def calc(self,stockCode):
cursor=self.connection.cursor()
cursor.execute("select date,open,high,low,close,volume from histDaily where code=%s and date>=%s and date<=%s",(stockCode,self.fromDate,self.toDate))
result = cursor.fetchall()
ohlc=[]
for row in result:
ohlc.append((row[0],row[1],row[2],row[3],row[4],row[5]))
cursor.close()
oprice=np.asfarray(np.asarray(ohlc)[:,1])
hprice=np.asfarray(np.asarray(ohlc)[:,2])
lprice=np.asfarray(np.asarray(ohlc)[:,3])
cprice=np.asfarray(np.asarray(ohlc)[:,4])
# cat a |awk '{print "self.__write(\""$1"\",stockCode,ohlc,ta."$1"(oprice,hprice,lprice,cprice))"}'
self.__write("CDL2CROWS", stockCode, ohlc, ta.CDL2CROWS(oprice, hprice, lprice, cprice))
self.__write("CDL3BLACKCROWS", stockCode, ohlc, ta.CDL3BLACKCROWS(oprice, hprice, lprice, cprice))
self.__write("CDL3INSIDE", stockCode, ohlc, ta.CDL3INSIDE(oprice, hprice, lprice, cprice))
self.__write("CDL3LINESTRIKE", stockCode, ohlc, ta.CDL3LINESTRIKE(oprice, hprice, lprice, cprice))
self.__write("CDL3OUTSIDE", stockCode, ohlc, ta.CDL3OUTSIDE(oprice, hprice, lprice, cprice))
self.__write("CDL3STARSINSOUTH", stockCode, ohlc, ta.CDL3STARSINSOUTH(oprice, hprice, lprice, cprice))
self.__write("CDL3WHITESOLDIERS", stockCode, ohlc, ta.CDL3WHITESOLDIERS(oprice, hprice, lprice, cprice))
self.__write("CDLABANDONEDBABY", stockCode, ohlc, ta.CDLABANDONEDBABY(oprice, hprice, lprice, cprice))
self.__write("CDLADVANCEBLOCK", stockCode, ohlc, ta.CDLADVANCEBLOCK(oprice, hprice, lprice, cprice))
self.__write("CDLBELTHOLD", stockCode, ohlc, ta.CDLBELTHOLD(oprice, hprice, lprice, cprice))
self.__write("CDLBREAKAWAY", stockCode, ohlc, ta.CDLBREAKAWAY(oprice, hprice, lprice, cprice))
self.__write("CDLCLOSINGMARUBOZU", stockCode, ohlc, ta.CDLCLOSINGMARUBOZU(oprice, hprice, lprice, cprice))
self.__write("CDLCONCEALBABYSWALL", stockCode, ohlc, ta.CDLCONCEALBABYSWALL(oprice, hprice, lprice, cprice))
self.__write("CDLCOUNTERATTACK", stockCode, ohlc, ta.CDLCOUNTERATTACK(oprice, hprice, lprice, cprice))
self.__write("CDLDARKCLOUDCOVER", stockCode, ohlc, ta.CDLDARKCLOUDCOVER(oprice, hprice, lprice, cprice))
self.__write("CDLDOJI", stockCode, ohlc, ta.CDLDOJI(oprice, hprice, lprice, cprice))
self.__write("CDLDOJISTAR", stockCode, ohlc, ta.CDLDOJISTAR(oprice, hprice, lprice, cprice))
self.__write("CDLDRAGONFLYDOJI", stockCode, ohlc, ta.CDLDRAGONFLYDOJI(oprice, hprice, lprice, cprice))
self.__write("CDLENGULFING", stockCode, ohlc, ta.CDLENGULFING(oprice, hprice, lprice, cprice))
self.__write("CDLEVENINGDOJISTAR", stockCode, ohlc, ta.CDLEVENINGDOJISTAR(oprice, hprice, lprice, cprice))
self.__write("CDLEVENINGSTAR", stockCode, ohlc, ta.CDLEVENINGSTAR(oprice, hprice, lprice, cprice))
self.__write("CDLGAPSIDESIDEWHITE", stockCode, ohlc, ta.CDLGAPSIDESIDEWHITE(oprice, hprice, lprice, cprice))
self.__write("CDLGRAVESTONEDOJI", stockCode, ohlc, ta.CDLGRAVESTONEDOJI(oprice, hprice, lprice, cprice))
self.__write("CDLHAMMER", stockCode, ohlc, ta.CDLHAMMER(oprice, hprice, lprice, cprice))
self.__write("CDLHANGINGMAN", stockCode, ohlc, ta.CDLHANGINGMAN(oprice, hprice, lprice, cprice))
self.__write("CDLHARAMI", stockCode, ohlc, ta.CDLHARAMI(oprice, hprice, lprice, cprice))
self.__write("CDLHARAMICROSS", stockCode, ohlc, ta.CDLHARAMICROSS(oprice, hprice, lprice, cprice))
self.__write("CDLHIGHWAVE", stockCode, ohlc, ta.CDLHIGHWAVE(oprice, hprice, lprice, cprice))
self.__write("CDLHIKKAKE", stockCode, ohlc, ta.CDLHIKKAKE(oprice, hprice, lprice, cprice))
self.__write("CDLHIKKAKEMOD", stockCode, ohlc, ta.CDLHIKKAKEMOD(oprice, hprice, lprice, cprice))
self.__write("CDLHOMINGPIGEON", stockCode, ohlc, ta.CDLHOMINGPIGEON(oprice, hprice, lprice, cprice))
self.__write("CDLIDENTICAL3CROWS", stockCode, ohlc, ta.CDLIDENTICAL3CROWS(oprice, hprice, lprice, cprice))
self.__write("CDLINNECK", stockCode, ohlc, ta.CDLINNECK(oprice, hprice, lprice, cprice))
self.__write("CDLINVERTEDHAMMER", stockCode, ohlc, ta.CDLINVERTEDHAMMER(oprice, hprice, lprice, cprice))
self.__write("CDLKICKING", stockCode, ohlc, ta.CDLKICKING(oprice, hprice, lprice, cprice))
self.__write("CDLKICKINGBYLENGTH", stockCode, ohlc, ta.CDLKICKINGBYLENGTH(oprice, hprice, lprice, cprice))
self.__write("CDLLADDERBOTTOM", stockCode, ohlc, ta.CDLLADDERBOTTOM(oprice, hprice, lprice, cprice))
self.__write("CDLLONGLEGGEDDOJI", stockCode, ohlc, ta.CDLLONGLEGGEDDOJI(oprice, hprice, lprice, cprice))
self.__write("CDLLONGLINE", stockCode, ohlc, ta.CDLLONGLINE(oprice, hprice, lprice, cprice))
self.__write("CDLMARUBOZU", stockCode, ohlc, ta.CDLMARUBOZU(oprice, hprice, lprice, cprice))
self.__write("CDLMATCHINGLOW", stockCode, ohlc, ta.CDLMATCHINGLOW(oprice, hprice, lprice, cprice))
self.__write("CDLMATHOLD", stockCode, ohlc, ta.CDLMATHOLD(oprice, hprice, lprice, cprice))
self.__write("CDLMORNINGDOJISTAR", stockCode, ohlc, ta.CDLMORNINGDOJISTAR(oprice, hprice, lprice, cprice))
self.__write("CDLMORNINGSTAR", stockCode, ohlc, ta.CDLMORNINGSTAR(oprice, hprice, lprice, cprice))
self.__write("CDLONNECK", stockCode, ohlc, ta.CDLONNECK(oprice, hprice, lprice, cprice))
self.__write("CDLPIERCING", stockCode, ohlc, ta.CDLPIERCING(oprice, hprice, lprice, cprice))
self.__write("CDLRICKSHAWMAN", stockCode, ohlc, ta.CDLRICKSHAWMAN(oprice, hprice, lprice, cprice))
self.__write("CDLRISEFALL3METHODS", stockCode, ohlc, ta.CDLRISEFALL3METHODS(oprice, hprice, lprice, cprice))
self.__write("CDLSEPARATINGLINES", stockCode, ohlc, ta.CDLSEPARATINGLINES(oprice, hprice, lprice, cprice))
self.__write("CDLSHOOTINGSTAR", stockCode, ohlc, ta.CDLSHOOTINGSTAR(oprice, hprice, lprice, cprice))
self.__write("CDLSHORTLINE", stockCode, ohlc, ta.CDLSHORTLINE(oprice, hprice, lprice, cprice))
self.__write("CDLSPINNINGTOP", stockCode, ohlc, ta.CDLSPINNINGTOP(oprice, hprice, lprice, cprice))
self.__write("CDLSTALLEDPATTERN", stockCode, ohlc, ta.CDLSTALLEDPATTERN(oprice, hprice, lprice, cprice))
self.__write("CDLSTICKSANDWICH", stockCode, ohlc, ta.CDLSTICKSANDWICH(oprice, hprice, lprice, cprice))
self.__write("CDLTAKURI", stockCode, ohlc, ta.CDLTAKURI(oprice, hprice, lprice, cprice))
self.__write("CDLTASUKIGAP", stockCode, ohlc, ta.CDLTASUKIGAP(oprice, hprice, lprice, cprice))
self.__write("CDLTHRUSTING", stockCode, ohlc, ta.CDLTHRUSTING(oprice, hprice, lprice, cprice))
self.__write("CDLTRISTAR", stockCode, ohlc, ta.CDLTRISTAR(oprice, hprice, lprice, cprice))
self.__write("CDLUNIQUE3RIVER", stockCode, ohlc, ta.CDLUNIQUE3RIVER(oprice, hprice, lprice, cprice))
self.__write("CDLUPSIDEGAP2CROWS", stockCode, ohlc, ta.CDLUPSIDEGAP2CROWS(oprice, hprice, lprice, cprice))
self.__write("CDLXSIDEGAP3METHODS", stockCode, ohlc, ta.CDLXSIDEGAP3METHODS(oprice, hprice, lprice, cprice))
self.connection.close()
self.connection2.close()
def CDLRICKSHAWMAN(data):
res = talib.CDLRICKSHAWMAN(
data.open.values, data.high.values, data.low.values, data.close.values)
return pd.DataFrame({'CDLRICKSHAWMAN': res}, index=data.index)
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 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
df['CDLIDENTICAL3CROWS'] = talib.CDLIDENTICAL3CROWS(op, hp, lp, cp) df['CDLINNECK'] = talib.CDLINNECK(op, hp, lp, cp) df['CDLINVERTEDHAMMER'] = talib.CDLINVERTEDHAMMER(op, hp, lp, cp) df['CDLKICKING'] = talib.CDLKICKING(op, hp, lp, cp) df['CDLKICKINGBYLENGTH'] = talib.CDLKICKINGBYLENGTH(op, hp, lp, cp) df['CDLLADDERBOTTOM'] = talib.CDLLADDERBOTTOM(op, hp, lp, cp) df['CDLLONGLEGGEDDOJI'] = talib.CDLLONGLEGGEDDOJI(op, hp, lp, cp) df['CDLLONGLINE'] = talib.CDLLONGLINE(op, hp, lp, cp) df['CDLMARUBOZU'] = talib.CDLMARUBOZU(op, hp, lp, cp) df['CDLMATCHINGLOW'] = talib.CDLMATCHINGLOW(op, hp, lp, cp) df['CDLMATHOLD'] = talib.CDLMATHOLD(op, hp, lp, cp) df['CDLMORNINGDOJISTAR'] = talib.CDLMORNINGDOJISTAR(op, hp, lp, cp) df['CDLMORNINGSTAR'] = talib.CDLMORNINGSTAR(op, hp, lp, cp) df['CDLONNECK'] = talib.CDLONNECK(op, hp, lp, cp) df['CDLPIERCING'] = talib.CDLPIERCING(op, hp, lp, cp) df['CDLRICKSHAWMAN'] = talib.CDLRICKSHAWMAN(op, hp, lp, cp) df['CDLRISEFALL3METHODS'] = talib.CDLRISEFALL3METHODS(op, hp, lp, cp) df['CDLSEPARATINGLINES'] = talib.CDLSEPARATINGLINES(op, hp, lp, cp) df['CDLSHOOTINGSTAR'] = talib.CDLSHOOTINGSTAR(op, hp, lp, cp) df['CDLSHORTLINE'] = talib.CDLSHORTLINE(op, hp, lp, cp) df['CDLSPINNINGTOP'] = talib.CDLSPINNINGTOP(op, hp, lp, cp) df['CDLSTALLEDPATTERN'] = talib.CDLSTALLEDPATTERN(op, hp, lp, cp) df['CDLSTICKSANDWICH'] = talib.CDLSTICKSANDWICH(op, hp, lp, cp) df['CDLTAKURI'] = talib.CDLTAKURI(op, hp, lp, cp) df['CDLTASUKIGAP'] = talib.CDLTASUKIGAP(op, hp, lp, cp) df['CDLTHRUSTING'] = talib.CDLTHRUSTING(op, hp, lp, cp) df['CDLTRISTAR'] = talib.CDLTRISTAR(op, hp, lp, cp) df['CDLUNIQUE3RIVER'] = talib.CDLUNIQUE3RIVER(op, hp, lp, cp) df['CDLUPSIDEGAP2CROWS'] = talib.CDLUPSIDEGAP2CROWS(op, hp, lp, cp) df['CDLXSIDEGAP3METHODS'] = talib.CDLXSIDEGAP3METHODS(op, hp, lp, cp) post_rc = df.shape[0]
#CDLMORNINGSTAR - Morning Star
cdlmorningstar = ta.CDLMORNINGSTAR(openp,
high,
low,
close,
penetration=0)
#CDLONNECK - On-Neck Pattern
cdlonneck = ta.CDLONNECK(openp, high, low, close)
#CDLPIERCING - Piercing Pattern
cdlpiercing = ta.CDLPIERCING(openp, high, low, close)
#CDLRICKSHAWMAN - Rickshaw Man
cdlrickshawman = ta.CDLRICKSHAWMAN(openp, high, low, close)
#CDLRISEFALL3METHODS - Rising/Falling Three Methods
cdlrisefall3methods = ta.CDLRISEFALL3METHODS(openp, high, low, close)
#CDLSEPARATINGLINES - Separating Lines
cdlseperatinglines = ta.CDLSEPARATINGLINES(openp, high, low, close)
#CDLSHOOTINGSTAR - Shooting Star
cdlshootingstar = ta.CDLSHOOTINGSTAR(openp, high, low, close)
#CDLSHORTLINE - Short Line Candle
cdlshortline = ta.CDLSHORTLINE(openp, high, low, close)
#CDLSPINNINGTOP - Spinning Top
cdlspinningtop = ta.CDLSPINNINGTOP(openp, high, low, close)
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 CDLRICKSHAWMAN(Open, High, Low, Close):
integer = pd.DataFrame()
for i in High.columns:
integer[i] = ta.CDLRICKSHAWMAN(Open[i], High[i], Low[i], Close[i])
return integer
def addIndi(self, enrich, endf):
# Overlays
if enrich == 'bbands':
bbup, bbmid, bblow = ta.BBANDS(endf['Close'], timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
endf.insert(0, column='bblow', value=bblow)
endf.insert(0, column='bbmid', value=bbmid)
endf.insert(0, column='bbup', value=bbup)
return endf
if enrich == 'mama':
mama, fama = ta.MAMA(endf['Close'])
endf.insert(0, column='fama', value=fama)
endf.insert(0, column='mama', value=mama)
return endf
if enrich == 'dema':
endf.insert(0, column='dema', value=ta.DEMA(endf['Close'], timeperiod=30))
return endf
if enrich == 'ema':
endf.insert(0, column='ema', value=ta.EMA(endf['Close'], timeperiod=30)) # Unstable period
return endf
if enrich == 'ht_trendline':
endf.insert(0, column='ht_trendline', value=ta.HT_TRENDLINE(endf['Close'])) # Unstable period
return endf
if enrich == 'kama':
endf.insert(0, column='kama', value=ta.KAMA(endf['Close'], timeperiod=30)) # Unstable period
return endf
if enrich == 'ma':
endf.insert(0, column='ma', value=ta.MA(endf['Close'], timeperiod=30, matype=0))
return endf
if enrich == 'midpoint':
endf.insert(0, column='midpoint', value=ta.MIDPOINT(endf['Close'], timeperiod=14))
return endf
if enrich == 'midprice':
endf.insert(0, column='midprice', value=ta.MIDPRICE(endf['High'], endf['Low'], timeperiod=14))
return endf
if enrich == 'sar':
endf.insert(0, column='sar', value=ta.SAR(endf['High'], endf['Low'], acceleration=0, maximum=0))
return endf
if enrich == 'sarext':
endf.insert(0, column='sarext', value=ta.SAREXT(endf['High'], endf['Low'], startvalue=0, offsetonreverse=0, accelerationinitlong=0, accelerationlong=0, accelerationmaxlong=0, accelerationinitshort=0, accelerationshort=0, accelerationmaxshort=0))
return endf
if enrich == 'sma':
endf.insert(0, column='sma', value=ta.SMA(endf['Close'], timeperiod=30))
return endf
if enrich == 't3':
endf.insert(0, column='t3', value=ta.T3(endf['Close'], timeperiod=5, vfactor=0)) # Unstable period
return endf
if enrich == 'tema':
endf.insert(0, column='tema', value=ta.TEMA(endf['Close'], timeperiod=30))
return endf
if enrich == 'trima':
endf.insert(0, column='trima', value=ta.TRIMA(endf['Close'], timeperiod=30))
return endf
if enrich == 'wma':
endf.insert(0, column='wma', value=ta.WMA(endf['Close'], timeperiod=30))
return endf
# Momentum
if enrich == 'adx':
endf.insert(0, column='adx', value=ta.ADX(endf['High'], endf['Low'], endf['Close'], timeperiod=14))
return endf
if enrich == 'adxr':
endf.insert(0, column='adxr', value=ta.ADXR(endf['High'], endf['Low'], endf['Close'], timeperiod=14))
return endf
if enrich == 'apo':
endf.insert(0, column='apo', value=ta.APO(endf['Close'], fastperiod=12, slowperiod=26, matype=0))
return endf
if enrich == 'aroonosc':
endf.insert(0, column='aroonosc', value=ta.AROONOSC(endf['High'], endf['Low'], timeperiod=14))
return endf
if enrich == 'aroon':
aroondown, aroonup = ta.AROON(endf['High'], endf['Low'], timeperiod=14)
endf.insert(0, column='aroonup', value=aroonup)
endf.insert(0, column='aroondown', value=aroondown)
return endf
if enrich == 'bop':
endf.insert(0, column='bop', value=ta.BOP(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cci':
endf.insert(0, column='cci', value=ta.CCI(endf['High'], endf['Low'], endf['Close'], timeperiod=14))
return endf
if enrich == 'cmo':
endf.insert(0, column='cmo', value=ta.CMO(endf['Close'], timeperiod=14))
return endf
if enrich == 'dx':
endf.insert(0, column='dx', value=ta.DX(endf['High'], endf['Low'], endf['Close'], timeperiod=14))
return endf
if enrich == 'macd':
macd, macdsignal, macdhist = ta.MACD(endf['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
endf.insert(0, column='macd', value=macd)
endf.insert(0, column='macdsignal', value=macdsignal)
endf.insert(0, column='macdhist', value=macdhist)
return endf
if enrich == 'mfi':
endf.insert(0, column='mfi', value=ta.MFI(endf['High'], endf['Low'], endf['Close'], endf['Volume'], timeperiod=14))
return endf
if enrich == 'mom':
endf.insert(0, column='mom', value=ta.MOM(endf['Close'], timeperiod=10))
return endf
if enrich == 'ppo':
endf.insert(0, column='ppo', value=ta.PPO(endf['Close'], fastperiod=12, slowperiod=26, matype=0))
return endf
if enrich == 'roc':
endf.insert(0, column='roc', value=ta.ROC(endf['Close'], timeperiod=10))
return endf
if enrich == 'rocp':
endf.insert(0, column='rocp', value=ta.ROCP(endf['Close'], timeperiod=10))
return endf
if enrich == 'rsi':
endf.insert(0, column='rsi', value=ta.RSI(endf['Close'], timeperiod=14))
return endf
if enrich == 'stoch':
slowk, slowd = ta.STOCH(endf['High'], endf['Low'], endf['Close'], fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
endf.insert(0, column='slowk', value=slowk)
endf.insert(0, column='slowd', value=slowd)
return endf
if enrich == 'trix':
endf.insert(0, column='trix', value=ta.TRIX(endf['Close'], timeperiod=30))
return endf
if enrich == 'ultosc':
endf.insert(0, column='ultosc', value=ta.ULTOSC(endf['High'], endf['Low'], endf['Close'], timeperiod1=7, timeperiod2=14, timeperiod3=28))
return endf
if enrich == 'willr':
endf.insert(0, column='willr', value=ta.WILLR(endf['High'], endf['Low'], endf['Close'], timeperiod=14))
return endf
# Volume
if enrich == 'ad':
endf.insert(0, column='ad', value=ta.AD(endf['High'], endf['Low'], endf['Close'], endf['Volume']))
return endf
if enrich == 'adosc':
endf.insert(0, column='adosc', value=ta.ADOSC(endf['High'], endf['Low'], endf['Close'], endf['Volume'], fastperiod=3, slowperiod=10))
return endf
if enrich == 'obv':
endf.insert(0, column='obv', value=ta.OBV(endf['Close'], endf['Volume']))
return endf
# Volitility
if enrich == 'atr':
endf.insert(0, column='atr', value=ta.ATR(endf['High'], endf['Low'], endf['Close'], timeperiod=14))
return endf
if enrich == 'natr':
endf.insert(0, column='natr', value=ta.NATR(endf['High'], endf['Low'], endf['Close'], timeperiod=14))
return endf
if enrich == 'trange':
endf.insert(0, column='trange', value=ta.TRANGE(endf['High'], endf['Low'], endf['Close']))
return endf
# Pattern
if enrich == 'cdl2crows':
endf.insert(0, column='cdl2crows', value=ta.CDL2CROWS(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdl3blackcrows':
endf.insert(0, column='cdl3blackcrows', value=ta.CDL3BLACKCROWS(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdl3inside':
endf.insert(0, column='cdl3inside', value=ta.CDL3INSIDE(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdl3linestrike':
endf.insert(0, column='cdl3linestrike', value=ta.CDL3LINESTRIKE(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdl3outside':
endf.insert(0, column='cdlcdl3outside', value=ta.CDL3OUTSIDE(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdl3starsinsouth':
