEWHITE(
open, high, low, close)
df['ta_pattern_cdlgravestonedoji'] = ta.CDLGRAVESTONEDOJI(
open, high, low, close)
df['ta_pattern_cdlhammer'] = ta.CDLHAMMER(open, high, low, close)
df['ta_pattern_cdlhangingman'] = ta.CDLHANGINGMAN(
open, high, low, close)
df['ta_pattern_cdlharami'] = ta.CDLHARAMI(open, high, low, close)
df['ta_pattern_cdlharamicross'] = ta.CDLHARAMICROSS(
open, high, low, close)
df['ta_pattern_cdlhighwave'] = ta.CDLHIGHWAVE(open, high, low, close)
df['ta_pattern_cdlhikkake'] = ta.CDLHIKKAKE(open, high, low, close)
df['ta_pattern_cdlhikkakemod'] = ta.CDLHIKKAKEMOD(
open, high, low, close)
df['ta_pattern_cdlhomingpigeon'] = ta.CDLHOMINGPIGEON(
open, high, low, close)
df['ta_pattern_cdlidentical3crows'] = ta.CDLIDENTICAL3CROWS(
open, high, low, close)
df['ta_pattern_cdlinneck'] = ta.CDLINNECK(open, high, low, close)
df['ta_pattern_cdlinvertedhammer'] = ta.CDLINVERTEDHAMMER(
open, high, low, close)
df['ta_pattern_cdlkicking'] = ta.CDLKICKING(open, high, low, close)
df['ta_pattern_cdlkickingbylength'] = ta.CDLKICKINGBYLENGTH(
open, high, low, close)
df['ta_pattern_cdlladderbottom'] = ta.CDLLADDERBOTTOM(
open, high, low, close)
df['ta_pattern_cdllongleggeddoji'] = ta.CDLLONGLEGGEDDOJI(
open, high, low, close)
df['ta_pattern_cdllongline'] = ta.CDLLONGLINE(open, high, low, close)
df['ta_pattern_cdlmarubozu'] = ta.CDLMARUBOZU(open, high, low, close)
df['ta_pattern_cdlmatchinglow'] = ta.CDLMATCHINGLOW(
open, high, low, close)
df['ta_pattern_cdlmathold'] = ta.CDLMATHOLD(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlmorningdojistar'] = ta.CDLMORNINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdlmorningstar'] = ta.CDLMORNINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdllonneck'] = ta.CDLONNECK(open, high, low, close)
df['ta_pattern_cdlpiercing'] = ta.CDLPIERCING(open, high, low, close)
df['ta_pattern_cdlrickshawman'] = ta.CDLRICKSHAWMAN(
open, high, low, close)
df['ta_pattern_cdlrisefall3methods'] = ta.CDLRISEFALL3METHODS(
open, high, low, close)
df['ta_pattern_cdlseparatinglines'] = ta.CDLSEPARATINGLINES(
open, high, low, close)
df['ta_pattern_cdlshootingstar'] = ta.CDLSHOOTINGSTAR(
open, high, low, close)
df['ta_pattern_cdlshortline'] = ta.CDLSHORTLINE(open, high, low, close)
df['ta_pattern_cdlspinningtop'] = ta.CDLSPINNINGTOP(
open, high, low, close)
df['ta_pattern_cdlstalledpattern'] = ta.CDLSTALLEDPATTERN(
open, high, low, close)
df['ta_pattern_cdlsticksandwich'] = ta.CDLSTICKSANDWICH(
open, high, low, close)
df['ta_pattern_cdltakuri'] = ta.CDLTAKURI(open, high, low, close)
df['ta_pattern_cdltasukigap'] = ta.CDLTASUKIGAP(open, high, low, close)
df['ta_pattern_cdlthrusting'] = ta.CDLTHRUSTING(open, high, low, close)
df['ta_pattern_cdltristar'] = ta.CDLTRISTAR(open, high, low, close)
df['ta_pattern_cdlunique3river'] = ta.CDLUNIQUE3RIVER(
open, high, low, close)
df['ta_pattern_cdlupsidegap2crows'] = ta.CDLUPSIDEGAP2CROWS(
open, high, low, close)
df['ta_pattern_cdlxsidegap3methods'] = ta.CDLXSIDEGAP3METHODS(
open, high, low, close)
if ta_settings['statistic']:
df['ta_statistic_beta'] = ta.BETA(high, low, timeperiod=5)
df['ta_statistic_correl'] = ta.CORREL(high, low, timeperiod=30)
df['ta_statistic_linearreg'] = ta.LINEARREG(close, timeperiod=14)
df['ta_statistic_linearreg_angle'] = ta.LINEARREG_ANGLE(close,
timeperiod=14)
df['ta_statistic_linearreg_intercept'] = ta.LINEARREG_INTERCEPT(
close, timeperiod=14)
df['ta_statistic_linearreg_slope'] = ta.LINEARREG_SLOPE(close,
timeperiod=14)
df['ta_statistic_stddev'] = ta.STDDEV(close, timeperiod=5, nbdev=1)
df['ta_statistic_tsf'] = ta.TSF(close, timeperiod=14)
df['ta_statistic_var'] = ta.VAR(close, timeperiod=5, nbdev=1)
if ta_settings['math_transforms']:
df['ta_math_transforms_atan'] = ta.ATAN(close)
df['ta_math_transforms_ceil'] = ta.CEIL(close)
df['ta_math_transforms_cos'] = ta.COS(close)
df['ta_math_transforms_floor'] = ta.FLOOR(close)
df['ta_math_transforms_ln'] = ta.LN(close)
df['ta_math_transforms_log10'] = ta.LOG10(close)
df['ta_math_transforms_sin'] = ta.SIN(close)
df['ta_math_transforms_sqrt'] = ta.SQRT(close)
df['ta_math_transforms_tan'] = ta.TAN(close)
if ta_settings['math_operators']:
df['ta_math_operators_add'] = ta.ADD(high, low)
df['ta_math_operators_div'] = ta.DIV(high, low)
df['ta_math_operators_min'], df['ta_math_operators_max'] = ta.MINMAX(
close, timeperiod=30)
df['ta_math_operators_minidx'], df[
'ta_math_operators_maxidx'] = ta.MINMAXINDEX(close, timeperiod=30)
df['ta_math_operators_mult'] = ta.MULT(high, low)
df['ta_math_operators_sub'] = ta.SUB(high, low)
df['ta_math_operators_sum'] = ta.SUM(close, timeperiod=30)
return df
def CDLSPINNINGTOP(data):
res = talib.CDLSPINNINGTOP(
data.open.values, data.high.values, data.low.values, data.close.values)
return pd.DataFrame({'CDLSPINNINGTOP': res}, index=data.index)
def candles(source):
open = source['open']
high = source['high']
low = source['low']
close = source['close']
source = source.join(
pd.Series(talib.CDL2CROWS(open, high, low, close), name='CDL2CROWS'))
source = source.join(
pd.Series(talib.CDL3BLACKCROWS(open, high, low, close),
name='CDL3BLACKCROWS'))
source = source.join(
pd.Series(talib.CDL3INSIDE(open, high, low, close), name='CDL3INSIDE'))
source = source.join(
pd.Series(talib.CDL3OUTSIDE(open, high, low, close),
name='CDL3OUTSIDE'))
source = source.join(
pd.Series(talib.CDL3STARSINSOUTH(open, high, low, close),
name='CDL3STARSINSOUTH'))
source = source.join(
pd.Series(talib.CDL3WHITESOLDIERS(open, high, low, close),
name='CDL3WHITESOLDIERS'))
source = source.join(
pd.Series(talib.CDLABANDONEDBABY(open, high, low, close),
name='CDLABANDONEDBABY'))
source = source.join(
pd.Series(talib.CDLADVANCEBLOCK(open, high, low, close),
name='CDLADVANCEBLOCK'))
source = source.join(
pd.Series(talib.CDLBELTHOLD(open, high, low, close),
name='CDLBELTHOLD'))
source = source.join(
pd.Series(talib.CDLBREAKAWAY(open, high, low, close),
name='CDLBREAKAWAY'))
source = source.join(
pd.Series(talib.CDLCLOSINGMARUBOZU(open, high, low, close),
name='CDLCLOSINGMARUBOZU'))
source = source.join(
pd.Series(talib.CDLCONCEALBABYSWALL(open, high, low, close),
name='CDLCONCEALBABYSWALL'))
source = source.join(
pd.Series(talib.CDLCOUNTERATTACK(open, high, low, close),
name='CDLCOUNTERATTACK'))
source = source.join(
pd.Series(talib.CDLDARKCLOUDCOVER(open, high, low, close),
name='CDLDARKCLOUDCOVER'))
source = source.join(
pd.Series(talib.CDLDOJI(open, high, low, close), name='CDLDOJI'))
source = source.join(
pd.Series(talib.CDLDOJISTAR(open, high, low, close),
name='CDLDOJISTAR'))
source = source.join(
pd.Series(talib.CDLDRAGONFLYDOJI(open, high, low, close),
name='CDLDRAGONFLYDOJI'))
source = source.join(
pd.Series(talib.CDLENGULFING(open, high, low, close),
name='CDLENGULFING'))
source = source.join(
pd.Series(talib.CDLEVENINGDOJISTAR(open, high, low, close),
name='CDLEVENINGDOJISTAR'))
source = source.join(
pd.Series(talib.CDLEVENINGSTAR(open, high, low, close),
name='CDLEVENINGSTAR'))
source = source.join(
pd.Series(talib.CDLGAPSIDESIDEWHITE(open, high, low, close),
name='CDLGAPSIDESIDEWHITE'))
source = source.join(
pd.Series(talib.CDLGRAVESTONEDOJI(open, high, low, close),
name='CDLGRAVESTONEDOJI'))
source = source.join(
pd.Series(talib.CDLHAMMER(open, high, low, close), name='CDLHAMMER'))
source = source.join(
pd.Series(talib.CDLHANGINGMAN(open, high, low, close),
name='CDLHANGINGMAN'))
source = source.join(
pd.Series(talib.CDLHARAMI(open, high, low, close), name='CDLHARAMI'))
source = source.join(
pd.Series(talib.CDLHARAMICROSS(open, high, low, close),
name='CDLHARAMICROSS'))
source = source.join(
pd.Series(talib.CDLHIGHWAVE(open, high, low, close),
name='CDLHIGHWAVE'))
source = source.join(
pd.Series(talib.CDLHIKKAKE(open, high, low, close), name='CDLHIKKAKE'))
source = source.join(
pd.Series(talib.CDLHIKKAKEMOD(open, high, low, close),
name='CDLHIKKAKEMOD'))
source = source.join(
