def get_cdlhighwave(ohlc):
cdlhighwave = ta.CDLHIGHWAVE(ohlc['1_open'], ohlc['2_high'], ohlc['3_low'],
ohlc['4_close'])
ohlc['cdlhighwave'] = cdlhighwave
return ohlc
def CDLHIGHWAVE(open, high, low, close):
''' High-Wave Candle 风高浪大线
分组: Pattern Recognition 形态识别
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。
integer = CDLHIGHWAVE(open, high, low, close)
'''
return talib.CDLHIGHWAVE(open, high, low, close)
def high_wave(self):
"""
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。
"""
result = talib.CDLHIGHWAVE(open=np.array(self.dataframe['open']),
high=np.array(self.dataframe['high']),
low=np.array(self.dataframe['low']),
close=np.array(self.dataframe['close']))
self.dataframe['high_wave'] = result
def high_wave(self, sym, frequency):
if not self.kbars_ready(sym, frequency):
return []
opens = self.open(sym, frequency)
highs = self.high(sym, frequency)
lows = self.low(sym, frequency)
closes = self.close(sym, frequency)
cdl = ta.CDLHIGHWAVE(opens, highs, lows, closes)
return cdl
def get_rates(codes, data, start, end):
rate = {}
code = codes['code'].get_values()
for i in code:
close = data[i]['close']
high = data[i]['high']
low = data[i]['low']
open = data[i]['open']
# 用CDLBELTHO/D进行测试
t1 = np.array(tl.CDLHIGHWAVE(open, high, low, close))
t2 = np.array(tl.CDLHANGINGMAN(open, high, low, close))
t3 = np.array(tl.CDLDRAGONFLYDOJI(open, high, low, close))
t4 = np.array(tl.CDLHARAMICROSS(open, high, low, close))
# t4 = np.minimum(t4,0)
t5 = np.array(tl.CDLDARKCLOUDCOVER(open, high, low, close))
test = t1 + t2 + t3 + t5 + t4
rate[i] = get_rate(close, test)
return rate
def _extract_feature(candle, params, candle_type, target_dt):
'''
前に余分に必要なデータ量: {(stockf_fastk_period_l + stockf_fastk_period_l) * 最大分足 (min)} + window_size
= (12 + 12) * 5 + 5 = 125 (min)
'''
o = candle.open
h = candle.high
l = candle.low
c = candle.close
v = candle.volume
# OHLCV
features = pd.DataFrame()
features['open'] = o
features['high'] = h
features['low'] = l
features['close'] = c
features['volume'] = v
####################################
#
# Momentum Indicator Functions
#
####################################
# ADX = SUM((+DI - (-DI)) / (+DI + (-DI)), N) / N
# N — 計算期間
# SUM (..., N) — N期間の合計
# +DI — プラスの価格変動の値（positive directional index)
# -DI — マイナスの価格変動の値（negative directional index）
# rsi_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要
features['adx_s'] = ta.ADX(h, l, c, timeperiod=params['adx_timeperiod_s'])
features['adx_m'] = ta.ADX(h, l, c, timeperiod=params['adx_timeperiod_m'])
features['adx_l'] = ta.ADX(h, l, c, timeperiod=params['adx_timeperiod_l'])
features['adxr_s'] = ta.ADXR(h, l, c, timeperiod=params['adxr_timeperiod_s'])
features['adxr_m'] = ta.ADXR(h, l, c, timeperiod=params['adxr_timeperiod_m'])
features['adxr_l'] = ta.ADXR(h, l, c, timeperiod=params['adxr_timeperiod_l'])
# APO = Shorter Period EMA – Longer Period EMA
features['apo_s'] = ta.APO(c, fastperiod=params['apo_fastperiod_s'], slowperiod=params['apo_slowperiod_s'], matype=ta.MA_Type.EMA)
features['apo_m'] = ta.APO(c, fastperiod=params['apo_fastperiod_m'], slowperiod=params['apo_slowperiod_m'], matype=ta.MA_Type.EMA)
# AroonUp = (N － 過去N日間の最高値からの経過期間) ÷ N × 100
# AroonDown = (N － 過去N日間の最安値からの経過期間) ÷ N × 100
# aroon_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要
#features['aroondown_s'], features['aroonup_s'] = ta.AROON(h, l, timeperiod=params['aroon_timeperiod_s'])
#features['aroondown_m'], features['aroonup_m'] = ta.AROON(h, l, timeperiod=params['aroon_timeperiod_m'])
#features['aroondown_l'], features['aroonup_l'] = ta.AROON(h, l, timeperiod=params['aroon_timeperiod_l'])
# Aronnオシレーター = AroonUp － AroonDown
# aroonosc_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要
features['aroonosc_s'] = ta.AROONOSC(h, l, timeperiod=params['aroonosc_timeperiod_s'])
features['aroonosc_m'] = ta.AROONOSC(h, l, timeperiod=params['aroonosc_timeperiod_m'])
features['aroonosc_l'] = ta.AROONOSC(h, l, timeperiod=params['aroonosc_timeperiod_l'])
# BOP = (close - open) / (high - low)
features['bop'] = ta.BOP(o, h, l, c)
# CCI = (TP - MA) / (0.015 * MD)
# TP: (高値+安値+終値) / 3
# MA: TPの移動平均
# MD: 平均偏差 = ((MA - TP1) + (MA - TP2) + ...) / N
features['cci_s'] = ta.CCI(h, l, c, timeperiod=params['cci_timeperiod_s'])
features['cci_m'] = ta.CCI(h, l, c, timeperiod=params['cci_timeperiod_m'])
features['cci_l'] = ta.CCI(h, l, c, timeperiod=params['cci_timeperiod_l'])
# CMO - Chande Momentum Oscillator
#features['cmo_s'] = ta.CMO(c, timeperiod=params['cmo_timeperiod_s'])
#features['cmo_m'] = ta.CMO(c, timeperiod=params['cmo_timeperiod_m'])
#features['cmo_l'] = ta.CMO(c, timeperiod=params['cmo_timeperiod_l'])
# DX - Directional Movement Index
features['dx_s'] = ta.DX(h, l, c, timeperiod=params['dx_timeperiod_s'])
features['dx_m'] = ta.DX(h, l, c, timeperiod=params['dx_timeperiod_m'])
features['dx_l'] = ta.DX(h, l, c, timeperiod=params['dx_timeperiod_l'])
# MACD＝基準線－相対線
# 基準線（EMA）：過去12日（週・月）間の終値指数平滑平均
# 相対線（EMA）：過去26日（週・月）間の終値指数平滑平均
# https://www.sevendata.co.jp/shihyou/technical/macd.html
# macd_slowperiod_m = 30 の場合30分足で（(30 + macd_signalperiod_m) * 30）/ 60 = 16.5時間必要（macd_signalperiod_m=3の時）
macd, macdsignal, macdhist = ta.MACDEXT(c, fastperiod=params['macd_fastperiod_s'],
slowperiod=params['macd_slowperiod_s'],
signalperiod=params['macd_signalperiod_s'],
fastmatype=ta.MA_Type.EMA, slowmatype=ta.MA_Type.EMA,
signalmatype=ta.MA_Type.EMA)
change_macd = calc_change(macd, macdsignal)
change_macd.index = macd.index
features['macd_s'] = macd
features['macdsignal_s'] = macdsignal
features['macdhist_s'] = macdhist
features['change_macd_s'] = change_macd
macd, macdsignal, macdhist = ta.MACDEXT(c, fastperiod=params['macd_fastperiod_m'],
slowperiod=params['macd_slowperiod_m'],
signalperiod=params['macd_signalperiod_m'],
fastmatype=ta.MA_Type.EMA, slowmatype=ta.MA_Type.EMA,
signalmatype=ta.MA_Type.EMA)
change_macd = calc_change(macd, macdsignal)
change_macd.index = macd.index
features['macd_m'] = macd
features['macdsignal_m'] = macdsignal
features['macdhist_m'] = macdhist
features['change_macd_m'] = change_macd
# MFI - Money Flow Index
features['mfi_s'] = ta.MFI(h, l, c, v, timeperiod=params['mfi_timeperiod_s'])
features['mfi_m'] = ta.MFI(h, l, c, v, timeperiod=params['mfi_timeperiod_m'])
features['mfi_l'] = ta.MFI(h, l, c, v, timeperiod=params['mfi_timeperiod_l'])
# MINUS_DI - Minus Directional Indicator
features['minus_di_s'] = ta.MINUS_DI(h, l, c, timeperiod=params['minus_di_timeperiod_s'])
features['minus_di_m'] = ta.MINUS_DI(h, l, c, timeperiod=params['minus_di_timeperiod_m'])
features['minus_di_l'] = ta.MINUS_DI(h, l, c, timeperiod=params['minus_di_timeperiod_l'])
# MINUS_DM - Minus Directional Movement
features['minus_dm_s'] = ta.MINUS_DM(h, l, timeperiod=params['minus_dm_timeperiod_s'])
features['minus_dm_m'] = ta.MINUS_DM(h, l, timeperiod=params['minus_dm_timeperiod_m'])
features['minus_dm_l'] = ta.MINUS_DM(h, l, timeperiod=params['minus_dm_timeperiod_l'])
# MOM - Momentum
features['mom_s'] = ta.MOM(c, timeperiod=params['mom_timeperiod_s'])
features['mom_m'] = ta.MOM(c, timeperiod=params['mom_timeperiod_m'])
features['mom_l'] = ta.MOM(c, timeperiod=params['mom_timeperiod_l'])
# PLUS_DI - Minus Directional Indicator
features['plus_di_s'] = ta.PLUS_DI(h, l, c, timeperiod=params['plus_di_timeperiod_s'])
features['plus_di_m'] = ta.PLUS_DI(h, l, c, timeperiod=params['plus_di_timeperiod_m'])
features['plus_di_l'] = ta.PLUS_DI(h, l, c, timeperiod=params['plus_di_timeperiod_l'])
# PLUS_DM - Minus Directional Movement
features['plus_dm_s'] = ta.PLUS_DM(h, l, timeperiod=params['plus_dm_timeperiod_s'])
features['plus_dm_m'] = ta.PLUS_DM(h, l, timeperiod=params['plus_dm_timeperiod_m'])
features['plus_dm_l'] = ta.PLUS_DM(h, l, timeperiod=params['plus_dm_timeperiod_l'])
# PPO - Percentage Price Oscillator
#features['ppo_s'] = ta.PPO(c, fastperiod=params['ppo_fastperiod_s'], slowperiod=params['ppo_slowperiod_s'], matype=ta.MA_Type.EMA)
#features['ppo_m'] = ta.PPO(c, fastperiod=params['ppo_fastperiod_m'], slowperiod=params['ppo_slowperiod_m'], matype=ta.MA_Type.EMA)
# ROC - Rate of change : ((price/prevPrice)-1)*100
features['roc_s'] = ta.ROC(c, timeperiod=params['roc_timeperiod_s'])
features['roc_m'] = ta.ROC(c, timeperiod=params['roc_timeperiod_m'])
features['roc_l'] = ta.ROC(c, timeperiod=params['roc_timeperiod_l'])
# ROCP = (price-prevPrice) / prevPrice
# http://www.tadoc.org/indicator/ROCP.htm
# rocp_timeperiod_l = 30 の場合、30分足で(30 * 30) / 60 = 15時間必要
rocp = ta.ROCP(c, timeperiod=params['rocp_timeperiod_s'])
change_rocp = calc_change(rocp, pd.Series(np.zeros(len(candle)), index=candle.index))
change_rocp.index = rocp.index
features['rocp_s'] = rocp
features['change_rocp_s'] = change_rocp
rocp = ta.ROCP(c, timeperiod=params['rocp_timeperiod_m'])
change_rocp = calc_change(rocp, pd.Series(np.zeros(len(candle)), index=candle.index))
change_rocp.index = rocp.index
features['rocp_m'] = rocp
features['change_rocp_m'] = change_rocp
rocp = ta.ROCP(c, timeperiod=params['rocp_timeperiod_l'])
change_rocp = calc_change(rocp, pd.Series(np.zeros(len(candle)), index=candle.index))
change_rocp.index = rocp.index
features['rocp_l'] = rocp
features['change_rocp_l'] = change_rocp
# ROCR - Rate of change ratio: (price/prevPrice)
features['rocr_s'] = ta.ROCR(c, timeperiod=params['rocr_timeperiod_s'])
features['rocr_m'] = ta.ROCR(c, timeperiod=params['rocr_timeperiod_m'])
features['rocr_l'] = ta.ROCR(c, timeperiod=params['rocr_timeperiod_l'])
# ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100
features['rocr100_s'] = ta.ROCR100(c, timeperiod=params['rocr100_timeperiod_s'])
features['rocr100_m'] = ta.ROCR100(c, timeperiod=params['rocr100_timeperiod_m'])
features['rocr100_l'] = ta.ROCR100(c, timeperiod=params['rocr100_timeperiod_l'])
# RSI = (100 * a) / (a + b) (a: x日間の値上がり幅の合計, b: x日間の値下がり幅の合計)
# https://www.sevendata.co.jp/shihyou/technical/rsi.html
# rsi_timeperiod_l=30の場合、30分足で、(30 * 30 / 60(min)) = 15時間必要
#features['rsi_s'] = ta.RSI(c, timeperiod=params['rsi_timeperiod_s'])
#features['rsi_m'] = ta.RSI(c, timeperiod=params['rsi_timeperiod_m'])
#features['rsi_l'] = ta.RSI(c, timeperiod=params['rsi_timeperiod_l'])
# FASTK(KPeriod) = 100 * (Today's Close - LowestLow) / (HighestHigh - LowestLow)
# FASTD(FastDperiod) = MA Smoothed FASTK over FastDperiod
# http://www.tadoc.org/indicator/STOCHF.htm
# stockf_fastk_period_l=30の場合30分足で、(((30 + 30) * 30) / 60(min)) = 30時間必要 (LowestLowが移動平均の30分余分に必要なので60period余分に計算する)
fastk, fastd = ta.STOCHF(h, l, c, fastk_period=params['stockf_fastk_period_s'], fastd_period=params['stockf_fastd_period_s'], fastd_matype=ta.MA_Type.EMA)
change_stockf = calc_change(fastk, fastd)
change_stockf.index = fastk.index
features['fastk_s'] = fastk
features['fastd_s'] = fastd
features['fast_change_s'] = change_stockf
fastk, fastd = ta.STOCHF(h, l, c, fastk_period=params['stockf_fastk_period_m'], fastd_period=params['stockf_fastd_period_m'], fastd_matype=ta.MA_Type.EMA)
change_stockf = calc_change(fastk, fastd)
change_stockf.index = fastk.index
features['fastk_m'] = fastk
features['fastd_m'] = fastd
features['fast_change_m'] = change_stockf
fastk, fastd = ta.STOCHF(h, l, c, fastk_period=params['stockf_fastk_period_l'], fastd_period=params['stockf_fastk_period_l'], fastd_matype=ta.MA_Type.EMA)
change_stockf = calc_change(fastk, fastd)