endf.insert(0, column='cdl3starsinsouth', value=ta.CDL3STARSINSOUTH(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdl3whitesoldiers':
endf.insert(0, column='cdl3whitesoldiers', value=ta.CDL3WHITESOLDIERS(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlabandonedbaby':
endf.insert(0, column='cdlabandonedbaby', value=ta.CDLABANDONEDBABY(endf['Open'], endf['High'], endf['Low'], endf['Close'], penetration=0))
return endf
if enrich == 'cdladvanceblock':
endf.insert(0, column='cdladvanceblock', value=ta.CDLADVANCEBLOCK(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlbelthold':
endf.insert(0, column='cdlbelthold', value=ta.CDLBELTHOLD(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlbreakaway':
endf.insert(0, column='cdlbreakaway', value=ta.CDLBREAKAWAY(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlclosingmarubozu':
endf.insert(0, column='cdlclosingmarubozu', value=ta.CDLCLOSINGMARUBOZU(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlconcealbabyswall':
endf.insert(0, column='cdlconcealbabyswall', value=ta.CDLCONCEALBABYSWALL(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlcounterattack':
endf.insert(0, column='cdlcounterattack', value=ta.CDLCOUNTERATTACK(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdldarkcloudcover':
endf.insert(0, column='cdldarkcloudcover', value=ta.CDLDARKCLOUDCOVER(endf['Open'], endf['High'], endf['Low'], endf['Close'], penetration=0))
return endf
if enrich == 'cdldoji':
endf.insert(0, column='cdldoji', value=ta.CDLDOJI(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdldojistar':
endf.insert(0, column='cdldojistar', value=ta.CDLDOJISTAR(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdldragonflydoji':
endf.insert(0, column='cdldragonflydoji', value=ta.CDLDRAGONFLYDOJI(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlengulfing':
endf.insert(0, column='cdlengulfing', value=ta.CDLENGULFING(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdleveningdojistar':
endf.insert(0, column='cdleveningdojistar', value=ta.CDLEVENINGDOJISTAR(endf['Open'], endf['High'], endf['Low'], endf['Close'], penetration=0))
return endf
if enrich == 'cdleveningstar':
endf.insert(0, column='cdleveningstar', value=ta.CDLEVENINGSTAR(endf['Open'], endf['High'], endf['Low'], endf['Close'], penetration=0))
return endf
if enrich == 'cdlgapsidesidewhite':
endf.insert(0, column='cdlgapsidesidewhite', value=ta.CDLGAPSIDESIDEWHITE(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlgravestonedoji':
endf.insert(0, column='cdlgravestonedoji', value=ta.CDLGRAVESTONEDOJI(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlhammer':
endf.insert(0, column='cdlhammer', value=ta.CDLHAMMER(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlhangingman':
endf.insert(0, column='cdlhangingman', value=ta.CDLHANGINGMAN(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlharami':
endf.insert(0, column='cdlharami', value=ta.CDLHARAMI(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlharamicross':
# print(endf.info(),file=sys.stderr)
endf.insert(0, column='cdlharamicross', value=ta.CDLHARAMICROSS(endf['Open'], endf['High'], endf['Low'], endf['Close']))
# print(endf.info(),file=sys.stderr)
return endf
if enrich == 'cdlhighwave':
print(endf.info(), file=sys.stderr)
endf.insert(0, column='cdlhighwave', value=ta.CDLHIGHWAVE(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlhikkake':
endf.insert(0, column='cdlhikkake', value=ta.CDLHIKKAKE(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlhikkakemod':
endf.insert(0, column='cdlhikkakemod', value=ta.CDLHIKKAKEMOD(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlhomingpigeon':
endf.insert(0, column='cdlhomingpigeon', value=ta.CDLHOMINGPIGEON(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlidentical3crows':
endf.insert(0, column='cdlidentical3crows', value=ta.CDLIDENTICAL3CROWS(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlinneck':
endf.insert(0, column='cdlinneck', value=ta.CDLINNECK(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlinvertedhammer':
endf.insert(0, column='cdlinvertedhammer', value=ta.CDLINVERTEDHAMMER(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlkicking':
endf.insert(0, column='cdlkicking', value=ta.CDLKICKING(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlkickingbylength':
endf.insert(0, column='cdlkickingbylength', value=ta.CDLKICKINGBYLENGTH(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlladderbottom':
endf.insert(0, column='cdlladderbottom', value=ta.CDLLADDERBOTTOM(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdllongleggeddoji':
endf.insert(0, column='cdllongleggeddoji', value=ta.CDLLONGLEGGEDDOJI(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdllongline':
endf.insert(0, column='cdllongline', value=ta.CDLLONGLINE(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlmarubozu':
endf.insert(0, column='cdlmarubozu', value=ta.CDLMARUBOZU(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlmatchinglow':
endf.insert(0, column='cdlmatchinglow', value=ta.CDLMATCHINGLOW(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlmathold':
endf.insert(0, column='cdlmathold', value=ta.CDLMATHOLD(endf['Open'], endf['High'], endf['Low'], endf['Close'], penetration=0))
return endf
if enrich == 'cdlmorningdojistar':
endf.insert(0, column='cdlmorningdojistar', value=ta.CDLMORNINGDOJISTAR(endf['Open'], endf['High'], endf['Low'], endf['Close'], penetration=0))
return endf
if enrich == 'cdlmorningstar':
endf.insert(0, column='cdlmorningstar', value=ta.CDLMORNINGSTAR(endf['Open'], endf['High'], endf['Low'], endf['Close'], penetration=0))
return endf
if enrich == 'cdlonneck':
endf.insert(0, column='cdlonneck', value=ta.CDLONNECK(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlpiercing':
endf.insert(0, column='cdlpiercing', value=ta.CDLPIERCING(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlrickshawman':
endf.insert(0, column='cdlrickshawman', value=ta.CDLRICKSHAWMAN(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlrisefall3methods':
endf.insert(0, column='cdlrisefall3methods', value=ta.CDLRISEFALL3METHODS(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlseparatinglines':
endf.insert(0, column='cdlseparatinglines', value=ta.CDLSEPARATINGLINES(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlshootingstar':
endf.insert(0, column='cdlshootingstar', value=ta.CDLSHOOTINGSTAR(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlshortline':
endf.insert(0, column='cdlshortline', value=ta.CDLSHORTLINE(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlspinningtop':
endf.insert(0, column='cdlspinningtop', value=ta.CDLSPINNINGTOP(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlstalledpattern':
endf.insert(0, column='cdlstalledpattern', value=ta.CDLSTALLEDPATTERN(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlsticksandwich':
endf.insert(0, column='cdlsticksandwich', value=ta.CDLSTICKSANDWICH(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdltakuri':
endf.insert(0, column='cdltakuri', value=ta.CDLTAKURI(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdltasukigap':
endf.insert(0, column='cdltasukigap', value=ta.CDLTASUKIGAP(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlthrusting':
endf.insert(0, column='cdlthrusting', value=ta.CDLTHRUSTING(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdltristar':
endf.insert(0, column='cdltristar', value=ta.CDLTRISTAR(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlunique3river':
endf.insert(0, column='cdlunique3river', value=ta.CDLUNIQUE3RIVER(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlupsidegap2crows':
endf.insert(0, column='cdlupsidegap2crows', value=ta.CDLUPSIDEGAP2CROWS(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'cdlxsidegap3methods':
endf.insert(0, column='cdlxsidegap3methods', value=ta.CDLXSIDEGAP3METHODS(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
# Price
if enrich == 'avgprice':
endf.insert(0, column='avgprice', value=ta.AVGPRICE(endf['Open'], endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'medprice':
endf.insert(0, column='medprice', value=ta.MEDPRICE(endf['High'], endf['Low']))
return endf
if enrich == 'typprice':
endf.insert(0, column='typprice', value=ta.TYPPRICE(endf['High'], endf['Low'], endf['Close']))
return endf
if enrich == 'wclprice':
endf.insert(0, column='wclprice', value=ta.WCLPRICE(endf['High'], endf['Low'], endf['Close']))
return endf
# Stats
if enrich == 'beta':
endf.insert(0, column='beta', value=ta.BETA(endf['High'], endf['Low'], timeperiod=5))
return endf
if enrich == 'correl':
endf.insert(0, column='correl', value=ta.CORREL(endf['High'], endf['Low'], timeperiod=30))
return endf
if enrich == 'linearreg':
endf.insert(0, column='linearreg', value=ta.LINEARREG(endf['Close'], timeperiod=14))
return endf
if enrich == 'linearreg_angle':
endf.insert(0, column='linearreg_angle', value=ta.LINEARREG_ANGLE(endf['Close'], timeperiod=14))
return endf
if enrich == 'linearreg_intercept':
endf.insert(0, column='linearreg_intercept', value=ta.LINEARREG_INTERCEPT(endf['Close'], timeperiod=14))
return endf
if enrich == 'linearreg_slope':
endf.insert(0, column='linearreg_slope', value=ta.LINEARREG_SLOPE(endf['Close'], timeperiod=14))
return endf
if enrich == 'stddev':
endf.insert(0, column='stddev', value=ta.STDDEV(endf['Close'], timeperiod=5, nbdev=1))
return endf
if enrich == 'tsf':
endf.insert(0, column='tsf', value=ta.TSF(endf['Close'], timeperiod=14))
return endf
if enrich == 'var':
endf.insert(0, column='var', value=ta.VAR(endf['Close'], timeperiod=5, nbdev=1))
return endf
def enhance_with_indicators(data):
set = []
OPEN = data[:, Candle.OPEN]
HIGH = data[:, Candle.HIGH]
LOW = data[:, Candle.LOW]
CLOSE = data[:, Candle.CLOSE]
VOLUME = data[:, Candle.VOLUME]
low_high = talib.BBANDS(CLOSE,
timeperiod=14,
nbdevup=2,
nbdevdn=2,
matype=1)
low_high = np.asarray([low_high[0][-1], low_high[2][-1]]).reshape(-1, 1)
low_high_scaler = StandardScaler()
low_high_scaler.fit(low_high)
one = np.asarray([-1, 1]).reshape(-1, 1)
one_scaler = StandardScaler()
one_scaler.fit(one)
hundred = np.asarray([-100, 100]).reshape(-1, 1)
hundred_scaler = StandardScaler()
hundred_scaler.fit(hundred)
thousand = np.asarray([-1000, 1000]).reshape(-1, 1)
thousand_scaler = StandardScaler()
thousand_scaler.fit(thousand)
million = np.asarray([-1000000, 1000000]).reshape(-1, 1)
million_scaler = StandardScaler()
million_scaler.fit(million)
set.append(scale(OPEN, low_high_scaler))
set.append(scale(HIGH, low_high_scaler))
set.append(scale(LOW, low_high_scaler))
set.append(scale(CLOSE, low_high_scaler))
#Bollinger Bands are envelopes plotted at a standard deviation level above and below a simple moving average of the price.
set.append(
scale(
talib.BBANDS(CLOSE, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0),
low_high_scaler)) #121.03399999999903 19719.281591268886
set.append(
scale(
talib.BBANDS(CLOSE, timeperiod=14, nbdevup=2, nbdevdn=2, matype=1),
low_high_scaler))
#The DEMA uses two exponential moving averages (EMAs) to eliminate lag, as some traders view lag as a problem.
set.append(scale(talib.DEMA(CLOSE, timeperiod=10),
low_high_scaler)) #122.0 19573.564771355504
set.append(scale(talib.DEMA(CLOSE, timeperiod=21),
low_high_scaler)) #122.0 19546.76082510694
set.append(scale(talib.DEMA(CLOSE, timeperiod=100),
low_high_scaler)) #123.84710711425136 19578.715808186673
#However, whereas SMA simply calculates an average of price data, EMA applies more weight to data that is more current.
set.append(scale(talib.EMA(CLOSE, timeperiod=10),
low_high_scaler)) #122.0 19499.362560116417
set.append(scale(talib.EMA(CLOSE, timeperiod=21),
low_high_scaler)) #122.0 19433.26416788178
set.append(scale(talib.EMA(CLOSE, timeperiod=100),
low_high_scaler)) #122.11270000000005 19059.124645340504
#The HTTrendline at a specific bar gives the current Hilbert Transform Trendline as instantaneously measured at that
#bar. In its Series form, the Instantaneous Trendline appears much like a Moving Average, but with minimal lag
#compared with the lag normally associated with such averages for equivalent periods.
set.append(scale(talib.HT_TRENDLINE(CLOSE),
low_high_scaler)) #122.0 19471.324
#Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility.
#KAMA will closely follow prices when the price swings are relatively small and the noise is low.
set.append(scale(talib.KAMA(CLOSE, timeperiod=10),
low_high_scaler)) #122.0 19397.611724437047
set.append(scale(talib.KAMA(CLOSE, timeperiod=21),
low_high_scaler)) #122.0 19336.434082122203
set.append(scale(talib.KAMA(CLOSE, timeperiod=100),
low_high_scaler)) #123.61 19301.746826077375
#The MESA adaptive moving average is a trend-following indicator. It adapts to price movements in a very unique way,
#based on the rate of change (ROC), as measured by the Hilbert Transform Discriminator.
set.append(
scale(talib.MAMA((HIGH + LOW) / 2., fastlimit=0.5, slowlimit=0.05),
low_high_scaler)) #121.04112572694972 19494.294994956996
set.append(scale(talib.MIDPOINT(CLOSE, timeperiod=5),
low_high_scaler)) #122.0 19544.95
set.append(scale(talib.MIDPRICE(LOW, HIGH, timeperiod=5),
low_high_scaler)) #122.0 19562.6
#The parabolic SAR indicator, developed by J. Welles Wilder Jr., is used by traders to determine trend direction
# and potential reversals in price.
set.append(
scale(talib.SAR(HIGH, LOW, acceleration=0.02, maximum=0.2),
low_high_scaler)) #122.0 19660.0
#A simple moving average (SMA) is an arithmetic moving average calculated by adding recent prices and then dividing
# that figure by the number of time periods in the calculation average.
set.append(scale(talib.SMA(CLOSE, timeperiod=5),
low_high_scaler)) #122.0 19553.340000000037
set.append(scale(talib.SMA(CLOSE, timeperiod=25),
low_high_scaler)) #122.0 19405.74400000004
set.append(scale(talib.SMA(CLOSE, timeperiod=50),
low_high_scaler)) #122.0 19286.443999999996
#The Triple Exponential Moving Average (T3) developed by Tim Tillson attempts to offer a moving average with better
#smoothing then traditional exponential moving average. It incorporates a smoothing technique which allows it to
#plot curves more gradual than ordinary moving averages and with a smaller lag.
set.append(scale(talib.T3(CLOSE, timeperiod=5, vfactor=0),
low_high_scaler)) #122.0 19498.31237177043
set.append(
scale(talib.T3(CLOSE, timeperiod=10, vfactor=0),
low_high_scaler)) #122.0 19419.991324685387
set.append(
scale(talib.T3(CLOSE, timeperiod=21, vfactor=0),
low_high_scaler)) #122.84310194419339 19306.63501695168
#The triple exponential moving average was designed to smooth price fluctuations, thereby making it easier to
#identify trends without the lag associated with traditional moving averages (MA).
set.append(scale(talib.TEMA(CLOSE, timeperiod=7),
low_high_scaler)) #122.0 19617.222402494965
set.append(scale(talib.TEMA(CLOSE, timeperiod=15),
low_high_scaler)) #122.0 19586.42515855386
#The Triangular Moving Average is basically a double-smoothed Simple Moving Average that gives more weight to the
#middle section of the data interval. The TMA has a significant lag to current prices and is not well-suited to
#fast moving markets.
set.append(scale(talib.TRIMA(CLOSE, timeperiod=5),
low_high_scaler)) #122.0 19567.31111092877
set.append(scale(talib.TRIMA(CLOSE, timeperiod=25),
low_high_scaler)) #122.0 19459.8816568341
set.append(scale(talib.TRIMA(CLOSE, timeperiod=50),
low_high_scaler)) #122.0 19359.257076923175
#The weighted moving average (WMA) is a technical indicator that assigns a greater weighting to the most recent data
#points, and less weighting to data points in the distant past. The WMA is obtained by multiplying each number in
#the data set by a predetermined weight and summing up the resulting values.
set.append(scale(talib.WMA(CLOSE, timeperiod=5),
low_high_scaler)) #122.0 19567.840000466134
set.append(scale(talib.WMA(CLOSE, timeperiod=10),
low_high_scaler)) #122.0 19527.127272724356
set.append(scale(talib.WMA(CLOSE, timeperiod=21),
low_high_scaler)) #122.0 19479.342424127473
set.append(scale(talib.WMA(CLOSE, timeperiod=50),
low_high_scaler)) #122.0 19355.600000135404
set.append(scale(talib.WMA(CLOSE, timeperiod=100),
low_high_scaler)) #122.21647326732675 19265.66566335264
set.append(scale(talib.LINEARREG(CLOSE, timeperiod=14),
low_high_scaler)) # 122.0 19585.157142857144
set.append(
scale(talib.LINEARREG_INTERCEPT(CLOSE, timeperiod=14),
low_high_scaler)) #121.54000000000003 19643.968571428577
set.append(scale(talib.TSF(CLOSE, timeperiod=14),
low_high_scaler)) #122.0 19609.668131868133
#ADX stands for Average Directional Movement Index and can be used to help measure the overall strength of a trend.