pd.Series(talib.CDLHOMINGPIGEON(open, high, low, close),
name='CDLHOMINGPIGEON'))
source = source.join(
pd.Series(talib.CDLIDENTICAL3CROWS(open, high, low, close),
name='CDLIDENTICAL3CROWS'))
source = source.join(
pd.Series(talib.CDLINNECK(open, high, low, close), name='CDLINNECK'))
source = source.join(
pd.Series(talib.CDLINVERTEDHAMMER(open, high, low, close),
name='CDLINVERTEDHAMMER'))
source = source.join(
pd.Series(talib.CDLKICKING(open, high, low, close), name='CDLKICKING'))
source = source.join(
pd.Series(talib.CDLKICKINGBYLENGTH(open, high, low, close),
name='CDLKICKINGBYLENGTH'))
source = source.join(
pd.Series(talib.CDLLADDERBOTTOM(open, high, low, close),
name='CDLLADDERBOTTOM'))
source = source.join(
pd.Series(talib.CDLLONGLEGGEDDOJI(open, high, low, close),
name='CDLLONGLEGGEDDOJI'))
source = source.join(
pd.Series(talib.CDLLONGLINE(open, high, low, close),
name='CDLLONGLINE'))
source = source.join(
pd.Series(talib.CDLMARUBOZU(open, high, low, close),
name='CDLMARUBOZU'))
source = source.join(
pd.Series(talib.CDLMATCHINGLOW(open, high, low, close),
name='CDLMATCHINGLOW'))
source = source.join(
pd.Series(talib.CDLMATHOLD(open, high, low, close), name='CDLMATHOLD'))
source = source.join(
pd.Series(talib.CDLMORNINGDOJISTAR(open, high, low, close),
name='CDLMORNINGDOJISTAR'))
source = source.join(
pd.Series(talib.CDLMORNINGSTAR(open, high, low, close),
name='CDLMORNINGSTAR'))
source = source.join(
pd.Series(talib.CDLONNECK(open, high, low, close), name='CDLONNECK'))
source = source.join(
pd.Series(talib.CDLPIERCING(open, high, low, close),
name='CDLPIERCING'))
source = source.join(
pd.Series(talib.CDLRICKSHAWMAN(open, high, low, close),
name='CDLRICKSHAWMAN'))
source = source.join(
pd.Series(talib.CDLRISEFALL3METHODS(open, high, low, close),
name='CDLRISEFALL3METHODS'))
source = source.join(
pd.Series(talib.CDLSEPARATINGLINES(open, high, low, close),
name='CDLSEPARATINGLINES'))
source = source.join(
pd.Series(talib.CDLSHOOTINGSTAR(open, high, low, close),
name='CDLSHOOTINGSTAR'))
source = source.join(
pd.Series(talib.CDLSHORTLINE(open, high, low, close),
name='CDLSHORTLINE'))
source = source.join(
pd.Series(talib.CDLSPINNINGTOP(open, high, low, close),
name='CDLSPINNINGTOP'))
source = source.join(
pd.Series(talib.CDLSTALLEDPATTERN(open, high, low, close),
name='CDLSTALLEDPATTERN'))
source = source.join(
pd.Series(talib.CDLSTICKSANDWICH(open, high, low, close),
name='CDLSTICKSANDWICH'))
source = source.join(
pd.Series(talib.CDLTAKURI(open, high, low, close), name='CDLTAKURI'))
source = source.join(
pd.Series(talib.CDLTASUKIGAP(open, high, low, close),
name='CDLTASUKIGAP'))
source = source.join(
pd.Series(talib.CDLTHRUSTING(open, high, low, close),
name='CDLTHRUSTING'))
source = source.join(
pd.Series(talib.CDLTRISTAR(open, high, low, close), name='CDLTRISTAR'))
source = source.join(
pd.Series(talib.CDLUNIQUE3RIVER(open, high, low, close),
name='CDLUNIQUE3RIVER'))
source = source.join(
pd.Series(talib.CDLUPSIDEGAP2CROWS(open, high, low, close),
name='CDLUPSIDEGAP2CROWS'))
source = source.join(
pd.Series(talib.CDLXSIDEGAP3METHODS(open, high, low, close),
name='CDLXSIDEGAP3METHODS'))
return source
df['CDLLADDERBOTTOM'] = talib.CDLLADDERBOTTOM(op, hp, lp, cp)
df['CDLLONGLEGGEDDOJI'] = talib.CDLLONGLEGGEDDOJI(op, hp, lp, cp)
df['CDLLONGLINE'] = talib.CDLLONGLINE(op, hp, lp, cp)
df['CDLMARUBOZU'] = talib.CDLMARUBOZU(op, hp, lp, cp)
df['CDLMATCHINGLOW'] = talib.CDLMATCHINGLOW(op, hp, lp, cp)
df['CDLMATHOLD'] = talib.CDLMATHOLD(op, hp, lp, cp)
df['CDLMORNINGDOJISTAR'] = talib.CDLMORNINGDOJISTAR(op, hp, lp, cp)
df['CDLMORNINGSTAR'] = talib.CDLMORNINGSTAR(op, hp, lp, cp)
df['CDLONNECK'] = talib.CDLONNECK(op, hp, lp, cp)
df['CDLPIERCING'] = talib.CDLPIERCING(op, hp, lp, cp)
df['CDLRICKSHAWMAN'] = talib.CDLRICKSHAWMAN(op, hp, lp, cp)
df['CDLRISEFALL3METHODS'] = talib.CDLRISEFALL3METHODS(op, hp, lp, cp)
df['CDLSEPARATINGLINES'] = talib.CDLSEPARATINGLINES(op, hp, lp, cp)
df['CDLSHOOTINGSTAR'] = talib.CDLSHOOTINGSTAR(op, hp, lp, cp)
df['CDLSHORTLINE'] = talib.CDLSHORTLINE(op, hp, lp, cp)
df['CDLSPINNINGTOP'] = talib.CDLSPINNINGTOP(op, hp, lp, cp)
df['CDLSTALLEDPATTERN'] = talib.CDLSTALLEDPATTERN(op, hp, lp, cp)
df['CDLSTICKSANDWICH'] = talib.CDLSTICKSANDWICH(op, hp, lp, cp)
df['CDLTAKURI'] = talib.CDLTAKURI(op, hp, lp, cp)
df['CDLTASUKIGAP'] = talib.CDLTASUKIGAP(op, hp, lp, cp)
df['CDLTHRUSTING'] = talib.CDLTHRUSTING(op, hp, lp, cp)
df['CDLTRISTAR'] = talib.CDLTRISTAR(op, hp, lp, cp)
df['CDLUNIQUE3RIVER'] = talib.CDLUNIQUE3RIVER(op, hp, lp, cp)
df['CDLUPSIDEGAP2CROWS'] = talib.CDLUPSIDEGAP2CROWS(op, hp, lp, cp)
df['CDLXSIDEGAP3METHODS'] = talib.CDLXSIDEGAP3METHODS(op, hp, lp, cp)
post_rc = df.shape[0]
df = df.drop(previous_columns, axis=1)
if post_rc == n:
print('Done Talib, Code', code, ' Pcc:', pcc, '/', lenCodes)
else:
print('Error At New Talib')
np.array(df['Open']), np.array(df['High']), np.array(df['Low']),
np.array(df['Adj Close']))
df['Separating_Lines'] = ta.CDLSEPARATINGLINES(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Shooting_Star'] = ta.CDLSHOOTINGSTAR(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Short_Line_Candle'] = ta.CDLSHORTLINE(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Spinning_Top'] = ta.CDLSPINNINGTOP(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Stalled_Pattern'] = ta.CDLSTALLEDPATTERN(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Stick_Sandwich'] = ta.CDLSTICKSANDWICH(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Takuri'] = ta.CDLTAKURI(np.array(df['Open']), np.array(df['High']),
np.array(df['Low']), np.array(df['Adj Close']))
df['Tasuki_Gap'] = ta.CDLTASUKIGAP(np.array(df['Open']), np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Thrusting_Pattern'] = ta.CDLTHRUSTING(np.array(df['Open']),
def pattern_recognition(candles: np.ndarray, pattern_type, penetration=0, sequential=False) -> Union[int, np.ndarray]:
"""
Pattern Recognition
:param candles: np.ndarray
:param penetration: int - default = 0
:param pattern_type: str
:param sequential: bool - default=False
:return: int | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
if pattern_type == "CDL2CROWS":
res = talib.CDL2CROWS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3BLACKCROWS":
res = talib.CDL3BLACKCROWS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3INSIDE":
res = talib.CDL3INSIDE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3LINESTRIKE":
res = talib.CDL3LINESTRIKE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3OUTSIDE":
res = talib.CDL3OUTSIDE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3STARSINSOUTH":
res = talib.CDL3STARSINSOUTH(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3WHITESOLDIERS":
res = talib.CDL3WHITESOLDIERS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLABANDONEDBABY":
res = talib.CDLABANDONEDBABY(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2],
penetration=penetration)
elif pattern_type == "CDLADVANCEBLOCK":
res = talib.CDLADVANCEBLOCK(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLBELTHOLD":
res = talib.CDLBELTHOLD(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLBREAKAWAY":
res = talib.CDLBREAKAWAY(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLCLOSINGMARUBOZU":
res = talib.CDLCLOSINGMARUBOZU(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLCONCEALBABYSWALL":
res = talib.CDLCONCEALBABYSWALL(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLCOUNTERATTACK":
res = talib.CDLCOUNTERATTACK(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLDARKCLOUDCOVER":
res = talib.CDLDARKCLOUDCOVER(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2],
penetration=penetration)
elif pattern_type == "CDLDOJI":
res = talib.CDLDOJI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLDOJISTAR":
res = talib.CDLDOJISTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLDRAGONFLYDOJI":
res = talib.CDLDRAGONFLYDOJI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLENGULFING":