change_stockf.index = fastk.index
features['fastk_l'] = fastk
features['fastd_l'] = fastd
features['fast_change_l'] = change_stockf
# TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
features['trix_s'] = ta.TRIX(c, timeperiod=params['trix_timeperiod_s'])
features['trix_m'] = ta.TRIX(c, timeperiod=params['trix_timeperiod_m'])
features['trix_l'] = ta.TRIX(c, timeperiod=params['trix_timeperiod_l'])
# ULTOSC - Ultimate Oscillator
features['ultosc_s'] = ta.ULTOSC(h, l, c, timeperiod1=params['ultosc_timeperiod_s1'], timeperiod2=params['ultosc_timeperiod_s2'], timeperiod3=params['ultosc_timeperiod_s3'])
# WILLR = (当日終値 - N日間の最高値) / (N日間の最高値 - N日間の最安値)× 100
# https://inet-sec.co.jp/study/technical-manual/williamsr/
# willr_timeperiod_l=30の場合30分足で、(30 * 30 / 60) = 15時間必要
features['willr_s'] = ta.WILLR(h, l, c, timeperiod=params['willr_timeperiod_s'])
features['willr_m'] = ta.WILLR(h, l, c, timeperiod=params['willr_timeperiod_m'])
features['willr_l'] = ta.WILLR(h, l, c, timeperiod=params['willr_timeperiod_l'])
####################################
#
# Volume Indicator Functions
#
####################################
# Volume Indicator Functions
# slowperiod_adosc_s = 10の場合、30分足で(10 * 30) / 60 = 5時間必要
features['ad'] = ta.AD(h, l, c, v)
features['adosc_s'] = ta.ADOSC(h, l, c, v, fastperiod=params['fastperiod_adosc_s'], slowperiod=params['slowperiod_adosc_s'])
features['obv'] = ta.OBV(c, v)
####################################
#
# Volatility Indicator Functions
#
####################################
# ATR - Average True Range
features['atr_s'] = ta.ATR(h, l, c, timeperiod=params['atr_timeperiod_s'])
features['atr_m'] = ta.ATR(h, l, c, timeperiod=params['atr_timeperiod_m'])
features['atr_l'] = ta.ATR(h, l, c, timeperiod=params['atr_timeperiod_l'])
# NATR - Normalized Average True Range
#features['natr_s'] = ta.NATR(h, l, c, timeperiod=params['natr_timeperiod_s'])
#features['natr_m'] = ta.NATR(h, l, c, timeperiod=params['natr_timeperiod_m'])
#features['natr_l'] = ta.NATR(h, l, c, timeperiod=params['natr_timeperiod_l'])
# TRANGE - True Range
features['trange'] = ta.TRANGE(h, l, c)
####################################
#
# Price Transform Functions
#
####################################
features['avgprice'] = ta.AVGPRICE(o, h, l, c)
features['medprice'] = ta.MEDPRICE(h, l)
#features['typprice'] = ta.TYPPRICE(h, l, c)
#features['wclprice'] = ta.WCLPRICE(h, l, c)
####################################
#
# Cycle Indicator Functions
#
####################################
#features['ht_dcperiod'] = ta.HT_DCPERIOD(c)
#features['ht_dcphase'] = ta.HT_DCPHASE(c)
#features['inphase'], features['quadrature'] = ta.HT_PHASOR(c)
#features['sine'], features['leadsine'] = ta.HT_SINE(c)
features['integer'] = ta.HT_TRENDMODE(c)
####################################
#
# Statistic Functions
#
####################################
# BETA - Beta
features['beta_s'] = ta.BETA(h, l, timeperiod=params['beta_timeperiod_s'])
features['beta_m'] = ta.BETA(h, l, timeperiod=params['beta_timeperiod_m'])
features['beta_l'] = ta.BETA(h, l, timeperiod=params['beta_timeperiod_l'])
# CORREL - Pearson's Correlation Coefficient (r)
#features['correl_s'] = ta.CORREL(h, l, timeperiod=params['correl_timeperiod_s'])
#features['correl_m'] = ta.CORREL(h, l, timeperiod=params['correl_timeperiod_m'])
#features['correl_l'] = ta.CORREL(h, l, timeperiod=params['correl_timeperiod_l'])
# LINEARREG - Linear Regression
#features['linearreg_s'] = ta.LINEARREG(c, timeperiod=params['linearreg_timeperiod_s'])
#features['linearreg_m'] = ta.LINEARREG(c, timeperiod=params['linearreg_timeperiod_m'])
#features['linearreg_l'] = ta.LINEARREG(c, timeperiod=params['linearreg_timeperiod_l'])
# LINEARREG_ANGLE - Linear Regression Angle
features['linearreg_angle_s'] = ta.LINEARREG_ANGLE(c, timeperiod=params['linearreg_angle_timeperiod_s'])
features['linearreg_angle_m'] = ta.LINEARREG_ANGLE(c, timeperiod=params['linearreg_angle_timeperiod_m'])
features['linearreg_angle_l'] = ta.LINEARREG_ANGLE(c, timeperiod=params['linearreg_angle_timeperiod_l'])
# LINEARREG_INTERCEPT - Linear Regression Intercept
features['linearreg_intercept_s'] = ta.LINEARREG_INTERCEPT(c, timeperiod=params['linearreg_intercept_timeperiod_s'])
features['linearreg_intercept_m'] = ta.LINEARREG_INTERCEPT(c, timeperiod=params['linearreg_intercept_timeperiod_m'])
features['linearreg_intercept_l'] = ta.LINEARREG_INTERCEPT(c, timeperiod=params['linearreg_intercept_timeperiod_l'])
# LINEARREG_SLOPE - Linear Regression Slope
features['linearreg_slope_s'] = ta.LINEARREG_SLOPE(c, timeperiod=params['linearreg_slope_timeperiod_s'])
features['linearreg_slope_m'] = ta.LINEARREG_SLOPE(c, timeperiod=params['linearreg_slope_timeperiod_m'])
features['linearreg_slope_l'] = ta.LINEARREG_SLOPE(c, timeperiod=params['linearreg_slope_timeperiod_l'])
# STDDEV - Standard Deviation
features['stddev_s'] = ta.STDDEV(c, timeperiod=params['stddev_timeperiod_s'], nbdev=1)
features['stddev_m'] = ta.STDDEV(c, timeperiod=params['stddev_timeperiod_m'], nbdev=1)
features['stddev_l'] = ta.STDDEV(c, timeperiod=params['stddev_timeperiod_l'], nbdev=1)
# TSF - Time Series Forecast
features['tsf_s'] = ta.TSF(c, timeperiod=params['tsf_timeperiod_s'])
features['tsf_m'] = ta.TSF(c, timeperiod=params['tsf_timeperiod_m'])
features['tsf_l'] = ta.TSF(c, timeperiod=params['tsf_timeperiod_l'])
# VAR - Variance
#features['var_s'] = ta.VAR(c, timeperiod=params['var_timeperiod_s'], nbdev=1)
#features['var_m'] = ta.VAR(c, timeperiod=params['var_timeperiod_m'], nbdev=1)
#features['var_l'] = ta.VAR(c, timeperiod=params['var_timeperiod_l'], nbdev=1)
# ボリンジャーバンド
# bbands_timeperiod_l = 30の場合、30分足で(30 * 30) / 60 = 15時間必要
bb_upper, bb_middle, bb_lower = ta.BBANDS(c, timeperiod=params['bbands_timeperiod_s'],
nbdevup=params['bbands_nbdevup_s'], nbdevdn=params['bbands_nbdevdn_s'],
matype=ta.MA_Type.EMA)
bb_trend1 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend1[c > bb_upper] = 1
bb_trend1[c < bb_lower] = -1
bb_trend2 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend2[c > bb_middle] = 1
bb_trend2[c < bb_middle] = -1
features['bb_upper_s'] = bb_upper
features['bb_middle_s'] = bb_middle
features['bb_lower_s'] = bb_lower
features['bb_trend1_s'] = bb_trend1
features['bb_trend2_s'] = bb_trend2
bb_upper, bb_middle, bb_lower = ta.BBANDS(c, timeperiod=params['bbands_timeperiod_m'],
nbdevup=params['bbands_nbdevup_m'], nbdevdn=params['bbands_nbdevdn_m'],
matype=ta.MA_Type.EMA)
bb_trend1 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend1[c > bb_upper] = 1
bb_trend1[c < bb_lower] = -1
bb_trend2 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend2[c > bb_middle] = 1
bb_trend2[c < bb_middle] = -1
features['bb_upper_m'] = bb_upper
features['bb_middle_m'] = bb_middle
features['bb_lower_m'] = bb_lower
features['bb_trend1_m'] = bb_trend1
features['bb_trend2_m'] = bb_trend2
bb_upper, bb_middle, bb_lower = ta.BBANDS(c, timeperiod=params['bbands_timeperiod_l'],
nbdevup=params['bbands_nbdevup_l'], nbdevdn=params['bbands_nbdevdn_l'],
matype=ta.MA_Type.EMA)
bb_trend1 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend1[c > bb_upper] = 1
bb_trend1[c < bb_lower] = -1
bb_trend2 = pd.Series(np.zeros(len(candle)), index=candle.index)
bb_trend2[c > bb_middle] = 1
bb_trend2[c < bb_middle] = -1
features['bb_upper_l'] = bb_upper
features['bb_middle_l'] = bb_middle
features['bb_lower_l'] = bb_lower
features['bb_trend1_l'] = bb_trend1
features['bb_trend2_l'] = bb_trend2
# ローソク足
features['CDL2CROWS'] = ta.CDL2CROWS(o, h, l, c)
features['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(o, h, l, c)
features['CDL3INSIDE'] = ta.CDL3INSIDE(o, h, l, c)
features['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(o, h, l, c)
features['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(o, h, l, c)
features['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(o, h, l, c)
features['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(o, h, l, c)
features['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY(o, h, l, c, penetration=0)
features['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(o, h, l, c)
features['CDLBELTHOLD'] = ta.CDLBELTHOLD(o, h, l, c)
features['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(o, h, l, c)
features['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU(o, h, l, c)
features['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL(o, h, l, c)
features['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK(o, h, l, c)
features['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(o, h, l, c, penetration=0)
#features['CDLDOJI'] = ta.CDLDOJI(o, h, l, c)
features['CDLDOJISTAR'] = ta.CDLDOJISTAR(o, h, l, c)
features['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(o, h, l, c)
features['CDLENGULFING'] = ta.CDLENGULFING(o, h, l, c)
features['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(o, h, l, c, penetration=0)
features['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(o, h, l, c, penetration=0)
#features['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE(o, h, l, c)
features['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(o, h, l, c)
features['CDLHAMMER'] = ta.CDLHAMMER(o, h, l, c)
#features['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(o, h, l, c)
features['CDLHARAMI'] = ta.CDLHARAMI(o, h, l, c)
features['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(o, h, l, c)
features['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(o, h, l, c)
#features['CDLHIKKAKE'] = ta.CDLHIKKAKE(o, h, l, c)
features['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(o, h, l, c)
features['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(o, h, l, c)
#features['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(o, h, l, c)
features['CDLINNECK'] = ta.CDLINNECK(o, h, l, c)
#features['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(o, h, l, c)
features['CDLKICKING'] = ta.CDLKICKING(o, h, l, c)
features['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH(o, h, l, c)
features['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM(o, h, l, c)
#features['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(o, h, l, c)
features['CDLMARUBOZU'] = ta.CDLMARUBOZU(o, h, l, c)
#features['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(o, h, l, c)
features['CDLMATHOLD'] = ta.CDLMATHOLD(o, h, l, c, penetration=0)
features['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(o, h, l, c, penetration=0)
features['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(o, h, l, c, penetration=0)
features['CDLONNECK'] = ta.CDLONNECK(o, h, l, c)
features['CDLPIERCING'] = ta.CDLPIERCING(o, h, l, c)
features['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(o, h, l, c)
features['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(o, h, l, c)
features['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(o, h, l, c)
#features['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(o, h, l, c)
features['CDLSHORTLINE'] = ta.CDLSHORTLINE(o, h, l, c)
#features['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(o, h, l, c)
features['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(o, h, l, c)
features['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(o, h, l, c)
features['CDLTAKURI'] = ta.CDLTAKURI(o, h, l, c)
features['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(o, h, l, c)
