def get_cdladvanceblock(ohlc): cdladvanceblock = ta.CDLADVANCEBLOCK(ohlc['1_open'], ohlc['2_high'], ohlc['3_low'], ohlc['4_close']) ohlc['cdladvanceblock'] = cdladvanceblock return ohlc
def add_pattern_reconition_factor(df): df['two_crows'] = 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['three_inside'] = talib.CDL3INSIDE(df['open'], df['high'], df['low'], df['close']) df['three_line_strike'] = talib.CDL3LINESTRIKE(df['open'], df['high'], df['low'], df['close']) df['three_outside'] = talib.CDL3OUTSIDE(df['open'], df['high'], df['low'], df['close']) df['three_star_south'] = talib.CDL3STARINSOUTH(df['open'], df['high'], df['low'], df['close']) df['three_white_soldiers'] = talib.CDL3WHITESOLDIERS( df['open'], df['high'], df['low'], df['close']) df['abandoned_baby'] = talib.CDLABANDONEDBABY(df['open'], df['high'], df['low'], df['close']) df['advance_block'] = talib.CDLADVANCEBLOCK(df['open'], df['high'], df['low'], df['close']) df['belt_hold'] = talib.CDLBELTHOLD(df['open'], df['high'], df['low'], df['close']) df['break_away'] = talib.CDLBREAKAWAY(df['open'], df['high'], df['low'], df['close']) df['closing_marubozu'] = talib.CDLCLOSINGMARUBOZU(df['open'], df['high'], df['low'], df['close']) df['conceal_baby_swall'] = talib.CDLCONCEALBABYSWALL( df['open'], df['high'], df['low'], df['close']) df['counter_attack'] = talib.CDLCOUNTERATTACK(df['open'], df['high'], df['low'], df['close']) df['dark_cloud_cover'] = talib.CDLDARKCLOUDCOVER(df['open'], df['high'], df['low'], df['close']) df['doji'] = talib.CDLDOJI(df['open'], df['high'], df['low'], df['close']) df['doji_star'] = talib.CDLDOJISTAR(df['open'], df['high'], df['low'], df['close']) df['dragon_fly_doji'] = talib.CDLDRAGONFLYDOJI(df['open'], df['high'], df['low'], df['close']) df['engulfing'] = talib.CDLENGULFING(df['open'], df['high'], df['low'], df['close']) df['evening_doji_star'] = talib.CDLEVENINGDOJISTAR(df['open'], df['high'], df['low'], df['close'], penetration=0) df['gap_sideside_white'] = talib.CDLGAPSIDESIDEWHITE( df['open'], df['high'], df['low'], df['close']) df['grave_stone_doji'] = talib.CDLGRAVESTONEDOJI(df['open'], df['high'], df['low'], df['close']) df['hammer'] = talib.CDLHAMMER(df['open'], df['high'], df['low'], df['close']) df['morning_doji_star'] = talib.CDLMORNINGDOJISTAR(df['open'], df['high'], df['low'], df['close'], penetration=0) df['on_neck'] = talib.CDLONNECK(df['open'], df['high'], df['low'], df['close']) return df
def advance_block(self): """ 名称:Belt-hold 捉腰带线 简介:两日K线模式,下跌趋势中,第一日阴线,第二日开盘价为最低价,阳线,收盘价接近最高价,预示价格上涨。 """ result = talib.CDLADVANCEBLOCK(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['advance_block'] = result
def CDLADVANCEBLOCK(open, high, low, close): ''' Advance Block 大敌当前 分组: Pattern Recognition 形态识别 简介: 三日K线模式,三日都收阳,每日收盘价都比前一日高, 开盘价都在前一日实体以内,实体变短,上影线变长。 integer = CDLADVANCEBLOCK(open, high, low, close) ''' return talib.CDLADVANCEBLOCK(open, high, low, close)
def advanced_block(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.CDLADVANCEBLOCK(opens, highs, lows, closes) return cdl
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 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 _extract_feature(candle, params, candle_type, target_dt): ''' 前に余分に必要なデータ量: {(stockf_fastk_period_l + stockf_fastk_period_l) * 最大分足 (min)} + window_size = (12 + 12) * 5 + 5 = 125 (min) ''' o = candle.open h = candle.high l = candle.low c = candle.close v = candle.volume # OHLCV features = pd.DataFrame() features['open'] = o features['high'] = h features['low'] = l features['close'] = c features['volume'] = v #################################### # # Momentum Indicator Functions # #################################### # ADX = SUM((+DI - (-DI)) / (+DI + (-DI)), N) / N # N — 計算期間 # SUM (..., N) — N期間の合計 # +DI — プラスの価格変動の値(positive directional index) # -DI — マイナスの価格変動の値(negative directional index) # rsi_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要 features['adx_s'] = ta.ADX(h, l, c, timeperiod=params['adx_timeperiod_s']) features['adx_m'] = ta.ADX(h, l, c, timeperiod=params['adx_timeperiod_m']) features['adx_l'] = ta.ADX(h, l, c, timeperiod=params['adx_timeperiod_l']) features['adxr_s'] = ta.ADXR(h, l, c, timeperiod=params['adxr_timeperiod_s']) features['adxr_m'] = ta.ADXR(h, l, c, timeperiod=params['adxr_timeperiod_m']) features['adxr_l'] = ta.ADXR(h, l, c, timeperiod=params['adxr_timeperiod_l']) # APO = Shorter Period EMA – Longer Period EMA