res = talib.CDLENGULFING(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLEVENINGDOJISTAR":
res = talib.CDLEVENINGDOJISTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2],
penetration=penetration)
elif pattern_type == "CDLEVENINGSTAR":
res = talib.CDLEVENINGSTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2], penetration=penetration)
elif pattern_type == "CDLGAPSIDESIDEWHITE":
res = talib.CDLGAPSIDESIDEWHITE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLGRAVESTONEDOJI":
res = talib.CDLGRAVESTONEDOJI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHAMMER":
res = talib.CDLHAMMER(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHANGINGMAN":
res = talib.CDLHANGINGMAN(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHARAMI":
res = talib.CDLHARAMI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHARAMICROSS":
res = talib.CDLHARAMICROSS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHIGHWAVE":
res = talib.CDLHIGHWAVE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHIKKAKE":
res = talib.CDLHIKKAKE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHIKKAKEMOD":
res = talib.CDLHIKKAKEMOD(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHOMINGPIGEON":
res = talib.CDLHOMINGPIGEON(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLIDENTICAL3CROWS":
res = talib.CDLIDENTICAL3CROWS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLINNECK":
res = talib.CDLINNECK(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLINVERTEDHAMMER":
res = talib.CDLINVERTEDHAMMER(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLKICKING":
res = talib.CDLKICKING(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLKICKINGBYLENGTH":
res = talib.CDLKICKINGBYLENGTH(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLLADDERBOTTOM":
res = talib.CDLLADDERBOTTOM(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLLONGLEGGEDDOJI":
res = talib.CDLLONGLEGGEDDOJI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLLONGLINE":
res = talib.CDLLONGLINE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLMARUBOZU":
res = talib.CDLMARUBOZU(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLMATCHINGLOW":
res = talib.CDLMATCHINGLOW(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLMATHOLD":
res = talib.CDLMATHOLD(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2], penetration=penetration)
elif pattern_type == "CDLMORNINGDOJISTAR":
res = talib.CDLMORNINGDOJISTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2],
penetration=penetration)
elif pattern_type == "CDLMORNINGSTAR":
res = talib.CDLMORNINGSTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2], penetration=penetration)
elif pattern_type == "CDLONNECK":
res = talib.CDLONNECK(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLPIERCING":
res = talib.CDLPIERCING(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLRICKSHAWMAN":
res = talib.CDLRICKSHAWMAN(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLRISEFALL3METHODS":
res = talib.CDLRISEFALL3METHODS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSEPARATINGLINES":
res = talib.CDLSEPARATINGLINES(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSHOOTINGSTAR":
res = talib.CDLSHOOTINGSTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSHORTLINE":
res = talib.CDLSHORTLINE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSPINNINGTOP":
res = talib.CDLSPINNINGTOP(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSTALLEDPATTERN":
res = talib.CDLSTALLEDPATTERN(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSTICKSANDWICH":
res = talib.CDLSTICKSANDWICH(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLTAKURI":
res = talib.CDLTAKURI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLTASUKIGAP":
res = talib.CDLTASUKIGAP(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLTHRUSTING":
res = talib.CDLTHRUSTING(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLTRISTAR":
res = talib.CDLTRISTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLUNIQUE3RIVER":
res = talib.CDLUNIQUE3RIVER(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLUPSIDEGAP2CROWS":
res = talib.CDLUPSIDEGAP2CROWS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLXSIDEGAP3METHODS":
res = talib.CDLXSIDEGAP3METHODS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
else:
raise ValueError('pattern type string not recognised')
return res / 100 if sequential else res[-1] / 100
def TALIB_CDLSPINNINGTOP(close):
'''00449,1,1'''
return talib.CDLSPINNINGTOP(close)
def get_technical_indicators(dataset):
# Create 7 and 21 days Moving Average
dataset['ma7'] = dataset['Adj Close'].rolling(window=7).mean()
dataset['ma21'] = dataset['Adj Close'].rolling(window=21).mean()
# Create Exponential moving average
dataset['ema'] = dataset['Adj Close'].ewm(com=0.5).mean()
# Create MACD
dataset['26ema'] = dataset['Adj Close'].ewm(span=26).mean()
dataset['12ema'] = dataset['Adj Close'].ewm(span=12).mean()
dataset['MACD'] = (dataset['12ema'] - dataset['26ema'])
# Create Momentum
dataset['momentum'] = dataset['Adj Close'] - 1
# Create Bollinger Bands
dataset['20sd'] = dataset['Adj Close'].rolling(20).std()
dataset['upper_band'] = dataset['ma21'] + (dataset['20sd'] * 2)
dataset['lower_band'] = dataset['ma21'] - (dataset['20sd'] * 2)
# Create RSI indicator
dataset['RSI'] = ta.RSI(np.array(dataset['Adj Close']))
#Part I: Create Cycle Indicators
#Create HT_DCPERIOD - Hilbert Transform - Dominant Cycle Period
dataset['HT_DCPERIOD'] = ta.HT_DCPERIOD(np.array(dataset['Adj Close']))
#Create HT_DCPHASE - Hilbert Transform - Dominant Cycle Phase
dataset['HT_DCPHASE'] = ta.HT_DCPHASE(np.array(dataset['Adj Close']))
#HT_TRENDMODE - Hilbert Transform - Trend vs Cycle Mode
dataset['HT_TRENDMODE'] = ta.HT_TRENDMODE(np.array(dataset['Adj Close']))
#Part II: Create Volatility Indicators
#Create Average True Range
dataset['ATR'] = ta.ATR(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create NATR - Normalized Average True Range
dataset['NATR'] = ta.NATR(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create TRANGE - True Range
dataset['TRANGE'] = ta.TRANGE(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Part III Overlap Studies
#Create DEMA - Double Exponential Moving Average
dataset['DEMA'] = ta.DEMA(np.array(dataset['Adj Close']), timeperiod=30)
#Create HT_TRENDLINE - Hilbert Transform - Instantaneous Trendline
dataset['HT_TRENDLINE'] = ta.HT_TRENDLINE(np.array(dataset['Adj Close']))
#Create KAMA - Kaufman Adaptive Moving Average
dataset['KAMA'] = ta.KAMA(np.array(dataset['Adj Close']), timeperiod=30)
#Create MIDPOINT - MidPoint over period
dataset['MIDPOINT'] = ta.MIDPOINT(np.array(dataset['Adj Close']),
timeperiod=14)
#Create MIDPRICE - Midpoint Price over period
dataset['MIDPRICE'] = ta.MIDPRICE(np.array(dataset['High']),
np.array(dataset['Low']),
timeperiod=14)
#Create SAR - Parabolic SAR
dataset['SAR'] = ta.SAR(np.array(dataset['High']),
np.array(dataset['Low']),
acceleration=0,
maximum=0)
#Create SMA - Simple Moving Average
dataset['SMA10'] = ta.SMA(np.array(dataset['Adj Close']), timeperiod=10)
#Create T3 - Triple Exponential Moving Average (T3)
dataset['T3'] = ta.T3(np.array(dataset['Adj Close']),
timeperiod=5,
vfactor=0)
#Create TRIMA - Triangular Moving Average
dataset['TRIMA'] = ta.TRIMA(np.array(dataset['Adj Close']), timeperiod=30)
#Create WMA - Weighted Moving Average
dataset['WMA'] = ta.WMA(np.array(dataset['Adj Close']), timeperiod=30)
#PART IV Momentum Indicators
#Create ADX - Average Directional Movement Index
dataset['ADX14'] = ta.ADX(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
dataset['ADX20'] = ta.ADX(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=20)
#Create ADXR - Average Directional Movement Index Rating
dataset['ADXR'] = ta.ADXR(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create APO - Absolute Price Oscillator