features['CDLTHRUSTING'] = ta.CDLTHRUSTING(o, h, l, c)
features['CDLTRISTAR'] = ta.CDLTRISTAR(o, h, l, c)
features['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER(o, h, l, c)
features['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS(o, h, l, c)
features['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS(o, h, l, c)
'''
# トレンドライン
for dt in datetimerange(candle.index[0], candle.index[-1] + timedelta(minutes=1)):
start_dt = (dt - timedelta(minutes=130)).strftime('%Y-%m-%d %H:%M:%S')
end_dt = dt.strftime('%Y-%m-%d %H:%M:%S')
tmp = candle.loc[(start_dt <= candle.index) & (candle.index <= end_dt)]
for w_size, stride in [(15, 5), (30, 10), (60, 10), (120, 10)]:
# for w_size, stride in [(120, 10)]:
trendlines = calc_trendlines(tmp, w_size, stride)
if len(trendlines) == 0:
continue
trendline_feature = calc_trendline_feature(tmp, trendlines)
print('{}-{} {} {} {}'.format(dt - timedelta(minutes=130), dt, trendline_feature['high_a'], trendline_feature['high_b'], trendline_feature['high_diff']))
features.loc[features.index == end_dt, 'trendline_high_a_{}'.format(w_size)] = trendline_feature['high_a']
features.loc[features.index == end_dt, 'trendline_high_b_{}'.format(w_size)] = trendline_feature['high_b']
features.loc[features.index == end_dt, 'trendline_high_diff_{}'.format(w_size)] = trendline_feature['high_diff']
features.loc[features.index == end_dt, 'trendline_low_a_{}'.format(w_size)] = trendline_feature['low_a']
features.loc[features.index == end_dt, 'trendline_low_b_{}'.format(w_size)] = trendline_feature['low_b']
features.loc[features.index == end_dt, 'trendline_low_diff_{}'.format(w_size)] = trendline_feature['low_diff']
'''
window = 5
features_ext = features
for w in range(window):
tmp = features.shift(periods=60 * (w + 1), freq='S')
tmp.columns = [c + '_' + str(w + 1) + 'w' for c in features.columns]
features_ext = pd.concat([features_ext, tmp], axis=1)
if candle_type == '5min':
features_ext = features_ext.shift(periods=300, freq='S')
features_ext = features_ext.fillna(method='ffill')
features_ext = features_ext[features_ext.index == target_dt]
return features_ext
def technical(df):
open = df['open'].values
close = df['close'].values
high = df['high'].values
low = df['low'].values
volume = df['volume'].values
# define the technical analysis matrix
retn = np.array([
tb.MA(close, timeperiod=60), # 1
tb.MA(close, timeperiod=120), # 2
tb.ADX(high, low, close, timeperiod=14), # 3
tb.ADXR(high, low, close, timeperiod=14), # 4
tb.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)[0], # 5
tb.RSI(close, timeperiod=14), # 6
tb.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[0], # 7
tb.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[1], # 8
tb.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[2], # 9
tb.AD(high, low, close, volume), # 10
tb.ATR(high, low, close, timeperiod=14), # 11
tb.HT_DCPERIOD(close), # 12
tb.CDL2CROWS(open, high, low, close), # 13
tb.CDL3BLACKCROWS(open, high, low, close), # 14
tb.CDL3INSIDE(open, high, low, close), # 15
tb.CDL3LINESTRIKE(open, high, low, close), # 16
tb.CDL3OUTSIDE(open, high, low, close), # 17
tb.CDL3STARSINSOUTH(open, high, low, close), # 18
tb.CDL3WHITESOLDIERS(open, high, low, close), # 19
tb.CDLABANDONEDBABY(open, high, low, close, penetration=0), # 20
tb.CDLADVANCEBLOCK(open, high, low, close), # 21
tb.CDLBELTHOLD(open, high, low, close), # 22
tb.CDLBREAKAWAY(open, high, low, close), # 23
tb.CDLCLOSINGMARUBOZU(open, high, low, close), # 24
tb.CDLCONCEALBABYSWALL(open, high, low, close), # 25
tb.CDLCOUNTERATTACK(open, high, low, close), # 26
tb.CDLDARKCLOUDCOVER(open, high, low, close, penetration=0), # 27
tb.CDLDOJI(open, high, low, close), # 28
tb.CDLDOJISTAR(open, high, low, close), # 29
tb.CDLDRAGONFLYDOJI(open, high, low, close), # 30
tb.CDLENGULFING(open, high, low, close), # 31
tb.CDLEVENINGDOJISTAR(open, high, low, close, penetration=0), # 32
tb.CDLEVENINGSTAR(open, high, low, close, penetration=0), # 33
tb.CDLGAPSIDESIDEWHITE(open, high, low, close), # 34
tb.CDLGRAVESTONEDOJI(open, high, low, close), # 35
tb.CDLHAMMER(open, high, low, close), # 36
tb.CDLHANGINGMAN(open, high, low, close), # 37
tb.CDLHARAMI(open, high, low, close), # 38
tb.CDLHARAMICROSS(open, high, low, close), # 39
tb.CDLHIGHWAVE(open, high, low, close), # 40
tb.CDLHIKKAKE(open, high, low, close), # 41
tb.CDLHIKKAKEMOD(open, high, low, close), # 42
tb.CDLHOMINGPIGEON(open, high, low, close), # 43
tb.CDLIDENTICAL3CROWS(open, high, low, close), # 44
tb.CDLINNECK(open, high, low, close), # 45
tb.CDLINVERTEDHAMMER(open, high, low, close), # 46
tb.CDLKICKING(open, high, low, close), # 47
tb.CDLKICKINGBYLENGTH(open, high, low, close), # 48
tb.CDLLADDERBOTTOM(open, high, low, close), # 49
tb.CDLLONGLEGGEDDOJI(open, high, low, close), # 50
tb.CDLLONGLINE(open, high, low, close), # 51
tb.CDLMARUBOZU(open, high, low, close), # 52
tb.CDLMATCHINGLOW(open, high, low, close), # 53
tb.CDLMATHOLD(open, high, low, close, penetration=0), # 54
tb.CDLMORNINGDOJISTAR(open, high, low, close, penetration=0), # 55
tb.CDLMORNINGSTAR(open, high, low, close, penetration=0), # 56
tb.CDLONNECK(open, high, low, close), # 57
tb.CDLPIERCING(open, high, low, close), # 58
tb.CDLRICKSHAWMAN(open, high, low, close), # 59
tb.CDLRISEFALL3METHODS(open, high, low, close), # 60
tb.CDLSEPARATINGLINES(open, high, low, close), # 61
tb.CDLSHOOTINGSTAR(open, high, low, close), # 62
tb.CDLSHORTLINE(open, high, low, close), # 63
tb.CDLSPINNINGTOP(open, high, low, close), # 64
tb.CDLSTALLEDPATTERN(open, high, low, close), # 65
tb.CDLSTICKSANDWICH(open, high, low, close), # 66
tb.CDLTAKURI(open, high, low, close), # 67
tb.CDLTASUKIGAP(open, high, low, close), # 68
tb.CDLTHRUSTING(open, high, low, close), # 69
tb.CDLTRISTAR(open, high, low, close), # 70
tb.CDLUNIQUE3RIVER(open, high, low, close), # 71
tb.CDLUPSIDEGAP2CROWS(open, high, low, close), # 72
tb.CDLXSIDEGAP3METHODS(open, high, low, close) # 73
]).T
return retn
def built_in_scanners(ticker="SPY"):
data = yf.download(ticker,
start="2020-01-01",
end=datetime.today().strftime('%Y-%m-%d'))
open = data['Open']
high = data['High']
low = data['Low']
close = data['Close']
# The library's functions runs on yesterday's date, so subtract 1 from today's date.
current_date = datetime.today() - timedelta(days=1)
current_date_formatted = current_date.strftime('%Y-%m-%d')
two_crows = talib.CDL2CROWS(open, high, low, close)[current_date_formatted]
three_black_crows = talib.CDL3BLACKCROWS(open, high, low,
close)[current_date_formatted]
three_inside = talib.CDL3INSIDE(open, high, low,
close)[current_date_formatted]
three_line_strike = talib.CDL3LINESTRIKE(open, high, low,
close)[current_date_formatted]
three_outside = talib.CDL3OUTSIDE(open, high, low,
close)[current_date_formatted]
three_stars_in_south = talib.CDL3STARSINSOUTH(
open, high, low, close)[current_date_formatted]
three_white_soldiers = talib.CDL3WHITESOLDIERS(
open, high, low, close)[current_date_formatted]
abandoned_baby = talib.CDLABANDONEDBABY(open, high, low,
close)[current_date_formatted]
advance_block = talib.CDLADVANCEBLOCK(open, high, low,
close)[current_date_formatted]
belt_hold = talib.CDLBELTHOLD(open, high, low,
close)[current_date_formatted]
breakaway = talib.CDLBREAKAWAY(open, high, low,
close)[current_date_formatted]
closing_marubozu = talib.CDLCLOSINGMARUBOZU(open, high, low,
close)[current_date_formatted]
concealing_baby_swallow = talib.CDLCONCEALBABYSWALL(
open, high, low, close)[current_date_formatted]
talib.CDLCOUNTERATTACK(open, high, low, close)[current_date_formatted]
dark_cloud_cover = talib.CDLDARKCLOUDCOVER(
open, high, low, close, penetration=0)[current_date_formatted]
doji = talib.CDLDOJI(open, high, low, close)[current_date_formatted]
doji_star = talib.CDLDOJISTAR(open, high, low,
close)[current_date_formatted]
dragonfly_doji = talib.CDLDRAGONFLYDOJI(open, high, low,
close)[current_date_formatted]
engulfing_candle = talib.CDLENGULFING(open, high, low,
close)[current_date_formatted]
evening_doji_star = talib.CDLEVENINGDOJISTAR(
open, high, low, close, penetration=0)[current_date_formatted]
evening_star = talib.CDLEVENINGSTAR(open, high, low, close,
penetration=0)[current_date_formatted]
gaps = talib.CDLGAPSIDESIDEWHITE(open, high, low,
close)[current_date_formatted]
gravestone_doji = talib.CDLGRAVESTONEDOJI(open, high, low,
close)[current_date_formatted]
hammer = talib.CDLHAMMER(open, high, low, close)[current_date_formatted]
hanging_man = talib.CDLHANGINGMAN(open, high, low,
close)[current_date_formatted]
harami = talib.CDLHARAMI(open, high, low, close)[current_date_formatted]
harami_cross = talib.CDLHARAMICROSS(open, high, low,
close)[current_date_formatted]
high_wave = talib.CDLHIGHWAVE(
open, high, low, close)[current_date_formatted][talib.CDLHIGHWAVE != 0]
hikkake = talib.CDLHIKKAKE(open, high, low, close)[current_date_formatted]
hikkakemod = talib.CDLHIKKAKEMOD(open, high, low,
close)[current_date_formatted]
homing_pigeon = talib.CDLHOMINGPIGEON(open, high, low,
close)[current_date_formatted]
identical_three_crows = talib.CDLIDENTICAL3CROWS(
open, high, low, close)[current_date_formatted]
in_neck = talib.CDLINNECK(open, high, low, close)[current_date_formatted]
inverted_hammer = talib.CDLINVERTEDHAMMER(open, high, low,
close)[current_date_formatted]
kicking = talib.CDLKICKING(open, high, low, close)[current_date_formatted]
kicking_by_length = talib.CDLKICKINGBYLENGTH(open, high, low,
close)[current_date_formatted]
ladder_bottom = talib.CDLLADDERBOTTOM(open, high, low,
close)[current_date_formatted]
long_legged_doji = talib.CDLLONGLEGGEDDOJI(open, high, low,
close)[current_date_formatted]
long_line = talib.CDLLONGLINE(open, high, low,
close)[current_date_formatted]
marubozu = talib.CDLMARUBOZU(open, high, low,
close)[current_date_formatted]
matching_low = talib.CDLMATCHINGLOW(open, high, low,
close)[current_date_formatted]
mat_hold = talib.CDLMATHOLD(open, high, low, close,
penetration=0)[current_date_formatted]
morning_doji_star = talib.CDLMORNINGDOJISTAR(
open, high, low, close, penetration=0)[current_date_formatted]
morning_star = talib.CDLMORNINGSTAR(open, high, low, close,
penetration=0)[current_date_formatted]
on_neck = talib.CDLONNECK(open, high, low, close)[current_date_formatted]
piercing = talib.CDLPIERCING(open, high, low,
close)[current_date_formatted]
rickshawman = talib.CDLRICKSHAWMAN(open, high, low,
close)[current_date_formatted]
rise_fall_3_methods = talib.CDLRISEFALL3METHODS(
open, high, low, close)[current_date_formatted]
separating_lines = talib.CDLSEPARATINGLINES(open, high, low,
close)[current_date_formatted]
shooting_star = talib.CDLSHOOTINGSTAR(open, high, low,
close)[current_date_formatted]
shortline = talib.CDLSHORTLINE(open, high, low,
close)[current_date_formatted]
spinning_top = talib.CDLSPINNINGTOP(open, high, low,
close)[current_date_formatted]
stalled_pattern = talib.CDLSTALLEDPATTERN(open, high, low,
close)[current_date_formatted]
stick_sandwich = talib.CDLSTICKSANDWICH(open, high, low,
close)[current_date_formatted]
takuri = talib.CDLTAKURI(open, high, low, close)[current_date_formatted]
tasuki_gap = talib.CDLTASUKIGAP(open, high, low,
close)[current_date_formatted]
thrusting = talib.CDLTHRUSTING(open, high, low,
close)[current_date_formatted]