features['apo_s'] = ta.APO(c, fastperiod=params['apo_fastperiod_s'], slowperiod=params['apo_slowperiod_s'], matype=ta.MA_Type.EMA) features['apo_m'] = ta.APO(c, fastperiod=params['apo_fastperiod_m'], slowperiod=params['apo_slowperiod_m'], matype=ta.MA_Type.EMA) # AroonUp = (N - 過去N日間の最高値からの経過期間) ÷ N × 100 # AroonDown = (N - 過去N日間の最安値からの経過期間) ÷ N × 100 # aroon_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要 #features['aroondown_s'], features['aroonup_s'] = ta.AROON(h, l, timeperiod=params['aroon_timeperiod_s']) #features['aroondown_m'], features['aroonup_m'] = ta.AROON(h, l, timeperiod=params['aroon_timeperiod_m']) #features['aroondown_l'], features['aroonup_l'] = ta.AROON(h, l, timeperiod=params['aroon_timeperiod_l']) # Aronnオシレーター = AroonUp - AroonDown # aroonosc_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要 features['aroonosc_s'] = ta.AROONOSC(h, l, timeperiod=params['aroonosc_timeperiod_s']) features['aroonosc_m'] = ta.AROONOSC(h, l, timeperiod=params['aroonosc_timeperiod_m']) features['aroonosc_l'] = ta.AROONOSC(h, l, timeperiod=params['aroonosc_timeperiod_l']) # BOP = (close - open) / (high - low) features['bop'] = ta.BOP(o, h, l, c) # CCI = (TP - MA) / (0.015 * MD) # TP: (高値+安値+終値) / 3 # MA: TPの移動平均 # MD: 平均偏差 = ((MA - TP1) + (MA - TP2) + ...) / N features['cci_s'] = ta.CCI(h, l, c, timeperiod=params['cci_timeperiod_s']) features['cci_m'] = ta.CCI(h, l, c, timeperiod=params['cci_timeperiod_m']) features['cci_l'] = ta.CCI(h, l, c, timeperiod=params['cci_timeperiod_l']) # CMO - Chande Momentum Oscillator #features['cmo_s'] = ta.CMO(c, timeperiod=params['cmo_timeperiod_s']) #features['cmo_m'] = ta.CMO(c, timeperiod=params['cmo_timeperiod_m']) #features['cmo_l'] = ta.CMO(c, timeperiod=params['cmo_timeperiod_l']) # DX - Directional Movement Index features['dx_s'] = ta.DX(h, l, c, timeperiod=params['dx_timeperiod_s']) features['dx_m'] = ta.DX(h, l, c, timeperiod=params['dx_timeperiod_m']) features['dx_l'] = ta.DX(h, l, c, timeperiod=params['dx_timeperiod_l']) # MACD=基準線-相対線 # 基準線(EMA):過去12日(週・月)間の終値指数平滑平均 # 相対線(EMA):過去26日(週・月)間の終値指数平滑平均 # https://www.sevendata.co.jp/shihyou/technical/macd.html # macd_slowperiod_m = 30 の場合30分足で((30 + macd_signalperiod_m) * 30)/ 60 = 16.5時間必要(macd_signalperiod_m=3の時) macd, macdsignal, macdhist = ta.MACDEXT(c, fastperiod=params['macd_fastperiod_s'], slowperiod=params['macd_slowperiod_s'], signalperiod=params['macd_signalperiod_s'], fastmatype=ta.MA_Type.EMA, slowmatype=ta.MA_Type.EMA, signalmatype=ta.MA_Type.EMA) change_macd = calc_change(macd, macdsignal) change_macd.index = macd.index features['macd_s'] = macd features['macdsignal_s'] = macdsignal features['macdhist_s'] = macdhist features['change_macd_s'] = change_macd macd, macdsignal, macdhist = ta.MACDEXT(c, fastperiod=params['macd_fastperiod_m'], slowperiod=params['macd_slowperiod_m'], signalperiod=params['macd_signalperiod_m'], fastmatype=ta.MA_Type.EMA, slowmatype=ta.MA_Type.EMA, signalmatype=ta.MA_Type.EMA) change_macd = calc_change(macd, macdsignal) change_macd.index = macd.index features['macd_m'] = macd features['macdsignal_m'] = macdsignal features['macdhist_m'] = macdhist features['change_macd_m'] = change_macd # MFI - Money Flow Index features['mfi_s'] = ta.MFI(h, l, c, v, timeperiod=params['mfi_timeperiod_s']) features['mfi_m'] = ta.MFI(h, l, c, v, timeperiod=params['mfi_timeperiod_m']) features['mfi_l'] = ta.MFI(h, l, c, v, timeperiod=params['mfi_timeperiod_l']) # MINUS_DI - Minus Directional Indicator features['minus_di_s'] = ta.MINUS_DI(h, l, c, timeperiod=params['minus_di_timeperiod_s']) features['minus_di_m'] = ta.MINUS_DI(h, l, c, timeperiod=params['minus_di_timeperiod_m']) features['minus_di_l'] = ta.MINUS_DI(h, l, c, timeperiod=params['minus_di_timeperiod_l']) # MINUS_DM - Minus Directional Movement features['minus_dm_s'] = ta.MINUS_DM(h, l, timeperiod=params['minus_dm_timeperiod_s']) features['minus_dm_m'] = ta.MINUS_DM(h, l, timeperiod=params['minus_dm_timeperiod_m']) features['minus_dm_l'] = ta.MINUS_DM(h, l, timeperiod=params['minus_dm_timeperiod_l']) # MOM - Momentum features['mom_s'] = ta.MOM(c, timeperiod=params['mom_timeperiod_s']) features['mom_m'] = ta.MOM(c, timeperiod=params['mom_timeperiod_m']) features['mom_l'] = ta.MOM(c, timeperiod=params['mom_timeperiod_l']) # PLUS_DI - Minus Directional Indicator features['plus_di_s'] = ta.PLUS_DI(h, l, c, timeperiod=params['plus_di_timeperiod_s']) features['plus_di_m'] = ta.PLUS_DI(h, l, c, timeperiod=params['plus_di_timeperiod_m']) features['plus_di_l'] = ta.PLUS_DI(h, l, c, timeperiod=params['plus_di_timeperiod_l']) # PLUS_DM - Minus Directional Movement