dataset['APO'] = ta.APO(np.array(dataset['Adj Close']),
fastperiod=12,
slowperiod=26,
matype=0)
#Create AROONOSC - Aroon Oscillator
dataset['AROONOSC'] = ta.AROONOSC(np.array(dataset['High']),
np.array(dataset['Low']),
timeperiod=14)
#Create BOP - Balance Of Power
dataset['BOP'] = ta.BOP(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CCI - Commodity Channel Index
dataset['CCI3'] = ta.CCI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=3)
dataset['CCI5'] = ta.CCI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=5)
dataset['CCI10'] = ta.CCI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=10)
dataset['CCI14'] = ta.CCI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create CMO - Chande Momentum Oscillator
dataset['CMO'] = ta.CMO(np.array(dataset['Adj Close']), timeperiod=14)
#Create DX - Directional Movement Index
dataset['DX'] = ta.DX(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create MINUS_DI - Minus Directional Indicator
dataset['MINUS_DI'] = ta.MINUS_DI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create MINUS_DM - Minus Directional Movement
dataset['MINUS_DM'] = ta.MINUS_DM(np.array(dataset['High']),
np.array(dataset['Low']),
timeperiod=14)
#Create MOM - Momentum
dataset['MOM3'] = ta.MOM(np.array(dataset['Adj Close']), timeperiod=3)
dataset['MOM5'] = ta.MOM(np.array(dataset['Adj Close']), timeperiod=5)
dataset['MOM10'] = ta.MOM(np.array(dataset['Adj Close']), timeperiod=10)
#Create PLUS_DI - Plus Directional Indicator
dataset['PLUS_DI'] = ta.PLUS_DI(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Create PLUS_DM - Plus Directional Movement
dataset['PLUS_DM'] = ta.PLUS_DM(np.array(dataset['High']),
np.array(dataset['Low']),
timeperiod=14)
#Create PPO - Percentage Price Oscillator
dataset['PPO'] = ta.PPO(np.array(dataset['Adj Close']),
fastperiod=12,
slowperiod=26,
matype=0)
#Create ROC - Rate of change : ((price/prevPrice)-1)*100
dataset['ROC'] = ta.ROC(np.array(dataset['Adj Close']), timeperiod=10)
#Create ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice
dataset['ROCP'] = ta.ROCP(np.array(dataset['Adj Close']), timeperiod=10)
#Create ROCR - Rate of change ratio: (price/prevPrice)
dataset['ROCR'] = ta.ROCR(np.array(dataset['Adj Close']), timeperiod=10)
#Create ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
dataset['ROCR100'] = ta.ROCR100(np.array(dataset['Adj Close']),
timeperiod=10)
#Create RSI - Relative Strength Index
dataset['RSI5'] = ta.RSI(np.array(dataset['Adj Close']), timeperiod=5)
dataset['RSI10'] = ta.RSI(np.array(dataset['Adj Close']), timeperiod=10)
dataset['RSI14'] = ta.RSI(np.array(dataset['Adj Close']), timeperiod=14)
#Create TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
dataset['TRIX'] = ta.TRIX(np.array(dataset['Adj Close']), timeperiod=30)
#Create ULTOSC - Ultimate Oscillator
dataset['ULTOSC'] = ta.ULTOSC(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
#Create WILLR - Williams' %R
dataset['WILLR'] = ta.WILLR(np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
timeperiod=14)
#Part V Pattern Recognition
#Create CDL2CROWS - Two Crows
dataset['CDL2CROWS'] = ta.CDL2CROWS(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDL3BLACKCROWS - Three Black Crows
dataset['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDL3INSIDE - Three Inside Up/Down
dataset['CDL3INSIDE'] = ta.CDL3INSIDE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDL3LINESTRIKE - Three-Line Strike
dataset['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDL3OUTSIDE - Three Outside Up/Down
dataset['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDL3STARSINSOUTH - Three Stars In The South
dataset['CDL3STARSINSOUTH '] = ta.CDL3STARSINSOUTH(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDL3WHITESOLDIERS - Three Advancing White Soldiers
dataset['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLABANDONEDBABY - Abandoned Baby
dataset['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY(
np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
penetration=0)
#Create CDLADVANCEBLOCK - Advance Block
dataset['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLBELTHOLD - Belt-hold
dataset['CDLBELTHOLD'] = ta.CDLBELTHOLD(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLBREAKAWAY - Breakaway
dataset['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLCLOSINGMARUBOZU - Closing Marubozu
dataset['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLCONCEALBABYSWALL - Concealing Baby Swalnp.array(dataset['Low'])
dataset['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLCOUNTERATTACK - Counterattack
dataset['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLDARKCLOUDCOVER - Dark Cloud Cover
dataset['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(
np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
penetration=0)
#Create CDLDOJI - Doji
dataset['CDLDOJI'] = ta.CDLDOJI(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLDOJISTAR - Doji Star
dataset['CDLDOJISTAR'] = ta.CDLDOJISTAR(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLDRAGONFLYDOJI - Dragonfly Doji
dataset['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLENGULFING - Engulfing Pattern
dataset['CDLENGULFING'] = ta.CDLENGULFING(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLEVENINGDOJISTAR - Evening Doji Star
dataset['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(
np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
penetration=0)
#Create CDLEVENINGSTAR - Evening Star
dataset['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(
dataset['Adj Close']),
penetration=0)
#Create CDLGAPSIDESIDEWHITE - Up/Down-gap side-by-side white lines
dataset['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLGRAVESTONEDOJI - Gravestone Doji
dataset['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLHAMMER - Hammer
dataset['CDLHAMMER'] = ta.CDLHAMMER(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHANGINGMAN - Hanging Man
dataset['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHARAMI - Harami Pattern
dataset['CDLHARAMI'] = ta.CDLHARAMI(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHARAMICROSS - Harami Cross Pattern
dataset['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLHIGHWAVE - High-Wave Candle
dataset['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHIKKAKE - Hikkake Pattern
dataset['CDLHIKKAKE'] = ta.CDLHIKKAKE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHIKKAKEMOD - Modified Hikkake Pattern
dataset['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLHOMINGPIGEON - Homing Pigeon
dataset['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLIDENTICAL3CROWS - Identical Three Crows
dataset['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLINNECK - In-Neck Pattern
dataset['CDLINNECK'] = ta.CDLINNECK(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLINVERTEDHAMMER - Inverted Hammer
dataset['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLKICKING - Kicking
dataset['CDLKICKING'] = ta.CDLKICKING(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLKICKINGBYLENGTH - Kicking - bull/bear determined by the longer marubozu
dataset['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLLADDERBOTTOM - Ladder Bottom
dataset['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLLONGLEGGEDDOJI - Long Legged Doji
dataset['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLLONGLINE - Long Line Candle
dataset['CDLLONGLINE'] = ta.CDLLONGLINE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLMARUBOZU - Marubozu
dataset['CDLMARUBOZU'] = ta.CDLMARUBOZU(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLMATCHINGLOW - Matching Low
dataset['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLMATHOLD - Mat Hold
dataset['CDLMATHOLD'] = ta.CDLMATHOLD(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
penetration=0)
#Create CDLMORNINGDOJISTAR - Morning Doji Star
dataset['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(