tristar = talib.CDLTRISTAR(open, high, low, close)[current_date_formatted]
unique_three_river = talib.CDLUNIQUE3RIVER(open, high, low,
close)[current_date_formatted]
upside_gap_two_crows = talib.CDLUPSIDEGAP2CROWS(
open, high, low, close)[current_date_formatted]
upside_downside_gap_three_methods = talib.CDLXSIDEGAP3METHODS(
open, high, low, close)[current_date_formatted]
patterns = list(vars().keys())[7:]
values = list(vars().values())[7:]
for index in range(0, len(patterns)):
if (values[index] != 0):
print(patterns[index])
print(values[index])
def CDLHIGHWAVE(data, **kwargs):
_check_talib_presence()
popen, phigh, plow, pclose, pvolume = _extract_ohlc(data)
return talib.CDLHIGHWAVE(popen, phigh, plow, pclose, **kwargs)
ohlc_df['CDLHAMMER'] = ta.CDLHAMMER(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLHARAMI'] = ta.CDLHARAMI(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLHIKKAKE'] = ta.CDLHIKKAKE(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDLINNECK'] = ta.CDLINNECK(ohlc_df['open'],
CDLHAMMER_real = talib.CDLHAMMER(resorted['open'],
resorted['high'],
resorted['low'],
resorted['close'])
CDLHANGINGMAN_real = talib.CDLHANGINGMAN(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLHARAMI_real = talib.CDLHARAMI(resorted['open'],
resorted['high'],
resorted['low'],
resorted['close'])
CDLHARAMICROSS_real = talib.CDLHARAMICROSS(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLHIGHWAVE_real = talib.CDLHIGHWAVE(resorted['open'],
resorted['high'],
resorted['low'],
resorted['close'])
CDLHIKKAKE_real = talib.CDLHIKKAKE(resorted['open'],
resorted['high'],
resorted['low'],
resorted['close'])
CDLHIKKAKEMOD_real = talib.CDLHIKKAKEMOD(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLHOMINGPIGEON_real = talib.CDLHOMINGPIGEON(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLIDENTICAL3CROWS_real = talib.CDLIDENTICAL3CROWS(
resorted['open'], resorted['high'], resorted['low'],
resorted['close'])
CDLINNECK_real = talib.CDLINNECK(resorted['open'],
def create_signal_dataframe(df_):
o = np.array(df_['始値'])
c = np.array(df_['終値'])
l = np.array(df_['安値'])
h = np.array(df_['高値'])
df = df_.copy()
df['CDL2CROWS'] = ta.CDL2CROWS(o, h, l, c)
df['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(o, h, l, c)
df['CDL3INSIDE'] = ta.CDL3INSIDE(o, h, l, c)
df['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(o, h, l, c)
df['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(o, h, l, c)
df['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(o, h, l, c)
df['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(o, h, l, c)
df['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY(o, h, l, c)
df['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(o, h, l, c)
df['CDLBELTHOLD'] = ta.CDLBELTHOLD(o, h, l, c)
df['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(o, h, l, c)
df['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU(o, h, l, c)
df['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL(o, h, l, c)
df['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK(o, h, l, c)
df['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(o, h, l, c)
df['CDLDOJI'] = ta.CDLDOJI(o, h, l, c)
df['CDLDOJISTAR'] = ta.CDLDOJISTAR(o, h, l, c)
df['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(o, h, l, c)
df['CDLENGULFING'] = ta.CDLENGULFING(o, h, l, c)
df['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(o, h, l, c)
df['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(o, h, l, c)
df['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE(o, h, l, c)
df['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(o, h, l, c)
df['CDLHAMMER'] = ta.CDLHAMMER(o, h, l, c)
df['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(o, h, l, c)
df['CDLHARAMI'] = ta.CDLHARAMI(o, h, l, c)
df['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(o, h, l, c)
df['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(o, h, l, c)
df['CDLHIKKAKE'] = ta.CDLHIKKAKE(o, h, l, c)
df['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(o, h, l, c)
df['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(o, h, l, c)
df['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(o, h, l, c)
df['CDLINNECK'] = ta.CDLINNECK(o, h, l, c)
df['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(o, h, l, c)
df['CDLKICKING'] = ta.CDLKICKING(o, h, l, c)
df['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH(o, h, l, c)
df['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM(o, h, l, c)
df['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(o, h, l, c)
df['CDLLONGLINE'] = ta.CDLLONGLINE(o, h, l, c)
df['CDLMARUBOZU'] = ta.CDLMARUBOZU(o, h, l, c)
df['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(o, h, l, c)
df['CDLMATHOLD'] = ta.CDLMATHOLD(o, h, l, c)
df['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(o, h, l, c)
df['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(o, h, l, c)
df['CDLONNECK'] = ta.CDLONNECK(o, h, l, c)
df['CDLPIERCING'] = ta.CDLPIERCING(o, h, l, c)
df['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(o, h, l, c)
df['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(o, h, l, c)
df['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(o, h, l, c)
df['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(o, h, l, c)
df['CDLSHORTLINE'] = ta.CDLSHORTLINE(o, h, l, c)
df['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(o, h, l, c)
df['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(o, h, l, c)
df['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(o, h, l, c)
df['CDLTAKURI'] = ta.CDLTAKURI(o, h, l, c)
df['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(o, h, l, c)
df['CDLTHRUSTING'] = ta.CDLTHRUSTING(o, h, l, c)
df['CDLTRISTAR'] = ta.CDLTRISTAR(o, h, l, c)
df['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER(o, h, l, c)
df['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS(o, h, l, c)
df['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS(o, h, l, c)
return df
def collectDATA(self, start_dt, end_dt, para_min, threshold):
# 建立数据库连接,剔除已入库的部分
db = pymysql.connect(host='127.0.0.1',
user='******',
passwd='admin',
db='stock',
charset='utf8')
cursor = db.cursor()
if para_min == 'day':
sql_done_set = "SELECT * FROM btc_day a where state_dt >= '%s' and state_dt <= '%s' order by state_dt asc" % (
start_dt, end_dt)
else:
sql_done_set = "SELECT * FROM btc_%smin a where state_dt >= '%s' and state_dt <= '%s' order by state_dt asc" % (
str(para_min), start_dt, end_dt)
cursor.execute(sql_done_set)
done_set = cursor.fetchall()
if len(done_set) == 0:
raise Exception
self.date_seq = []
self.open_list = []
self.close_list = []
self.high_list = []
self.low_list = []
self.vol_list = []
self.amount_list = []
self.tor_list = []
self.vr_list = []
self.ma5_list = []
self.ma10_list = []
self.ma20_list = []
self.ma30_list = []
self.ma60_list = []
for i in range(len(done_set)):
self.date_seq.append(done_set[i][0])
self.open_list.append(float(done_set[i][1]))
self.close_list.append(float(done_set[i][2]))
self.high_list.append(float(done_set[i][3]))
self.low_list.append(float(done_set[i][4]))
self.vol_list.append(float(done_set[i][6]))
self.amount_list.append(float(done_set[i][5]))
db.close()
cdl_2crows = ta.CDL2CROWS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3blackcrows = ta.CDL3BLACKCROWS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3inside = ta.CDL3INSIDE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3linestrike = ta.CDL3LINESTRIKE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3outside = ta.CDL3OUTSIDE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3starsinsouth = ta.CDL3STARSINSOUTH(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3whitesoldiers = ta.CDL3WHITESOLDIERS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_abandonedbaby = ta.CDLABANDONEDBABY(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_advancedblock = ta.CDLADVANCEBLOCK(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_belthold = ta.CDLBELTHOLD(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_breakaway = ta.CDLBREAKAWAY(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_closing = ta.CDLCLOSINGMARUBOZU(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_conbaby = ta.CDLCONCEALBABYSWALL(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_counterattack = ta.CDLCOUNTERATTACK(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_darkcloud = ta.CDLDARKCLOUDCOVER(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_doji = ta.CDLDOJI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_dojistar = ta.CDLDOJISTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_dragondoji = ta.CDLDRAGONFLYDOJI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_eng = ta.CDLENGULFING(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_evedoji = ta.CDLEVENINGDOJISTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_evestar = ta.CDLEVENINGSTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_gapside = ta.CDLGAPSIDESIDEWHITE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_gravedoji = ta.CDLGRAVESTONEDOJI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_hammer = ta.CDLHAMMER(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_hanging = ta.CDLHANGINGMAN(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_hara = ta.CDLHARAMI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_haracross = ta.CDLHARAMICROSS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_highwave = ta.CDLHIGHWAVE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_hikk = ta.CDLHIKKAKE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_hikkmod = ta.CDLHIKKAKEMOD(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_homing = ta.CDLHOMINGPIGEON(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_i3crows = ta.CDLIDENTICAL3CROWS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_inneck = ta.CDLINNECK(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_inverhammer = ta.CDLINVERTEDHAMMER(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_kicking = ta.CDLKICKING(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_kicking2 = ta.CDLKICKINGBYLENGTH(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_ladder = ta.CDLLADDERBOTTOM(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_longdoji = ta.CDLLONGLEGGEDDOJI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_longline = ta.CDLLONGLINE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_marubo = ta.CDLMARUBOZU(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_matchinglow = ta.CDLMATCHINGLOW(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_mathold = ta.CDLMATHOLD(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_morningdoji = ta.CDLMORNINGDOJISTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_morningstar = ta.CDLMORNINGSTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_onneck = ta.CDLONNECK(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_pier = ta.CDLPIERCING(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_rick = ta.CDLRICKSHAWMAN(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_3methords = ta.CDLRISEFALL3METHODS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_seprate = ta.CDLSEPARATINGLINES(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_shoot = ta.CDLSHOOTINGSTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_shortcandle = ta.CDLSHORTLINE(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_spin = ta.CDLSPINNINGTOP(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_stalled = ta.CDLSTALLEDPATTERN(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_sandwich = ta.CDLSTICKSANDWICH(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_taku = ta.CDLTAKURI(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_takugap = ta.CDLTASUKIGAP(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_thrust = ta.CDLTHRUSTING(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_tristar = ta.CDLTRISTAR(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_uni = ta.CDLUNIQUE3RIVER(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_upgap = ta.CDLUPSIDEGAP2CROWS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