#The ADX indicator is an average of expanding price range values.
set.append(
scale(talib.ADX(HIGH, LOW, CLOSE, timeperiod=10),
hundred_scaler)) #0.0 99.99999999999963
set.append(
scale(talib.ADX(HIGH, LOW, CLOSE, timeperiod=14),
hundred_scaler)) #0.0 99.9999999940751
set.append(
scale(talib.ADX(HIGH, LOW, CLOSE, timeperiod=21),
hundred_scaler)) #0.0 99.99998408446837
#ADXR stands for Average Directional Movement Index Rating. ADXR is a component of the Directional Movement System
#developed by Welles Wilder.
set.append(
scale(talib.ADXR(HIGH, LOW, CLOSE, timeperiod=14),
hundred_scaler)) #0.0 99.9999999892742
#The Aroon indicator is a technical indicator that is used to identify trend changes in the price of an asset,
#as well as the strength of that trend. In essence, the indicator measures the time between highs and the time
#between lows over a time period. ... The indicator signals when this is happening, and when it isn't
set.append(scale(talib.AROON(HIGH, LOW), hundred_scaler)) #0.0 100.0
#The Directional Movement Index, or DMI, is an indicator developed by J. ... An optional third line, called
#directional movement (DX) shows the difference between the lines. When +DI is above -DI, there is more upward
#pressure than downward pressure in the price.
set.append(scale(talib.DX(HIGH, LOW, CLOSE, timeperiod=5),
hundred_scaler)) #0.0 100.0
set.append(scale(talib.DX(HIGH, LOW, CLOSE, timeperiod=21),
hundred_scaler)) #0.0 100.0
set.append(scale(talib.DX(HIGH, LOW, CLOSE, timeperiod=50),
hundred_scaler)) #0.0 100.0
set.append(
scale(talib.MFI(HIGH, LOW, CLOSE, VOLUME, timeperiod=14),
hundred_scaler)) #-5.888410733172162e-08 100.00000000707982
set.append(
scale(talib.MFI(HIGH, LOW, CLOSE, VOLUME, timeperiod=26),
hundred_scaler)) #-1.3802451942902055e-09 100.00000001185656
set.append(
scale(talib.MFI(HIGH, LOW, CLOSE, VOLUME, timeperiod=100),
hundred_scaler)) #-5.3901183535126315e-08 100.00000000091877
set.append(
scale(talib.MINUS_DI(HIGH, LOW, CLOSE, timeperiod=14),
hundred_scaler)) #0.0 99.99999996020233
set.append(
scale(talib.PLUS_DI(HIGH, LOW, CLOSE, timeperiod=14),
hundred_scaler)) #0.0 100.0
set.append(scale(talib.RSI(CLOSE, timeperiod=14),
hundred_scaler)) #0.0 100.0
set.append(
scale(
talib.STOCH(HIGH,
LOW,
CLOSE,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0),
hundred_scaler)) #-1.0137076363510762e-12 100.00000000000279
set.append(
scale(
talib.STOCHF(HIGH,
LOW,
CLOSE,
fastk_period=5,
fastd_period=3,
fastd_matype=0),
hundred_scaler)) #-1.0137076363510762e-12 100.0000000000012
set.append(
scale(
talib.STOCHRSI(CLOSE,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0),
hundred_scaler)) #-1.2166564052525548e-12 100.00000000000011
set.append(
scale(
talib.ULTOSC(HIGH,
LOW,
CLOSE,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28),
hundred_scaler)) # 0.0 100.00000032481957
set.append(
scale(talib.CDL3WHITESOLDIERS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(scale(talib.CDLDOJI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLDRAGONFLYDOJI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLGRAVESTONEDOJI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(scale(talib.CDLHAMMER(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLHOMINGPIGEON(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLINVERTEDHAMMER(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLLADDERBOTTOM(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLLONGLEGGEDDOJI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLMATCHINGLOW(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLMORNINGDOJISTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLMORNINGSTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(scale(talib.CDLPIERCING(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLRICKSHAWMAN(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLSTICKSANDWICH(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(scale(talib.CDLTAKURI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLUNIQUE3RIVER(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
#Absolute Price Oscillator crossing above zero is considered bullish, while crossing below zero is bearish. A
#positive indicator value indicates an upward movement, while negative readings signal a downward trend.
set.append(
scale(talib.APO(CLOSE, fastperiod=12, slowperiod=26, matype=1),
thousand_scaler)) #-536.1910463572985 443.13971636041424
set.append(scale(VOLUME, thousand_scaler))
#The Commodity Channel Index (CCI) measures the current price level relative to an average price level over a given
#period of time. CCI is relatively high when prices are far above their average.
set.append(scale(talib.CCI(HIGH, LOW, CLOSE),
thousand_scaler)) #-466.66671166042244 466.66673951370416
#Moving average convergence divergence (MACD) is a trend-following momentum indicator that shows the relationship
#between two moving averages of a security’s price.
set.append(
scale(talib.MACD(CLOSE, fastperiod=5, slowperiod=14, signalperiod=5),
thousand_scaler)) # -536.1910463572985 443.13971636041424
set.append(
scale(talib.MACD(CLOSE, fastperiod=12, slowperiod=26, signalperiod=9),
thousand_scaler)) #-536.1910463572985 443.13971636041424
set.append(
scale(talib.MACD(CLOSE, fastperiod=14, slowperiod=50, signalperiod=25),
thousand_scaler)) # -536.1910463572985 443.13971636041424
set.append(scale(talib.ATR(HIGH, LOW, CLOSE),
thousand_scaler)) #0.0 672.1715311610562
set.append(scale(talib.HT_DCPHASE(CLOSE),
hundred_scaler)) #-44.99298332517037 314.99654478107004
set.append(
scale(talib.LINEARREG_SLOPE(CLOSE, timeperiod=14),
hundred_scaler)) #-222.33604395604476 152.37032967033085
set.append(
scale(talib.STDDEV(CLOSE, timeperiod=5, nbdev=1),
thousand_scaler)) #0.0 709.4023698851089
set.append(scale(talib.MINUS_DM(HIGH, LOW, timeperiod=14),
thousand_scaler)) #0.0 2909.3760999618785
set.append(scale(talib.MOM(CLOSE, timeperiod=10),
thousand_scaler)) #-2508.0 1711.2000000000007
set.append(scale(talib.MOM(CLOSE, timeperiod=25),
thousand_scaler)) # -2508.0 1711.2000000000007
set.append(scale(talib.PLUS_DM(HIGH, LOW, timeperiod=14),
thousand_scaler)) #0.0 3697.0008310558483
set.append(scale(talib.ADOSC(HIGH, LOW, CLOSE, VOLUME),
thousand_scaler)) #-1843.4418435977714 1237.4131984846026
set.append(scale(talib.TRANGE(HIGH, LOW, CLOSE),
thousand_scaler)) #0.0 4000.0
set.append(scale(talib.HT_PHASOR(CLOSE),
thousand_scaler)) #-2873.977625168652 3422.2535328187428
#The Balance of Power indicator measures the market strength of buyers against sellers by assessing the ability of
#each side to drive prices to an extreme level. The calculation is: Balance of Power = (Close price – Open price) /
#(High price – Low price) The resulting value can be smoothed by a moving average.
set.append(scale(talib.BOP(OPEN, HIGH, LOW, CLOSE), one_scaler)) #-1.0 1.0
set.append(scale(talib.ROCP(CLOSE, timeperiod=10),
one_scaler)) #-0.30688987999999995 0.46745909457773854
set.append(scale(talib.ROCR(CLOSE, timeperiod=10),
one_scaler)) #0.69311012 1.4674590945777386
set.append(scale(talib.TRIX(CLOSE, timeperiod=30),
one_scaler)) #-0.6044429731575707 0.434667877456385
set.append(scale(talib.HT_SINE(CLOSE),
one_scaler)) #-0.9999999999996187 0.9999999999940317
set.append(scale(talib.HT_TRENDMODE(CLOSE), one_scaler)) #0 1
set.append(
scale(talib.PPO(CLOSE, fastperiod=12, slowperiod=26, matype=0),
hundred_scaler)) #-13.640389725420714 13.383459677599681
set.append(scale(talib.ROC(CLOSE, timeperiod=10),
hundred_scaler)) #-30.688987999999995 46.74590945777386
set.append(scale(talib.NATR(HIGH, LOW, CLOSE),
one_scaler)) #0.0 7.881777549670427
set.append(scale(talib.HT_DCPERIOD(CLOSE),
hundred_scaler)) #6.91050087362864 49.99951053223339
set.append(scale(talib.CORREL(HIGH, LOW, timeperiod=30),
one_scaler)) #-2.4748737341529163 2.2135943621178655
set.append(scale(talib.AD(HIGH, LOW, CLOSE, VOLUME),
million_scaler)) #-3719.2404462314116 199644.25743563366
set.append(scale(talib.OBV(CLOSE, VOLUME),
million_scaler)) #-23849.75116020021 29139.83770172981
set.append(scale(talib.BETA(HIGH, LOW, timeperiod=5),
million_scaler)) #-2971957.111723269 1250567.1172035346
set.append(
scale(talib.VAR(CLOSE, timeperiod=5, nbdev=1),
million_scaler)) #-1.4901161193847656e-07 503251.7223986089
# The Aroon Oscillator is a trend-following indicator that uses aspects of the Aroon Indicator (Aroon Up and Aroon
# Down) to gauge the strength of a current trend and the likelihood that it will continue. Readings above zero
# indicate that an uptrend is present, while readings below zero indicate that a downtrend is present.
set.append(scale(talib.AROONOSC(HIGH, LOW),
hundred_scaler)) # -100.0 100.0
# The Chande Momentum Oscillator (CMO) is a technical momentum indicator developed by Tushar Chande. The CMO
# indicator is created by calculating the difference between the sum of all recent higher closes and the sum of all
# recent lower closes and then dividing the result by the sum of all price movement over a given time period.
set.append(scale(talib.CMO(CLOSE, timeperiod=5),
hundred_scaler)) # -100.0 100.0
set.append(scale(talib.CMO(CLOSE, timeperiod=14),
hundred_scaler)) # -99.99999998652726 100.0
set.append(scale(talib.CMO(CLOSE, timeperiod=25),
hundred_scaler)) # -99.99854211548185 100.0
set.append(scale(talib.CDL3INSIDE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDL3LINESTRIKE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDL3OUTSIDE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLABANDONEDBABY(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLBELTHOLD(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLBREAKAWAY(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLCLOSINGMARUBOZU(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLCOUNTERATTACK(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLDOJISTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLENGULFING(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLGAPSIDESIDEWHITE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLHARAMI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLHARAMICROSS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLHIGHWAVE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLKICKING(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLKICKINGBYLENGTH(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLLONGLINE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLMARUBOZU(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLRISEFALL3METHODS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLSEPARATINGLINES(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLSHORTLINE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLSPINNINGTOP(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLTASUKIGAP(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLTRISTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLXSIDEGAP3METHODS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.LINEARREG_ANGLE(CLOSE, timeperiod=14),
hundred_scaler)) #-89.74230272272693 89.62397563202859
set.append(
scale(talib.WILLR(HIGH, LOW, CLOSE, timeperiod=14),
hundred_scaler)) # -100.00000000000001 0.0
#The Two Crows is a three-line bearish reversal candlestick pattern. The pattern requires confirmation, that is,
#the following candles should break a trendline or the nearest support area which may be formed by the first
#candle's line. If the pattern is not confirmed it may act only as a temporary pause within an uptrend.
set.append(scale(talib.CDL2CROWS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDL3BLACKCROWS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLADVANCEBLOCK(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) # -100 0
set.append(
scale(talib.CDLDARKCLOUDCOVER(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLEVENINGDOJISTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLEVENINGSTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLHANGINGMAN(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLIDENTICAL3CROWS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(scale(talib.CDLINNECK(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(scale(talib.CDLONNECK(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLSHOOTINGSTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLSTALLEDPATTERN(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLTHRUSTING(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(scale(talib.CDLHIKKAKE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-200 200
set.append(
scale(talib.CDLHIKKAKEMOD(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-200 200
return np.concatenate(set)
def appendAllTAData(df=pd.DataFrame([])):
resDF = pd.DataFrame([])
# 函数名：AD名称：ChaikinA/DLine累积/派发线（Accumulation/DistributionLine）
# 简介：MarcChaikin提出的一种平衡交易量指标，以当日的收盘价位来估算成交流量，用于估定一段时间内该证券累积的资金流量。
# 计算公式：A/D=昨日A/D+多空对比*今日成交量多空对比=[（收盘价-最低价）-（最高价-收盘价）]/（最高价-最低价）
# 若最高价等于最低价：多空对比=（收盘价/昨收盘）-1
# 研判：1、A/D测量资金流向，向上的A/D表明买方占优势，而向下的A/D表明卖方占优势
# 2、A/D与价格的背离可视为买卖信号，即底背离考虑买入，顶背离考虑卖出
# 3、应当注意A/D忽略了缺口的影响，事实上，跳空缺口的意义是不能轻易忽略的
# A/D指标无需设置参数，但在应用时，可结合指标的均线进行分析例子：real=AD(high,low,close,volume)
resDF['AD'] = ta.AD(df['max_price'].values, df['min_price'].values,
df['price'].values, df['vol'].values)
# 函数名：ADOSC名称：Chaikin A/D Oscillator Chaikin震荡指标
# 简介：将资金流动情况与价格行为相对比，检测市场中资金流入和流出的情况
# 计算公式：fastperiod A/D - slowperiod A/D
# 研判：1、交易信号是背离：看涨背离做多，看跌背离做空
# 2、股价与90天移动平均结合，与其他指标结合
# 3、由正变负卖出，由负变正买进
# 例子：real = ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)
resDF['ADOSC'] = ta.ADOSC(df['max_price'].values,
df['min_price'].values,
df['price'].values,
df['vol'].values,
fastperiod=3,
slowperiod=10)
resDF['ADX'] = ta.ADX(df['max_price'].values, df['min_price'].values,
df['price'].values)
resDF['ADXR'] = ta.ADXR(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
resDF['APO'] = ta.APO(df['price'].values,
fastperiod=12,
slowperiod=26,
matype=0)
resDF['aroondown'], resDF['aroonup'] = ta.AROON(df['max_price'].values,
df['min_price'].values,
timeperiod=14)
resDF['AROONOSC'] = ta.AROONOSC(df['max_price'].values,
df['min_price'].values,
timeperiod=14)
resDF['ATR'] = ta.ATR(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
resDF['AVGPRICE'] = ta.AVGPRICE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values, df['price'].values)
resDF['upperband'], resDF['middleband'], resDF['lowerband'] = ta.BBANDS(
df['price'].values, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
resDF['BETA'] = ta.BETA(df['max_price'].values,
df['min_price'].values,
timeperiod=5)
resDF['BOP'] = ta.BOP(df['price_today_open'].values,
df['max_price'].values, df['min_price'].values,
df['price'].values)
resDF['CCI'] = ta.CCI(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=10)[-1]
# 函数名：CDL2CROWS名称：Two Crows 两只乌鸦
# 简介：三日K线模式，第一天长阳，第二天高开收阴，第三天再次高开继续收阴，收盘比前一日收盘价低，预示股价下跌。
# 例子：integer = CDL2CROWS(open, high, low, close)
resDF['CDL2CROWS'] = ta.CDL2CROWS(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDL3BLACKCROWS名称：Three Black Crows 三只乌鸦
# 简介：三日K线模式，连续三根阴线，每日收盘价都下跌且接近最低价，每日开盘价都在上根K线实体内，预示股价下跌。
# 例子：integer = CD3BLACKCROWS(open, high, low, close)
resDF['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDL3INSIDE名称： Three Inside Up/Down 三内部上涨和下跌
# 简介：三日K线模式，母子信号+长K线，以三内部上涨为例，K线为阴阳阳，第三天收盘价高于第一天开盘价，第二天K线在第一天K线内部，预示着股价上涨。
# 例子：integer = CDL3INSIDE(open, high, low, close)
resDF['CDL3INSIDE'] = ta.CDL3INSIDE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDL3LINESTRIKE名称： Three-Line Strike 三线打击
# 简介：四日K线模式，前三根阳线，每日收盘价都比前一日高，开盘价在前一日实体内，第四日市场高开，收盘价低于第一日开盘价，预示股价下跌。
# 例子：integer = CDL3LINESTRIKE(open, high, low, close)
resDF['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDL3OUTSIDE名称：Three Outside Up/Down 三外部上涨和下跌
# 简介：三日K线模式，与三内部上涨和下跌类似，K线为阴阳阳，但第一日与第二日的K线形态相反，以三外部上涨为例，第一日K线在第二日K线内部，预示着股价上涨。
# 例子：integer = CDL3OUTSIDE(open, high, low, close)
resDF['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDL3STARSINSOUTH名称：Three Stars In The South 南方三星
# 简介：三日K线模式，与大敌当前相反，三日K线皆阴，第一日有长下影线，第二日与第一日类似，K线整体小于第一日，第三日无下影线实体信号，成交价格都在第一日振幅之内，预示下跌趋势反转，股价上升。
# 例子：integer = CDL3STARSINSOUTH(open, high, low, close)
resDF['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDL3WHITESOLDIERS名称：Three Advancing White Soldiers 三个白兵
# 简介：三日K线模式，三日K线皆阳，每日收盘价变高且接近最高价，开盘价在前一日实体上半部，预示股价上升。
# 例子：integer = CDL3WHITESOLDIERS(open, high, low, close)
resDF['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLABANDONEDBABY名称：Abandoned Baby 弃婴
# 简介：三日K线模式，第二日价格跳空且收十字星（开盘价与收盘价接近，最高价最低价相差不大），预示趋势反转，发生在顶部下跌，底部上涨。
# 例子：integer = CDLABANDONEDBABY(open, high, low, close, penetration=0)
resDF['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY(
df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLADVANCEBLOCK名称：Advance Block 大敌当前
# 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。
# 例子：integer = CDLADVANCEBLOCK(open, high, low, close)
resDF['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLBELTHOLD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。
# 例子：integer = CDLBELTHOLD(open, high, low, close)
resDF['CDLBELTHOLD'] = ta.CDLBELTHOLD(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLBREAKAWAY名称：Breakaway 脱离
# 简介：五日K线模式，以看涨脱离为例，下跌趋势中，第一日长阴线，第二日跳空阴线，延续趋势开始震荡，第五日长阳线，收盘价在第一天收盘价与第二天开盘价之间，预示价格上涨。
# 例子：integer = CDLBREAKAWAY(open, high, low, close)
resDF['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名： CDLCLOSINGMARUBOZU 名称：Closing Marubozu 收盘缺影线