np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']),
penetration=0)
#Create CDLMORNINGSTAR - Morning Star
dataset['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(
dataset['Adj Close']),
penetration=0)
#Create CDLONNECK - On-Neck Pattern
dataset['CDLONNECK'] = ta.CDLONNECK(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLPIERCING - Piercing Pattern
dataset['CDLPIERCING'] = ta.CDLPIERCING(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLRICKSHAWMAN - Rickshaw Man
dataset['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLRISEFALL3METHODS - Rising/Falling Three Methods
dataset['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLSEPARATINGLINES - Separating Lines
dataset['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLSHOOTINGSTAR - Shooting Star
dataset['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLSHORTLINE - Short Line Candle
dataset['CDLSHORTLINE'] = ta.CDLSHORTLINE(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLSPINNINGTOP - Spinning Top
dataset['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLSTALLEDPATTERN - Stalled Pattern
dataset['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLSTICKSANDWICH - Stick Sandwich
dataset['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLTAKURI - Takuri (Dragonfly Doji with very long np.array(dataset['Low'])er shadow)
dataset['CDLTAKURI'] = ta.CDLTAKURI(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLTASUKIGAP - Tasuki Gap
dataset['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLTHRUSTING - Thrusting Pattern
dataset['CDLTHRUSTING'] = ta.CDLTHRUSTING(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLTRISTAR - Tristar Pattern
dataset['CDLTRISTAR'] = ta.CDLTRISTAR(np.array(dataset['Open']),
np.array(dataset['High']),
np.array(dataset['Low']),
np.array(dataset['Adj Close']))
#Create CDLUNIQUE3RIVER - Unique 3 River
dataset['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLUPSIDEGAP2CROWS - Upside Gap Two Crows
dataset['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
#Create CDLXSIDEGAP3METHODS - Upside/Downside Gap Three Methods
dataset['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS(
np.array(dataset['Open']), np.array(dataset['High']),
np.array(dataset['Low']), np.array(dataset['Adj Close']))
return dataset
def calculate(self, para):
self.t = self.inputdata[:, 0]
self.op = self.inputdata[:, 1]
self.high = self.inputdata[:, 2]
self.low = self.inputdata[:, 3]
self.close = self.inputdata[:, 4]
#adjusted close
self.close1 = self.inputdata[:, 5]
self.volume = self.inputdata[:, 6]
#Overlap study
#Overlap Studies
#Overlap Studies
if para is 'BBANDS': #Bollinger Bands
upperband, middleband, lowerband = ta.BBANDS(self.close,
timeperiod=self.tp,
nbdevup=2,
nbdevdn=2,
matype=0)
self.output = [upperband, middleband, lowerband]
elif para is 'DEMA': #Double Exponential Moving Average
self.output = ta.DEMA(self.close, timeperiod=self.tp)
elif para is 'EMA': #Exponential Moving Average
self.output = ta.EMA(self.close, timeperiod=self.tp)
elif para is 'HT_TRENDLINE': #Hilbert Transform - Instantaneous Trendline
self.output = ta.HT_TRENDLINE(self.close)
elif para is 'KAMA': #Kaufman Adaptive Moving Average
self.output = ta.KAMA(self.close, timeperiod=self.tp)
elif para is 'MA': #Moving average
self.output = ta.MA(self.close, timeperiod=self.tp, matype=0)
elif para is 'MAMA': #MESA Adaptive Moving Average
mama, fama = ta.MAMA(self.close, fastlimit=0, slowlimit=0)
elif para is 'MAVP': #Moving average with variable period
self.output = ta.MAVP(self.close,
periods=10,
minperiod=self.tp,
maxperiod=self.tp1,
matype=0)
elif para is 'MIDPOINT': #MidPoint over period
self.output = ta.MIDPOINT(self.close, timeperiod=self.tp)
elif para is 'MIDPRICE': #Midpoint Price over period
self.output = ta.MIDPRICE(self.high, self.low, timeperiod=self.tp)
elif para is 'SAR': #Parabolic SAR
self.output = ta.SAR(self.high,
self.low,
acceleration=0,
maximum=0)
elif para is 'SAREXT': #Parabolic SAR - Extended
self.output = ta.SAREXT(self.high,
self.low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
elif para is 'SMA': #Simple Moving Average
self.output = ta.SMA(self.close, timeperiod=self.tp)
elif para is 'T3': #Triple Exponential Moving Average (T3)
self.output = ta.T3(self.close, timeperiod=self.tp, vfactor=0)
elif para is 'TEMA': #Triple Exponential Moving Average
self.output = ta.TEMA(self.close, timeperiod=self.tp)
elif para is 'TRIMA': #Triangular Moving Average
self.output = ta.TRIMA(self.close, timeperiod=self.tp)
elif para is 'WMA': #Weighted Moving Average
self.output = ta.WMA(self.close, timeperiod=self.tp)
#Momentum Indicators
elif para is 'ADX': #Average Directional Movement Index
self.output = ta.ADX(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'ADXR': #Average Directional Movement Index Rating
self.output = ta.ADXR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'APO': #Absolute Price Oscillator
self.output = ta.APO(self.close,
fastperiod=12,
slowperiod=26,
matype=0)
elif para is 'AROON': #Aroon
aroondown, aroonup = ta.AROON(self.high,
self.low,
timeperiod=self.tp)
self.output = [aroondown, aroonup]
elif para is 'AROONOSC': #Aroon Oscillator
self.output = ta.AROONOSC(self.high, self.low, timeperiod=self.tp)
elif para is 'BOP': #Balance Of Power
self.output = ta.BOP(self.op, self.high, self.low, self.close)
elif para is 'CCI': #Commodity Channel Index
self.output = ta.CCI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'CMO': #Chande Momentum Oscillator
self.output = ta.CMO(self.close, timeperiod=self.tp)
elif para is 'DX': #Directional Movement Index
self.output = ta.DX(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'MACD': #Moving Average Convergence/Divergence
macd, macdsignal, macdhist = ta.MACD(self.close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
self.output = [macd, macdsignal, macdhist]
elif para is 'MACDEXT': #MACD with controllable MA type
macd, macdsignal, macdhist = ta.MACDEXT(self.close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
self.output = [macd, macdsignal, macdhist]
elif para is 'MACDFIX': #Moving Average Convergence/Divergence Fix 12/26
macd, macdsignal, macdhist = ta.MACDFIX(self.close, signalperiod=9)
self.output = [macd, macdsignal, macdhist]
elif para is 'MFI': #Money Flow Index
self.output = ta.MFI(self.high,
self.low,
self.close,
self.volume,
timeperiod=self.tp)
elif para is 'MINUS_DI': #Minus Directional Indicator
self.output = ta.MINUS_DI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'MINUS_DM': #Minus Directional Movement
self.output = ta.MINUS_DM(self.high, self.low, timeperiod=self.tp)
elif para is 'MOM': #Momentum
self.output = ta.MOM(self.close, timeperiod=10)
elif para is 'PLUS_DI': #Plus Directional Indicator
self.output = ta.PLUS_DI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'PLUS_DM': #Plus Directional Movement
self.output = ta.PLUS_DM(self.high, self.low, timeperiod=self.tp)
elif para is 'PPO': #Percentage Price Oscillator
self.output = ta.PPO(self.close,
fastperiod=12,
slowperiod=26,
matype=0)
elif para is 'ROC': #Rate of change : ((price/prevPrice)-1)*100
self.output = ta.ROC(self.close, timeperiod=10)
elif para is 'ROCP': #Rate of change Percentage: (price-prevPrice)/prevPrice
self.output = ta.ROCP(self.close, timeperiod=10)
elif para is 'ROCR': #Rate of change ratio: (price/prevPrice)
self.output = ta.ROCR(self.close, timeperiod=10)
elif para is 'ROCR100': #Rate of change ratio 100 scale: (price/prevPrice)*100
self.output = ta.ROCR100(self.close, timeperiod=10)
elif para is 'RSI': #Relative Strength Index
self.output = ta.RSI(self.close, timeperiod=self.tp)
elif para is 'STOCH': #Stochastic
slowk, slowd = ta.STOCH(self.high,
self.low,
self.close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
self.output = [slowk, slowd]
elif para is 'STOCHF': #Stochastic Fast
fastk, fastd = ta.STOCHF(self.high,
self.low,