cdl_xside = ta.CDLXSIDEGAP3METHODS(np.array(self.open_list),
np.array(self.high_list),
np.array(self.low_list),
np.array(self.close_list))
self.data_train = []
self.data_target = []
self.data_target_onehot = []
for i in range(len(self.close_list) - 5):
train = [
cdl_2crows[i], cdl_3blackcrows[i], cdl_3inside[i],
cdl_3linestrike[i], cdl_3outside[i], cdl_3starsinsouth[i],
cdl_3whitesoldiers[i], cdl_abandonedbaby[i],
cdl_advancedblock[i], cdl_belthold[i], cdl_breakaway[i],
cdl_closing[i], cdl_conbaby[i], cdl_counterattack[i],
cdl_darkcloud[i], cdl_doji[i], cdl_dojistar[i],
cdl_dragondoji[i], cdl_eng[i], cdl_evedoji[i], cdl_evestar[i],
cdl_gapside[i], cdl_gravedoji[i], cdl_hammer[i],
cdl_hanging[i], cdl_hara[i], cdl_haracross[i], cdl_highwave[i],
cdl_hikk[i], cdl_hikkmod[i], cdl_homing[i], cdl_i3crows[i],
cdl_inneck[i], cdl_inverhammer[i], cdl_kicking[i],
cdl_kicking2[i], cdl_ladder[i], cdl_longdoji[i],
cdl_longline[i], cdl_marubo[i], cdl_matchinglow[i],
cdl_mathold[i], cdl_morningdoji[i], cdl_morningstar[i],
cdl_onneck[i], cdl_pier[i], cdl_rick[i], cdl_3methords[i],
cdl_seprate[i], cdl_shoot[i], cdl_shortcandle[i], cdl_spin[i],
cdl_stalled[i], cdl_sandwich[i], cdl_taku[i], cdl_takugap[i],
cdl_thrust[i], cdl_tristar[i], cdl_uni[i], cdl_upgap[i],
cdl_xside[i]
]
self.data_train.append(np.array(train))
# after_max_price = max(self.close_list[i+1:i + 5])
# after_min_price = min(self.close_list[i+1:i+5])
# if after_max_price / self.close_list[i] >= 1.01:
# self.data_target.append(float(1.00))
# self.data_target_onehot.append([1,0,0])
# elif after_min_price / self.close_list[i] < 0.99:
# self.data_target.append(float(-1.00))
# self.data_target_onehot.append([0,1,0])
# else:
# self.data_target.append(float(0.00))
# self.data_target_onehot.append([0,0,1])
after_mean_price = np.array(self.close_list[i + 1:i + 5]).mean()
if after_mean_price / self.close_list[i] > threshold:
self.data_target.append(float(1.00))
self.data_target_onehot.append([1, 0, 0])
else:
self.data_target.append(float(-1.00))
self.data_target_onehot.append([0, 1, 0])
self.cnt_pos = 0
self.cnt_pos = len([x for x in self.data_target if x == 1.00])
self.test_case = []
self.test_case = np.array([
cdl_2crows[-1], cdl_3blackcrows[-1], cdl_3inside[-1],
cdl_3linestrike[-1], cdl_3outside[-1], cdl_3starsinsouth[-1],
cdl_3whitesoldiers[-1], cdl_abandonedbaby[-1],
cdl_advancedblock[-1], cdl_belthold[-1], cdl_breakaway[-1],
cdl_closing[-1], cdl_conbaby[-1], cdl_counterattack[-1],
cdl_darkcloud[-1], cdl_doji[-1], cdl_dojistar[-1],
cdl_dragondoji[-1], cdl_eng[-1], cdl_evedoji[-1], cdl_evestar[-1],
cdl_gapside[-1], cdl_gravedoji[-1], cdl_hammer[-1],
cdl_hanging[-1], cdl_hara[-1], cdl_haracross[-1], cdl_highwave[-1],
cdl_hikk[-1], cdl_hikkmod[-1], cdl_homing[-1], cdl_i3crows[-1],
cdl_inneck[-1], cdl_inverhammer[-1], cdl_kicking[-1],
cdl_kicking2[-1], cdl_ladder[-1], cdl_longdoji[-1],
cdl_longline[-1], cdl_marubo[-1], cdl_matchinglow[-1],
cdl_mathold[-1], cdl_morningdoji[-1], cdl_morningstar[-1],
cdl_onneck[-1], cdl_pier[-1], cdl_rick[-1], cdl_3methords[-1],
cdl_seprate[-1], cdl_shoot[-1], cdl_shortcandle[-1], cdl_spin[-1],
cdl_stalled[-1], cdl_sandwich[-1], cdl_taku[-1], cdl_takugap[-1],
cdl_thrust[-1], cdl_tristar[-1], cdl_uni[-1], cdl_upgap[-1],
cdl_xside[-1]
])
self.data_train = np.array(self.data_train)
self.data_target = np.array(self.data_target)
def add_indicator(data):
open = data.Open
high = data.High
low = data.Low
close = data.Close
volume = data.Volume
data['CDL2CROWS'] = talib.CDL2CROWS(open, high, low, close)
data['CDL3BLACKCROWS'] = talib.CDL3BLACKCROWS(open, high, low, close)
data['CDL3INSIDE'] = talib.CDL3INSIDE(open, high, low, close)
data['CDL3LINESTRIKE'] = talib.CDL3LINESTRIKE(open, high, low, close)
data['CDL3OUTSIDE'] = talib.CDL3OUTSIDE(open, high, low, close)
data['CDL3STARSINSOUTH'] = talib.CDL3STARSINSOUTH(open, high, low, close)
data['CDL3WHITESOLDIERS'] = talib.CDL3WHITESOLDIERS(open, high, low, close)
data['CDLABANDONEDBABY'] = talib.CDLABANDONEDBABY(open,
high,
low,
close,
penetration=0)
data['CDLADVANCEBLOCK'] = talib.CDLADVANCEBLOCK(open, high, low, close)
data['CDLBELTHOLD'] = talib.CDLBELTHOLD(open, high, low, close)
data['CDLBREAKAWAY'] = talib.CDLBREAKAWAY(open, high, low, close)
data['CDLCLOSINGMARUBOZU'] = talib.CDLCLOSINGMARUBOZU(
open, high, low, close)
data['CDLCONCEALBABYSWALL'] = talib.CDLCONCEALBABYSWALL(
open, high, low, close)
data['CDLCOUNTERATTACK'] = talib.CDLCOUNTERATTACK(open, high, low, close)
data['CDLDARKCLOUDCOVER'] = talib.CDLDARKCLOUDCOVER(open,
high,
low,
close,
penetration=0)
data['CDLDOJI'] = talib.CDLDOJI(open, high, low, close)
data['CDLDOJISTAR'] = talib.CDLDOJISTAR(open, high, low, close)
data['CDLDRAGONFLYDOJI'] = talib.CDLDRAGONFLYDOJI(open, high, low, close)
data['CDLENGULFING'] = talib.CDLENGULFING(open, high, low, close)
data['CDLEVENINGDOJISTAR'] = talib.CDLEVENINGDOJISTAR(open,
high,
low,
close,
penetration=0)
data['CDLEVENINGSTAR'] = talib.CDLEVENINGSTAR(open,
high,
low,
close,
penetration=0)
data['CDLGAPSIDESIDEWHITE'] = talib.CDLGAPSIDESIDEWHITE(
open, high, low, close)
data['CDLGRAVESTONEDOJI'] = talib.CDLGRAVESTONEDOJI(open, high, low, close)
data['CDLHAMMER'] = talib.CDLHAMMER(open, high, low, close)
data['CDLHANGINGMAN'] = talib.CDLHANGINGMAN(open, high, low, close)
data['CDLHARAMI'] = talib.CDLHARAMI(open, high, low, close)
data['CDLHARAMICROSS'] = talib.CDLHARAMICROSS(open, high, low, close)
data['CDLHIGHWAVE'] = talib.CDLHIGHWAVE(open, high, low, close)
data['CDLHIKKAKE'] = talib.CDLHIKKAKE(open, high, low, close)
data['CDLHIKKAKEMOD'] = talib.CDLHIKKAKEMOD(open, high, low, close)
data['CDLHOMINGPIGEON'] = talib.CDLHOMINGPIGEON(open, high, low, close)
data['CDLIDENTICAL3CROWS'] = talib.CDLIDENTICAL3CROWS(
open, high, low, close)
data['CDLINNECK'] = talib.CDLINNECK(open, high, low, close)
data['CDLINVERTEDHAMMER'] = talib.CDLINVERTEDHAMMER(open, high, low, close)
data['CDLKICKING'] = talib.CDLKICKING(open, high, low, close)
data['CDLKICKINGBYLENGTH'] = talib.CDLKICKINGBYLENGTH(
open, high, low, close)
data['CDLLADDERBOTTOM'] = talib.CDLLADDERBOTTOM(open, high, low, close)
data['CDLLONGLEGGEDDOJI'] = talib.CDLLONGLEGGEDDOJI(open, high, low, close)
data['CDLLONGLINE'] = talib.CDLLONGLINE(open, high, low, close)
data['CDLMARUBOZU'] = talib.CDLMARUBOZU(open, high, low, close)
data['CDLMATCHINGLOW'] = talib.CDLMATCHINGLOW(open, high, low, close)
data['CDLMATHOLD'] = talib.CDLMATHOLD(open,
high,
low,
close,
penetration=0)
data['CDLMORNINGDOJISTAR'] = talib.CDLMORNINGDOJISTAR(open,
high,
low,
close,
penetration=0)
data['CDLMORNINGSTAR'] = talib.CDLMORNINGSTAR(open,
high,
low,
close,
penetration=0)
data['CDLONNECK'] = talib.CDLONNECK(open, high, low, close)
data['CDLPIERCING'] = talib.CDLPIERCING(open, high, low, close)
data['CDLRICKSHAWMAN'] = talib.CDLRICKSHAWMAN(open, high, low, close)
data['CDLRISEFALL3METHODS'] = talib.CDLRISEFALL3METHODS(
open, high, low, close)
data['CDLSEPARATINGLINES'] = talib.CDLSEPARATINGLINES(
open, high, low, close)
data['CDLSHOOTINGSTAR'] = talib.CDLSHOOTINGSTAR(open, high, low, close)
data['CDLSHORTLINE'] = talib.CDLSHORTLINE(open, high, low, close)
data['CDLSPINNINGTOP'] = talib.CDLSPINNINGTOP(open, high, low, close)
data['CDLSTALLEDPATTERN'] = talib.CDLSTALLEDPATTERN(open, high, low, close)
data['CDLSTICKSANDWICH'] = talib.CDLSTICKSANDWICH(open, high, low, close)
data['CDLTAKURI'] = talib.CDLTAKURI(open, high, low, close)
data['CDLTASUKIGAP'] = talib.CDLTASUKIGAP(open, high, low, close)
data['CDLTHRUSTING'] = talib.CDLTHRUSTING(open, high, low, close)
data['CDLTRISTAR'] = talib.CDLTRISTAR(open, high, low, close)
data['CDLUNIQUE3RIVER'] = talib.CDLUNIQUE3RIVER(open, high, low, close)
data['CDLUPSIDEGAP2CROWS'] = talib.CDLUPSIDEGAP2CROWS(
open, high, low, close)
data['CDLXSIDEGAP3METHODS'] = talib.CDLXSIDEGAP3METHODS(
open, high, low, close)
# data['ADX'] = talib.ADX(high, low, close, timeperiod=14)
data['MACDFAS'], data['MACDSLO'], data['MACDSIGNA'] = talib.MACD(
close, fastperiod=12, slowperiod=26, signalperiod=9)
data['3day MA'] = close.shift(1).rolling(window=3).mean()
data['10day MA'] = close.shift(1).rolling(window=10).mean()
data['30day MA'] = close.shift(1).rolling(window=30).mean()
data['RSI_9'] = talib.RSI(close.values, timeperiod=9)
data['S_10'] = close.rolling(window=10).mean()
data['Corr'] = close.rolling(window=10).corr(data['S_10'])
data['Williams %R'] = talib.WILLR(data['High'].values, data['Low'].values,
data['Close'].values, 7)
return data
def CDLHIGHWAVE(self):
integer = talib.CDLHIGHWAVE(self.open, self.high, self.low, self.close)
return integer
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)
def ta(name, price_h, price_l, price_c, price_v, price_o):
# function 'MAX'/'MAXINDEX'/'MIN'/'MININDEX'/'MINMAX'/'MINMAXINDEX'/'SUM' is missing
if name == 'AD':
return talib.AD(np.array(price_h), np.array(price_l),
np.array(price_c), np.asarray(price_v, dtype='float'))
if name == 'ADOSC':
return talib.ADOSC(np.array(price_h),
np.array(price_l),
np.array(price_c),
np.asarray(price_v, dtype='float'),
fastperiod=2,
slowperiod=10)
if name == 'ADX':
return talib.ADX(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'ADXR':
return talib.ADXR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'APO':
return talib.APO(np.asarray(price_c, dtype='float'),
fastperiod=12,
slowperiod=26,
matype=0)
if name == 'AROON':
AROON_DWON, AROON2_UP = talib.AROON(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=90)
return (AROON_DWON, AROON2_UP)
if name == 'AROONOSC':
return talib.AROONOSC(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=14)
if name == 'ATR':
return talib.ATR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'AVGPRICE':
return talib.AVGPRICE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'BBANDS':
BBANDS1, BBANDS2, BBANDS3 = talib.BBANDS(np.asarray(price_c,
dtype='float'),
matype=MA_Type.T3)
return BBANDS1
if name == 'BETA':
return talib.BETA(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=5)
if name == 'BOP':
return talib.BOP(np.array(price_o), np.array(price_h),
np.array(price_l), np.asarray(price_c, dtype='float'))
if name == 'CCI':
return talib.CCI(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'CDL2CROWS':
return talib.CDL2CROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3BLACKCROWS':
return talib.CDL3BLACKCROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3INSIDE':
return talib.CDL3INSIDE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3LINESTRIKE':