# 简介：一日K线模式，以阳线为例，最低价低于开盘价，收盘价等于最高价，预示着趋势持续。
# 例子：integer = CDLCLOSINGMARUBOZU(open, high, low, close)
resDF['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLCONCEALBABYSWALL名称： Concealing Baby Swallow 藏婴吞没
# 简介：四日K线模式，下跌趋势中，前两日阴线无影线，第二日开盘、收盘价皆低于第二日，第三日倒锤头，第四日开盘价高于前一日最高价，收盘价低于前一日最低价，预示着底部反转。
# 例子：integer = CDLCONCEALBABYSWALL(open, high, low, close)
resDF['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLCOUNTERATTACK
# 名称：Counterattack 反击线
# 简介：二日K线模式，与分离线类似。
# 例子：integer = CDLCOUNTERATTACK(open, high, low, close)
resDF['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLDARKCLOUDCOVER名称：Dark Cloud Cover 乌云压顶
# 简介：二日K线模式，第一日长阳，第二日开盘价高于前一日最高价，收盘价处于前一日实体中部以下，预示着股价下跌。
# 例子：integer = CDLDARKCLOUDCOVER(open, high, low, close, penetration=0)
resDF['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(
df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名： CDLDOJI
# 名称：Doji 十字
# 简介：一日K线模式，开盘价与收盘价基本相同。
# 例子：integer = CDLDOJI(open, high, low, close)
resDF['CDLDOJI'] = ta.CDLDOJI(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名： CDLDOJISTAR
# 名称：Doji Star 十字星
# 简介：一日K线模式，开盘价与收盘价基本相同，上下影线不会很长，预示着当前趋势反转。
# 例子：integer = CDLDOJISTAR(open, high, low, close)
resDF['CDLDOJISTAR'] = ta.CDLDOJISTAR(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLDRAGONFLYDOJI名称：Dragonfly Doji 蜻蜓十字/T形十字
# 简介：一日K线模式，开盘后价格一路走低，之后收复，收盘价与开盘价相同，预示趋势反转。
# 例子：integer = CDLDRAGONFLYDOJI(open, high, low, close)
resDF['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLENGULFING名称：Engulfing Pattern 吞噬模式
# 简介：两日K线模式，分多头吞噬和空头吞噬，以多头吞噬为例，第一日为阴线，第二日阳线，第一日的开盘价和收盘价在第二日开盘价收盘价之内，但不能完全相同。
# 例子：integer = CDLENGULFING(open, high, low, close)
resDF['CDLENGULFING'] = ta.CDLENGULFING(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLEVENINGDOJISTAR名称：Evening Doji Star 十字暮星
# 简介：三日K线模式，基本模式为暮星，第二日收盘价和开盘价相同，预示顶部反转。
# 例子：integer = CDLEVENINGDOJISTAR(open, high, low, close, penetration=0)
resDF['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(
df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLEVENINGSTAR名称：Evening Star 暮星
# 简介：三日K线模式，与晨星相反，上升趋势中，第一日阳线，第二日价格振幅较小，第三日阴线，预示顶部反转。
# 例子：integer = CDLEVENINGSTAR(open, high, low, close, penetration=0)
resDF['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLGAPSIDESIDEWHITE名称：Up/Down-gap side-by-side white lines 向上/下跳空并列阳线
# 简介：二日K线模式，上升趋势向上跳空，下跌趋势向下跳空，第一日与第二日有相同开盘价，实体长度差不多，则趋势持续。
# 例子：integer = CDLGAPSIDESIDEWHITE(open, high, low, close)
resDF['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLGRAVESTONEDOJI名称：Gravestone Doji 墓碑十字/倒T十字
# 简介：一日K线模式，开盘价与收盘价相同，上影线长，无下影线，预示底部反转。
# 例子：integer = CDLGRAVESTONEDOJI(open, high, low, close)
resDF['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLHAMMER
# 名称：Hammer 锤头
# 简介：一日K线模式，实体较短，无上影线，下影线大于实体长度两倍，处于下跌趋势底部，预示反转。
# 例子：integer = CDLHAMMER(open, high, low, close)
resDF['CDLHAMMER'] = ta.CDLHAMMER(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHANGINGMAN
# 名称：Hanging Man 上吊线
# 简介：一日K线模式，形状与锤子类似，处于上升趋势的顶部，预示着趋势反转。
# 例子：integer = CDLHANGINGMAN(open, high, low, close)
resDF['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHARAMI名称：Harami Pattern 母子线
# 简介：二日K线模式，分多头母子与空头母子，两者相反，以多头母子为例，在下跌趋势中，第一日K线长阴，第二日开盘价收盘价在第一日价格振幅之内，为阳线，预示趋势反转，股价上升。
# 例子：integer = CDLHARAMI(open, high, low, close)
resDF['CDLHARAMI'] = ta.CDLHARAMI(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHARAMICROSS名称：Harami Cross Pattern 十字孕线
# 简介：二日K线模式，与母子县类似，若第二日K线是十字线，便称为十字孕线，预示着趋势反转。
# 例子：integer = CDLHARAMICROSS(open, high, low, close)
resDF['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHIGHWAVE
# 名称：High-Wave Candle 风高浪大线
# 简介：三日K线模式，具有极长的上/下影线与短的实体，预示着趋势反转。
# 例子：integer = CDLHIGHWAVE(open, high, low, close)
resDF['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHIKKAKE名称：Hikkake Pattern 陷阱
# 简介：三日K线模式，与母子类似，第二日价格在前一日实体范围内，第三日收盘价高于前两日，反转失败，趋势继续。
# 例子：integer = CDLHIKKAKE(open, high, low, close)
resDF['CDLHIKKAKE'] = ta.CDLHIKKAKE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHIKKAKEMOD名称：Modified Hikkake Pattern 修正陷阱
# 简介：三日K线模式，与陷阱类似，上升趋势中，第三日跳空高开；下跌趋势中，第三日跳空低开，反转失败，趋势继续。
# 例子：integer = CDLHIKKAKEMOD(open, high, low, close)
resDF['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHOMINGPIGEON名称：Homing Pigeon 家鸽
# 简介：二日K线模式，与母子线类似，不同的的是二日K线颜色相同，第二日最高价、最低价都在第一日实体之内，预示着趋势反转。
# 例子：integer = CDLHOMINGPIGEON(open, high, low, close)
resDF['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLIDENTICAL3CROWS名称：Identical Three Crows 三胞胎乌鸦
# 简介：三日K线模式，上涨趋势中，三日都为阴线，长度大致相等，每日开盘价等于前一日收盘价，收盘价接近当日最低价，预示价格下跌。
# 例子：integer = CDLIDENTICAL3CROWS(open, high, low, close)
resDF['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLINNECK名称：In-Neck Pattern 颈内线
# 简介：二日K线模式，下跌趋势中，第一日长阴线，第二日开盘价较低，收盘价略高于第一日收盘价，阳线，实体较短，预示着下跌继续。
# 例子：integer = CDLINNECK(open, high, low, close)
resDF['CDLINNECK'] = ta.CDLINNECK(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLINVERTEDHAMMER名称：Inverted Hammer 倒锤头
# 简介：一日K线模式，上影线较长，长度为实体2倍以上，无下影线，在下跌趋势底部，预示着趋势反转。
# 例子：integer = CDLINVERTEDHAMMER(open, high, low, close)
resDF['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLKICKING
# 名称：Kicking 反冲形态
# 简介：二日K线模式，与分离线类似，两日K线为秃线，颜色相反，存在跳空缺口。
# 例子：integer = CDLKICKING(open, high, low, close)
resDF['CDLKICKING'] = ta.CDLKICKING(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLKICKINGBYLENGTH名称：Kicking - bull/bear determined by the longer marubozu 由较长缺影线决定的反冲形态
# 简介：二日K线模式，与反冲形态类似，较长缺影线决定价格的涨跌。
# 例子：integer = CDLKICKINGBYLENGTH(open, high, low, close)
resDF['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLLADDERBOTTOM名称：Ladder Bottom 梯底
# 简介：五日K线模式，下跌趋势中，前三日阴线，开盘价与收盘价皆低于前一日开盘、收盘价，第四日倒锤头，第五日开盘价高于前一日开盘价，阳线，收盘价高于前几日价格振幅，预示着底部反转。
# 例子：integer = CDLLADDERBOTTOM(open, high, low, close)
resDF['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLLONGLEGGEDDOJI名称：Long Legged Doji 长脚十字
# 简介：一日K线模式，开盘价与收盘价相同居当日价格中部，上下影线长，表达市场不确定性。
# 例子：integer = CDLLONGLEGGEDDOJI(open, high, low, close)
resDF['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLLONGLINE
# 名称：Long Line Candle 长蜡烛
# 简介：一日K线模式，K线实体长，无上下影线。
# 例子：integer = CDLLONGLINE(open, high, low, close)
resDF['CDLLONGLINE'] = ta.CDLLONGLINE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLMARUBOZU
# 名称：Marubozu 光头光脚/缺影线
# 简介：一日K线模式，上下两头都没有影线的实体，阴线预示着熊市持续或者牛市反转，阳线相反。
# 例子：integer = CDLMARUBOZU(open, high, low, close)
resDF['CDLMARUBOZU'] = ta.CDLMARUBOZU(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLMATCHINGLOW名称：Matching Low 相同低价
# 简介：二日K线模式，下跌趋势中，第一日长阴线，第二日阴线，收盘价与前一日相同，预示底部确认，该价格为支撑位。
# 例子：integer = CDLMATCHINGLOW(open, high, low, close)
resDF['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLMATHOLD名称：Mat Hold 铺垫
# 简介：五日K线模式，上涨趋势中，第一日阳线，第二日跳空高开影线，第三、四日短实体影线，第五日阳线，收盘价高于前四日，预示趋势持续。
# 例子：integer = CDLMATHOLD(open, high, low, close, penetration=0)
resDF['CDLMATHOLD'] = ta.CDLMATHOLD(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLMORNINGDOJISTAR名称：Morning Doji Star 十字晨星
# 简介：三日K线模式，基本模式为晨星，第二日K线为十字星，预示底部反转。
# 例子：integer = CDLMORNINGDOJISTAR(open, high, low, close, penetration=0)
resDF['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(
df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLMORNINGSTAR名称：Morning Star 晨星
# 简介：三日K线模式，下跌趋势，第一日阴线，第二日价格振幅较小，第三天阳线，预示底部反转。
# 例子：integer = CDLMORNINGSTAR(open, high, low, close, penetration=0)
resDF['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLONNECK名称：On-Neck Pattern 颈上线
# 简介：二日K线模式，下跌趋势中，第一日长阴线，第二日开盘价较低，收盘价与前一日最低价相同，阳线，实体较短，预示着延续下跌趋势。
# 例子：integer = CDLONNECK(open, high, low, close)
resDF['CDLONNECK'] = ta.CDLONNECK(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLPIERCING名称：Piercing Pattern 刺透形态
# 简介：两日K线模式，下跌趋势中，第一日阴线，第二日收盘价低于前一日最低价，收盘价处在第一日实体上部，预示着底部反转。
# 例子：integer = CDLPIERCING(open, high, low, close)
resDF['CDLPIERCING'] = ta.CDLPIERCING(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLRICKSHAWMAN名称：Rickshaw Man 黄包车夫
# 简介：一日K线模式，与长腿十字线类似，若实体正好处于价格振幅中点，称为黄包车夫。
# 例子：integer = CDLRICKSHAWMAN(open, high, low, close)
resDF['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLRISEFALL3METHODS名称：Rising/Falling Three Methods 上升/下降三法
# 简介： 五日K线模式，以上升三法为例，上涨趋势中，第一日长阳线，中间三日价格在第一日范围内小幅震荡，第五日长阳线，收盘价高于第一日收盘价，预示股价上升。
# 例子：integer = CDLRISEFALL3METHODS(open, high, low, close)
resDF['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLSEPARATINGLINES名称：Separating Lines 分离线
# 简介：二日K线模式，上涨趋势中，第一日阴线，第二日阳线，第二日开盘价与第一日相同且为最低价，预示着趋势继续。
# 例子：integer = CDLSEPARATINGLINES(open, high, low, close)
resDF['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLSHOOTINGSTAR名称：Shooting Star 射击之星
# 简介：一日K线模式，上影线至少为实体长度两倍，没有下影线，预示着股价下跌
# 例子：integer = CDLSHOOTINGSTAR(open, high, low, close)
resDF['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLSHORTLINE
# 名称：Short Line Candle 短蜡烛
# 简介：一日K线模式，实体短，无上下影线。
# 例子：integer = CDLSHORTLINE(open, high, low, close)
resDF['CDLSHORTLINE'] = ta.CDLSHORTLINE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLSPINNINGTOP
# 名称：Spinning Top 纺锤
# 简介：一日K线，实体小。
# 例子：integer = CDLSPINNINGTOP(open, high, low, close)
resDF['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLSTALLEDPATTERN名称：Stalled Pattern 停顿形态
# 简介：三日K线模式，上涨趋势中，第二日长阳线，第三日开盘于前一日收盘价附近，短阳线，预示着上涨结束。
# 例子：integer = CDLSTALLEDPATTERN(open, high, low, close)
resDF['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLSTICKSANDWICH名称：Stick Sandwich 条形三明治
# 简介：三日K线模式，第一日长阴线，第二日阳线，开盘价高于前一日收盘价，第三日开盘价高于前两日最高价，收盘价于第一日收盘价相同。
# 例子：integer = CDLSTICKSANDWICH(open, high, low, close)
resDF['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLTAKURI名称：Takuri (Dragonfly Doji with very long lower shadow) 探水竿
# 简介：一日K线模式，大致与蜻蜓十字相同，下影线长度长。
# 例子：integer = CDLTAKURI(open, high, low, close)
resDF['CDLTAKURI'] = ta.CDLTAKURI(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLTASUKIGAP名称：Tasuki Gap 跳空并列阴阳线
# 简介：三日K线模式，分上涨和下跌，以上升为例，前两日阳线，第二日跳空，第三日阴线，收盘价于缺口中，上升趋势持续。
# 例子：integer = CDLTASUKIGAP(open, high, low, close)
resDF['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLTHRUSTING名称：Thrusting Pattern 插入
# 简介：二日K线模式，与颈上线类似，下跌趋势中，第一日长阴线，第二日开盘价跳空，收盘价略低于前一日实体中部，与颈上线相比实体较长，预示着趋势持续。
# 例子：integer = CDLTHRUSTING(open, high, low, close)
resDF['CDLTHRUSTING'] = ta.CDLTHRUSTING(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLTRISTAR
# 名称：Tristar Pattern 三星
# 简介：三日K线模式，由三个十字组成，第二日十字必须高于或者低于第一日和第三日，预示着反转。
# 例子：integer = CDLTRISTAR(open, high, low, close)
resDF['CDLTRISTAR'] = ta.CDLTRISTAR(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLUNIQUE3RIVER名称：Unique 3 River 奇特三河床
# 简介：三日K线模式，下跌趋势中，第一日长阴线，第二日为锤头，最低价创新低，第三日开盘价低于第二日收盘价，收阳线，收盘价不高于第二日收盘价，预示着反转，第二日下影线越长可能性越大。
# 例子：integer = CDLUNIQUE3RIVER(open, high, low, close)
resDF['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLUPSIDEGAP2CROWS名称：Upside Gap Two Crows 向上跳空的两只乌鸦
# 简介：三日K线模式，第一日阳线，第二日跳空以高于第一日最高价开盘，收阴线，第三日开盘价高于第二日，收阴线，与第一日比仍有缺口。
# 例子：integer = CDLUPSIDEGAP2CROWS(open, high, low, close)
resDF['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLXSIDEGAP3METHODS名称：Upside/Downside Gap Three Methods 上升/下降跳空三法
# 简介：五日K线模式，以上升跳空三法为例，上涨趋势中，第一日长阳线，第二日短阳线，第三日跳空阳线，第四日阴线，开盘价与收盘价于前两日实体内，第五日长阳线，收盘价高于第一日收盘价，预示股价上升。
# 例子：integer = CDLXSIDEGAP3METHODS(open, high, low, close)
resDF['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
resDF['CMO'] = ta.CMO(df['price'].values, timeperiod=14)
resDF['CORREL'] = ta.CORREL(df['max_price'].values,
df['min_price'].values,
timeperiod=30)
resDF['DEMA'] = ta.DEMA(df['price'].values, timeperiod=30)
resDF['DX'] = ta.DX(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
resDF['EMA'] = ta.EMA(df['price'].values, timeperiod=30)
resDF['HT_DCPERIOD'] = ta.HT_DCPERIOD(df['price'].values)
resDF['HT_DCPHASE'] = ta.HT_DCPHASE(df['price'].values)
resDF['inphase'], resDF['quadrature'] = ta.HT_PHASOR(df['price'].values)
resDF['sine'], resDF['leadsine'] = ta.HT_SINE(df['price'].values)
resDF['HT_TRENDLINE'] = ta.HT_TRENDLINE(df['price'].values)
resDF['HT_TRENDMODE'] = ta.HT_TRENDMODE(df['price'].values)
resDF['KAMA'] = ta.KAMA(df['price'].values, timeperiod=30)
resDF['LINEARREG'] = ta.LINEARREG(df['price'].values, timeperiod=14)
resDF['LINEARREG_ANGLE'] = ta.LINEARREG_ANGLE(df['price'].values,
timeperiod=14)
resDF['LINEARREG_INTERCEPT'] = ta.LINEARREG_INTERCEPT(df['price'].values,
timeperiod=14)
resDF['LINEARREG_SLOPE'] = ta.LINEARREG_SLOPE(df['price'].values,
timeperiod=14)
resDF['MA'] = ta.MA(df['price'].values, timeperiod=30, matype=0)
resDF['macd'], resDF['macdsignal'], resDF['macdhist'] = ta.MACD(
df['price'].values, fastperiod=12, slowperiod=26, signalperiod=9)
resDF['macd'], resDF['macdsignal'], resDF['macdhist'] = ta.MACDEXT(
df['price'].values,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
resDF['macd'], resDF['macdsignal'], resDF['macdhist'] = ta.MACDFIX(
df['price'].values, signalperiod=9)
#resDF['mama'], resDF['fama'] = ta.MAMA (df['price'].values, fastlimit=0, slowlimit=0)
resDF['MAX'] = ta.MAX(df['price'].values, timeperiod=30)
resDF['MAXINDEX'] = ta.MAXINDEX(df['price'].values, timeperiod=30)
resDF['MEDPRICE'] = ta.MEDPRICE(df['max_price'].values,
df['min_price'].values)
resDF['MFI'] = ta.MFI(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
resDF['MIDPOINT'] = ta.MIDPOINT(df['price'].values, timeperiod=14)
resDF['MIDPRICE'] = ta.MIDPRICE(df['max_price'].values,
df['min_price'].values,
timeperiod=14)
resDF['MIN'] = ta.MIN(df['price'].values, timeperiod=30)
resDF['MININDEX'] = ta.MININDEX(df['price'].values, timeperiod=30)
resDF['min'], resDF['max'] = ta.MINMAX(df['price'].values, timeperiod=30)
resDF['minidx'], resDF['maxidx'] = ta.MINMAXINDEX(df['price'].values,
timeperiod=30)
resDF['MINUS_DI'] = ta.MINUS_DI(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
resDF['MINUS_DM'] = ta.MINUS_DM(df['max_price'].values,
df['min_price'].values,
timeperiod=14)
resDF['MOM'] = ta.MOM(df['max_price'].values, timeperiod=10)
resDF['NATR'] = ta.NATR(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
# 函数名：OBV 名称：On Balance Volume 能量潮
# 简介：Joe Granville提出，通过统计成交量变动的趋势推测股价趋势计算公式：以某日为基期，逐日累计每日上市股票总成交量，若隔日指数或股票上涨，则基期OBV加上本日成交量为本日OBV。隔日指数或股票下跌，则基期OBV减去本日成交量为本日OBV
# 研判：1、以“N”字型为波动单位，一浪高于一浪称“上升潮”，下跌称“跌潮”；上升潮买进，跌潮卖出
# 2、须配合K线图走势
# 3、用多空比率净额法进行修正，但不知TA-Lib采用哪种方法
# 多空比率净额= [（收盘价－最低价）－（最高价-收盘价）] ÷（ 最高价－最低价）×成交量
# 例子：real = OBV(close, volume)
resDF['OBV'] = ta.OBV(df['price'].values, df['vol'].values)
# resDF['PLUS_DI'] = ta.PLUS_DI
# resDF['PLUS_DM'] = ta.PLUS_DM
resDF['PPO'] = ta.PPO(df['price'].values,
fastperiod=12,
slowperiod=26,
matype=0)
resDF['ROC'] = ta.ROC(df['price'].values, timeperiod=10)
resDF['ROCP'] = ta.ROCP(df['price'].values, timeperiod=10)
resDF['ROCR'] = ta.ROCR(df['price'].values, timeperiod=10)
resDF['ROCR100'] = ta.ROCR100(df['price'].values, timeperiod=10)
resDF['RSI'] = ta.RSI(df['price'].values, timeperiod=14)
resDF['SAR'] = ta.SAR(df['max_price'].values,
df['min_price'].values,
acceleration=0,
maximum=0)
resDF['SAREXT'] = ta.SAREXT(df['max_price'].values,
df['min_price'].values,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
resDF['SMA'] = ta.SMA(df['price'].values, timeperiod=30)
resDF['STDDEV'] = ta.STDDEV(df['price'].values, timeperiod=5, nbdev=1)
# resDF['STOCH'] = ta.STOCH
# resDF['STOCHF'] = ta.STOCHF
resDF['fastk'], resDF['fastd'] = ta.STOCHRSI(df['price'].values,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
resDF['SUM'] = ta.SUM(df['price'].values, timeperiod=30)
resDF['T3'] = ta.T3(df['price'].values, timeperiod=5, vfactor=0)
resDF['TEMA'] = ta.TEMA(df['price'].values, timeperiod=30)
resDF['TRANGE'] = ta.TRANGE(df['max_price'].values, df['min_price'].values,
df['price'].values)
resDF['TRIMA'] = ta.TRIMA(df['price'].values, timeperiod=30)
resDF['TRIX'] = ta.TRIX(df['price'].values, timeperiod=30)
resDF['TSF'] = ta.TSF(df['price'].values, timeperiod=14)
resDF['TYPPRICE'] = ta.TYPPRICE(df['max_price'].values,
df['min_price'].values, df['price'].values)
# resDF['ULTOSC'] = ta.ULTOSC
resDF['VAR'] = ta.VAR(df['price'].values, timeperiod=5, nbdev=1)
resDF['WCLPRICE'] = ta.WCLPRICE(df['max_price'].values,
df['min_price'].values, df['price'].values)
# resDF['WILLR'] = ta.WILLR
resDF['WMA'] = ta.WMA(df['price'].values, timeperiod=30)
return resDF
close,
penetration=0)
df['CDLDOJI'] = talib.CDLDOJI(open, high, low, close)
df['CDLMORNINGDOJISTAR'] = talib.CDLMORNINGDOJISTAR(open,
high,
low,
close,
penetration=0)
df['CDLMORNINGSTAR'] = talib.CDLMORNINGSTAR(open,
high,
low,
close,
penetration=0)
df['CDLONNECK'] = talib.CDLONNECK(open, high, low, close)
df['CDLPIERCING'] = talib.CDLPIERCING(open, high, low, close)
df['CDLRICKSHAWMAN'] = talib.CDLRICKSHAWMAN(open, high, low, close)
df['CDLRISEFALL3METHODS'] = talib.CDLRISEFALL3METHODS(open, high, low, close)
df['CDLSEPARATINGLINES'] = talib.CDLSEPARATINGLINES(open, high, low, close)
df['CDLSHOOTINGSTAR'] = talib.CDLSHOOTINGSTAR(open, high, low, close)
df['CDLSHORTLINE'] = talib.CDLSHORTLINE(open, high, low, close)
df['CDLSPINNINGTOP'] = talib.CDLSPINNINGTOP(open, high, low, close)
df['CDLSTALLEDPATTERN'] = talib.CDLSTALLEDPATTERN(open, high, low, close)
df['CDLUPSIDEGAP2CROWS'] = talib.CDLUPSIDEGAP2CROWS(open, high, low, close)
df['CDLXSIDEGAP3METHODS'] = talib.CDLXSIDEGAP3METHODS(open, high, low, close)
df['CDLUNIQUE3RIVER'] = talib.CDLUNIQUE3RIVER(open, high, low, close)
df['ADOSC'] = talib.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)
def TA(S, s, c):
S = S.fillna(0)
ope = numpy.asfarray(S.Open)
high = numpy.asfarray(S.High)
low = numpy.asfarray(S.Low)
close = numpy.asfarray(S.Close)