features['plus_dm_s'] = ta.PLUS_DM(h, l, timeperiod=params['plus_dm_timeperiod_s']) features['plus_dm_m'] = ta.PLUS_DM(h, l, timeperiod=params['plus_dm_timeperiod_m']) features['plus_dm_l'] = ta.PLUS_DM(h, l, timeperiod=params['plus_dm_timeperiod_l']) # PPO - Percentage Price Oscillator #features['ppo_s'] = ta.PPO(c, fastperiod=params['ppo_fastperiod_s'], slowperiod=params['ppo_slowperiod_s'], matype=ta.MA_Type.EMA) #features['ppo_m'] = ta.PPO(c, fastperiod=params['ppo_fastperiod_m'], slowperiod=params['ppo_slowperiod_m'], matype=ta.MA_Type.EMA) # ROC - Rate of change : ((price/prevPrice)-1)*100 features['roc_s'] = ta.ROC(c, timeperiod=params['roc_timeperiod_s']) features['roc_m'] = ta.ROC(c, timeperiod=params['roc_timeperiod_m']) features['roc_l'] = ta.ROC(c, timeperiod=params['roc_timeperiod_l']) # ROCP = (price-prevPrice) / prevPrice # http://www.tadoc.org/indicator/ROCP.htm # rocp_timeperiod_l = 30 の場合、30分足で(30 * 30) / 60 = 15時間必要 rocp = ta.ROCP(c, timeperiod=params['rocp_timeperiod_s']) change_rocp = calc_change(rocp, pd.Series(np.zeros(len(candle)), index=candle.index)) change_rocp.index = rocp.index features['rocp_s'] = rocp features['change_rocp_s'] = change_rocp rocp = ta.ROCP(c, timeperiod=params['rocp_timeperiod_m']) change_rocp = calc_change(rocp, pd.Series(np.zeros(len(candle)), index=candle.index)) change_rocp.index = rocp.index features['rocp_m'] = rocp features['change_rocp_m'] = change_rocp rocp = ta.ROCP(c, timeperiod=params['rocp_timeperiod_l']) change_rocp = calc_change(rocp, pd.Series(np.zeros(len(candle)), index=candle.index)) change_rocp.index = rocp.index features['rocp_l'] = rocp features['change_rocp_l'] = change_rocp # ROCR - Rate of change ratio: (price/prevPrice) features['rocr_s'] = ta.ROCR(c, timeperiod=params['rocr_timeperiod_s']) features['rocr_m'] = ta.ROCR(c, timeperiod=params['rocr_timeperiod_m']) features['rocr_l'] = ta.ROCR(c, timeperiod=params['rocr_timeperiod_l']) # ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100 features['rocr100_s'] = ta.ROCR100(c, timeperiod=params['rocr100_timeperiod_s']) features['rocr100_m'] = ta.ROCR100(c, timeperiod=params['rocr100_timeperiod_m']) features['rocr100_l'] = ta.ROCR100(c, timeperiod=params['rocr100_timeperiod_l']) # RSI = (100 * a) / (a + b) (a: x日間の値上がり幅の合計, b: x日間の値下がり幅の合計) # https://www.sevendata.co.jp/shihyou/technical/rsi.html # rsi_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要 #features['rsi_s'] = ta.RSI(c, timeperiod=params['rsi_timeperiod_s']) #features['rsi_m'] = ta.RSI(c, timeperiod=params['rsi_timeperiod_m']) #features['rsi_l'] = ta.RSI(c, timeperiod=params['rsi_timeperiod_l']) # FASTK(KPeriod) = 100 * (Today's Close - LowestLow) / (HighestHigh - LowestLow) # FASTD(FastDperiod) = MA Smoothed FASTK over FastDperiod # http://www.tadoc.org/indicator/STOCHF.htm # stockf_fastk_period_l=30の場合30分足で、(((30 + 30) * 30) / 60(min)) = 30時間必要 (LowestLowが移動平均の30分余分に必要なので60period余分に計算する) fastk, fastd = ta.STOCHF(h, l, c, fastk_period=params['stockf_fastk_period_s'], fastd_period=params['stockf_fastd_period_s'], fastd_matype=ta.MA_Type.EMA) change_stockf = calc_change(fastk, fastd) change_stockf.index = fastk.index features['fastk_s'] = fastk features['fastd_s'] = fastd features['fast_change_s'] = change_stockf fastk, fastd = ta.STOCHF(h, l, c, fastk_period=params['stockf_fastk_period_m'], fastd_period=params['stockf_fastd_period_m'], fastd_matype=ta.MA_Type.EMA) change_stockf = calc_change(fastk, fastd) change_stockf.index = fastk.index features['fastk_m'] = fastk features['fastd_m'] = fastd features['fast_change_m'] = change_stockf fastk, fastd = ta.STOCHF(h, l, c, fastk_period=params['stockf_fastk_period_l'], fastd_period=params['stockf_fastk_period_l'], fastd_matype=ta.MA_Type.EMA) change_stockf = calc_change(fastk, fastd) change_stockf.index = fastk.index features['fastk_l'] = fastk features['fastd_l'] = fastd features['fast_change_l'] = change_stockf # TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA features['trix_s'] = ta.TRIX(c, timeperiod=params['trix_timeperiod_s']) features['trix_m'] = ta.TRIX(c, timeperiod=params['trix_timeperiod_m']) features['trix_l'] = ta.TRIX(c, timeperiod=params['trix_timeperiod_l']) # ULTOSC - Ultimate Oscillator features['ultosc_s'] = ta.ULTOSC(h, l, c, timeperiod1=params['ultosc_timeperiod_s1'], timeperiod2=params['ultosc_timeperiod_s2'], timeperiod3=params['ultosc_timeperiod_s3']) # WILLR = (当日終値 - N日間の最高値) / (N日間の最高値 - N日間の最安値)× 100 # https://inet-sec.co.jp/study/technical-manual/williamsr/ # willr_timeperiod_l=30の場合30分足で、(30 * 30 / 60) = 15時間必要 features['willr_s'] = ta.WILLR(h, l, c, timeperiod=params['willr_timeperiod_s']) features['willr_m'] = ta.WILLR(h, l, c, timeperiod=params['willr_timeperiod_m']) features['willr_l'] = ta.WILLR(h, l, c, timeperiod=params['willr_timeperiod_l']) #################################### # # Volume Indicator Functions # #################################### # Volume Indicator Functions # slowperiod_adosc_s = 10の場合、30分足で(10 * 30) / 60 = 5時間必要 features['ad'] = ta.AD(h, l, c, v) features['adosc_s'] = ta.ADOSC(h, l, c, v, fastperiod=params['fastperiod_adosc_s'], slowperiod=params['slowperiod_adosc_s']) features['obv'] = ta.OBV(c, v) #################################### # # Volatility Indicator Functions # #################################### # ATR - Average True Range features['atr_s'] = ta.ATR(h, l, c, timeperiod=params['atr_timeperiod_s']) features['atr_m'] = ta.ATR(h, l, c, timeperiod=params['atr_timeperiod_m']) features['atr_l'] = ta.ATR(h, l, c, timeperiod=params['atr_timeperiod_l']) # NATR - Normalized Average True Range #features['natr_s'] = ta.NATR(h, l, c, timeperiod=params['natr_timeperiod_s']) #features['natr_m'] = ta.NATR(h, l, c, timeperiod=params['natr_timeperiod_m']) #features['natr_l'] = ta.NATR(h, l, c, timeperiod=params['natr_timeperiod_l']) # TRANGE - True Range features['trange'] = ta.TRANGE(h, l, c) #################################### # # Price Transform Functions # #################################### features['avgprice'] = ta.AVGPRICE(o, h, l, c) features['medprice'] = ta.MEDPRICE(h, l) #features['typprice'] = ta.TYPPRICE(h, l, c) #features['wclprice'] = ta.WCLPRICE(h, l, c) #################################### # # Cycle Indicator Functions # #################################### #features['ht_dcperiod'] = ta.HT_DCPERIOD(c) #features['ht_dcphase'] = ta.HT_DCPHASE(c) #features['inphase'], features['quadrature'] = ta.HT_PHASOR(c) #features['sine'], features['leadsine'] = ta.HT_SINE(c) features['integer'] = ta.HT_TRENDMODE(c) #################################### # # Statistic Functions # #################################### # BETA - Beta features['beta_s'] = ta.BETA(h, l, timeperiod=params['beta_timeperiod_s']) features['beta_m'] = ta.BETA(h, l, timeperiod=params['beta_timeperiod_m']) features['beta_l'] = ta.BETA(h, l, timeperiod=params['beta_timeperiod_l']) # CORREL - Pearson's Correlation Coefficient (r) #features['correl_s'] = ta.CORREL(h, l, timeperiod=params['correl_timeperiod_s']) #features['correl_m'] = ta.CORREL(h, l, timeperiod=params['correl_timeperiod_m']) #features['correl_l'] = ta.CORREL(h, l, timeperiod=params['correl_timeperiod_l']) # LINEARREG - Linear Regression #features['linearreg_s'] = ta.LINEARREG(c, timeperiod=params['linearreg_timeperiod_s']) #features['linearreg_m'] = ta.LINEARREG(c, timeperiod=params['linearreg_timeperiod_m']) #features['linearreg_l'] = ta.LINEARREG(c, timeperiod=params['linearreg_timeperiod_l']) # LINEARREG_ANGLE - Linear Regression Angle features['linearreg_angle_s'] = ta.LINEARREG_ANGLE(c, timeperiod=params['linearreg_angle_timeperiod_s']) features['linearreg_angle_m'] = ta.LINEARREG_ANGLE(c, timeperiod=params['linearreg_angle_timeperiod_m']) features['linearreg_angle_l'] = ta.LINEARREG_ANGLE(c, timeperiod=params['linearreg_angle_timeperiod_l']) # LINEARREG_INTERCEPT - Linear Regression Intercept features['linearreg_intercept_s'] = ta.LINEARREG_INTERCEPT(c, timeperiod=params['linearreg_intercept_timeperiod_s']) features['linearreg_intercept_m'] = ta.LINEARREG_INTERCEPT(c, timeperiod=params['linearreg_intercept_timeperiod_m']) features['linearreg_intercept_l'] = ta.LINEARREG_INTERCEPT(c, timeperiod=params['linearreg_intercept_timeperiod_l']) # LINEARREG_SLOPE - Linear Regression Slope features['linearreg_slope_s'] = ta.LINEARREG_SLOPE(c, timeperiod=params['linearreg_slope_timeperiod_s']) features['linearreg_slope_m'] = ta.LINEARREG_SLOPE(c, timeperiod=params['linearreg_slope_timeperiod_m']) features['linearreg_slope_l'] = ta.LINEARREG_SLOPE(c, timeperiod=params['linearreg_slope_timeperiod_l']) # STDDEV - Standard Deviation features['stddev_s'] = ta.STDDEV(c, timeperiod=params['stddev_timeperiod_s'], nbdev=1) features['stddev_m'] = ta.STDDEV(c, timeperiod=params['stddev_timeperiod_m'], nbdev=1) features['stddev_l'] = ta.STDDEV(c, timeperiod=params['stddev_timeperiod_l'], nbdev=1) # TSF - Time Series Forecast features['tsf_s'] = ta.TSF(c, timeperiod=params['tsf_timeperiod_s']) features['tsf_m'] = ta.TSF(c, timeperiod=params['tsf_timeperiod_m']) features['tsf_l'] = ta.TSF(c, timeperiod=params['tsf_timeperiod_l']) # VAR - Variance #features['var_s'] = ta.VAR(c, timeperiod=params['var_timeperiod_s'], nbdev=1) #features['var_m'] = ta.VAR(c, timeperiod=params['var_timeperiod_m'], nbdev=1) #features['var_l'] = ta.VAR(c, timeperiod=params['var_timeperiod_l'], nbdev=1) # ボリンジャーバンド # bbands_timeperiod_l = 30の場合、30分足で(30 * 30) / 60 = 15時間必要 bb_upper, bb_middle, bb_lower = ta.BBANDS(c, timeperiod=params['bbands_timeperiod_s'], nbdevup=params['bbands_nbdevup_s'], nbdevdn=params['bbands_nbdevdn_s'], matype=ta.MA_Type.EMA) bb_trend1 = pd.Series(np.zeros(len(candle)), index=candle.index) bb_trend1[c > bb_upper] = 1 bb_trend1[c < bb_lower] = -1 bb_trend2 = pd.Series(np.zeros(len(candle)), index=candle.index) bb_trend2[c > bb_middle] = 1 bb_trend2[c < bb_middle] = -1 features['bb_upper_s'] = bb_upper features['bb_middle_s'] = bb_middle features['bb_lower_s'] = bb_lower features['bb_trend1_s'] = bb_trend1 features['bb_trend2_s'] = bb_trend2 bb_upper, bb_middle, bb_lower = ta.BBANDS(c, timeperiod=params['bbands_timeperiod_m'], nbdevup=params['bbands_nbdevup_m'], nbdevdn=params['bbands_nbdevdn_m'], matype=ta.MA_Type.EMA) bb_trend1 = pd.Series(np.zeros(len(candle)), index=candle.index) bb_trend1[c > bb_upper] = 1 bb_trend1[c < bb_lower] = -1 bb_trend2 = pd.Series(np.zeros(len(candle)), index=candle.index) bb_trend2[c > bb_middle] = 1 bb_trend2[c < bb_middle] = -1 features['bb_upper_m'] = bb_upper features['bb_middle_m'] = bb_middle features['bb_lower_m'] = bb_lower features['bb_trend1_m'] = bb_trend1 features['bb_trend2_m'] = bb_trend2 bb_upper, bb_middle, bb_lower = ta.BBANDS(c, timeperiod=params['bbands_timeperiod_l'], nbdevup=params['bbands_nbdevup_l'], nbdevdn=params['bbands_nbdevdn_l'], matype=ta.MA_Type.EMA) bb_trend1 = pd.Series(np.zeros(len(candle)), index=candle.index) bb_trend1[c > bb_upper] = 1 bb_trend1[c < bb_lower] = -1 bb_trend2 = pd.Series(np.zeros(len(candle)), index=candle.index) bb_trend2[c > bb_middle] = 1 bb_trend2[c < bb_middle] = -1 features['bb_upper_l'] = bb_upper features['bb_middle_l'] = bb_middle features['bb_lower_l'] = bb_lower features['bb_trend1_l'] = bb_trend1 features['bb_trend2_l'] = bb_trend2 # ローソク足 features['CDL2CROWS'] = ta.CDL2CROWS(o, h, l, c) features['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(o, h, l, c) features['CDL3INSIDE'] = ta.CDL3INSIDE(o, h, l, c) features['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(o, h, l, c) features['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(o, h, l, c) features['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(o, h, l, c) features['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(o, h, l, c) features['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY(o, h, l, c, penetration=0) features['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(o, h, l, c) features['CDLBELTHOLD'] = ta.CDLBELTHOLD(o, h, l, c) features['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(o, h, l, c) features['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU(o, h, l, c) features['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL(o, h, l, c) features['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK(o, h, l, c) features['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(o, h, l, c, penetration=0) #features['CDLDOJI'] = ta.CDLDOJI(o, h, l, c) features['CDLDOJISTAR'] = ta.CDLDOJISTAR(o, h, l, c) features['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(o, h, l, c) features['CDLENGULFING'] = ta.CDLENGULFING(o, h, l, c) features['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(o, h, l, c, penetration=0) features['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(o, h, l, c, penetration=0) #features['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE(o, h, l, c) features['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(o, h, l, c) features['CDLHAMMER'] = ta.CDLHAMMER(o, h, l, c) #features['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(o, h, l, c) features['CDLHARAMI'] = ta.CDLHARAMI(o, h, l, c) features['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(o, h, l, c) features['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(o, h, l, c) #features['CDLHIKKAKE'] = ta.CDLHIKKAKE(o, h, l, c) features['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(o, h, l, c) features['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(o, h, l, c) #features['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(o, h, l, c) features['CDLINNECK