return talib.CDL3LINESTRIKE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3OUTSIDE':
return talib.CDL3OUTSIDE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3STARSINSOUTH':
return talib.CDL3STARSINSOUTH(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDL3WHITESOLDIERS':
return talib.CDL3WHITESOLDIERS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLABANDONEDBABY':
return talib.CDLABANDONEDBABY(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLADVANCEBLOCK':
return talib.CDLADVANCEBLOCK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLBELTHOLD':
return talib.CDLBELTHOLD(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLBREAKAWAY':
return talib.CDLBREAKAWAY(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLCLOSINGMARUBOZU':
return talib.CDLCLOSINGMARUBOZU(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLCONCEALBABYSWALL':
return talib.CDLCONCEALBABYSWALL(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLCOUNTERATTACK':
return talib.CDLCOUNTERATTACK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLDARKCLOUDCOVER':
return talib.CDLDARKCLOUDCOVER(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLDOJI':
return talib.CDLDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLDOJISTAR':
return talib.CDLDOJISTAR(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLDRAGONFLYDOJI':
return talib.CDLDRAGONFLYDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLENGULFING':
return talib.CDLENGULFING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLEVENINGDOJISTAR':
return talib.CDLEVENINGDOJISTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLEVENINGSTAR':
return talib.CDLEVENINGSTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLGAPSIDESIDEWHITE':
return talib.CDLGAPSIDESIDEWHITE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLGRAVESTONEDOJI':
return talib.CDLGRAVESTONEDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHAMMER':
return talib.CDLHAMMER(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHANGINGMAN':
return talib.CDLHANGINGMAN(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHARAMI':
return talib.CDLHARAMI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHARAMICROSS':
return talib.CDLHARAMICROSS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHIGHWAVE':
return talib.CDLHIGHWAVE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHIKKAKE':
return talib.CDLHIKKAKE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHIKKAKEMOD':
return talib.CDLHIKKAKEMOD(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLHOMINGPIGEON':
return talib.CDLHOMINGPIGEON(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLIDENTICAL3CROWS':
return talib.CDLIDENTICAL3CROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLINNECK':
return talib.CDLINNECK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLINVERTEDHAMMER':
return talib.CDLINVERTEDHAMMER(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLKICKING':
return talib.CDLKICKING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLKICKINGBYLENGTH':
return talib.CDLKICKINGBYLENGTH(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLLADDERBOTTOM':
return talib.CDLLADDERBOTTOM(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLLONGLEGGEDDOJI':
return talib.CDLLONGLEGGEDDOJI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLLONGLINE':
return talib.CDLLONGLINE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMARUBOZU':
return talib.CDLMARUBOZU(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMATCHINGLOW':
return talib.CDLMATCHINGLOW(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMATHOLD':
return talib.CDLMATHOLD(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLMORNINGDOJISTAR':
return talib.CDLMORNINGDOJISTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLMORNINGSTAR':
return talib.CDLMORNINGSTAR(np.array(price_o),
np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
penetration=0)
if name == 'CDLONNECK':
return talib.CDLONNECK(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLPIERCING':
return talib.CDLPIERCING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLRICKSHAWMAN':
return talib.CDLRICKSHAWMAN(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLRISEFALL3METHODS':
return talib.CDLRISEFALL3METHODS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSEPARATINGLINES':
return talib.CDLSEPARATINGLINES(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSHOOTINGSTAR':
return talib.CDLSHOOTINGSTAR(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSHORTLINE':
return talib.CDLSHORTLINE(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSPINNINGTOP':
return talib.CDLSPINNINGTOP(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSTALLEDPATTERN':
return talib.CDLSTALLEDPATTERN(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLSTICKSANDWICH':
return talib.CDLSTICKSANDWICH(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTAKURI':
return talib.CDLTAKURI(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTASUKIGAP':
return talib.CDLTASUKIGAP(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTHRUSTING':
return talib.CDLTHRUSTING(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLTRISTAR':
return talib.CDLTRISTAR(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLUNIQUE3RIVER':
return talib.CDLUNIQUE3RIVER(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLUPSIDEGAP2CROWS':
return talib.CDLUPSIDEGAP2CROWS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CDLXSIDEGAP3METHODS':
return talib.CDLXSIDEGAP3METHODS(np.array(price_o), np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'CMO':
return talib.CMO(np.asarray(price_c, dtype='float'), timeperiod=14)
if name == 'CORREL':
return talib.CORREL(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=30)
if name == 'DEMA':
return talib.DEMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'DX':
return talib.DX(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'EMA':
return talib.EMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'HT_DCPERIOD':
return talib.HT_DCPERIOD(np.asarray(price_c, dtype='float'))
if name == 'HT_DCPHASE':
return talib.HT_DCPHASE(np.asarray(price_c, dtype='float'))
if name == 'HT_PHASOR':
HT_PHASOR1, HT_PHASOR2 = talib.HT_PHASOR(
np.asarray(price_c, dtype='float')
) # use HT_PHASOR1 for the indication of up and down
return HT_PHASOR1
if name == 'HT_SINE':
HT_SINE1, HT_SINE2 = talib.HT_SINE(np.asarray(price_c, dtype='float'))
return HT_SINE1
if name == 'HT_TRENDLINE':
return talib.HT_TRENDLINE(np.asarray(price_c, dtype='float'))
if name == 'HT_TRENDMODE':
return talib.HT_TRENDMODE(np.asarray(price_c, dtype='float'))
if name == 'KAMA':
return talib.KAMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'LINEARREG':
return talib.LINEARREG(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'LINEARREG_ANGLE':
return talib.LINEARREG_ANGLE(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'LINEARREG_INTERCEPT':
return talib.LINEARREG_INTERCEPT(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'LINEARREG_SLOPE':
return talib.LINEARREG_SLOPE(np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'MA':
return talib.MA(np.asarray(price_c, dtype='float'),
timeperiod=30,
matype=0)
if name == 'MACD':
MACD1, MACD2, MACD3 = talib.MACD(np.asarray(price_c, dtype='float'),
fastperiod=12,
slowperiod=26,
signalperiod=9)
return MACD1
if nam == 'MACDEXT':
return talib.MACDEXT(np.asarray(price_c, dtype='float'),
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
if name == 'MACDFIX':
MACDFIX1, MACDFIX2, MACDFIX3 = talib.MACDFIX(np.asarray(price_c,
dtype='float'),
signalperiod=9)
return MACDFIX1
if name == 'MAMA':
MAMA1, MAMA2 = talib.MAMA(np.asarray(price_c, dtype='float'),
fastlimit=0,
slowlimit=0)
return MAMA1
if name == 'MEDPRICE':
return talib.MEDPRICE(np.array(price_h),
np.asarray(price_l, dtype='float'))
if name == 'MINUS_DI':
return talib.MINUS_DI(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'MINUS_DM':
return talib.MINUS_DM(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=14)
if name == 'MOM':
return talib.MOM(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'NATR':
return talib.NATR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'OBV':
return talib.OBV(np.array(price_c), np.asarray(price_v, dtype='float'))
if name == 'PLUS_DI':
return talib.PLUS_DI(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'PLUS_DM':
return talib.PLUS_DM(np.array(price_h),
np.asarray(price_l, dtype='float'),
timeperiod=14)
if name == 'PPO':
return talib.PPO(np.asarray(price_c, dtype='float'),
fastperiod=12,
slowperiod=26,
matype=0)
if name == 'ROC':
return talib.ROC(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'ROCP':
return talib.ROCP(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'ROCR100':
return talib.ROCR100(np.asarray(price_c, dtype='float'), timeperiod=10)
if name == 'RSI':
return talib.RSI(np.asarray(price_c, dtype='float'), timeperiod=14)
if name == 'SAR':
return talib.SAR(np.array(price_h),
np.asarray(price_l, dtype='float'),
acceleration=0,
maximum=0)
if name == 'SAREXT':
return talib.SAREXT(np.array(price_h),
np.asarray(price_l, dtype='float'),
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
if name == 'SMA':
return talib.SMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'STDDEV':
return talib.STDDEV(np.asarray(price_c, dtype='float'),
timeperiod=5,
nbdev=1)
if name == 'STOCH':
STOCH1, STOCH2 = talib.STOCH(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
return STOCH1
if name == 'STOCHF':
STOCHF1, STOCHF2 = talib.STOCHF(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
fastk_period=5,
fastd_period=3,
fastd_matype=0)
return STOCHF1
if name == 'STOCHRSI':
STOCHRSI1, STOCHRSI2 = talib.STOCHRSI(np.asarray(price_c,
dtype='float'),
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
return STOCHRSI1
if name == 'T3':
return talib.T3(np.asarray(price_c, dtype='float'),
timeperiod=5,
vfactor=0)
if name == 'TEMA':
return talib.TEMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'TRANGE':
return talib.TRANGE(np.array(price_h), np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'TRIMA':
return talib.TRIMA(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'TRIX':
return talib.TRIX(np.asarray(price_c, dtype='float'), timeperiod=30)
if name == 'TSF':
return talib.TSF(np.asarray(price_c, dtype='float'), timeperiod=14)
if name == 'TYPPRICE':
return talib.TYPPRICE(np.array(price_h), np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'ULTOSC':
return talib.ULTOSC(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
if name == 'VAR':
return talib.VAR(np.asarray(price_c, dtype='float'),
timeperiod=5,
nbdev=1)
if name == 'WCLPRICE':
return talib.WCLPRICE(np.array(price_h), np.array(price_l),
np.asarray(price_c, dtype='float'))
if name == 'WILLR':
return talib.WILLR(np.array(price_h),
np.array(price_l),
np.asarray(price_c, dtype='float'),
timeperiod=14)
if name == 'WMA':
return talib.WMA(np.asarray(price_c, dtype='float'), timeperiod=30)
def add_ta_features(df, ta_settings):
"""Add technial analysis features from typical financial dataset that
typically include columns such as "open", "high", "low", "price" and
"volume".
http://mrjbq7.github.io/ta-lib/
Args:
df(pandas.DataFrame): original DataFrame.
ta_settings(dict): configuration.
Returns:
pandas.DataFrame: DataFrame with new features included.