volume = numpy.asfarray(S.Volume)
##ROI calculation
ROI = [(close[i + 1] - close[i]) / close[i] for i in range(len(close) - 1)]
ROI.append(0) #add zero value for last day
d = pandas.DataFrame(ROI, index=S.index, columns=['ROI'])
d.to_csv("C:\\Users\\...\\Documents\\Data_TA\\{0}\\ROI_{1}.csv".format(
s, c))
##Baselines
try:
bah.append((S.Close['2013-04-30'] - S.Close['2002-05-01']) /
S.Close['2002-05-01'])
sah.append((-S.Close['2013-04-30'] + S.Close['2002-05-01']) /
S.Close['2002-05-01'])
except:
bah.append((S.Close['2013-04-30'] - S.Close['2002-05-02']) /
S.Close['2002-05-02'])
sah.append((-S.Close['2013-04-30'] + S.Close['2002-05-02']) /
S.Close['2002-05-02'])
rp.append(
numpy.dot(numpy.random.uniform(-1, 1, len(S)), numpy.asfarray(ROI)))
##talib application
#overlap
BBANDS = ta.BBANDS(close)
DEMA = ta.DEMA(close)
EMA = ta.EMA(close)
HT_TRENDLINE = ta.HT_TRENDLINE(close)
KAMA = ta.KAMA(close)
MA = ta.MA(close)
MAMA = ta.MAMA(close)
MIDPOINT = ta.MIDPOINT(close)
MIDPRICE = ta.MIDPRICE(high, low)
SAR = ta.SAR(high, low)
SAREXT = ta.SAREXT(high, low)
SMA = ta.SMA(close)
T3 = ta.T3(close)
TEMA = ta.TEMA(close)
TRIMA = ta.TRIMA(close)
WMA = ta.WMA(close)
#momentum
ADX = ta.ADX(high, low, close)
ADXR = ta.ADXR(high, low, close)
APO = ta.APO(close)
AROON = ta.AROON(high, low)
AROONOSC = ta.AROONOSC(high, low)
BOP = ta.BOP(ope, high, low, close)
CCI = ta.CCI(high, low, close)
CMO = ta.CMO(close)
DX = ta.DX(high, low, close)
MACD = ta.MACD(close)
MACDEXT = ta.MACDEXT(close)
MFI = ta.MFI(high, low, close, volume)
MINUS_DI = ta.MINUS_DI(high, low, close)
MINUS_DM = ta.MINUS_DM(high, low)
MOM = ta.MOM(close)
PLUS_DI = ta.PLUS_DI(high, low, close)
PLUS_DM = ta.PLUS_DM(high, low)
PPO = ta.PPO(close)
ROC = ta.ROC(close)
ROCP = ta.ROCP(close)
ROCR = ta.ROCR(close)
RSI = ta.RSI(close)
STOCH = ta.STOCH(high, low, close)
STOCHF = ta.STOCHF(high, low, close)
STOCHRSI = ta.STOCHRSI(close)
TRIX = ta.TRIX(close)
ULTOSC = ta.ULTOSC(high, low, close)
WILLR = ta.WILLR(high, low, close)
#volume
AD = ta.AD(high, low, close, volume)
ADOSC = ta.ADOSC(high, low, close, volume)
OBV = ta.OBV(close, volume)
#cycle
HT_DCPERIOD = ta.HT_DCPERIOD(close)
HT_DCPHASE = ta.HT_DCPHASE(close)
HT_PHASOR = ta.HT_PHASOR(close)
HT_SINE = ta.HT_SINE(close)
HT_TRENDMODE = ta.HT_TRENDMODE(close)
#price
AVGPRICE = ta.AVGPRICE(ope, high, low, close)
MEDPRICE = ta.MEDPRICE(high, low)
TYPPRICE = ta.TYPPRICE(high, low, close)
WCLPRICE = ta.WCLPRICE(high, low, close)
#volatility
ATR = ta.ATR(high, low, close)
NATR = ta.NATR(high, low, close)
TRANGE = ta.TRANGE(high, low, close)
#pattern
CDL2CROWS = ta.CDL2CROWS(ope, high, low, close)
CDL3BLACKCROWS = ta.CDL3BLACKCROWS(ope, high, low, close)
CDL3INSIDE = ta.CDL3INSIDE(ope, high, low, close)
CDL3LINESTRIKE = ta.CDL3LINESTRIKE(ope, high, low, close)
CDL3OUTSIDE = ta.CDL3OUTSIDE(ope, high, low, close)
CDL3STARSINSOUTH = ta.CDL3STARSINSOUTH(ope, high, low, close)
CDL3WHITESOLDIERS = ta.CDL3WHITESOLDIERS(ope, high, low, close)
CDLABANDONEDBABY = ta.CDLABANDONEDBABY(ope, high, low, close)
CDLADVANCEBLOCK = ta.CDLADVANCEBLOCK(ope, high, low, close)
CDLBELTHOLD = ta.CDLBELTHOLD(ope, high, low, close)
CDLBREAKAWAY = ta.CDLBREAKAWAY(ope, high, low, close)
CDLCLOSINGMARUBOZU = ta.CDLCLOSINGMARUBOZU(ope, high, low, close)
CDLCONCEALBABYSWALL = ta.CDLCONCEALBABYSWALL(ope, high, low, close)
CDLCOUNTERATTACK = ta.CDLCOUNTERATTACK(ope, high, low, close)
CDLDARKCLOUDCOVER = ta.CDLDARKCLOUDCOVER(ope, high, low, close)
CDLDOJI = ta.CDLDOJI(ope, high, low, close)
CDLDOJISTAR = ta.CDLDOJISTAR(ope, high, low, close)
CDLDRAGONFLYDOJI = ta.CDLDRAGONFLYDOJI(ope, high, low, close)
CDLENGULFING = ta.CDLENGULFING(ope, high, low, close)
CDLEVENINGDOJISTAR = ta.CDLEVENINGDOJISTAR(ope, high, low, close)
CDLEVENINGSTAR = ta.CDLEVENINGSTAR(ope, high, low, close)
CDLGAPSIDESIDEWHITE = ta.CDLGAPSIDESIDEWHITE(ope, high, low, close)
CDLGRAVESTONEDOJI = ta.CDLGRAVESTONEDOJI(ope, high, low, close)
CDLHAMMER = ta.CDLHAMMER(ope, high, low, close)
CDLHANGINGMAN = ta.CDLHANGINGMAN(ope, high, low, close)
CDLHARAMI = ta.CDLHARAMI(ope, high, low, close)
CDLHARAMICROSS = ta.CDLHARAMICROSS(ope, high, low, close)
CDLHIGHWAVE = ta.CDLHIGHWAVE(ope, high, low, close)
CDLHIKKAKE = ta.CDLHIKKAKE(ope, high, low, close)
CDLHIKKAKEMOD = ta.CDLHIKKAKEMOD(ope, high, low, close)
CDLHOMINGPIGEON = ta.CDLHOMINGPIGEON(ope, high, low, close)
CDLIDENTICAL3CROWS = ta.CDLIDENTICAL3CROWS(ope, high, low, close)
CDLINNECK = ta.CDLINNECK(ope, high, low, close)
CDLINVERTEDHAMMER = ta.CDLINVERTEDHAMMER(ope, high, low, close)
CDLKICKING = ta.CDLKICKING(ope, high, low, close)
CDLKICKINGBYLENGTH = ta.CDLKICKINGBYLENGTH(ope, high, low, close)
CDLLADDERBOTTOM = ta.CDLLADDERBOTTOM(ope, high, low, close)
CDLLONGLEGGEDDOJI = ta.CDLLONGLEGGEDDOJI(ope, high, low, close)
CDLLONGLINE = ta.CDLLONGLINE(ope, high, low, close)
CDLMARUBOZU = ta.CDLMARUBOZU(ope, high, low, close)
CDLMATCHINGLOW = ta.CDLMATCHINGLOW(ope, high, low, close)
CDLMATHOLD = ta.CDLMATHOLD(ope, high, low, close)
CDLMORNINGDOJISTAR = ta.CDLMORNINGDOJISTAR(ope, high, low, close)
CDLMORNINGSTAR = ta.CDLMORNINGSTAR(ope, high, low, close)
CDLONNECK = ta.CDLONNECK(ope, high, low, close)
CDLPIERCING = ta.CDLPIERCING(ope, high, low, close)
CDLRICKSHAWMAN = ta.CDLRICKSHAWMAN(ope, high, low, close)
CDLRISEFALL3METHODS = ta.CDLRISEFALL3METHODS(ope, high, low, close)
CDLSEPARATINGLINES = ta.CDLSEPARATINGLINES(ope, high, low, close)
CDLSHOOTINGSTAR = ta.CDLSHOOTINGSTAR(ope, high, low, close)
CDLSHORTLINE = ta.CDLSHORTLINE(ope, high, low, close)
CDLSPINNINGTOP = ta.CDLSPINNINGTOP(ope, high, low, close)
CDLSTALLEDPATTERN = ta.CDLSTALLEDPATTERN(ope, high, low, close)
CDLSTICKSANDWICH = ta.CDLSTICKSANDWICH(ope, high, low, close)
CDLTAKURI = ta.CDLTAKURI(ope, high, low, close)
CDLTASUKIGAP = ta.CDLTASUKIGAP(ope, high, low, close)
CDLTHRUSTING = ta.CDLTHRUSTING(ope, high, low, close)
CDLTRISTAR = ta.CDLTRISTAR(ope, high, low, close)
CDLUNIQUE3RIVER = ta.CDLUNIQUE3RIVER(ope, high, low, close)
CDLUPSIDEGAP2CROWS = ta.CDLUPSIDEGAP2CROWS(ope, high, low, close)
CDLXSIDEGAP3METHODS = ta.CDLXSIDEGAP3METHODS(ope, high, low, close)
f = numpy.column_stack(
(ATR, NATR, TRANGE, HT_DCPERIOD, HT_DCPHASE, HT_PHASOR[0],
HT_PHASOR[1], HT_SINE[0], HT_SINE[1], HT_TRENDMODE, AVGPRICE,
MEDPRICE, TYPPRICE, WCLPRICE, ADX, ADXR, APO, AROON[0], AROON[1],
AROONOSC, BOP, CCI, CMO, DX, MACD[0], MACD[1], MACD[2], MACDEXT[0],
MACDEXT[1], MACDEXT[2], MFI, MINUS_DI, MINUS_DM, MOM, PLUS_DI,
PLUS_DM, PPO, ROC, ROCP, ROCR, RSI, STOCH[0], STOCH[1], STOCHF[0],
STOCHF[1], STOCHRSI[0], STOCHRSI[1], TRIX, ULTOSC, WILLR, CDL2CROWS,
CDL3BLACKCROWS, CDL3INSIDE, CDL3LINESTRIKE, CDL3OUTSIDE,
CDL3STARSINSOUTH, CDL3WHITESOLDIERS, CDLABANDONEDBABY,
CDLADVANCEBLOCK, CDLBELTHOLD, CDLBREAKAWAY, CDLCLOSINGMARUBOZU,
CDLCONCEALBABYSWALL, CDLCOUNTERATTACK, CDLDARKCLOUDCOVER, CDLDOJI,
CDLDOJISTAR, CDLDRAGONFLYDOJI, CDLENGULFING, CDLEVENINGDOJISTAR,
CDLEVENINGSTAR, CDLGAPSIDESIDEWHITE, CDLGRAVESTONEDOJI, CDLHAMMER,
CDLHANGINGMAN, CDLHARAMI, CDLHARAMICROSS, CDLHIGHWAVE, CDLHIKKAKE,
CDLHIKKAKEMOD, CDLHOMINGPIGEON, CDLIDENTICAL3CROWS, CDLINNECK,
CDLINVERTEDHAMMER, CDLKICKING, CDLKICKINGBYLENGTH, CDLLADDERBOTTOM,
CDLLONGLEGGEDDOJI, CDLLONGLINE, CDLMARUBOZU, CDLMATCHINGLOW,
CDLMATHOLD, CDLMORNINGDOJISTAR, CDLMORNINGSTAR, CDLONNECK,
CDLPIERCING, CDLRICKSHAWMAN, CDLRISEFALL3METHODS, CDLSEPARATINGLINES,
CDLSHOOTINGSTAR, CDLSHORTLINE, CDLSPINNINGTOP, CDLSTALLEDPATTERN,
CDLSTICKSANDWICH, CDLTAKURI, CDLTASUKIGAP, CDLTHRUSTING, CDLTRISTAR,
CDLUNIQUE3RIVER, CDLUPSIDEGAP2CROWS, CDLXSIDEGAP3METHODS, BBANDS[0],
BBANDS[1], BBANDS[2], DEMA, EMA, HT_TRENDLINE, KAMA, MA, MAMA[0],
MAMA[1], MIDPOINT, MIDPRICE, SAR, SAREXT, SMA, T3, TEMA, TRIMA, WMA,
AD, ADOSC, OBV))
h = numpy.apply_along_axis(nor, 0, f) # normalize columnwise
df = pandas.DataFrame(
h,
index=S.index,
columns=[
'ATR', 'NATR', 'TRANGE', 'HT_DCPERIOD', 'HT_DCPHASE',
'HT_PHASOR[0]', 'HT_PHASOR[1]', 'HT_SINE[0]', 'HT_SINE[1]',
'HT_TRENDMODE', 'AVGPRICE', 'MEDPRICE', 'TYPPRICE', 'WCLPRICE',
'ADX', 'ADXR', 'APO', 'AROON[0]', 'AROON[1]', 'AROONOSC', 'BOP',
'CCI', 'CMO', 'DX', 'MACD[0]', 'MACD[1]', 'MACD[2]', 'MACDEXT[0]',
'MACDEXT[1]', 'MACDEXT[2]', 'MFI', 'MINUS_DI', 'MINUS_DM', 'MOM',
'PLUS_DI', 'PLUS_DM', 'PPO', 'ROC', 'ROCP', 'ROCR', 'RSI',
'STOCH[0]', 'STOCH[1]', 'STOCHF[0]', 'STOCHF[1]', 'STOCHRSI[0]',
'STOCHRSI[1]', 'TRIX', 'ULTOSC', 'WILLR', 'CDL2CROWS',
'CDL3BLACKCROWS', 'CDL3INSIDE', 'CDL3LINESTRIKE', 'CDL3OUTSIDE',
'CDL3STARSINSOUTH', 'CDL3WHITESOLDIERS', 'CDLABANDONEDBABY',
'CDLADVANCEBLOCK', 'CDLBELTHOLD', 'CDLBREAKAWAY',
'CDLCLOSINGMARUBOZU', 'CDLCONCEALBABYSWALL', 'CDLCOUNTERATTACK',
'CDLDARKCLOUDCOVER', 'CDLDOJI', 'CDLDOJISTAR', 'CDLDRAGONFLYDOJI',
'CDLENGULFING', 'CDLEVENINGDOJISTAR', 'CDLEVENINGSTAR',
'CDLGAPSIDESIDEWHITE', 'CDLGRAVESTONEDOJI', 'CDLHAMMER',
'CDLHANGINGMAN', 'CDLHARAMI', 'CDLHARAMICROSS', 'CDLHIGHWAVE',
'CDLHIKKAKE', 'CDLHIKKAKEMOD', 'CDLHOMINGPIGEON',
'CDLIDENTICAL3CROWS', 'CDLINNECK', 'CDLINVERTEDHAMMER',
'CDLKICKING', 'CDLKICKINGBYLENGTH', 'CDLLADDERBOTTOM',
'CDLLONGLEGGEDDOJI', 'CDLLONGLINE', 'CDLMARUBOZU',
'CDLMATCHINGLOW', 'CDLMATHOLD', 'CDLMORNINGDOJISTAR',
'CDLMORNINGSTAR', 'CDLONNECK', 'CDLPIERCING', 'CDLRICKSHAWMAN',
'CDLRISEFALL3METHODS', 'CDLSEPARATINGLINES', 'CDLSHOOTINGSTAR',
'CDLSHORTLINE', 'CDLSPINNINGTOP', 'CDLSTALLEDPATTERN',
'CDLSTICKSANDWICH', 'CDLTAKURI', 'CDLTASUKIGAP', 'CDLTHRUSTING',
'CDLTRISTAR', 'CDLUNIQUE3RIVER', 'CDLUPSIDEGAP2CROWS',
'CDLXSIDEGAP3METHODS', 'BBANDS[0]', 'BBANDS[1]', 'BBANDS[2]',
'DEMA', 'EMA', 'HT_TRENDLINE', 'KAMA', 'MA', 'MAMA[0]', 'MAMA[1]',
'MIDPOINT', 'MIDPRICE', 'SAR', 'SAREXT', 'SMA', 'T3', 'TEMA',
'TRIMA', 'WMA', 'AD', 'ADOSC', 'OBV'
])
df.to_csv("C:\\Users\\...\\Documents\\Data_TA\\{0}\\{1}.csv".format(s, c))
def _extract_feature(candle, params):
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
# RSI
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'])
# STOCHF
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.T3)
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.T3)
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_fastd_period_l'],
fastd_matype=ta.MA_Type.T3)
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
# WILLR
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'])
# MACD
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.T3,
slowmatype=ta.MA_Type.T3,
signalmatype=ta.MA_Type.T3)
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.T3,
slowmatype=ta.MA_Type.T3,
signalmatype=ta.MA_Type.T3)
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
# ROCP
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
# ボリンジャーバンド
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.T3)
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.T3)
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.T3)
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
# 出来高
features['obv'] = ta.OBV(c, v)
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'])
# 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)
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)
return features_ext
def pattern_recognition(candles: np.ndarray, pattern_type: str, penetration: int = 0, sequential: bool = 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
"""
candles = slice_candles(candles, sequential)
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 pattern_recognition(df):
val = 0
# test_len = 5
dates = np.array(df.index)
open = np.array(df.open)
close = np.array(df.close)
high = np.array(df.high)
low = np.array(df.low)
# volume = np.array(df.volume)
tristar = talib.CDLTRISTAR(open, high, low, close)
Two_Crows = talib.CDL2CROWS(open, high, low, close)
Three_Black_Crows = talib.CDL3BLACKCROWS(open, high, low, close)
Three_Inside_Up_Down = talib.CDL3INSIDE(open, high, low, close)
Three_Line_Strike = talib.CDL3LINESTRIKE(open, high, low, close)
Three_Outside_Up_Down = talib.CDL3OUTSIDE(open, high, low, close)
Three_Stars_In_The_South = talib.CDL3STARSINSOUTH(open, high, low, close)
Three_Advancing_White_Soldiers = talib.CDL3WHITESOLDIERS(
open, high, low, close)
Abandoned_Baby = talib.CDLABANDONEDBABY(
open, high, low, close, penetration=0)
Advance_Block = talib.CDLADVANCEBLOCK(open, high, low, close)
Belt_hold = talib.CDLBELTHOLD(open, high, low, close)
Breakaway = talib.CDLBREAKAWAY(open, high, low, close)
Closing_Marubozu = talib.CDLCLOSINGMARUBOZU(open, high, low, close)
Concealing_Baby_Swallow = talib.CDLCONCEALBABYSWALL(open, high, low, close)
Counterattack = talib.CDLCOUNTERATTACK(open, high, low, close)
Dark_Cloud_Cover = talib.CDLDARKCLOUDCOVER(
open, high, low, close, penetration=0)
Doji = talib.CDLDOJI(open, high, low, close)
Doji_Star = talib.CDLDOJISTAR(open, high, low, close)
Dragonfly_Doji = talib.CDLDRAGONFLYDOJI(open, high, low, close)
Engulfing_Pattern = talib.CDLENGULFING(open, high, low, close)
Evening_Doji_Star = talib.CDLEVENINGDOJISTAR(
open, high, low, close, penetration=0)
Evening_Star = talib.CDLEVENINGSTAR(open, high, low, close, penetration=0)
Up_Down_gap_side_by_side_white_lines = talib.CDLGAPSIDESIDEWHITE(
open, high, low, close)
Gravestone_Doji = talib.CDLGRAVESTONEDOJI(open, high, low, close)
Hammer = talib.CDLHAMMER(open, high, low, close)
Hanging_Man = talib.CDLHANGINGMAN(open, high, low, close)
Harami_Pattern = talib.CDLHARAMI(open, high, low, close)
Harami_Cross_Pattern = talib.CDLHARAMICROSS(open, high, low, close)
High_Wave_Candle = talib.CDLHIGHWAVE(open, high, low, close)
Hikkake_Pattern = talib.CDLHIKKAKE(open, high, low, close)
Modified_Hikkake_Pattern = talib.CDLHIKKAKEMOD(open, high, low, close)
Homing_Pigeon = talib.CDLHOMINGPIGEON(open, high, low, close)
Identical_Three_Crows = talib.CDLIDENTICAL3CROWS(open, high, low, close)
In_Neck_Pattern = talib.CDLINNECK(open, high, low, close)
Inverted_Hammer = talib.CDLINVERTEDHAMMER(open, high, low, close)
Kicking = talib.CDLKICKING(open, high, low, close)
Kicking_bull_bear_determined_by_the_longer_marubozu = talib.CDLKICKINGBYLENGTH(
open, high, low, close)
Ladder_Bottom = talib.CDLLADDERBOTTOM(open, high, low, close)
Long_Legged_Doji = talib.CDLLONGLEGGEDDOJI(open, high, low, close)
Long_Line_Candle = talib.CDLLONGLINE(open, high, low, close)
Marubozu = talib.CDLMARUBOZU(open, high, low, close)
Matching_Low = talib.CDLMATCHINGLOW(open, high, low, close)
Mat_Hold = talib.CDLMATHOLD(open, high, low, close, penetration=0)
Morning_Doji_Star = talib.CDLMORNINGDOJISTAR(
open, high, low, close, penetration=0)
Morning_Star = talib.CDLMORNINGSTAR(open, high, low, close, penetration=0)
On_Neck_Pattern = talib.CDLONNECK(open, high, low, close)
Piercing_Pattern = talib.CDLPIERCING(open, high, low, close)
Rickshaw_Man = talib.CDLRICKSHAWMAN(open, high, low, close)
Rising_Falling_Three_Methods = talib.CDLRISEFALL3METHODS(
open, high, low, close)
Separating_Lines = talib.CDLSEPARATINGLINES(open, high, low, close)
Shooting_Star = talib.CDLSHOOTINGSTAR(open, high, low, close)
Short_Line_Candle = talib.CDLSHORTLINE(open, high, low, close)
Spinning_Top = talib.CDLSPINNINGTOP(open, high, low, close)
Stalled_Pattern = talib.CDLSTALLEDPATTERN(open, high, low, close)
Stick_Sandwich = talib.CDLSTICKSANDWICH(open, high, low, close)
Takuri_Dragonfly_Doji_with_very_long_lower_shadow = talib.CDLTAKURI(
open, high, low, close)
Tasuki_Gap = talib.CDLTASUKIGAP(open, high, low, close)
Thrusting_Pattern = talib.CDLTHRUSTING(open, high, low, close)
Tristar_Pattern = talib.CDLTRISTAR(open, high, low, close)
Unique_3_River = talib.CDLUNIQUE3RIVER(open, high, low, close)
Upside_Gap_Two_Crows = talib.CDLUPSIDEGAP2CROWS(open, high, low, close)
Upside_Downside_Gap_Three_Methods = talib.CDLXSIDEGAP3METHODS(
open, high, low, close)
pattern_arrays = [tristar, Two_Crows, Three_Black_Crows, Three_Inside_Up_Down, Three_Line_Strike, Three_Outside_Up_Down, Three_Stars_In_The_South, Three_Advancing_White_Soldiers, Abandoned_Baby, Advance_Block, Belt_hold, Breakaway, Closing_Marubozu, Concealing_Baby_Swallow, Counterattack, Dark_Cloud_Cover, Doji, Doji_Star, Dragonfly_Doji, Engulfing_Pattern, Evening_Doji_Star, Evening_Star, Up_Down_gap_side_by_side_white_lines, Gravestone_Doji, Hammer, Hanging_Man, Harami_Pattern, Harami_Cross_Pattern, High_Wave_Candle, Hikkake_Pattern, Modified_Hikkake_Pattern, Homing_Pigeon,
Identical_Three_Crows, In_Neck_Pattern, Inverted_Hammer, Kicking, Kicking_bull_bear_determined_by_the_longer_marubozu, Ladder_Bottom, Long_Legged_Doji, Long_Line_Candle, Marubozu, Matching_Low, Mat_Hold, Morning_Doji_Star, Morning_Star, On_Neck_Pattern, Piercing_Pattern, Rickshaw_Man, Rising_Falling_Three_Methods, Separating_Lines, Shooting_Star, Short_Line_Candle, Spinning_Top, Stalled_Pattern, Stick_Sandwich, Takuri_Dragonfly_Doji_with_very_long_lower_shadow, Tasuki_Gap, Thrusting_Pattern, Tristar_Pattern, Unique_3_River, Upside_Gap_Two_Crows, Upside_Downside_Gap_Three_Methods]
pattern_names = ['tristar', 'Two_Crows', 'Three_Black_Crows', 'Three_Inside_Up_Down', 'Three_Line_Strike', 'Three_Outside_Up_Down', 'Three_Stars_In_The_South', 'Three_Advancing_White_Soldiers', 'Abandoned_Baby', 'Advance_Block', 'Belt_hold', 'Breakaway', 'Closing_Marubozu', 'Concealing_Baby_Swallow', 'Counterattack', 'Dark_Cloud_Cover', 'Doji', 'Doji_Star', 'Dragonfly_Doji', 'Engulfing_Pattern', 'Evening_Doji_Star', 'Evening_Star', 'Up_Down_gap_side_by_side_white_lines', 'Gravestone_Doji', 'Hammer', 'Hanging_Man', 'Harami_Pattern', 'Harami_Cross_Pattern', 'High_Wave_Candle', 'Hikkake_Pattern', 'Modified_Hikkake_Pattern', 'Homing_Pigeon',