self.close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.output = [fastk, fastd]
elif para is 'STOCHRSI': #Stochastic Relative Strength Index
fastk, fastd = ta.STOCHRSI(self.close,
timeperiod=self.tp,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.output = [fastk, fastd]
elif para is 'TRIX': #1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
self.output = ta.TRIX(self.close, timeperiod=self.tp)
elif para is 'ULTOSC': #Ultimate Oscillator
self.output = ta.ULTOSC(self.high,
self.low,
self.close,
timeperiod1=self.tp,
timeperiod2=self.tp1,
timeperiod3=self.tp2)
elif para is 'WILLR': #Williams' %R
self.output = ta.WILLR(self.high,
self.low,
self.close,
timeperiod=self.tp)
# Volume Indicators : #
elif para is 'AD': #Chaikin A/D Line
self.output = ta.AD(self.high, self.low, self.close, self.volume)
elif para is 'ADOSC': #Chaikin A/D Oscillator
self.output = ta.ADOSC(self.high,
self.low,
self.close,
self.volume,
fastperiod=3,
slowperiod=10)
elif para is 'OBV': #On Balance Volume
self.output = ta.OBV(self.close, self.volume)
# Volatility Indicators: #
elif para is 'ATR': #Average True Range
self.output = ta.ATR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'NATR': #Normalized Average True Range
self.output = ta.NATR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'TRANGE': #True Range
self.output = ta.TRANGE(self.high, self.low, self.close)
#Price Transform : #
elif para is 'AVGPRICE': #Average Price
self.output = ta.AVGPRICE(self.op, self.high, self.low, self.close)
elif para is 'MEDPRICE': #Median Price
self.output = ta.MEDPRICE(self.high, self.low)
elif para is 'TYPPRICE': #Typical Price
self.output = ta.TYPPRICE(self.high, self.low, self.close)
elif para is 'WCLPRICE': #Weighted Close Price
self.output = ta.WCLPRICE(self.high, self.low, self.close)
#Cycle Indicators : #
elif para is 'HT_DCPERIOD': #Hilbert Transform - Dominant Cycle Period
self.output = ta.HT_DCPERIOD(self.close)
elif para is 'HT_DCPHASE': #Hilbert Transform - Dominant Cycle Phase
self.output = ta.HT_DCPHASE(self.close)
elif para is 'HT_PHASOR': #Hilbert Transform - Phasor Components
inphase, quadrature = ta.HT_PHASOR(self.close)
self.output = [inphase, quadrature]
elif para is 'HT_SINE': #Hilbert Transform - SineWave #2
sine, leadsine = ta.HT_SINE(self.close)
self.output = [sine, leadsine]
elif para is 'HT_TRENDMODE': #Hilbert Transform - Trend vs Cycle Mode
self.integer = ta.HT_TRENDMODE(self.close)
#Pattern Recognition : #
elif para is 'CDL2CROWS': #Two Crows
self.integer = ta.CDL2CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDL3BLACKCROWS': #Three Black Crows
self.integer = ta.CDL3BLACKCROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDL3INSIDE': #Three Inside Up/Down
self.integer = ta.CDL3INSIDE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3LINESTRIKE': #Three-Line Strike
self.integer = ta.CDL3LINESTRIKE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3OUTSIDE': #Three Outside Up/Down
self.integer = ta.CDL3OUTSIDE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3STARSINSOUTH': #Three Stars In The South
self.integer = ta.CDL3STARSINSOUTH(self.op, self.high, self.low,
self.close)
elif para is 'CDL3WHITESOLDIERS': #Three Advancing White Soldiers
self.integer = ta.CDL3WHITESOLDIERS(self.op, self.high, self.low,
self.close)
elif para is 'CDLABANDONEDBABY': #Abandoned Baby
self.integer = ta.CDLABANDONEDBABY(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLBELTHOLD': #Belt-hold
self.integer = ta.CDLBELTHOLD(self.op, self.high, self.low,
self.close)
elif para is 'CDLBREAKAWAY': #Breakaway
self.integer = ta.CDLBREAKAWAY(self.op, self.high, self.low,
self.close)
elif para is 'CDLCLOSINGMARUBOZU': #Closing Marubozu
self.integer = ta.CDLCLOSINGMARUBOZU(self.op, self.high, self.low,
self.close)
elif para is 'CDLCONCEALBABYSWALL': #Concealing Baby Swallow
self.integer = ta.CDLCONCEALBABYSWALL(self.op, self.high, self.low,
self.close)
elif para is 'CDLCOUNTERATTACK': #Counterattack
self.integer = ta.CDLCOUNTERATTACK(self.op, self.high, self.low,
self.close)
elif para is 'CDLDARKCLOUDCOVER': #Dark Cloud Cover
self.integer = ta.CDLDARKCLOUDCOVER(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLDOJI': #Doji
self.integer = ta.CDLDOJI(self.op, self.high, self.low, self.close)
elif para is 'CDLDOJISTAR': #Doji Star
self.integer = ta.CDLDOJISTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLDRAGONFLYDOJI': #Dragonfly Doji
self.integer = ta.CDLDRAGONFLYDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLENGULFING': #Engulfing Pattern
self.integer = ta.CDLENGULFING(self.op, self.high, self.low,
self.close)
elif para is 'CDLEVENINGDOJISTAR': #Evening Doji Star
self.integer = ta.CDLEVENINGDOJISTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLEVENINGSTAR': #Evening Star
self.integer = ta.CDLEVENINGSTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLGAPSIDESIDEWHITE': #Up/Down-gap side-by-side white lines
self.integer = ta.CDLGAPSIDESIDEWHITE(self.op, self.high, self.low,
self.close)
elif para is 'CDLGRAVESTONEDOJI': #Gravestone Doji
self.integer = ta.CDLGRAVESTONEDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLHAMMER': #Hammer
self.integer = ta.CDLHAMMER(self.op, self.high, self.low,
self.close)
elif para is 'CDLHANGINGMAN': #Hanging Man
self.integer = ta.CDLHANGINGMAN(self.op, self.high, self.low,
self.close)
elif para is 'CDLHARAMI': #Harami Pattern
self.integer = ta.CDLHARAMI(self.op, self.high, self.low,
self.close)
elif para is 'CDLHARAMICROSS': #Harami Cross Pattern
self.integer = ta.CDLHARAMICROSS(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIGHWAVE': #High-Wave Candle
self.integer = ta.CDLHIGHWAVE(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIKKAKE': #Hikkake Pattern
self.integer = ta.CDLHIKKAKE(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIKKAKEMOD': #Modified Hikkake Pattern
self.integer = ta.CDLHIKKAKEMOD(self.op, self.high, self.low,
self.close)
elif para is 'CDLHOMINGPIGEON': #Homing Pigeon
self.integer = ta.CDLHOMINGPIGEON(self.op, self.high, self.low,
self.close)
elif para is 'CDLIDENTICAL3CROWS': #Identical Three Crows
self.integer = ta.CDLIDENTICAL3CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDLINNECK': #In-Neck Pattern
self.integer = ta.CDLINNECK(self.op, self.high, self.low,
self.close)
elif para is 'CDLINVERTEDHAMMER': #Inverted Hammer
self.integer = ta.CDLINVERTEDHAMMER(self.op, self.high, self.low,
self.close)
elif para is 'CDLKICKING': #Kicking
self.integer = ta.CDLKICKING(self.op, self.high, self.low,
self.close)
elif para is 'CDLKICKINGBYLENGTH': #Kicking - bull/bear determined by the longer marubozu
self.integer = ta.CDLKICKINGBYLENGTH(self.op, self.high, self.low,
self.close)
elif para is 'CDLLADDERBOTTOM': #Ladder Bottom
self.integer = ta.CDLLADDERBOTTOM(self.op, self.high, self.low,
self.close)
elif para is 'CDLLONGLEGGEDDOJI': #Long Legged Doji
self.integer = ta.CDLLONGLEGGEDDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLLONGLINE': #Long Line Candle
self.integer = ta.CDLLONGLINE(self.op, self.high, self.low,
self.close)
elif para is 'CDLMARUBOZU': #Marubozu
self.integer = ta.CDLMARUBOZU(self.op, self.high, self.low,
self.close)
elif para is 'CDLMATCHINGLOW': #Matching Low
self.integer = ta.CDLMATCHINGLOW(self.op, self.high, self.low,
self.close)
elif para is 'CDLMATHOLD': #Mat Hold
self.integer = ta.CDLMATHOLD(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLMORNINGDOJISTAR': #Morning Doji Star
self.integer = ta.CDLMORNINGDOJISTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLMORNINGSTAR': #Morning Star
self.integer = ta.CDLMORNINGSTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLONNECK': #On-Neck Pattern
self.integer = ta.CDLONNECK(self.op, self.high, self.low,
self.close)
elif para is 'CDLPIERCING': #Piercing Pattern
self.integer = ta.CDLPIERCING(self.op, self.high, self.low,
self.close)
elif para is 'CDLRICKSHAWMAN': #Rickshaw Man