"""
open = df['open']
high = df['high']
low = df['low']
close = df['price']
volume = df['volume']
if ta_settings['overlap']:
df['ta_overlap_bbands_upper'], df['ta_overlap_bbands_middle'], df[
'ta_overlap_bbands_lower'] = ta.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
df['ta_overlap_dema'] = ta.DEMA(
close, timeperiod=15) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_ema'] = ta.EMA(close, timeperiod=30)
df['ta_overlap_kama'] = ta.KAMA(close, timeperiod=30)
df['ta_overlap_ma'] = ta.MA(close, timeperiod=30, matype=0)
df['ta_overlap_mama_mama'], df['ta_overlap_mama_fama'] = ta.MAMA(close)
period = np.random.randint(10, 20, size=len(close)).astype(float)
df['ta_overlap_mavp'] = ta.MAVP(close,
period,
minperiod=2,
maxperiod=30,
matype=0)
df['ta_overlap_midpoint'] = ta.MIDPOINT(close, timeperiod=14)
df['ta_overlap_midprice'] = ta.MIDPRICE(high, low, timeperiod=14)
df['ta_overlap_sar'] = ta.SAR(high, low, acceleration=0, maximum=0)
df['ta_overlap_sarext'] = ta.SAREXT(high,
low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
df['ta_overlap_sma'] = ta.SMA(close, timeperiod=30)
df['ta_overlap_t3'] = ta.T3(close, timeperiod=5, vfactor=0)
df['ta_overlap_tema'] = ta.TEMA(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_trima'] = ta.TRIMA(close, timeperiod=30)
df['ta_overlap_wma'] = ta.WMA(close, timeperiod=30)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_overlap_ht_trendline'] = ta.HT_TRENDLINE(close)
if ta_settings['momentum']:
df['ta_momentum_adx'] = ta.ADX(high, low, close, timeperiod=14)
df['ta_momentum_adxr'] = ta.ADXR(high, low, close, timeperiod=14)
df['ta_momentum_apo'] = ta.APO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_aroondown'], df['ta_momentum_aroonup'] = ta.AROON(
high, low, timeperiod=14)
df['ta_momentum_aroonosc'] = ta.AROONOSC(high, low, timeperiod=14)
df['ta_momentum_bop'] = ta.BOP(open, high, low, close)
df['ta_momentum_cci'] = ta.CCI(high, low, close, timeperiod=14)
df['ta_momentum_cmo'] = ta.CMO(close, timeperiod=14)
df['ta_momentum_dx'] = ta.DX(high, low, close, timeperiod=14)
df['ta_momentum_macd_macd'], df['ta_momentum_macd_signal'], df[
'ta_momentum_macd_hist'] = ta.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
df['ta_momentum_macdext_macd'], df['ta_momentum_macdext_signal'], df[
'ta_momentum_macdext_hist'] = ta.MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
df['ta_momentum_macdfix_macd'], df['ta_momentum_macdfix_signal'], df[
'ta_momentum_macdfix_hist'] = ta.MACDFIX(close, signalperiod=9)
df['ta_momentum_mfi'] = ta.MFI(high, low, close, volume, timeperiod=14)
df['ta_momentum_minus_di'] = ta.MINUS_DI(high,
low,
close,
timeperiod=14)
df['ta_momentum_minus_dm'] = ta.MINUS_DM(high, low, timeperiod=14)
df['ta_momentum_mom'] = ta.MOM(close, timeperiod=10)
df['ta_momentum_plus_di'] = ta.PLUS_DI(high, low, close, timeperiod=14)
df['ta_momentum_plus_dm'] = ta.PLUS_DM(high, low, timeperiod=14)
df['ta_momentum_ppo'] = ta.PPO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_roc'] = ta.ROC(close, timeperiod=10)
df['ta_momentum_rocp'] = ta.ROCP(close, timeperiod=10)
df['ta_momentum_rocr'] = ta.ROCR(close, timeperiod=10)
df['ta_momentum_rocr100'] = ta.ROCR100(close, timeperiod=10)
df['ta_momentum_rsi'] = ta.RSI(close, timeperiod=14)
df['ta_momentum_slowk'], df['ta_momentum_slowd'] = ta.STOCH(
high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHF(
high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHRSI(
close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['ta_momentum_trix'] = ta.TRIX(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_momentum_ultosc'] = ta.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df['ta_momentum_willr'] = ta.WILLR(high, low, close, timeperiod=14)
if ta_settings['volume']:
df['ta_volume_ad'] = ta.AD(high, low, close, volume)
df['ta_volume_adosc'] = ta.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)
df['ta_volume_obv'] = ta.OBV(close, volume)
if ta_settings['volatility']:
df['ta_volatility_atr'] = ta.ATR(high, low, close, timeperiod=14)
df['ta_volatility_natr'] = ta.NATR(high, low, close, timeperiod=14)
df['ta_volatility_trange'] = ta.TRANGE(high, low, close)
if ta_settings['price']:
df['ta_price_avgprice'] = ta.AVGPRICE(open, high, low, close)
df['ta_price_medprice'] = ta.MEDPRICE(high, low)
df['ta_price_typprice'] = ta.TYPPRICE(high, low, close)
df['ta_price_wclprice'] = ta.WCLPRICE(high, low, close)
if ta_settings['cycle']:
df['ta_cycle_ht_dcperiod'] = ta.HT_DCPERIOD(close)
df['ta_cycle_ht_phasor_inphase'], df[
'ta_cycle_ht_phasor_quadrature'] = ta.HT_PHASOR(close)
df['ta_cycle_ht_trendmode'] = ta.HT_TRENDMODE(close)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_cycle_ht_dcphase'] = ta.HT_DCPHASE(close)
# df['ta_cycle_ht_sine_sine'], df['ta_cycle_ht_sine_leadsine'] = ta.HT_SINE(close)
if ta_settings['pattern']:
df['ta_pattern_cdl2crows'] = ta.CDL2CROWS(open, high, low, close)
df['ta_pattern_cdl3blackrows'] = ta.CDL3BLACKCROWS(
open, high, low, close)
df['ta_pattern_cdl3inside'] = ta.CDL3INSIDE(open, high, low, close)
df['ta_pattern_cdl3linestrike'] = ta.CDL3LINESTRIKE(
open, high, low, close)
df['ta_pattern_cdl3outside'] = ta.CDL3OUTSIDE(open, high, low, close)
df['ta_pattern_cdl3starsinsouth'] = ta.CDL3STARSINSOUTH(
open, high, low, close)
df['ta_pattern_cdl3whitesoldiers'] = ta.CDL3WHITESOLDIERS(
open, high, low, close)
df['ta_pattern_cdlabandonedbaby'] = ta.CDLABANDONEDBABY(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdladvanceblock'] = ta.CDLADVANCEBLOCK(
open, high, low, close)
df['ta_pattern_cdlbelthold'] = ta.CDLBELTHOLD(open, high, low, close)
df['ta_pattern_cdlbreakaway'] = ta.CDLBREAKAWAY(open, high, low, close)
df['ta_pattern_cdlclosingmarubozu'] = ta.CDLCLOSINGMARUBOZU(
open, high, low, close)
df['ta_pattern_cdlconcealbabyswall'] = ta.CDLCONCEALBABYSWALL(
open, high, low, close)
df['ta_pattern_cdlcounterattack'] = ta.CDLCOUNTERATTACK(
open, high, low, close)
df['ta_pattern_cdldarkcloudcover'] = ta.CDLDARKCLOUDCOVER(
open, high, low, close, penetration=0)
df['ta_pattern_cdldoji'] = ta.CDLDOJI(open, high, low, close)
df['ta_pattern_cdldojistar'] = ta.CDLDOJISTAR(open, high, low, close)
df['ta_pattern_cdldragonflydoji'] = ta.CDLDRAGONFLYDOJI(
open, high, low, close)
df['ta_pattern_cdlengulfing'] = ta.CDLENGULFING(open, high, low, close)
df['ta_pattern_cdleveningdojistar'] = ta.CDLEVENINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdleveningstar'] = ta.CDLEVENINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlgapsidesidewhite'] = ta.CDLGAPSIDESIDEWHITE(
open, high, low, close)
df['ta_pattern_cdlgravestonedoji'] = ta.CDLGRAVESTONEDOJI(
open, high, low, close)
df['ta_pattern_cdlhammer'] = ta.CDLHAMMER(open, high, low, close)
df['ta_pattern_cdlhangingman'] = ta.CDLHANGINGMAN(
open, high, low, close)
df['ta_pattern_cdlharami'] = ta.CDLHARAMI(open, high, low, close)
df['ta_pattern_cdlharamicross'] = ta.CDLHARAMICROSS(
open, high, low, close)
df['ta_pattern_cdlhighwave'] = ta.CDLHIGHWAVE(open, high, low, close)
df['ta_pattern_cdlhikkake'] = ta.CDLHIKKAKE(open, high, low, close)
df['ta_pattern_cdlhikkakemod'] = ta.CDLHIKKAKEMOD(
open, high, low, close)
df['ta_pattern_cdlhomingpigeon'] = ta.CDLHOMINGPIGEON(
open, high, low, close)
df['ta_pattern_cdlidentical3crows'] = ta.CDLIDENTICAL3CROWS(
open, high, low, close)
df['ta_pattern_cdlinneck'] = ta.CDLINNECK(open, high, low, close)
df['ta_pattern_cdlinvertedhammer'] = ta.CDLINVERTEDHAMMER(
open, high, low, close)
df['ta_pattern_cdlkicking'] = ta.CDLKICKING(open, high, low, close)
df['ta_pattern_cdlkickingbylength'] = ta.CDLKICKINGBYLENGTH(
open, high, low, close)
df['ta_pattern_cdlladderbottom'] = ta.CDLLADDERBOTTOM(
open, high, low, close)
df['ta_pattern_cdllongleggeddoji'] = ta.CDLLONGLEGGEDDOJI(
open, high, low, close)
df['ta_pattern_cdllongline'] = ta.CDLLONGLINE(open, high, low, close)
df['ta_pattern_cdlmarubozu'] = ta.CDLMARUBOZU(open, high, low, close)
df['ta_pattern_cdlmatchinglow'] = ta.CDLMATCHINGLOW(
open, high, low, close)
df['ta_pattern_cdlmathold'] = ta.CDLMATHOLD(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlmorningdojistar'] = ta.CDLMORNINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdlmorningstar'] = ta.CDLMORNINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdllonneck'] = ta.CDLONNECK(open, high, low, close)
df['ta_pattern_cdlpiercing'] = ta.CDLPIERCING(open, high, low, close)
df['ta_pattern_cdlrickshawman'] = ta.CDLRICKSHAWMAN(
open, high, low, close)
df['ta_pattern_cdlrisefall3methods'] = ta.CDLRISEFALL3METHODS(
open, high, low, close)
df['ta_pattern_cdlseparatinglines'] = ta.CDLSEPARATINGLINES(
open, high, low, close)
df['ta_pattern_cdlshootingstar'] = ta.CDLSHOOTINGSTAR(
open, high, low, close)
df['ta_pattern_cdlshortline'] = ta.CDLSHORTLINE(open, high, low, close)
df['ta_pattern_cdlspinningtop'] = ta.CDLSPINNINGTOP(
open, high, low, close)
df['ta_pattern_cdlstalledpattern'] = ta.CDLSTALLEDPATTERN(
open, high, low, close)
df['ta_pattern_cdlsticksandwich'] = ta.CDLSTICKSANDWICH(
open, high, low, close)
df['ta_pattern_cdltakuri'] = ta.CDLTAKURI(open, high, low, close)
df['ta_pattern_cdltasukigap'] = ta.CDLTASUKIGAP(open, high, low, close)
df['ta_pattern_cdlthrusting'] = ta.CDLTHRUSTING(open, high, low, close)
df['ta_pattern_cdltristar'] = ta.CDLTRISTAR(open, high, low, close)
df['ta_pattern_cdlunique3river'] = ta.CDLUNIQUE3RIVER(
open, high, low, close)
df['ta_pattern_cdlupsidegap2crows'] = ta.CDLUPSIDEGAP2CROWS(
open, high, low, close)
df['ta_pattern_cdlxsidegap3methods'] = ta.CDLXSIDEGAP3METHODS(
open, high, low, close)
if ta_settings['statistic']:
df['ta_statistic_beta'] = ta.BETA(high, low, timeperiod=5)
df['ta_statistic_correl'] = ta.CORREL(high, low, timeperiod=30)
df['ta_statistic_linearreg'] = ta.LINEARREG(close, timeperiod=14)
df['ta_statistic_linearreg_angle'] = ta.LINEARREG_ANGLE(close,
timeperiod=14)
df['ta_statistic_linearreg_intercept'] = ta.LINEARREG_INTERCEPT(
close, timeperiod=14)
df['ta_statistic_linearreg_slope'] = ta.LINEARREG_SLOPE(close,
timeperiod=14)
df['ta_statistic_stddev'] = ta.STDDEV(close, timeperiod=5, nbdev=1)
df['ta_statistic_tsf'] = ta.TSF(close, timeperiod=14)
df['ta_statistic_var'] = ta.VAR(close, timeperiod=5, nbdev=1)
if ta_settings['math_transforms']:
df['ta_math_transforms_atan'] = ta.ATAN(close)
df['ta_math_transforms_ceil'] = ta.CEIL(close)
df['ta_math_transforms_cos'] = ta.COS(close)
df['ta_math_transforms_floor'] = ta.FLOOR(close)
df['ta_math_transforms_ln'] = ta.LN(close)
df['ta_math_transforms_log10'] = ta.LOG10(close)
df['ta_math_transforms_sin'] = ta.SIN(close)
df['ta_math_transforms_sqrt'] = ta.SQRT(close)
df['ta_math_transforms_tan'] = ta.TAN(close)
if ta_settings['math_operators']:
df['ta_math_operators_add'] = ta.ADD(high, low)
df['ta_math_operators_div'] = ta.DIV(high, low)
df['ta_math_operators_min'], df['ta_math_operators_max'] = ta.MINMAX(
close, timeperiod=30)
df['ta_math_operators_minidx'], df[
'ta_math_operators_maxidx'] = ta.MINMAXINDEX(close, timeperiod=30)
df['ta_math_operators_mult'] = ta.MULT(high, low)
df['ta_math_operators_sub'] = ta.SUB(high, low)
df['ta_math_operators_sum'] = ta.SUM(close, timeperiod=30)
return df
def CDLHIGHWAVE(data):
res = talib.CDLHIGHWAVE(
data.open.values, data.high.values, data.low.values, data.close.values)
return pd.DataFrame({'CDLHIGHWAVE': res}, index=data.index)
def candles(source):
open = source['open']
high = source['high']
low = source['low']