'] = ta.CDLINNECK(o, h, l, c) #features['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(o, h, l, c) features['CDLKICKING'] = ta.CDLKICKING(o, h, l, c) features['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH(o, h, l, c) features['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM(o, h, l, c) #features['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(o, h, l, c) features['CDLMARUBOZU'] = ta.CDLMARUBOZU(o, h, l, c) #features['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(o, h, l, c) features['CDLMATHOLD'] = ta.CDLMATHOLD(o, h, l, c, penetration=0) features['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(o, h, l, c, penetration=0) features['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(o, h, l, c, penetration=0) features['CDLONNECK'] = ta.CDLONNECK(o, h, l, c) features['CDLPIERCING'] = ta.CDLPIERCING(o, h, l, c) features['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(o, h, l, c) features['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(o, h, l, c) features['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(o, h, l, c) #features['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(o, h, l, c) features['CDLSHORTLINE'] = ta.CDLSHORTLINE(o, h, l, c) #features['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(o, h, l, c) features['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(o, h, l, c) features['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(o, h, l, c) features['CDLTAKURI'] = ta.CDLTAKURI(o, h, l, c) features['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(o, h, l, c) features['CDLTHRUSTING'] = ta.CDLTHRUSTING(o, h, l, c) features['CDLTRISTAR'] = ta.CDLTRISTAR(o, h, l, c) features['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER(o, h, l, c) features['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS(o, h, l, c) features['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS(o, h, l, c) ''' # トレンドライン for dt in datetimerange(candle.index[0], candle.index[-1] + timedelta(minutes=1)): start_dt = (dt - timedelta(minutes=130)).strftime('%Y-%m-%d %H:%M:%S') end_dt = dt.strftime('%Y-%m-%d %H:%M:%S') tmp = candle.loc[(start_dt <= candle.index) & (candle.index <= end_dt)] for w_size, stride in [(15, 5), (30, 10), (60, 10), (120, 10)]: # for w_size, stride in [(120, 10)]: trendlines = calc_trendlines(tmp, w_size, stride) if len(trendlines) == 0: continue trendline_feature = calc_trendline_feature(tmp, trendlines) print('{}-{} {} {} {}'.format(dt - timedelta(minutes=130), dt, trendline_feature['high_a'], trendline_feature['high_b'], trendline_feature['high_diff'])) features.loc[features.index == end_dt, 'trendline_high_a_{}'.format(w_size)] = trendline_feature['high_a'] features.loc[features.index == end_dt, 'trendline_high_b_{}'.format(w_size)] = trendline_feature['high_b'] features.loc[features.index == end_dt, 'trendline_high_diff_{}'.format(w_size)] = trendline_feature['high_diff'] features.loc[features.index == end_dt, 'trendline_low_a_{}'.format(w_size)] = trendline_feature['low_a'] features.loc[features.index == end_dt, 'trendline_low_b_{}'.format(w_size)] = trendline_feature['low_b'] features.loc[features.index == end_dt, 'trendline_low_diff_{}'.format(w_size)] = trendline_feature['low_diff'] ''' window = 5 features_ext = features for w in range(window): tmp = features.shift(periods=60 * (w + 1), freq='S') tmp.columns = [c + '_' + str(w + 1) + 'w' for c in features.columns] features_ext = pd.concat([features_ext, tmp], axis=1) if candle_type == '5min': features_ext = features_ext.shift(periods=300, freq='S') features_ext = features_ext.fillna(method='ffill') features_ext = features_ext[features_ext.index == target_dt] return features_ext
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
ohlc_df['close']) ohlc_df['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(ohlc_df['open'], ohlc_df['high'], ohlc_df['low'], ohlc_df['close']) ohlc_df['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH( ohlc_df['open'], ohlc_df['high'], ohlc_df['low'], ohlc_df['close']) ohlc_df['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS( ohlc_df['open'], ohlc_df['high'], ohlc_df['low'], ohlc_df['close']) ohlc_df['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY( ohlc_df['open'], ohlc_df['high'], ohlc_df['low'], ohlc_df['close']) ohlc_df['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK( ohlc_df['open'], ohlc_df['high'], ohlc_df['low'], ohlc_df['close']) ohlc_df['CDLBELTHOLD'] = ta.CDLBELTHOLD(ohlc_df['open'], ohlc_df['high'], ohlc_df['low'], ohlc_df['close']) ohlc_df['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(ohlc_df['open'], ohlc_df['high'], ohlc_df['low'], ohlc_df['close']) ohlc_df['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU( ohlc_df['open'], ohlc_df['high'], ohlc_df['low'], ohlc_df['close']) ohlc_df['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL( ohlc_df['open'], ohlc_df['high'], ohlc_df['low'], ohlc_df['close'])