'Identical_Three_Crows', 'In_Neck_Pattern', 'Inverted_Hammer', 'Kicking', 'Kicking_bull_bear_determined_by_the_longer_marubozu', 'Ladder_Bottom', 'Long_Legged_Doji', 'Long_Line_Candle', 'Marubozu', 'Matching_Low', 'Mat_Hold', 'Morning_Doji_Star', 'Morning_Star', 'On_Neck_Pattern', 'Piercing_Pattern', 'Rickshaw_Man', 'Rising_Falling_Three_Methods', 'Separating_Lines', 'Shooting_Star', 'Short_Line_Candle', 'Spinning_Top', 'Stalled_Pattern', 'Stick_Sandwich', 'Takuri_Dragonfly_Doji_with_very_long_lower_shadow', 'Tasuki_Gap', 'Thrusting_Pattern', 'Tristar_Pattern', 'Unique_3_River', 'Upside_Gap_Two_Crows', 'Upside_Downside_Gap_Three_Methods']
for pattern_results in range(len(pattern_arrays)):
# print(f'Found {pattern_names[pattern_results]}')
# print(pattern_arrays[pattern_results])
for index in range(len(pattern_arrays[pattern_results])):
value = pattern_arrays[pattern_results][index]
val = val + value
# if value != 0:
# print(
# f'{pattern_arrays[pattern_results]} index - {index} value - {value}')
# print('-----------------------------------------------')
# print(f'Total val = {val}')
return val
def CDLRICKSHAWMAN(data, **kwargs):
_check_talib_presence()
popen, phigh, plow, pclose, pvolume = _extract_ohlc(data)
return talib.CDLRICKSHAWMAN(popen, phigh, plow, pclose, **kwargs)
def Candlesticks_Pattern(data):
data["Upside/Downside Gap Three Methods"]= ta.CDLXSIDEGAP3METHODS(data["Open"],data["High"],data["Low"],data["Close"])
data["Upside Gap Two Crows"]= ta.CDLUPSIDEGAP2CROWS(data["Open"],data["High"],data["Low"],data["Close"])
data["Unique 3 River"]= ta.CDLUNIQUE3RIVER(data["Open"],data["High"],data["Low"],data["Close"])
data["Tristar Pattern"] =ta.CDLTRISTAR (data["Open"],data["High"],data["Low"],data["Close"])
data["Thrusting Pattern"]= ta.CDLTHRUSTING(data["Open"],data["High"],data["Low"],data["Close"])
data["Tasuki Gap"]= ta.CDLTASUKIGAP(data["Open"],data["High"],data["Low"],data["Close"])
data["Takuri (Dragonfly Doji with very long lower shadow)"]= ta.CDLTAKURI(data["Open"],data["High"],data["Low"],data["Close"])
data["Stick Sandwich"]= ta.CDLSTICKSANDWICH(data["Open"],data["High"],data["Low"],data["Close"])
data["Stalled Pattern"]= ta.CDLSTALLEDPATTERN(data["Open"],data["High"],data["Low"],data["Close"])
data["Spinning Top"]= ta.CDLSPINNINGTOP(data["Open"],data["High"],data["Low"],data["Close"])
data["Short Line Candle"]= ta.CDLSHORTLINE(data["Open"],data["High"],data["Low"],data["Close"])
data["Shooting Star"]= ta.CDLSHOOTINGSTAR(data["Open"],data["High"],data["Low"],data["Close"])
data["Separating Lines"]= ta.CDLSEPARATINGLINES(data["Open"],data["High"],data["Low"],data["Close"])
data["Rising/Falling Three Methods"]= ta.CDLRISEFALL3METHODS(data["Open"],data["High"],data["Low"],data["Close"])
data["Rickshaw Man"]= ta.CDLRICKSHAWMAN(data["Open"],data["High"],data["Low"],data["Close"])
data["Piercing Pattern"]= ta.CDLPIERCING(data["Open"],data["High"],data["Low"],data["Close"])
data["On-Neck Pattern"]= ta.CDLONNECK(data["Open"],data["High"],data["Low"],data["Close"])
data["Morning Star"]= ta.CDLMORNINGSTAR(data["Open"],data["High"],data["Low"],data["Close"])
data["Morning Doji Star"]= ta.CDLMORNINGDOJISTAR(data["Open"],data["High"],data["Low"],data["Close"])
data["Mat Hold"]= ta.CDLMATHOLD(data["Open"],data["High"],data["Low"],data["Close"])
data["Matching Low"]= ta.CDLMATCHINGLOW(data["Open"],data["High"],data["Low"],data["Close"])
data["Marubozu"]= ta.CDLMARUBOZU(data["Open"],data["High"],data["Low"],data["Close"])
data["Long Line Candle"]= ta.CDLLONGLINE(data["Open"],data["High"],data["Low"],data["Close"])
data["Long Legged Doji"]= ta.CDLLONGLEGGEDDOJI(data["Open"],data["High"],data["Low"],data["Close"])
data["Ladder Bottom"]= ta.CDLLADDERBOTTOM(data["Open"],data["High"],data["Low"],data["Close"])
data["Kicking - bull/bear determined by the longer marubozu"]= ta.CDLKICKINGBYLENGTH(data["Open"],data["High"],data["Low"],data["Close"])
data["Kicking"]= ta.CDLKICKING(data["Open"],data["High"],data["Low"],data["Close"])
data["Inverted Hammer"]= ta.CDLINVERTEDHAMMER(data["Open"],data["High"],data["Low"],data["Close"])
data["Identical Three Crows"]= ta.CDLIDENTICAL3CROWS(data["Open"],data["High"],data["Low"],data["Close"])
data["Two Crows"]=ta.CDL2CROWS(data["Open"],data["High"],data["Low"],data["Close"])
data["Three Black Crows"]=ta.CDL3BLACKCROWS(data["Open"],data["High"],data["Low"],data["Close"])
data["Three Inside Up/Down"]=ta.CDL3INSIDE(data["Open"],data["High"],data["Low"],data["Close"])
data["Three-Line Strike"]=ta.CDL3LINESTRIKE(data["Open"],data["High"],data["Low"],data["Close"])
data["Three Outside Up/Down"]=ta.CDL3OUTSIDE(data["Open"],data["High"],data["Low"],data["Close"])
data["Three Stars In The South"]=ta.CDL3STARSINSOUTH(data["Open"],data["High"],data["Low"],data["Close"])
data["Three Advancing White Soldiers"]=ta.CDL3WHITESOLDIERS(data["Open"],data["High"],data["Low"],data["Close"])
data["Abandoned Baby"]=ta.CDLABANDONEDBABY(data["Open"],data["High"],data["Low"],data["Close"])
data["Advance Block"]=ta.CDLADVANCEBLOCK(data["Open"],data["High"],data["Low"],data["Close"])
data["Belt-hold"]=ta.CDLBELTHOLD(data["Open"],data["High"],data["Low"],data["Close"])
data["Breakaway"]=ta.CDLBREAKAWAY(data["Open"],data["High"],data["Low"],data["Close"])
data["Closing Marubozu"]=ta.CDLCLOSINGMARUBOZU(data["Open"],data["High"],data["Low"],data["Close"])
data["Concealing Baby Swallow"]=ta.CDLCONCEALBABYSWALL(data["Open"],data["High"],data["Low"],data["Close"])
data["Counterattack"]=ta.CDLCOUNTERATTACK(data["Open"],data["High"],data["Low"],data["Close"])
data["Dark Cloud Cover"]=ta.CDLDARKCLOUDCOVER(data["Open"],data["High"],data["Low"],data["Close"])
data["Doji"]=ta.CDLDOJI(data["Open"],data["High"],data["Low"],data["Close"])
data["Doji Star"]=ta.CDLDOJISTAR(data["Open"],data["High"],data["Low"],data["Close"])
data["Dragonfly Doji"]=ta.CDLDRAGONFLYDOJI(data["Open"],data["High"],data["Low"],data["Close"])
data["Engulfing Pattern"]=ta.CDLENGULFING(data["Open"],data["High"],data["Low"],data["Close"])
data["Evening Doji Star"]=ta.CDLEVENINGDOJISTAR(data["Open"],data["High"],data["Low"],data["Close"])
data["Evening Star"]=ta.CDLEVENINGSTAR(data["Open"],data["High"],data["Low"],data["Close"])
data["Up/Down-gap side-by-side white lines"]=ta.CDLGAPSIDESIDEWHITE(data["Open"],data["High"],data["Low"],data["Close"])
data["Gravestone Doji"]=ta.CDLGRAVESTONEDOJI(data["Open"],data["High"],data["Low"],data["Close"])
data["Hammer"]=ta.CDLHAMMER(data["Open"],data["High"],data["Low"],data["Close"])
data["Hanging Man"]=ta.CDLHANGINGMAN(data["Open"],data["High"],data["Low"],data["Close"])
data["Harami Pattern"]=ta.CDLHARAMI(data["Open"],data["High"],data["Low"],data["Close"])
data["Harami Cross Pattern"]=ta.CDLHARAMICROSS(data["Open"],data["High"],data["Low"],data["Close"])
data["High-Wave Candle"]=ta.CDLHIGHWAVE(data["Open"],data["High"],data["Low"],data["Close"])
data["Hikkake Pattern"]=ta.CDLHIKKAKE(data["Open"],data["High"],data["Low"],data["Close"])
data["Modified Hikkake Pattern"]=ta.CDLHIKKAKEMOD(data["Open"],data["High"],data["Low"],data["Close"])
data["Homing Pigeon"]=ta.CDLHOMINGPIGEON(data["Open"],data["High"],data["Low"],data["Close"])
return(data)
def CDLRICKSHAWMAN(self):
integer = talib.CDLRICKSHAWMAN(self.open, self.high, self.low, self.close)
return integer
def match(open: np.ndarray, high: np.ndarray, low: np.ndarray,
close: np.ndarray, volumn: np.ndarray) -> ['PattrnResult']:
if (len(open) < 20):
raise RuntimeError("len must >= 20")
rets = []
##61个形态识别模式
KPattern.__checkIfAdd(rets, talib.CDL2CROWS(open, high, low, close),
"CDL2CROWS")
KPattern.__checkIfAdd(rets,
talib.CDL3BLACKCROWS(open, high, low,
close), "CDL3BLACKCROWS")
KPattern.__checkIfAdd(rets, talib.CDL3INSIDE(open, high, low, close),
"CDL3INSIDE")
KPattern.__checkIfAdd(rets,
talib.CDL3LINESTRIKE(open, high, low,
close), "CDL3LINESTRIKE")
KPattern.__checkIfAdd(rets, talib.CDL3OUTSIDE(open, high, low, close),
"CDL3OUTSIDE")
KPattern.__checkIfAdd(rets,
talib.CDL3STARSINSOUTH(open, high, low, close),
"CDL3STARSINSOUTH")
KPattern.__checkIfAdd(rets,
talib.CDL3WHITESOLDIERS(open, high, low, close),
"CDL3WHITESOLDIERS")
KPattern.__checkIfAdd(
rets, talib.CDLABANDONEDBABY(open, high, low, close,
penetration=0), "CDLABANDONEDBABY")
KPattern.__checkIfAdd(rets,
talib.CDLADVANCEBLOCK(open, high, low, close),
"CDLADVANCEBLOCK")
KPattern.__checkIfAdd(rets, talib.CDLBELTHOLD(open, high, low, close),
"CDLBELTHOLD")
KPattern.__checkIfAdd(rets, talib.CDLBREAKAWAY(open, high, low, close),
"CDLBREAKAWAY")
KPattern.__checkIfAdd(rets,
talib.CDLCLOSINGMARUBOZU(open, high, low, close),
"CDLCLOSINGMARUBOZU")
KPattern.__checkIfAdd(
rets, talib.CDLCONCEALBABYSWALL(open, high, low, close),
"CDLCONCEALBABYSWALL")
KPattern.__checkIfAdd(rets,
talib.CDLCOUNTERATTACK(open, high, low, close),
"CDLCOUNTERATTACK")
KPattern.__checkIfAdd(
rets, talib.CDLDARKCLOUDCOVER(open,
high,
low,
close,
penetration=0), "CDLDARKCLOUDCOVER")
KPattern.__checkIfAdd(rets, talib.CDLDOJI(open, high, low, close),
"CDLDOJI")
KPattern.__checkIfAdd(rets, talib.CDLDOJISTAR(open, high, low, close),
"CDLDOJISTAR")
KPattern.__checkIfAdd(rets,
talib.CDLDRAGONFLYDOJI(open, high, low, close),
"CDLDRAGONFLYDOJI")
KPattern.__checkIfAdd(rets, talib.CDLENGULFING(open, high, low, close),
"CDLENGULFING")
KPattern.__checkIfAdd(rets,
talib.CDLEVENINGDOJISTAR(open, high, low, close),
"CDLEVENINGDOJISTAR")
KPattern.__checkIfAdd(
rets, talib.CDLEVENINGSTAR(open, high, low, close, penetration=0),
"CDLEVENINGSTAR")
KPattern.__checkIfAdd(
rets, talib.CDLGAPSIDESIDEWHITE(open, high, low, close),
"CDLGAPSIDESIDEWHITE")
KPattern.__checkIfAdd(rets,
talib.CDLGRAVESTONEDOJI(open, high, low, close),
"CDLGRAVESTONEDOJI")
KPattern.__checkIfAdd(rets, talib.CDLHAMMER(open, high, low, close),
"CDLHAMMER")
KPattern.__checkIfAdd(rets,
talib.CDLHANGINGMAN(open, high, low,
close), "CDLHANGINGMAN")
KPattern.__checkIfAdd(rets, talib.CDLHARAMI(open, high, low, close),
"CDLHARAMI")
KPattern.__checkIfAdd(rets,
talib.CDLHARAMICROSS(open, high, low,
close), "CDLHARAMICROSS")
KPattern.__checkIfAdd(rets, talib.CDLHIGHWAVE(open, high, low, close),
"CDLHIGHWAVE")
KPattern.__checkIfAdd(rets, talib.CDLHIKKAKE(open, high, low, close),
"CDLHIKKAKE")
KPattern.__checkIfAdd(rets,
talib.CDLHIKKAKEMOD(open, high, low,
close), "CDLHIKKAKEMOD")
KPattern.__checkIfAdd(rets,
talib.CDLHOMINGPIGEON(open, high, low, close),
"CDLHOMINGPIGEON")
KPattern.__checkIfAdd(rets,
talib.CDLIDENTICAL3CROWS(open, high, low, close),
"CDLIDENTICAL3CROWS")
KPattern.__checkIfAdd(rets, talib.CDLINNECK(open, high, low, close),
"CDLINNECK")
KPattern.__checkIfAdd(rets,
talib.CDLINVERTEDHAMMER(open, high, low, close),
"CDLINVERTEDHAMMER")
KPattern.__checkIfAdd(rets, talib.CDLKICKING(open, high, low, close),
"CDLKICKING")
KPattern.__checkIfAdd(rets,
talib.CDLKICKINGBYLENGTH(open, high, low, close),
"CDLKICKINGBYLENGTH")
KPattern.__checkIfAdd(rets,
talib.CDLLADDERBOTTOM(open, high, low, close),
"CDLLADDERBOTTOM")
KPattern.__checkIfAdd(rets,
talib.CDLLONGLEGGEDDOJI(open, high, low, close),
"CDLLONGLEGGEDDOJI")
KPattern.__checkIfAdd(rets, talib.CDLLONGLINE(open, high, low, close),
"CDLLONGLINE")
KPattern.__checkIfAdd(rets, talib.CDLMARUBOZU(open, high, low, close),
"CDLMARUBOZU")
KPattern.__checkIfAdd(rets,
talib.CDLMATCHINGLOW(open, high, low,
close), "CDLMATCHINGLOW")
KPattern.__checkIfAdd(rets, talib.CDLMATHOLD(open, high, low, close),
"CDLMATHOLD")
KPattern.__checkIfAdd(rets,
talib.CDLMORNINGDOJISTAR(open, high, low, close),
"CDLMORNINGDOJISTAR")
KPattern.__checkIfAdd(rets,
talib.CDLMORNINGSTAR(open, high, low,
close), "CDLMORNINGSTAR")
KPattern.__checkIfAdd(rets, talib.CDLONNECK(open, high, low, close),
"CDLONNECK")
KPattern.__checkIfAdd(rets, talib.CDLPIERCING(open, high, low, close),
"CDLPIERCING")
KPattern.__checkIfAdd(rets,
talib.CDLRICKSHAWMAN(open, high, low,
close), "CDLRICKSHAWMAN")
KPattern.__checkIfAdd(
rets, talib.CDLRISEFALL3METHODS(open, high, low, close),
"CDLRISEFALL3METHODS")
KPattern.__checkIfAdd(rets,
talib.CDLSEPARATINGLINES(open, high, low, close),
"CDLSEPARATINGLINES")
KPattern.__checkIfAdd(rets,
talib.CDLSHOOTINGSTAR(open, high, low, close),
"CDLSHOOTINGSTAR")
KPattern.__checkIfAdd(rets, talib.CDLSHORTLINE(open, high, low, close),
"CDLSHORTLINE")
KPattern.__checkIfAdd(rets,
talib.CDLSPINNINGTOP(open, high, low,
close), "CDLSPINNINGTOP")
KPattern.__checkIfAdd(rets,
talib.CDLSTALLEDPATTERN(open, high, low, close),
"CDLSTALLEDPATTERN")
KPattern.__checkIfAdd(rets,
talib.CDLSTICKSANDWICH(open, high, low, close),
"CDLSTICKSANDWICH")
KPattern.__checkIfAdd(rets, talib.CDLTAKURI(open, high, low, close),
"CDLTAKURI")
KPattern.__checkIfAdd(rets, talib.CDLTASUKIGAP(open, high, low, close),
"CDLTASUKIGAP")
KPattern.__checkIfAdd(rets, talib.CDLTHRUSTING(open, high, low, close),
"CDLTHRUSTING")
KPattern.__checkIfAdd(rets, talib.CDLTRISTAR(open, high, low, close),
"CDLTRISTAR")
KPattern.__checkIfAdd(rets,
talib.CDLUNIQUE3RIVER(open, high, low, close),
"CDLUNIQUE3RIVER")
KPattern.__checkIfAdd(rets,
talib.CDLUPSIDEGAP2CROWS(open, high, low, close),
"CDLUPSIDEGAP2CROWS")
KPattern.__checkIfAdd(
rets, talib.CDLXSIDEGAP3METHODS(open, high, low, close),
"CDLXSIDEGAP3METHODS")
return rets
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_candle_patters(self):
CDL2CROWS = talib.CDL2CROWS(self.data["askopen"], self.data["askhigh"],
self.data["asklow"], self.data["askclose"])
self.data['CDL2CROWS'] = CDL2CROWS
CDL3BLACKCROWS = talib.CDL3BLACKCROWS(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDL3BLACKCROWS'] = CDL3BLACKCROWS
CDL3INSIDE = talib.CDL3INSIDE(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDL3INSIDE'] = CDL3INSIDE
CDL3LINESTRIKE = talib.CDL3LINESTRIKE(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDL3LINESTRIKE'] = CDL3LINESTRIKE
CDL3OUTSIDE = talib.CDL3OUTSIDE(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDL3OUTSIDE'] = CDL3OUTSIDE
CDL3STARSINSOUTH = talib.CDL3STARSINSOUTH(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDL3STARSINSOUTH'] = CDL3STARSINSOUTH
CDL3WHITESOLDIERS = talib.CDL3WHITESOLDIERS(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDL3WHITESOLDIERS'] = CDL3WHITESOLDIERS
CDLABANDONEDBABY = talib.CDLABANDONEDBABY(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLABANDONEDBABY'] = CDLABANDONEDBABY
CDLADVANCEBLOCK = talib.CDLADVANCEBLOCK(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLADVANCEBLOCK'] = CDLADVANCEBLOCK
CDLBELTHOLD = talib.CDLBELTHOLD(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLBELTHOLD'] = CDLBELTHOLD
CDLBREAKAWAY = talib.CDLBREAKAWAY(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLBREAKAWAY'] = CDLBREAKAWAY
CDLCLOSINGMARUBOZU = talib.CDLCLOSINGMARUBOZU(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLCLOSINGMARUBOZU'] = CDLCLOSINGMARUBOZU
CDLCONCEALBABYSWALL = talib.CDLCONCEALBABYSWALL(
self.data["askopen"], self.data["askhigh"], self.data["asklow"],
self.data["askclose"])
self.data['CDLCONCEALBABYSWALL'] = CDLCONCEALBABYSWALL
CDLCOUNTERATTACK = talib.CDLCOUNTERATTACK(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLCOUNTERATTACK'] = CDLCOUNTERATTACK
CDLDARKCLOUDCOVER = talib.CDLDARKCLOUDCOVER(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLDARKCLOUDCOVER'] = CDLDARKCLOUDCOVER
CDLDOJI = talib.CDLDOJI(self.data["askopen"], self.data["askhigh"],
self.data["asklow"], self.data["askclose"])
self.data['CDLDOJI'] = CDLDOJI
CDLDOJISTAR = talib.CDLDOJISTAR(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLDOJISTAR'] = CDLDOJISTAR
CDLDRAGONFLYDOJI = talib.CDLDRAGONFLYDOJI(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLDRAGONFLYDOJI'] = CDLDRAGONFLYDOJI
CDLENGULFING = talib.CDLENGULFING(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLENGULFING'] = CDLENGULFING
CDLEVENINGDOJISTAR = talib.CDLEVENINGDOJISTAR(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLEVENINGDOJISTAR'] = CDLEVENINGDOJISTAR
CDLEVENINGSTAR = talib.CDLEVENINGSTAR(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLEVENINGSTAR'] = CDLEVENINGSTAR
CDLGAPSIDESIDEWHITE = talib.CDLGAPSIDESIDEWHITE(
self.data["askopen"], self.data["askhigh"], self.data["asklow"],
self.data["askclose"])
self.data['CDLGAPSIDESIDEWHITE'] = CDLGAPSIDESIDEWHITE
CDLGRAVESTONEDOJI = talib.CDLGRAVESTONEDOJI(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLGRAVESTONEDOJI'] = CDLGRAVESTONEDOJI
CDLHAMMER = talib.CDLHAMMER(self.data["askopen"], self.data["askhigh"],
self.data["asklow"], self.data["askclose"])
self.data['CDLHAMMER'] = CDLHAMMER
CDLHANGINGMAN = talib.CDLHANGINGMAN(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLHANGINGMAN'] = CDLHANGINGMAN
CDLHARAMI = talib.CDLHARAMI(self.data["askopen"], self.data["askhigh"],
self.data["asklow"], self.data["askclose"])
self.data['CDLHARAMI'] = CDLHARAMI
CDLHARAMICROSS = talib.CDLHARAMICROSS(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLHARAMICROSS'] = CDLHARAMICROSS
CDLHIGHWAVE = talib.CDLHIGHWAVE(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLHIGHWAVE'] = CDLHIGHWAVE
CDLHIKKAKE = talib.CDLHIKKAKE(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLHIKKAKE'] = CDLHIKKAKE
