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])