#CDL3STARSINSOUTH - Three Stars In The South cdl3starinsouth = ta.CDL3STARSINSOUTH(openp, high, low, close) #CDL3WHITESOLDIERS - Three Advancing White Soldiers cdl3whitesoldiers = ta.CDL3WHITESOLDIERS(openp, high, low, close) #CDLABANDONEDBABY - Abandoned Baby cdlabandonbaby = ta.CDLABANDONEDBABY(openp, high, low, close, penetration=0) #CDLADVANCEBLOCK - Advance Block cdladvanceblock = ta.CDLADVANCEBLOCK(openp, high, low, close) #CDLBELTHOLD - Belt-hold cdlbelthold = ta.CDLBELTHOLD(openp, high, low, close) #CDLBREAKAWAY - Breakaway cdlbreakway = ta.CDLBREAKAWAY(openp, high, low, close) #CDLCLOSINGMARUBOZU - Closing Marubozu cdlclosingmarubozu = ta.CDLCLOSINGMARUBOZU(openp, high, low, close) #CDLCONCEALBABYSWALL - Concealing Baby Swallow cdlconcealbabyswall = ta.CDLCONCEALBABYSWALL(openp, high, low, close) #CDLCOUNTERATTACK - Counterattack cdlcounterattack = ta.CDLCOUNTERATTACK(openp, high, low, close)
def CDLADVANCEBLOCK(data): res = talib.CDLADVANCEBLOCK( data.open.values, data.high.values, data.low.values, data.close.values) return pd.DataFrame({'CDLADVANCEBLOCK': res}, index=data.index)
def CDLADVANCEBLOCK(data, **kwargs): _check_talib_presence() popen, phigh, plow, pclose, pvolume = _extract_ohlc(data) return talib.CDLADVANCEBLOCK(popen, phigh, plow, pclose, **kwargs)
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
resorted['close']) CDL3OUTSIDE_real = talib.CDL3OUTSIDE(resorted['open'], resorted['high'], resorted['low'], resorted['close']) CDL3STARSINSOUTH_real = talib.CDL3STARSINSOUTH( resorted['open'], resorted['high'], resorted['low'], resorted['close']) CDL3WHITESOLDIERS_real = talib.CDL3WHITESOLDIERS( resorted['open'], resorted['high'], resorted['low'], resorted['close']) CDLABANDONEDBABY_real = talib.CDLABANDONEDBABY( resorted['open'], resorted['high'], resorted['low'], resorted['close']) CDLADVANCEBLOCK_real = talib.CDLADVANCEBLOCK( resorted['open'], resorted['high'], resorted['low'], resorted['close']) CDLBELTHOLD_real = talib.CDLBELTHOLD(resorted['open'], resorted['high'], resorted['low'], resorted['close']) CDLBREAKAWAY_real = talib.CDLBREAKAWAY(resorted['open'], resorted['high'], resorted['low'], resorted['close']) CDLCLOSINGMARUBOZU_real = talib.CDLCLOSINGMARUBOZU( resorted['open'], resorted['high'], resorted['low'], resorted['close']) CDLCONCEALBABYSWALL_real = talib.CDLCONCEALBABYSWALL( resorted['open'], resorted['high'], resorted['low'], resorted['close'])
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 CDLADVANCEBLOCK(self): integer = talib.CDLADVANCEBLOCK(self.open, self.high, self.low, self.close) return integer
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
# Feature Space New Talib previous_columns = df.columns.values op = df['Open'] hp = df['High'] lp = df['Low'] cp = df['Close'] df['CDL2CROWS'] = talib.CDL2CROWS(op, hp, lp, cp) df['CDL3BLACKCROWS'] = talib.CDL3BLACKCROWS(op, hp, lp, cp) df['CDL3INSIDE'] = talib.CDL3INSIDE(op, hp, lp, cp) df['CDL3LINESTRIKE'] = talib.CDL3LINESTRIKE(op, hp, lp, cp) df['CDL3OUTSIDE'] = talib.CDL3OUTSIDE(op, hp, lp, cp) df['CDL3STARSINSOUTH'] = talib.CDL3STARSINSOUTH(op, hp, lp, cp) df['CDL3WHITESOLDIERS'] = talib.CDL3WHITESOLDIERS(op, hp, lp, cp) df['CDLABANDONEDBABY'] = talib.CDLABANDONEDBABY(op, hp, lp, cp) df['CDLADVANCEBLOCK'] = talib.CDLADVANCEBLOCK(op, hp, lp, cp) df['CDLBELTHOLD'] = talib.CDLBELTHOLD(op, hp, lp, cp) df['CDLBREAKAWAY'] = talib.CDLBREAKAWAY(op, hp, lp, cp) df['CDLCLOSINGMARUBOZU'] = talib.CDLCLOSINGMARUBOZU(op, hp, lp, cp) df['CDLCONCEALBABYSWALL'] = talib.CDLCONCEALBABYSWALL(op, hp, lp, cp) df['CDLCOUNTERATTACK'] = talib.CDLCOUNTERATTACK(op, hp, lp, cp) df['CDLDARKCLOUDCOVER'] = talib.CDLDARKCLOUDCOVER(op, hp, lp, cp) df['CDLDOJI'] = talib.CDLDOJI(op, hp, lp, cp) df['CDLDOJISTAR'] = talib.CDLDOJISTAR(op, hp, lp, cp) df['CDLDRAGONFLYDOJI'] = talib.CDLDRAGONFLYDOJI(op, hp, lp, cp) df['CDLENGULFING'] = talib.CDLENGULFING(op, hp, lp, cp) df['CDLEVENINGDOJISTAR'] = talib.CDLEVENINGDOJISTAR(op, hp, lp, cp) df['CDLEVENINGSTAR'] = talib.CDLEVENINGSTAR(op, hp, lp, cp) df['CDLGAPSIDESIDEWHITE'] = talib.CDLGAPSIDESIDEWHITE(op, hp, lp, cp) df['CDLGRAVESTONEDOJI'] = talib.CDLGRAVESTONEDOJI(op, hp, lp, cp) df['CDLHAMMER'] = talib.CDLHAMMER(op, hp, lp, cp)