CDLHIKKAKEMOD = talib.CDLHIKKAKEMOD(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLHIKKAKEMOD'] = CDLHIKKAKEMOD
CDLHOMINGPIGEON = talib.CDLHOMINGPIGEON(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLHOMINGPIGEON'] = CDLHOMINGPIGEON
CDLIDENTICAL3CROWS = talib.CDLIDENTICAL3CROWS(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLIDENTICAL3CROWS'] = CDLIDENTICAL3CROWS
CDLINNECK = talib.CDLINNECK(self.data["askopen"], self.data["askhigh"],
self.data["asklow"], self.data["askclose"])
self.data['CDLINNECK'] = CDLINNECK
CDLINVERTEDHAMMER = talib.CDLINVERTEDHAMMER(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLINVERTEDHAMMER'] = CDLINVERTEDHAMMER
CDLKICKING = talib.CDLKICKING(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLKICKING'] = CDLKICKING
CDLKICKINGBYLENGTH = talib.CDLKICKINGBYLENGTH(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLKICKINGBYLENGTH'] = CDLKICKINGBYLENGTH
CDLLADDERBOTTOM = talib.CDLLADDERBOTTOM(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLLADDERBOTTOM'] = CDLLADDERBOTTOM
CDLLONGLEGGEDDOJI = talib.CDLLONGLEGGEDDOJI(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLLONGLEGGEDDOJI'] = CDLLONGLEGGEDDOJI
CDLLONGLINE = talib.CDLLONGLINE(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLLONGLINE'] = CDLLONGLINE
CDLMARUBOZU = talib.CDLMARUBOZU(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLMARUBOZU'] = CDLMARUBOZU
CDLMATCHINGLOW = talib.CDLMATCHINGLOW(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLMATCHINGLOW'] = CDLMATCHINGLOW
CDLMATHOLD = talib.CDLMATHOLD(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLMATHOLD'] = CDLMATHOLD
CDLMORNINGDOJISTAR = talib.CDLMORNINGDOJISTAR(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLMORNINGDOJISTAR'] = CDLMORNINGDOJISTAR
CDLMORNINGSTAR = talib.CDLMORNINGSTAR(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLMORNINGSTAR'] = CDLMORNINGSTAR
CDLONNECK = talib.CDLONNECK(self.data["askopen"], self.data["askhigh"],
self.data["asklow"], self.data["askclose"])
self.data['CDLONNECK'] = CDLONNECK
CDLPIERCING = talib.CDLPIERCING(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLPIERCING'] = CDLPIERCING
CDLRICKSHAWMAN = talib.CDLRICKSHAWMAN(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLRICKSHAWMAN'] = CDLRICKSHAWMAN
CDLRISEFALL3METHODS = talib.CDLRISEFALL3METHODS(
self.data["askopen"], self.data["askhigh"], self.data["asklow"],
self.data["askclose"])
self.data['CDLRISEFALL3METHODS'] = CDLRISEFALL3METHODS
CDLSEPARATINGLINES = talib.CDLSEPARATINGLINES(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLSEPARATINGLINES'] = CDLSEPARATINGLINES
CDLSHOOTINGSTAR = talib.CDLSHOOTINGSTAR(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLSHOOTINGSTAR'] = CDLSHOOTINGSTAR
CDLSHORTLINE = talib.CDLSHORTLINE(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLSHORTLINE'] = CDLSHORTLINE
CDLSPINNINGTOP = talib.CDLSPINNINGTOP(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLSPINNINGTOP'] = CDLSPINNINGTOP
CDLSTALLEDPATTERN = talib.CDLSTALLEDPATTERN(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLSTALLEDPATTERN'] = CDLSTALLEDPATTERN
CDLSTICKSANDWICH = talib.CDLSTICKSANDWICH(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLSTICKSANDWICH'] = CDLSTICKSANDWICH
CDLTAKURI = talib.CDLTAKURI(self.data["askopen"], self.data["askhigh"],
self.data["asklow"], self.data["askclose"])
self.data['CDLTAKURI'] = CDLTAKURI
CDLTASUKIGAP = talib.CDLTASUKIGAP(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLTASUKIGAP'] = CDLTASUKIGAP
CDLTHRUSTING = talib.CDLTHRUSTING(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLTHRUSTING'] = CDLTHRUSTING
CDLTRISTAR = talib.CDLTRISTAR(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLTRISTAR'] = CDLTRISTAR
CDLUNIQUE3RIVER = talib.CDLUNIQUE3RIVER(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLUNIQUE3RIVER'] = CDLUNIQUE3RIVER
CDLUPSIDEGAP2CROWS = talib.CDLUPSIDEGAP2CROWS(self.data["askopen"],
self.data["askhigh"],
self.data["asklow"],
self.data["askclose"])
self.data['CDLUPSIDEGAP2CROWS'] = CDLUPSIDEGAP2CROWS
CDLXSIDEGAP3METHODS = talib.CDLXSIDEGAP3METHODS(
self.data["askopen"], self.data["askhigh"], self.data["asklow"],
self.data["askclose"])
self.data['CDLXSIDEGAP3METHODS'] = CDLXSIDEGAP3METHODS
CDLMORNINGDOJISTAR_real = talib.CDLMORNINGDOJISTAR(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLMORNINGSTAR_real = talib.CDLMORNINGSTAR(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLONNECK_real = talib.CDLONNECK(resorted['open'],
resorted['high'],
resorted['low'],
resorted['close'])
CDLPIERCING_real = talib.CDLPIERCING(resorted['open'],
resorted['high'],
resorted['low'],
resorted['close'])
CDLRICKSHAWMAN_real = talib.CDLRICKSHAWMAN(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLRISEFALL3METHODS_real = talib.CDLRISEFALL3METHODS(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLSEPARATINGLINES_real = talib.CDLSEPARATINGLINES(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLSHOOTINGSTAR_real = talib.CDLSHOOTINGSTAR(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLSHORTLINE_real = talib.CDLSHORTLINE(resorted['open'],
resorted['high'],
resorted['low'],
resorted['close'])
CDLSPINNINGTOP_real = talib.CDLSPINNINGTOP(
def add_pattern(self, df):
ret_parrern = []
result = []
result_add = []
result_minus = []
output = talib.CDL2CROWS(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDL2CROWS', 63, ret_parrern)
for i in range(len(output)):
result.append(0)
result_add.append(0)
result_minus.append(0)
result = self.get_pattern(result, result_add, result_minus, output, 1)
# output = talib.CDL3BLACKCROWS(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
# self.add_machine(df, output, 'CDL3BLACKCROWS', 2, ret_parrern)
# result = self.get_pattern(result, result_add, result_minus, output, 2)
output = talib.CDL3INSIDE(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDL3INSIDE', 3, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 3)
# output = talib.CDL3LINESTRIKE(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
# self.add_machine(df, output, 'CDL3LINESTRIKE', 4, ret_parrern)
# result = self.get_pattern(result, result_add, result_minus, output, 4)
output = talib.CDL3OUTSIDE(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDL3OUTSIDE', 5, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 5)
output = talib.CDL3STARSINSOUTH(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDL3STARSINSOUTH', 6, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 6)
output = talib.CDL3WHITESOLDIERS(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDL3WHITESOLDIERS', 7, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 7)
output = talib.CDLABANDONEDBABY(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLABANDONEDBABY', 8, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 8)
output = talib.CDLADVANCEBLOCK(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLADVANCEBLOCK', 9, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 9)
output = talib.CDLBELTHOLD(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLBELTHOLD', 10, ret_parrern)
result = self.get_pattern(result,result_add, result_minus, output, 10)
output = talib.CDLBREAKAWAY(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLBREAKAWAY', 11, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 11)
output = talib.CDLCLOSINGMARUBOZU(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLCLOSINGMARUBOZU', 12, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 12)
output = talib.CDLCONCEALBABYSWALL(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLCONCEALBABYSWALL', 13, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 13)
output = talib.CDLCOUNTERATTACK(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLCOUNTERATTACK', 14, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 14)
output = talib.CDLDARKCLOUDCOVER(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLDARKCLOUDCOVER', 15, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 15)
output = talib.CDLDOJISTAR(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLDOJISTAR',17, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 17)
output = talib.CDLDRAGONFLYDOJI(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLDRAGONFLYDOJI',18, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 18)
output = talib.CDLENGULFING(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLENGULFING',19, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 19)
output = talib.CDLEVENINGDOJISTAR(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLEVENINGDOJISTAR',20, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 20)
output = talib.CDLEVENINGSTAR(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLEVENINGSTAR',21, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 21)
# output = talib.CDLGAPSIDESIDEWHITE(df['시가'].values, df['고가'].values, 7XXZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ29io
# output = talib.CDLGRAVESTONEDOJI(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLGRAVESTONEDOJI',23, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 23)
output = talib.CDLHAMMER(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLHAMMER',24, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 24)
output = talib.CDLHANGINGMAN(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLHANGINGMAN',25, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 25)
output = talib.CDLHARAMI(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLHARAMI',26, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 26)
output = talib.CDLHARAMICROSS(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLHARAMICROSS',27, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 27)
output = talib.CDLHIGHWAVE(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLHIGHWAVE',28, ret_parrern)
result = self.get_pattern(result,result_add, result_minus, output, 28)
output = talib.CDLHIKKAKE(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLHIKKAKE',29, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 29)
output = talib.CDLHIKKAKEMOD(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLHIKKAKEMOD',30, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 30)
output = talib.CDLHOMINGPIGEON(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLHOMINGPIGEON',31, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 31)
output = talib.CDLIDENTICAL3CROWS(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLIDENTICAL3CROWS',32, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 32)
output = talib.CDLINNECK(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLINNECK',33, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 33)
output = talib.CDLINVERTEDHAMMER(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLINVERTEDHAMMER',34, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 34)
output = talib.CDLKICKING(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLKICKING',35, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 35)
output = talib.CDLKICKINGBYLENGTH(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLKICKINGBYLENGTH',36, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 36)
output = talib.CDLLADDERBOTTOM(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLLADDERBOTTOM',37, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 37)
output = talib.CDLLONGLEGGEDDOJI(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLLONGLEGGEDDOJI',38, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 38)
output = talib.CDLLONGLINE(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLLONGLINE',39, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 39)
output = talib.CDLMARUBOZU(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLMARUBOZU',40, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 40)
output = talib.CDLMATCHINGLOW(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLMATCHINGLOW',41, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 41)
output = talib.CDLMATHOLD(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLMATHOLD',42, ret_parrern)
result = self.get_pattern(result,result_add, result_minus, output, 42)
# output = talib.CDLMATCHINGLOW(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
# self.add_machine(df, output, 'CDLMATCHINGLOW',43, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 43)
output = talib.CDLMORNINGDOJISTAR(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLMORNINGDOJISTAR',44, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 44)
output = talib.CDLMORNINGSTAR(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLMORNINGSTAR',45, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 45)
output = talib.CDLONNECK(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLONNECK',46, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 46)
output = talib.CDLPIERCING(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLPIERCING',47, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 47)
output = talib.CDLRICKSHAWMAN(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLRICKSHAWMAN',48, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 48)
output = talib.CDLRISEFALL3METHODS(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLRISEFALL3METHODS',49, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 49)
output = talib.CDLSEPARATINGLINES(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLSEPARATINGLINES',50, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 50)
output = talib.CDLSHOOTINGSTAR(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLSHOOTINGSTAR',51, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 51)
output = talib.CDLSHORTLINE(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLSHORTLINE',52, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 52)
output = talib.CDLSPINNINGTOP(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLSPINNINGTOP',53, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 53)
output = talib.CDLSTALLEDPATTERN(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLSTALLEDPATTERN',54, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 54)
output = talib.CDLSTICKSANDWICH(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLSTICKSANDWICH',55, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 55)
output = talib.CDLTAKURI(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLTAKURI',56, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 56)
output = talib.CDLTASUKIGAP(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLTASUKIGAP',57, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 57)
output = talib.CDLTHRUSTING(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLTHRUSTING',58, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 58)
output = talib.CDLTRISTAR(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLTRISTAR',59, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 59)
output = talib.CDLUNIQUE3RIVER(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLUNIQUE3RIVER',60, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 60)
output = talib.CDLUPSIDEGAP2CROWS(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLUPSIDEGAP2CROWS',61, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 61)
output = talib.CDLXSIDEGAP3METHODS(df['시가'].values, df['고가'].values, df['저가'].values, df['종가'].values)
self.add_machine(df, output, 'CDLXSIDEGAP3METHODS',62, ret_parrern)
result = self.get_pattern(result, result_add, result_minus, output, 62)
df['PATTERN'] = result
df['PLUS_PATTERN'] = result_add
df['MINUS_PATTERN'] = result_minus
# if 'PLUS_PATTERN' in services.get('configurator').get('input_column'):
# print('')
# else:
# services.get('configurator').get('input_column').append('PLUS_PATTERN')
# if 'MINUS_PATTERN' in services.get('configurator').get('input_column'):
# print('')
# else:
# services.get('configurator').get('input_column').append('MINUS_PATTERN')
# for i in range(len(result)):
# print('[%s]: [%s][%s][%s][%s] plus[%s] minus[%s] %s' % (i, df.iloc[i].name, df['고가'].values[i], df['종가'].values[i], df['저가'].values[i], result_add[i], result_minus[i], result[i]))
# i = len(result) -1
# print('[%s]: [%s][%s][%s][%s] plus[%s] minus[%s] %s' % (i, df.iloc[i].name, df['고가'].values[i], df['종가'].values[i], df['저가'].values[i], result_add[i], result_minus[i],result[i]))
return ret_parrern
ohlc_df['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLONNECK'] = ta.CDLONNECK(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLPIERCING'] = ta.CDLPIERCING(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLSHORTLINE'] = ta.CDLSHORTLINE(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(
def compute_candlestick_patterns(self):
df = self.df
open = df.open
close = df.close
high = df.high
low = df.low
candle_patterns = pd.DataFrame(deepcopy(close))
candle_patterns['two_crows'] = talib.CDL2CROWS(open, high, low, close)
candle_patterns['three_black_crows'] = talib.CDL3BLACKCROWS(open, high, low, close)
candle_patterns['three_inside'] = talib.CDL3INSIDE(open, high, low, close)
candle_patterns['three_line_strike'] = talib.CDL3LINESTRIKE(open, high, low, close)
candle_patterns['three_outside'] = talib.CDL3OUTSIDE(open, high, low, close)
candle_patterns['three_star_south'] = talib.CDL3STARSINSOUTH(open, high, low, close)
candle_patterns['three_white_soldiers'] = talib.CDL3WHITESOLDIERS(open, high, low, close)