self.integer = ta.CDLRICKSHAWMAN(self.op, self.high, self.low,
self.close)
elif para is 'CDLRISEFALL3METHODS': #Rising/Falling Three Methods
self.integer = ta.CDLRISEFALL3METHODS(self.op, self.high, self.low,
self.close)
elif para is 'CDLSEPARATINGLINES': #Separating Lines
self.integer = ta.CDLSEPARATINGLINES(self.op, self.high, self.low,
self.close)
elif para is 'CDLSHOOTINGSTAR': #Shooting Star
self.integer = ta.CDLSHOOTINGSTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLSHORTLINE': #Short Line Candle
self.integer = ta.CDLSHORTLINE(self.op, self.high, self.low,
self.close)
elif para is 'CDLSPINNINGTOP': #Spinning Top
self.integer = ta.CDLSPINNINGTOP(self.op, self.high, self.low,
self.close)
elif para is 'CDLSTALLEDPATTERN': #Stalled Pattern
self.integer = ta.CDLSTALLEDPATTERN(self.op, self.high, self.low,
self.close)
elif para is 'CDLSTICKSANDWICH': #Stick Sandwich
self.integer = ta.CDLSTICKSANDWICH(self.op, self.high, self.low,
self.close)
elif para is 'CDLTAKURI': #Takuri (Dragonfly Doji with very long lower shadow)
self.integer = ta.CDLTAKURI(self.op, self.high, self.low,
self.close)
elif para is 'CDLTASUKIGAP': #Tasuki Gap
self.integer = ta.CDLTASUKIGAP(self.op, self.high, self.low,
self.close)
elif para is 'CDLTHRUSTING': #Thrusting Pattern
self.integer = ta.CDLTHRUSTING(self.op, self.high, self.low,
self.close)
elif para is 'CDLTRISTAR': #Tristar Pattern
self.integer = ta.CDLTRISTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLUNIQUE3RIVER': #Unique 3 River
self.integer = ta.CDLUNIQUE3RIVER(self.op, self.high, self.low,
self.close)
elif para is 'CDLUPSIDEGAP2CROWS': #Upside Gap Two Crows
self.integer = ta.CDLUPSIDEGAP2CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDLXSIDEGAP3METHODS': #Upside/Downside Gap Three Methods
self.integer = ta.CDLXSIDEGAP3METHODS(self.op, self.high, self.low,
self.close)
#Statistic Functions : #
elif para is 'BETA': #Beta
self.output = ta.BETA(self.high, self.low, timeperiod=5)
elif para is 'CORREL': #Pearson's Correlation Coefficient (r)
self.output = ta.CORREL(self.high, self.low, timeperiod=self.tp)
elif para is 'LINEARREG': #Linear Regression
self.output = ta.LINEARREG(self.close, timeperiod=self.tp)
elif para is 'LINEARREG_ANGLE': #Linear Regression Angle
self.output = ta.LINEARREG_ANGLE(self.close, timeperiod=self.tp)
elif para is 'LINEARREG_INTERCEPT': #Linear Regression Intercept
self.output = ta.LINEARREG_INTERCEPT(self.close,
timeperiod=self.tp)
elif para is 'LINEARREG_SLOPE': #Linear Regression Slope
self.output = ta.LINEARREG_SLOPE(self.close, timeperiod=self.tp)
elif para is 'STDDEV': #Standard Deviation
self.output = ta.STDDEV(self.close, timeperiod=5, nbdev=1)
elif para is 'TSF': #Time Series Forecast
self.output = ta.TSF(self.close, timeperiod=self.tp)
elif para is 'VAR': #Variance
self.output = ta.VAR(self.close, timeperiod=5, nbdev=1)
else:
print('You issued command:' + para)
def all_candels(df):
df['two_crow'] = talib.CDL2CROWS(df.open,df.high,df.low,df.close)
df['three_black_crows'] = talib.CDL3BLACKCROWS(df.open,df.high,df.low,df.close)
df['threeinside updown'] = talib.CDL3INSIDE(df.open,df.high,df.low,df.close)
df['threelinestrike'] = talib.CDL3LINESTRIKE(df.open,df.high,df.low,df.close)
df['3outside'] = talib.CDL3OUTSIDE(df.open,df.high,df.low,df.close)
df['3starsinsouth'] = talib.CDL3STARSINSOUTH(df.open,df.high,df.low,df.close)
df['3WHITESOLDIERS'] = talib.CDL3WHITESOLDIERS(df.open,df.high,df.low,df.close)
df['ABANDONEDBABY'] = talib.CDLABANDONEDBABY(df.open,df.high,df.low,df.close)
df['ADVANCEBLOCK'] = talib.CDLADVANCEBLOCK(df.open,df.high,df.low,df.close)
df['BELTHOLD'] = talib.CDLBELTHOLD(df.open,df.high,df.low,df.close)
df['BREAKAWAY'] = talib.CDLBREAKAWAY(df.open,df.high,df.low,df.close)
df['CLOSINGMARUBOZU'] = talib.CDLCLOSINGMARUBOZU(df.open,df.high,df.low,df.close)
df['CONCEALBABYSWALL'] = talib.CDLCONCEALBABYSWALL(df.open,df.high,df.low,df.close)
df['COUNTERATTACK'] = talib.CDLCOUNTERATTACK(df.open,df.high,df.low,df.close)
df['DARKCLOUDCOVER'] = talib.CDLDARKCLOUDCOVER(df.open,df.high,df.low,df.close)
df['DOJI'] = talib.CDLDOJI(df.open,df.high,df.low,df.close)
df['DOJISTAR'] = talib.CDLDOJISTAR(df.open,df.high,df.low,df.close)
df['DRAGONFLYDOJI'] = talib.CDLDRAGONFLYDOJI(df.open,df.high,df.low,df.close)
df['ENGULFING'] = talib.CDLENGULFING(df.open,df.high,df.low,df.close)
df['EVENINGDOJISTAR'] = talib.CDLEVENINGDOJISTAR(df.open,df.high,df.low,df.close)
df['EVENINGSTAR'] = talib.CDLEVENINGSTAR(df.open,df.high,df.low,df.close)
df['GAPSIDESIDEWHITE'] = talib.CDLGAPSIDESIDEWHITE(df.open,df.high,df.low,df.close)
df['GRAVESTONEDOJI'] = talib.CDLGRAVESTONEDOJI(df.open,df.high,df.low,df.close)
df['HAMMER'] = talib.CDLHAMMER(df.open,df.high,df.low,df.close)
df['HANGINGMAN'] = talib.CDLHANGINGMAN(df.open,df.high,df.low,df.close)
df['HARAMI'] = talib.CDLHARAMI(df.open,df.high,df.low,df.close)
df['HARAMICROSS'] = talib.CDLHARAMICROSS(df.open,df.high,df.low,df.close)
df['HIGHWAVE'] = talib.CDLHIGHWAVE(df.open,df.high,df.low,df.close)
df['HIKKAKE'] = talib.CDLHIKKAKE(df.open,df.high,df.low,df.close)
df['HIKKAKEMOD'] = talib.CDLHIKKAKEMOD(df.open,df.high,df.low,df.close)
df['HOMINGPIGEON'] = talib.CDLHOMINGPIGEON(df.open,df.high,df.low,df.close)
df['IDENTICAL3CROWS'] = talib.CDLIDENTICAL3CROWS(df.open,df.high,df.low,df.close)
df['INNECK'] = talib.CDLINNECK(df.open,df.high,df.low,df.close)
df['INVERTEDHAMMER'] = talib.CDLINVERTEDHAMMER(df.open,df.high,df.low,df.close)
df['KICKING'] = talib.CDLKICKING(df.open,df.high,df.low,df.close)
df['KICKINGBYLENGTH'] = talib.CDLKICKINGBYLENGTH(df.open,df.high,df.low,df.close)
df['LADDERBOTTOM'] = talib.CDLLADDERBOTTOM(df.open,df.high,df.low,df.close)
df['LONGLEGGEDDOJI'] = talib.CDLLONGLEGGEDDOJI(df.open,df.high,df.low,df.close)
df['LONGLINE'] = talib.CDLLONGLINE(df.open,df.high,df.low,df.close)
df['MARUBOZU'] = talib.CDLMARUBOZU(df.open,df.high,df.low,df.close)
df['MATCHINGLOW'] = talib.CDLMATCHINGLOW(df.open,df.high,df.low,df.close)
df['MATHOLD'] = talib.CDLMATHOLD(df.open,df.high,df.low,df.close)
df['MORNINGDOJISTAR'] = talib.CDLMORNINGDOJISTAR(df.open,df.high,df.low,df.close)
df['MORNINGSTAR'] = talib.CDLMORNINGSTAR(df.open,df.high,df.low,df.close)
df['ONNECK'] = talib.CDLONNECK(df.open,df.high,df.low,df.close)
df['PIERCING'] = talib.CDLPIERCING(df.open,df.high,df.low,df.close)
df['RICKSHAWMAN'] = talib.CDLRICKSHAWMAN(df.open,df.high,df.low,df.close)
df['RISEFALL3METHODS'] = talib.CDLRISEFALL3METHODS(df.open,df.high,df.low,df.close)
df['SEPARATINGLINES'] = talib.CDLSEPARATINGLINES(df.open,df.high,df.low,df.close)
df['SHOOTINGSTAR'] = talib.CDLSHOOTINGSTAR(df.open,df.high,df.low,df.close)
df['SHORTLINE'] = talib.CDLSHORTLINE(df.open,df.high,df.low,df.close)
df['SPINNINGTOP'] = talib.CDLSPINNINGTOP(df.open,df.high,df.low,df.close)
df['STALLEDPATTERN'] = talib.CDLSTALLEDPATTERN(df.open,df.high,df.low,df.close)
df['STICKSANDWICH'] = talib.CDLSTICKSANDWICH(df.open,df.high,df.low,df.close)
df['TAKURI'] = talib.CDLTAKURI(df.open,df.high,df.low,df.close)
df['TASUKIGAP'] = talib.CDLTASUKIGAP(df.open,df.high,df.low,df.close)
df['THRUSTING'] = talib.CDLTHRUSTING(df.open,df.high,df.low,df.close)
df['TRISTAR'] = talib.CDLTRISTAR(df.open,df.high,df.low,df.close)
df['UNIQUE3RIVER'] = talib.CDLUNIQUE3RIVER(df.open,df.high,df.low,df.close)
df['UPSIDEGAP2CROWS'] = talib.CDLUPSIDEGAP2CROWS(df.open,df.high,df.low,df.close)
df['XSIDEGAP3METHODS'] = talib.CDLXSIDEGAP3METHODS(df.open,df.high,df.low,df.close)
return df
def CDLSPINNINGTOP(DataFrame):
res = talib.CDLSPINNINGTOP(DataFrame.open.values, DataFrame.high.values,
DataFrame.low.values, DataFrame.close.values)
return pd.DataFrame({'CDLSPINNINGTOP': res}, index=DataFrame.index)
def built_in_scanners(ticker="SPY"):
data = yf.download(ticker,
start="2020-01-01",
end=datetime.today().strftime('%Y-%m-%d'))
open = data['Open']
high = data['High']
low = data['Low']
close = data['Close']