def handle_pattern_recognition(args, axes, i, klines_df, close_times, display_count): # talib if args.CDL2CROWS: name = 'CDL2CROWS' integer = talib.CDL2CROWS(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"]) i += 1 axes[i].set_ylabel(name) axes[i].grid(True) axes[i].plot(close_times, integer[-display_count:], "y:", label=name) if args.CDL3BLACKCROWS: name = 'CDL3BLACKCROWS' integer = talib.CDL3BLACKCROWS(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"]) i += 1 axes[i].set_ylabel(name) axes[i].grid(True) axes[i].plot(close_times, integer[-display_count:], "y:", label=name) if args.CDL3INSIDE: name = 'CDL3INSIDE' integer = talib.CDL3INSIDE(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"]) i += 1 axes[i].set_ylabel(name) axes[i].grid(True) axes[i].plot(close_times, integer[-display_count:], "y:", label=name) if args.CDL3LINESTRIKE: name = 'CDL3LINESTRIKE' integer = talib.CDL3LINESTRIKE(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"]) i += 1 axes[i].set_ylabel(name) axes[i].grid(True) axes[i].plot(close_times, integer[-display_count:], "y:", label=name) if args.CDL3OUTSIDE: name = 'CDL3OUTSIDE' integer = talib.CDL3OUTSIDE(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"]) i += 1 axes[i].set_ylabel(name) axes[i].grid(True) axes[i].plot(close_times, integer[-display_count:], "y:", label=name) if args.CDL3STARSINSOUTH: name = 'CDL3STARSINSOUTH' integer = talib.CDL3STARSINSOUTH(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"]) i += 1 axes[i].set_ylabel(name) axes[i].grid(True) axes[i].plot(close_times, integer[-display_count:], "y:", label=name) if args.CDL3WHITESOLDIERS: name = 'CDL3WHITESOLDIERS' integer = talib.CDL3WHITESOLDIERS(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"]) i += 1 axes[i].set_ylabel(name) axes[i].grid(True) axes[i].plot(close_times, integer[-display_count:], "y:", label=name) if args.CDLABANDONEDBABY: name = 'CDLABANDONEDBABY' integer = talib.CDLABANDONEDBABY(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"]) i += 1 axes[i].set_ylabel(name) axes[i].grid(True) axes[i].plot(close_times, integer[-display_count:], "y:", label=name) if args.CDLADVANCEBLOCK: name = 'CDLADVANCEBLOCK' integer = talib.CDLADVANCEBLOCK(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"]) i += 1 axes[i].set_ylabel(name) axes[i].grid(True) axes[i].plot(close_times, integer[-display_count:], "y:", label=name) '''
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 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
def feature_build(self, open_price, high_price, low_price, close_price, adjust_close, volume, index, feature_result_list): result = talib.CDLADVANCEBLOCK(open_price, high_price, low_price, close_price) return result
np.array(df['Low']), np.array(df['Adj Close'])) df['Thre_Stars_In_The_South'] = ta.CDL3STARSINSOUTH(np.array(df['Open']), np.array(df['High']), np.array(df['Low']), np.array(df['Adj Close'])) df['Three_Advancing_White_Soldiers'] = ta.CDL3WHITESOLDIERS( np.array(df['Open']), np.array(df['High']), np.array(df['Low']), np.array(df['Adj Close'])) df['Abandoned_Baby'] = ta.CDLABANDONEDBABY(np.array(df['Open']), np.array(df['High']), np.array(df['Low']), np.array(df['Adj Close']), penetration=0) df['Advanced_Block'] = ta.CDLADVANCEBLOCK(np.array(df['Open']), np.array(df['High']), np.array(df['Low']), np.array(df['Adj Close'])) df['Belt_hold'] = ta.CDLBELTHOLD(np.array(df['Open']), np.array(df['High']), np.array(df['Low']), np.array(df['Adj Close'])) df['Breakaway'] = ta.CDLBREAKAWAY(np.array(df['Open']), np.array(df['High']), np.array(df['Low']), np.array(df['Adj Close'])) df['Closing_Marubozu'] = ta.CDLCLOSINGMARUBOZU(np.array(df['Open']), np.array(df['High']), np.array(df['Low']), np.array(df['Adj Close'])) df['Concealing_Baby_Swallow'] = ta.CDLCONCEALBABYSWALL( np.array(df['Open']), np.array(df['High']), np.array(df['Low']), np.array(df['Adj Close'])) df['Counterattack'] = ta.CDLCOUNTERATTACK(np.array(df['Open']),
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 TALIB_CDLADVANCEBLOCK(close): '''00406,1,1''' return talib.CDLADVANCEBLOCK(close)
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 CDLADVANCEBLOCK(DataFrame): res = talib.CDLADVANCEBLOCK(DataFrame.open.values, DataFrame.high.values, DataFrame.low.values, DataFrame.close.values) return pd.DataFrame({'CDLADVANCEBLOCK': 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])