candle_patterns['abandoned_baby'] = talib.CDLABANDONEDBABY(open, high, low, close)
candle_patterns['advance_block'] = talib.CDLADVANCEBLOCK(open, high, low, close)
candle_patterns['belt_hold'] = talib.CDLBELTHOLD(open, high, low, close)
candle_patterns['breakaway'] = talib.CDLBREAKAWAY(open, high, low, close)
candle_patterns['closing_marubozu'] = talib.CDLCLOSINGMARUBOZU(open, high, low, close)
candle_patterns['concealing_baby_swallow'] = talib.CDLCONCEALBABYSWALL(open, high, low, close)
candle_patterns['counterattack'] = talib.CDLCOUNTERATTACK(open, high, low, close)
candle_patterns['dark_cloud_cover'] = talib.CDLDARKCLOUDCOVER(open, high, low, close)
candle_patterns['doji'] = talib.CDLDOJI(open, high, low, close)
candle_patterns['doji_star'] = talib.CDLDOJISTAR(open, high, low, close)
candle_patterns['gravestone_doji'] = talib.CDLGRAVESTONEDOJI(open, high, low, close)
candle_patterns['dragonfly_doji'] = talib.CDLDRAGONFLYDOJI(open, high, low, close)
candle_patterns['engulfing'] = talib.CDLENGULFING(open, high, low, close)
candle_patterns['eveningstar_doji'] = talib.CDLEVENINGDOJISTAR(open, high, low, close)
candle_patterns['eveningstar'] = talib.CDLEVENINGSTAR(open, high, low, close)
candle_patterns['gap_side_white'] = talib.CDLGAPSIDESIDEWHITE(open, high, low, close)
candle_patterns['hammer'] = talib.CDLHAMMER(open, high, low, close)
candle_patterns['doji'] = talib.CDLDOJI(open, high, low, close)
candle_patterns['hanging_man'] = talib.CDLHANGINGMAN(open, high, low, close)
candle_patterns['harami'] = talib.CDLHARAMI(open, high, low, close)
candle_patterns['harami_cross'] = talib.CDLHARAMICROSS(open, high, low, close)
candle_patterns['high_wave'] = talib.CDLHIGHWAVE(open, high, low, close)
candle_patterns['hikkake'] = talib.CDLHIKKAKE(open, high, low, close)
candle_patterns['hikkake_mod'] = talib.CDLHIKKAKEMOD(open, high, low, close)
candle_patterns['homing_pigeon'] = talib.CDLHOMINGPIGEON(open, high, low, close)
candle_patterns['identical_3crows'] = talib.CDLIDENTICAL3CROWS(open, high, low, close)
candle_patterns['in_neck'] = talib.CDLINNECK(open, high, low, close)
candle_patterns['inverted_hammer'] = talib.CDLINVERTEDHAMMER(open, high, low, close)
candle_patterns['kicking'] = talib.CDLKICKING(open, high, low, close)
candle_patterns['kicking_marubozu'] = talib.CDLKICKINGBYLENGTH(open, high, low, close)
candle_patterns['ladder_bottom'] = talib.CDLLADDERBOTTOM(open, high, low, close)
candle_patterns['long_leg_doji'] = talib.CDLLONGLEGGEDDOJI(open, high, low, close)
candle_patterns['long_line'] = talib.CDLLONGLINE(open, high, low, close)
candle_patterns['marubozu'] = talib.CDLMARUBOZU(open, high, low, close)
candle_patterns['matching_low'] = talib.CDLMATCHINGLOW(open, high, low, close)
candle_patterns['mat_hold'] = talib.CDLMATHOLD(open, high, low, close)
candle_patterns['morningstar_doji'] = talib.CDLMORNINGDOJISTAR(open, high, low, close)
candle_patterns['morningstar'] = talib.CDLMORNINGSTAR(open, high, low, close)
candle_patterns['on_neck'] = talib.CDLONNECK(open, high, low, close)
candle_patterns['piercing'] = talib.CDLPIERCING(open, high, low, close)
candle_patterns['rickshaw_man'] = talib.CDLRICKSHAWMAN(open, high, low, close)
candle_patterns['rising_fall_3methods'] = talib.CDLRISEFALL3METHODS(open, high, low, close)
candle_patterns['separating_lines'] = talib.CDLSEPARATINGLINES(open, high, low, close)
candle_patterns['shooting_star'] = talib.CDLSHOOTINGSTAR(open, high, low, close)
candle_patterns['short_line'] = talib.CDLSHORTLINE(open, high, low, close)
candle_patterns['spinning_top'] = talib.CDLSPINNINGTOP(open, high, low, close)
candle_patterns['stalled'] = talib.CDLSTALLEDPATTERN(open, high, low, close)
candle_patterns['stick_sandwich'] = talib.CDLSTICKSANDWICH(open, high, low, close)
candle_patterns['takuri'] = talib.CDLTAKURI(open, high, low, close)
candle_patterns['tasuki_gap'] = talib.CDLTASUKIGAP(open, high, low, close)
candle_patterns['thrusting'] = talib.CDLTHRUSTING(open, high, low, close)
candle_patterns['tristar'] = talib.CDLTRISTAR(open, high, low, close)
candle_patterns['unique_3river'] = talib.CDLUNIQUE3RIVER(open, high, low, close)
candle_patterns['upside_gap_2crows'] = talib.CDLUPSIDEGAP2CROWS(open, high, low, close)
candle_patterns['upside_gap_3methods'] = talib.CDLXSIDEGAP3METHODS(open, high, low, close)
self.candlestick_patterns = candle_patterns
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 get_ta(self):
# define pivot variables for easy use
open_price = self.data['open_price'].values
close = self.data['close'].values
high = self.data['high'].values
low = self.data['low'].values
volume = self.data['volume'].values
# define the technical analysis matrix
retn = np.array([
tb.MA(close, timeperiod=5), # 1
tb.MA(close, timeperiod=10), # 2
tb.MA(close, timeperiod=20), # 3
tb.MA(close, timeperiod=60), # 4
tb.MA(close, timeperiod=90), # 5
tb.MA(close, timeperiod=120), # 6
tb.ADX(high, low, close, timeperiod=20), # 7
tb.ADXR(high, low, close, timeperiod=20), # 8
tb.MACD(close, fastperiod=12, slowperiod=26,
signalperiod=9)[0], # 9
tb.RSI(close, timeperiod=14), # 10
tb.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2,
matype=0)[0], # 11
tb.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2,
matype=0)[1], # 12
tb.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2,
matype=0)[2], # 13
tb.AD(high, low, close, volume), # 14
tb.ATR(high, low, close, timeperiod=14), # 15
tb.HT_DCPERIOD(close), # 16
tb.CDL2CROWS(open_price, high, low, close), # 17
tb.CDL3BLACKCROWS(open_price, high, low, close), # 18
tb.CDL3INSIDE(open_price, high, low, close), # 19
tb.CDL3LINESTRIKE(open_price, high, low, close), # 20
tb.CDL3OUTSIDE(open_price, high, low, close), # 21
tb.CDL3STARSINSOUTH(open_price, high, low, close), # 22
tb.CDL3WHITESOLDIERS(open_price, high, low, close), # 23
tb.CDLABANDONEDBABY(open_price, high, low, close,
penetration=0), # 24
tb.CDLADVANCEBLOCK(open_price, high, low, close), # 25
tb.CDLBELTHOLD(open_price, high, low, close), # 26
tb.CDLBREAKAWAY(open_price, high, low, close), # 27
tb.CDLCLOSINGMARUBOZU(open_price, high, low, close), # 28
tb.CDLCONCEALBABYSWALL(open_price, high, low, close), # 29
tb.CDLCOUNTERATTACK(open_price, high, low, close), # 30
tb.CDLDARKCLOUDCOVER(open_price, high, low, close,
penetration=0), # 31
tb.CDLDOJI(open_price, high, low, close), # 32
tb.CDLDOJISTAR(open_price, high, low, close), # 33
tb.CDLDRAGONFLYDOJI(open_price, high, low, close), # 34
tb.CDLENGULFING(open_price, high, low, close), # 35
tb.CDLEVENINGDOJISTAR(open_price, high, low, close,
penetration=0), # 36
tb.CDLEVENINGSTAR(open_price, high, low, close,
penetration=0), # 37
tb.CDLGAPSIDESIDEWHITE(open_price, high, low, close), # 38
tb.CDLGRAVESTONEDOJI(open_price, high, low, close), # 39
tb.CDLHAMMER(open_price, high, low, close), # 40
tb.CDLHANGINGMAN(open_price, high, low, close), # 41
tb.CDLHARAMI(open_price, high, low, close), # 42
tb.CDLHARAMICROSS(open_price, high, low, close), # 43
tb.CDLHIGHWAVE(open_price, high, low, close), # 44
tb.CDLHIKKAKE(open_price, high, low, close), # 45
tb.CDLHIKKAKEMOD(open_price, high, low, close), # 46
tb.CDLHOMINGPIGEON(open_price, high, low, close), # 47
tb.CDLIDENTICAL3CROWS(open_price, high, low, close), # 48
tb.CDLINNECK(open_price, high, low, close), # 49
tb.CDLINVERTEDHAMMER(open_price, high, low, close), # 50
tb.CDLKICKING(open_price, high, low, close), # 51
tb.CDLKICKINGBYLENGTH(open_price, high, low, close), # 52
tb.CDLLADDERBOTTOM(open_price, high, low, close), # 53
tb.CDLLONGLEGGEDDOJI(open_price, high, low, close), # 54
tb.CDLLONGLINE(open_price, high, low, close), # 55
tb.CDLMARUBOZU(open_price, high, low, close), # 56
tb.CDLMATCHINGLOW(open_price, high, low, close), # 57
tb.CDLMATHOLD(open_price, high, low, close, penetration=0), # 58
tb.CDLMORNINGDOJISTAR(open_price, high, low, close,
penetration=0), # 59
tb.CDLMORNINGSTAR(open_price, high, low, close,
penetration=0), # 60
tb.CDLONNECK(open_price, high, low, close), # 61
tb.CDLPIERCING(open_price, high, low, close), # 62
tb.CDLRICKSHAWMAN(open_price, high, low, close), # 63
tb.CDLRISEFALL3METHODS(open_price, high, low, close), # 64
tb.CDLSEPARATINGLINES(open_price, high, low, close), # 65
tb.CDLSHOOTINGSTAR(open_price, high, low, close), # 66
tb.CDLSHORTLINE(open_price, high, low, close), # 67
tb.CDLSPINNINGTOP(open_price, high, low, close), # 68
tb.CDLSTALLEDPATTERN(open_price, high, low, close), # 69
tb.CDLSTICKSANDWICH(open_price, high, low, close), # 70
tb.CDLTAKURI(open_price, high, low, close), # 71
tb.CDLTASUKIGAP(open_price, high, low, close), # 72
tb.CDLTHRUSTING(open_price, high, low, close), # 73
tb.CDLTRISTAR(open_price, high, low, close), # 74
tb.CDLUNIQUE3RIVER(open_price, high, low, close), # 75
tb.CDLUPSIDEGAP2CROWS(open_price, high, low, close), # 76
tb.CDLXSIDEGAP3METHODS(open_price, high, low, close) # 77
]).T
retdf = pd.DataFrame(retn)
retdf['date'] = self.data.index.values
retdf.set_index('date', inplace=True)
self.data = self.data.join(retdf)
return self.data
def CDLRICKSHAWMAN(DataFrame):
res = talib.CDLRICKSHAWMAN(DataFrame.open.values, DataFrame.high.values,
DataFrame.low.values, DataFrame.close.values)
return pd.DataFrame({'CDLRICKSHAWMAN': res}, index=DataFrame.index)
def main():
# read csv file and transform it to datafeed (df):
df = pd.read_csv(current_dir + "/" + base_dir + "/" + in_dir + "/" +
in_dir + '_' + stock_symbol + '.csv')
# set numpy datafeed from df:
df_numpy = {
'Date': np.array(df['date']),
'Open': np.array(df['open'], dtype='float'),
'High': np.array(df['high'], dtype='float'),
'Low': np.array(df['low'], dtype='float'),
'Close': np.array(df['close'], dtype='float'),
'Volume': np.array(df['volume'], dtype='float')
}
date = df_numpy['Date']
openp = df_numpy['Open']
high = df_numpy['High']
low = df_numpy['Low']
close = df_numpy['Close']
volume = df_numpy['Volume']
#########################################
##### Pattern Recognition Functions #####
#########################################
#CDL2CROWS - Two Crows
cdl2crows = ta.CDL2CROWS(openp, high, low, close)
#CDL3BLACKCROWS - Three Black Crows
cdl3blackcrows = ta.CDL3BLACKCROWS(openp, high, low, close)
#CDL3INSIDE - Three Inside Up/Down
cdl3inside = ta.CDL3INSIDE(openp, high, low, close)
#CDL3LINESTRIKE - Three-Line Strike
cdl3linestrike = ta.CDL3LINESTRIKE(openp, high, low, close)
#CDL3OUTSIDE - Three Outside Up/Down
cdl3outside = ta.CDL3OUTSIDE(openp, high, low, 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)
#CDLDARKCLOUDCOVER - Dark Cloud Cover
cdldarkcloudcover = ta.CDLDARKCLOUDCOVER(openp,
high,
low,
close,
penetration=0)
#CDLDOJI - Doji
cdldoji = ta.CDLDOJI(openp, high, low, close)
#CDLDOJISTAR - Doji Star
cdldojistar = ta.CDLDOJISTAR(openp, high, low, close)
#CDLDRAGONFLYDOJI - Dragonfly Doji
cdldragonflydoji = ta.CDLDRAGONFLYDOJI(openp, high, low, close)
#CDLENGULFING - Engulfing Pattern
cdlengulfing = ta.CDLENGULFING(openp, high, low, close)
#CDLEVENINGDOJISTAR - Evening Doji Star
cdeveningdojistar = ta.CDLEVENINGDOJISTAR(openp,
high,
low,
close,
penetration=0)
#CDLEVENINGSTAR - Evening Star
cdeveningstar = ta.CDLEVENINGSTAR(openp, high, low, close, penetration=0)
#CDLGAPSIDESIDEWHITE - Up/Down-gap side-by-side white lines
cdlgapsidesidewhite = ta.CDLGAPSIDESIDEWHITE(openp, high, low, close)
#CDLGRAVESTONEDOJI - Gravestone Doji
cdlgravestonedoji = ta.CDLGRAVESTONEDOJI(openp, high, low, close)
#CDLHAMMER - Hammer
cdlhammer = ta.CDLHAMMER(openp, high, low, close)
#CDLHANGINGMAN - Hanging Man
cdlhangman = ta.CDLHANGINGMAN(openp, high, low, close)
#CDLHARAMI - Harami Pattern
cdlharami = ta.CDLHARAMI(openp, high, low, close)
#CDLHARAMICROSS - Harami Cross Pattern
cdlharamicross = ta.CDLHARAMICROSS(openp, high, low, close)
#CDLHIGHWAVE - High-Wave Candle
cdlhighwave = ta.CDLHIGHWAVE(openp, high, low, close)
#CDLHIKKAKE - Hikkake Pattern
cdlhikakke = ta.CDLHIKKAKE(openp, high, low, close)
#CDLHIKKAKEMOD - Modified Hikkake Pattern
cdlhikkakemod = ta.CDLHIKKAKEMOD(openp, high, low, close)
#CDLHOMINGPIGEON - Homing Pigeon
cdlhomingpigeon = ta.CDLHOMINGPIGEON(openp, high, low, close)
#CDLIDENTICAL3CROWS - Identical Three Crows
cdlidentical3crows = ta.CDLIDENTICAL3CROWS(openp, high, low, close)
#CDLINNECK - In-Neck Pattern
cdlinneck = ta.CDLINNECK(openp, high, low, close)
#CDLINVERTEDHAMMER - Inverted Hammer
cdlinvertedhammer = ta.CDLINVERTEDHAMMER(openp, high, low, close)
#CDLKICKING - Kicking
cdkicking = ta.CDLKICKING(openp, high, low, close)
#CDLKICKINGBYLENGTH - Kicking - bull/bear determined by the longer marubozu
cdkickingbylength = ta.CDLKICKINGBYLENGTH(openp, high, low, close)
#CDLLADDERBOTTOM - Ladder Bottom
cdlladderbottom = ta.CDLLADDERBOTTOM(openp, high, low, close)
#CDLLONGLEGGEDDOJI - Long Legged Doji
cdllongleggeddoji = ta.CDLLONGLEGGEDDOJI(openp, high, low, close)
#CDLLONGLINE - Long Line Candle
cdllongline = ta.CDLLONGLINE(openp, high, low, close)
#CDLMARUBOZU - Marubozu
cdlmarubozu = ta.CDLMARUBOZU(openp, high, low, close)
#CDLMATCHINGLOW - Matching Low
cdlmatchinglow = ta.CDLMATCHINGLOW(openp, high, low, close)
#CDLMATHOLD - Mat Hold
cdlmathold = ta.CDLMATHOLD(openp, high, low, close, penetration=0)
#CDLMORNINGDOJISTAR - Morning Doji Star
cdlmorningdojistar = ta.CDLMORNINGDOJISTAR(openp,
high,
low,
close,
penetration=0)
#CDLMORNINGSTAR - Morning Star
cdlmorningstar = ta.CDLMORNINGSTAR(openp, high, low, close, penetration=0)
#CDLONNECK - On-Neck Pattern
cdlonneck = ta.CDLONNECK(openp, high, low, close)
#CDLPIERCING - Piercing Pattern
cdlpiercing = ta.CDLPIERCING(openp, high, low, close)
#CDLRICKSHAWMAN - Rickshaw Man
cdlrickshawman = ta.CDLRICKSHAWMAN(openp, high, low, close)
#CDLRISEFALL3METHODS - Rising/Falling Three Methods
cdlrisefall3methods = ta.CDLRISEFALL3METHODS(openp, high, low, close)
#CDLSEPARATINGLINES - Separating Lines
cdlseperatinglines = ta.CDLSEPARATINGLINES(openp, high, low, close)
#CDLSHOOTINGSTAR - Shooting Star
cdlshootingstar = ta.CDLSHOOTINGSTAR(openp, high, low, close)
#CDLSHORTLINE - Short Line Candle
cdlshortline = ta.CDLSHORTLINE(openp, high, low, close)
#CDLSPINNINGTOP - Spinning Top
cdlspinningtop = ta.CDLSPINNINGTOP(openp, high, low, close)
#CDLSTALLEDPATTERN - Stalled Pattern
cdlstalledpattern = ta.CDLSTALLEDPATTERN(openp, high, low, close)
#CDLSTICKSANDWICH - Stick Sandwich
cdlsticksandwich = ta.CDLSTICKSANDWICH(openp, high, low, close)
#CDLTAKURI - Takuri (Dragonfly Doji with very long lower shadow)
cdltakuri = ta.CDLTAKURI(openp, high, low, close)
#CDLTASUKIGAP - Tasuki Gap
cdltasukigap = ta.CDLTASUKIGAP(openp, high, low, close)
#CDLTHRUSTING - Thrusting Pattern
cdlthrusting = ta.CDLTHRUSTING(openp, high, low, close)
#CDLTRISTAR - Tristar Pattern
cdltristar = ta.CDLTRISTAR(openp, high, low, close)
#CDLUNIQUE3RIVER - Unique 3 River
cdlunique3river = ta.CDLUNIQUE3RIVER(openp, high, low, close)
#CDLUPSIDEGAP2CROWS - Upside Gap Two Crows
cdlupsidegap2crows = ta.CDLUPSIDEGAP2CROWS(openp, high, low, close)
#CDLXSIDEGAP3METHODS - Upside/Downside Gap Three Methods
cdlxsidegap3methods = ta.CDLXSIDEGAP3METHODS(openp, high, low, close)
df_save = pd.DataFrame(
data={
'date': np.array(df['date']),
'cdl2crows': cdl2crows,
'cdl3blackcrows': cdl3blackcrows,
'cdl3inside': cdl3inside,
'cdl3linestrike': cdl3linestrike,
'cdl3outside': cdl3outside,
'cdl3starinsouth': cdl3starinsouth,
'cdl3whitesoldiers': cdl3whitesoldiers,
'cdlabandonbaby': cdlabandonbaby,
'cdladvanceblock': cdladvanceblock,
'cdlbelthold': cdlbelthold,
'cdlbreakway': cdlbreakway,
'cdlclosingmarubozu': cdlclosingmarubozu,
'cdlconcealbabyswall': cdlconcealbabyswall,
'cdlcounterattack': cdlcounterattack,
'cdldarkcloudcover': cdldarkcloudcover,
'cdldoji': cdldoji,
'cdldojistar': cdldojistar,
'cdldragonflydoji': cdldragonflydoji,
'cdlengulfing': cdlengulfing,
'cdeveningdojistar': cdeveningdojistar,
'cdeveningstar': cdeveningstar,
'cdlgapsidesidewhite': cdlgapsidesidewhite,
'cdlgravestonedoji': cdlgravestonedoji,
'cdlhammer': cdlhammer,
'cdlhangman': cdlhangman,
'cdlharami': cdlharami,
'cdlharamicross': cdlharamicross,
'cdlhighwave': cdlhighwave,
'cdlhikakke': cdlhikakke,
'cdlhikkakemod': cdlhikkakemod,
'cdlhomingpigeon': cdlhomingpigeon,
'cdlidentical3crows': cdlidentical3crows,
'cdlinneck': cdlinneck,
'cdlinvertedhammer': cdlinvertedhammer,
'cdkicking': cdkicking,
'cdkickingbylength': cdkickingbylength,
'cdlladderbottom': cdlladderbottom,
'cdllongleggeddoji': cdllongleggeddoji,
'cdllongline': cdllongline,
'cdlmarubozu': cdlmarubozu,
'cdlmatchinglow': cdlmatchinglow,
'cdlmathold': cdlmathold,
'cdlmorningdojistar': cdlmorningdojistar,
'cdlmorningstar': cdlmorningstar,
'cdlonneck': cdlonneck,
'cdlpiercing': cdlpiercing,
'cdlrickshawman': cdlrickshawman,
'cdlrisefall3methods': cdlrisefall3methods,
'cdlseperatinglines': cdlseperatinglines,
'cdlshootingstar': cdlshootingstar,
'cdlshortline': cdlshortline,
'cdlspinningtop': cdlspinningtop,
'cdlstalledpattern': cdlstalledpattern,
'cdlsticksandwich': cdlsticksandwich,
'cdltakuri': cdltakuri,
'cdltasukigap': cdltasukigap,
'cdlthrusting': cdlthrusting,
'cdltristar': cdltristar,
'cdlunique3river': cdlunique3river,
'cdlupsidegap2crows': cdlupsidegap2crows,
'cdlxsidegap3methods': cdlxsidegap3methods
})
df_save.to_csv(current_dir + "/" + base_dir + "/" + out_dir + '/' +
stock_symbol + "/" + out_dir + '_ta_pattern_reognition_' +
stock_symbol + '.csv',
index=False)