# The library's functions runs on yesterday's date, so subtract 1 from today's date.
current_date = datetime.today() - timedelta(days=1)
current_date_formatted = current_date.strftime('%Y-%m-%d')
two_crows = talib.CDL2CROWS(open, high, low, close)[current_date_formatted]
three_black_crows = talib.CDL3BLACKCROWS(open, high, low,
close)[current_date_formatted]
three_inside = talib.CDL3INSIDE(open, high, low,
close)[current_date_formatted]
three_line_strike = talib.CDL3LINESTRIKE(open, high, low,
close)[current_date_formatted]
three_outside = talib.CDL3OUTSIDE(open, high, low,
close)[current_date_formatted]
three_stars_in_south = talib.CDL3STARSINSOUTH(
open, high, low, close)[current_date_formatted]
three_white_soldiers = talib.CDL3WHITESOLDIERS(
open, high, low, close)[current_date_formatted]
abandoned_baby = talib.CDLABANDONEDBABY(open, high, low,
close)[current_date_formatted]
advance_block = talib.CDLADVANCEBLOCK(open, high, low,
close)[current_date_formatted]
belt_hold = talib.CDLBELTHOLD(open, high, low,
close)[current_date_formatted]
breakaway = talib.CDLBREAKAWAY(open, high, low,
close)[current_date_formatted]
closing_marubozu = talib.CDLCLOSINGMARUBOZU(open, high, low,
close)[current_date_formatted]
concealing_baby_swallow = talib.CDLCONCEALBABYSWALL(
open, high, low, close)[current_date_formatted]
talib.CDLCOUNTERATTACK(open, high, low, close)[current_date_formatted]
dark_cloud_cover = talib.CDLDARKCLOUDCOVER(
open, high, low, close, penetration=0)[current_date_formatted]
doji = talib.CDLDOJI(open, high, low, close)[current_date_formatted]
doji_star = talib.CDLDOJISTAR(open, high, low,
close)[current_date_formatted]
dragonfly_doji = talib.CDLDRAGONFLYDOJI(open, high, low,
close)[current_date_formatted]
engulfing_candle = talib.CDLENGULFING(open, high, low,
close)[current_date_formatted]
evening_doji_star = talib.CDLEVENINGDOJISTAR(
open, high, low, close, penetration=0)[current_date_formatted]
evening_star = talib.CDLEVENINGSTAR(open, high, low, close,
penetration=0)[current_date_formatted]
gaps = talib.CDLGAPSIDESIDEWHITE(open, high, low,
close)[current_date_formatted]
gravestone_doji = talib.CDLGRAVESTONEDOJI(open, high, low,
close)[current_date_formatted]
hammer = talib.CDLHAMMER(open, high, low, close)[current_date_formatted]
hanging_man = talib.CDLHANGINGMAN(open, high, low,
close)[current_date_formatted]
harami = talib.CDLHARAMI(open, high, low, close)[current_date_formatted]
harami_cross = talib.CDLHARAMICROSS(open, high, low,
close)[current_date_formatted]
high_wave = talib.CDLHIGHWAVE(
open, high, low, close)[current_date_formatted][talib.CDLHIGHWAVE != 0]
hikkake = talib.CDLHIKKAKE(open, high, low, close)[current_date_formatted]
hikkakemod = talib.CDLHIKKAKEMOD(open, high, low,
close)[current_date_formatted]
homing_pigeon = talib.CDLHOMINGPIGEON(open, high, low,
close)[current_date_formatted]
identical_three_crows = talib.CDLIDENTICAL3CROWS(
open, high, low, close)[current_date_formatted]
in_neck = talib.CDLINNECK(open, high, low, close)[current_date_formatted]
inverted_hammer = talib.CDLINVERTEDHAMMER(open, high, low,
close)[current_date_formatted]
kicking = talib.CDLKICKING(open, high, low, close)[current_date_formatted]
kicking_by_length = talib.CDLKICKINGBYLENGTH(open, high, low,
close)[current_date_formatted]
ladder_bottom = talib.CDLLADDERBOTTOM(open, high, low,
close)[current_date_formatted]
long_legged_doji = talib.CDLLONGLEGGEDDOJI(open, high, low,
close)[current_date_formatted]
long_line = talib.CDLLONGLINE(open, high, low,
close)[current_date_formatted]
marubozu = talib.CDLMARUBOZU(open, high, low,
close)[current_date_formatted]
matching_low = talib.CDLMATCHINGLOW(open, high, low,
close)[current_date_formatted]
mat_hold = talib.CDLMATHOLD(open, high, low, close,
penetration=0)[current_date_formatted]
morning_doji_star = talib.CDLMORNINGDOJISTAR(
open, high, low, close, penetration=0)[current_date_formatted]
morning_star = talib.CDLMORNINGSTAR(open, high, low, close,
penetration=0)[current_date_formatted]
on_neck = talib.CDLONNECK(open, high, low, close)[current_date_formatted]
piercing = talib.CDLPIERCING(open, high, low,
close)[current_date_formatted]
rickshawman = talib.CDLRICKSHAWMAN(open, high, low,
close)[current_date_formatted]
rise_fall_3_methods = talib.CDLRISEFALL3METHODS(
open, high, low, close)[current_date_formatted]
separating_lines = talib.CDLSEPARATINGLINES(open, high, low,
close)[current_date_formatted]
shooting_star = talib.CDLSHOOTINGSTAR(open, high, low,
close)[current_date_formatted]
shortline = talib.CDLSHORTLINE(open, high, low,
close)[current_date_formatted]
spinning_top = talib.CDLSPINNINGTOP(open, high, low,
close)[current_date_formatted]
stalled_pattern = talib.CDLSTALLEDPATTERN(open, high, low,
close)[current_date_formatted]
stick_sandwich = talib.CDLSTICKSANDWICH(open, high, low,
close)[current_date_formatted]
takuri = talib.CDLTAKURI(open, high, low, close)[current_date_formatted]
tasuki_gap = talib.CDLTASUKIGAP(open, high, low,
close)[current_date_formatted]
thrusting = talib.CDLTHRUSTING(open, high, low,
close)[current_date_formatted]
tristar = talib.CDLTRISTAR(open, high, low, close)[current_date_formatted]
unique_three_river = talib.CDLUNIQUE3RIVER(open, high, low,
close)[current_date_formatted]
upside_gap_two_crows = talib.CDLUPSIDEGAP2CROWS(
open, high, low, close)[current_date_formatted]
upside_downside_gap_three_methods = talib.CDLXSIDEGAP3METHODS(
open, high, low, close)[current_date_formatted]
patterns = list(vars().keys())[7:]
values = list(vars().values())[7:]
for index in range(0, len(patterns)):
if (values[index] != 0):
print(patterns[index])
print(values[index])