close = source['close']
source = source.join(
pd.Series(talib.CDL2CROWS(open, high, low, close), name='CDL2CROWS'))
source = source.join(
pd.Series(talib.CDL3BLACKCROWS(open, high, low, close),
name='CDL3BLACKCROWS'))
source = source.join(
pd.Series(talib.CDL3INSIDE(open, high, low, close), name='CDL3INSIDE'))
source = source.join(
pd.Series(talib.CDL3OUTSIDE(open, high, low, close),
name='CDL3OUTSIDE'))
source = source.join(
pd.Series(talib.CDL3STARSINSOUTH(open, high, low, close),
name='CDL3STARSINSOUTH'))
source = source.join(
pd.Series(talib.CDL3WHITESOLDIERS(open, high, low, close),
name='CDL3WHITESOLDIERS'))
source = source.join(
pd.Series(talib.CDLABANDONEDBABY(open, high, low, close),
name='CDLABANDONEDBABY'))
source = source.join(
pd.Series(talib.CDLADVANCEBLOCK(open, high, low, close),
name='CDLADVANCEBLOCK'))
source = source.join(
pd.Series(talib.CDLBELTHOLD(open, high, low, close),
name='CDLBELTHOLD'))
source = source.join(
pd.Series(talib.CDLBREAKAWAY(open, high, low, close),
name='CDLBREAKAWAY'))
source = source.join(
pd.Series(talib.CDLCLOSINGMARUBOZU(open, high, low, close),
name='CDLCLOSINGMARUBOZU'))
source = source.join(
pd.Series(talib.CDLCONCEALBABYSWALL(open, high, low, close),
name='CDLCONCEALBABYSWALL'))
source = source.join(
pd.Series(talib.CDLCOUNTERATTACK(open, high, low, close),
name='CDLCOUNTERATTACK'))
source = source.join(
pd.Series(talib.CDLDARKCLOUDCOVER(open, high, low, close),
name='CDLDARKCLOUDCOVER'))
source = source.join(
pd.Series(talib.CDLDOJI(open, high, low, close), name='CDLDOJI'))
source = source.join(
pd.Series(talib.CDLDOJISTAR(open, high, low, close),
name='CDLDOJISTAR'))
source = source.join(
pd.Series(talib.CDLDRAGONFLYDOJI(open, high, low, close),
name='CDLDRAGONFLYDOJI'))
source = source.join(
pd.Series(talib.CDLENGULFING(open, high, low, close),
name='CDLENGULFING'))
source = source.join(
pd.Series(talib.CDLEVENINGDOJISTAR(open, high, low, close),
name='CDLEVENINGDOJISTAR'))
source = source.join(
pd.Series(talib.CDLEVENINGSTAR(open, high, low, close),
name='CDLEVENINGSTAR'))
source = source.join(
pd.Series(talib.CDLGAPSIDESIDEWHITE(open, high, low, close),
name='CDLGAPSIDESIDEWHITE'))
source = source.join(
pd.Series(talib.CDLGRAVESTONEDOJI(open, high, low, close),
name='CDLGRAVESTONEDOJI'))
source = source.join(
pd.Series(talib.CDLHAMMER(open, high, low, close), name='CDLHAMMER'))
source = source.join(
pd.Series(talib.CDLHANGINGMAN(open, high, low, close),
name='CDLHANGINGMAN'))
source = source.join(
pd.Series(talib.CDLHARAMI(open, high, low, close), name='CDLHARAMI'))
source = source.join(
pd.Series(talib.CDLHARAMICROSS(open, high, low, close),
name='CDLHARAMICROSS'))
source = source.join(
pd.Series(talib.CDLHIGHWAVE(open, high, low, close),
name='CDLHIGHWAVE'))
source = source.join(
pd.Series(talib.CDLHIKKAKE(open, high, low, close), name='CDLHIKKAKE'))
source = source.join(
pd.Series(talib.CDLHIKKAKEMOD(open, high, low, close),
name='CDLHIKKAKEMOD'))
source = source.join(
pd.Series(talib.CDLHOMINGPIGEON(open, high, low, close),
name='CDLHOMINGPIGEON'))
source = source.join(
pd.Series(talib.CDLIDENTICAL3CROWS(open, high, low, close),
name='CDLIDENTICAL3CROWS'))
source = source.join(
pd.Series(talib.CDLINNECK(open, high, low, close), name='CDLINNECK'))
source = source.join(
pd.Series(talib.CDLINVERTEDHAMMER(open, high, low, close),
name='CDLINVERTEDHAMMER'))
source = source.join(
pd.Series(talib.CDLKICKING(open, high, low, close), name='CDLKICKING'))
source = source.join(
pd.Series(talib.CDLKICKINGBYLENGTH(open, high, low, close),
name='CDLKICKINGBYLENGTH'))
source = source.join(
pd.Series(talib.CDLLADDERBOTTOM(open, high, low, close),
name='CDLLADDERBOTTOM'))
source = source.join(
pd.Series(talib.CDLLONGLEGGEDDOJI(open, high, low, close),
name='CDLLONGLEGGEDDOJI'))
source = source.join(
pd.Series(talib.CDLLONGLINE(open, high, low, close),
name='CDLLONGLINE'))
source = source.join(
pd.Series(talib.CDLMARUBOZU(open, high, low, close),
name='CDLMARUBOZU'))
source = source.join(
pd.Series(talib.CDLMATCHINGLOW(open, high, low, close),
name='CDLMATCHINGLOW'))
source = source.join(
pd.Series(talib.CDLMATHOLD(open, high, low, close), name='CDLMATHOLD'))
source = source.join(
pd.Series(talib.CDLMORNINGDOJISTAR(open, high, low, close),
name='CDLMORNINGDOJISTAR'))
source = source.join(
pd.Series(talib.CDLMORNINGSTAR(open, high, low, close),
name='CDLMORNINGSTAR'))
source = source.join(
pd.Series(talib.CDLONNECK(open, high, low, close), name='CDLONNECK'))
source = source.join(
pd.Series(talib.CDLPIERCING(open, high, low, close),
name='CDLPIERCING'))
source = source.join(
pd.Series(talib.CDLRICKSHAWMAN(open, high, low, close),
name='CDLRICKSHAWMAN'))
source = source.join(
pd.Series(talib.CDLRISEFALL3METHODS(open, high, low, close),
name='CDLRISEFALL3METHODS'))
source = source.join(
pd.Series(talib.CDLSEPARATINGLINES(open, high, low, close),
name='CDLSEPARATINGLINES'))
source = source.join(
pd.Series(talib.CDLSHOOTINGSTAR(open, high, low, close),
name='CDLSHOOTINGSTAR'))
source = source.join(
pd.Series(talib.CDLSHORTLINE(open, high, low, close),
name='CDLSHORTLINE'))
source = source.join(
pd.Series(talib.CDLSPINNINGTOP(open, high, low, close),
name='CDLSPINNINGTOP'))
source = source.join(
pd.Series(talib.CDLSTALLEDPATTERN(open, high, low, close),
name='CDLSTALLEDPATTERN'))
source = source.join(
pd.Series(talib.CDLSTICKSANDWICH(open, high, low, close),
name='CDLSTICKSANDWICH'))
source = source.join(
pd.Series(talib.CDLTAKURI(open, high, low, close), name='CDLTAKURI'))
source = source.join(
pd.Series(talib.CDLTASUKIGAP(open, high, low, close),
name='CDLTASUKIGAP'))
source = source.join(
pd.Series(talib.CDLTHRUSTING(open, high, low, close),
name='CDLTHRUSTING'))
source = source.join(
pd.Series(talib.CDLTRISTAR(open, high, low, close), name='CDLTRISTAR'))
source = source.join(
pd.Series(talib.CDLUNIQUE3RIVER(open, high, low, close),
name='CDLUNIQUE3RIVER'))
source = source.join(
pd.Series(talib.CDLUPSIDEGAP2CROWS(open, high, low, close),
name='CDLUPSIDEGAP2CROWS'))
source = source.join(
pd.Series(talib.CDLXSIDEGAP3METHODS(open, high, low, close),
name='CDLXSIDEGAP3METHODS'))
return source
np.array(df['Low']),
np.array(df['Adj Close']))
df['Hammer'] = ta.CDLHAMMER(np.array(df['Open']), np.array(df['High']),
np.array(df['Low']), np.array(df['Adj Close']))
df['Hanging_Man'] = ta.CDLHANGINGMAN(np.array(df['Open']),
np.array(df['High']), np.array(df['Low']),
np.array(df['Adj Close']))
df['Harami_Pattern'] = ta.CDLHARAMI(np.array(df['Open']), np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Harami_Cross_Pattern'] = ta.CDLHARAMICROSS(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['High_Wave_Candle'] = ta.CDLHIGHWAVE(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Hikkake_Pattern'] = ta.CDLHIKKAKE(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Modified_Hikkake_Pattern'] = ta.CDLHIKKAKEMOD(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Homing_Pigeon'] = ta.CDLHOMINGPIGEON(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Identical_Three_Crows'] = ta.CDLIDENTICAL3CROWS(np.array(df['Open']),
np.array(df['High']),
def TALIB_CDLHIGHWAVE(close):
'''00425,1,1'''
return talib.CDLHIGHWAVE(close)
df['Low'], df['Close'])
df['Evening Star'] = ta.CDLEVENINGSTAR(df['Open'], df['High'], df['Low'],
df['Close'])
df['Up/Down-gap side-by-side white lines'] = ta.CDLGAPSIDESIDEWHITE(
df['Open'], df['High'], df['Low'], df['Close'])
df['Gravestone Doji'] = ta.CDLGRAVESTONEDOJI(df['Open'], df['High'], df['Low'],
df['Close'])
df['Hammer'] = ta.CDLHAMMER(df['Open'], df['High'], df['Low'], df['Close'])
df['Hanging Man'] = ta.CDLHANGINGMAN(df['Open'], df['High'], df['Low'],
df['Close'])
df['Harami Pattern'] = ta.CDLHARAMI(df['Open'], df['High'], df['Low'],
df['Close'])
df['Harami Cross Pattern'] = ta.CDLHARAMICROSS(df['Open'], df['High'],
df['Low'], df['Close'])
df['High-Wave Candle'] = ta.CDLHIGHWAVE(df['Open'], df['High'], df['Low'],
df['Close'])
df['Hikkake Pattern'] = ta.CDLHIKKAKE(df['Open'], df['High'], df['Low'],
df['Close'])
df['Modified Hikkake Pattern'] = ta.CDLHIKKAKEMOD(df['Open'], df['High'],
df['Low'], df['Close'])
df['Homing Pigeon'] = ta.CDLHOMINGPIGEON(df['Open'], df['High'], df['Low'],
df['Close'])
df['Identical Three Crows'] = ta.CDLIDENTICAL3CROWS(df['Open'], df['High'],
df['Low'], df['Close'])
df['In-Neck Pattern'] = ta.CDLINNECK(df['Open'], df['High'], df['Low'],
df['Close'])
df['Inverted Hammer'] = ta.CDLINVERTEDHAMMER(df['Open'], df['High'], df['Low'],
df['Close'])
df['Kicking'] = ta.CDLKICKING(df['Open'], df['High'], df['Low'], df['Close'])
df['Kicking By Length'] = ta.CDLKICKINGBYLENGTH(df['Open'], df['High'],
df['CDLCONCEALBABYSWALL'] = talib.CDLCONCEALBABYSWALL(op, hp, lp, cp) df['CDLCOUNTERATTACK'] = talib.CDLCOUNTERATTACK(op, hp, lp, cp) df['CDLDARKCLOUDCOVER'] = talib.CDLDARKCLOUDCOVER(op, hp, lp, cp) df['CDLDOJI'] = talib.CDLDOJI(op, hp, lp, cp) df['CDLDOJISTAR'] = talib.CDLDOJISTAR(op, hp, lp, cp) df['CDLDRAGONFLYDOJI'] = talib.CDLDRAGONFLYDOJI(op, hp, lp, cp) df['CDLENGULFING'] = talib.CDLENGULFING(op, hp, lp, cp) df['CDLEVENINGDOJISTAR'] = talib.CDLEVENINGDOJISTAR(op, hp, lp, cp) df['CDLEVENINGSTAR'] = talib.CDLEVENINGSTAR(op, hp, lp, cp) df['CDLGAPSIDESIDEWHITE'] = talib.CDLGAPSIDESIDEWHITE(op, hp, lp, cp) df['CDLGRAVESTONEDOJI'] = talib.CDLGRAVESTONEDOJI(op, hp, lp, cp) df['CDLHAMMER'] = talib.CDLHAMMER(op, hp, lp, cp) df['CDLHANGINGMAN'] = talib.CDLHANGINGMAN(op, hp, lp, cp) df['CDLHARAMI'] = talib.CDLHARAMI(op, hp, lp, cp) df['CDLHARAMICROSS'] = talib.CDLHARAMICROSS(op, hp, lp, cp) df['CDLHIGHWAVE'] = talib.CDLHIGHWAVE(op, hp, lp, cp) df['CDLHIKKAKE'] = talib.CDLHIKKAKE(op, hp, lp, cp) df['CDLHIKKAKEMOD'] = talib.CDLHIKKAKEMOD(op, hp, lp, cp) df['CDLHOMINGPIGEON'] = talib.CDLHOMINGPIGEON(op, hp, lp, cp) 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)
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 pattern_recognition(candles: np.ndarray, pattern_type, penetration=0, sequential=False) -> Union[int, np.ndarray]:
"""
Pattern Recognition
:param candles: np.ndarray
:param penetration: int - default = 0
:param pattern_type: str
:param sequential: bool - default=False
:return: int | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
if pattern_type == "CDL2CROWS":
res = talib.CDL2CROWS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3BLACKCROWS":
res = talib.CDL3BLACKCROWS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3INSIDE":
res = talib.CDL3INSIDE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3LINESTRIKE":
res = talib.CDL3LINESTRIKE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3OUTSIDE":
res = talib.CDL3OUTSIDE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3STARSINSOUTH":
res = talib.CDL3STARSINSOUTH(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDL3WHITESOLDIERS":
res = talib.CDL3WHITESOLDIERS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLABANDONEDBABY":
res = talib.CDLABANDONEDBABY(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2],
penetration=penetration)
elif pattern_type == "CDLADVANCEBLOCK":
res = talib.CDLADVANCEBLOCK(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLBELTHOLD":
res = talib.CDLBELTHOLD(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLBREAKAWAY":
res = talib.CDLBREAKAWAY(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLCLOSINGMARUBOZU":
res = talib.CDLCLOSINGMARUBOZU(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLCONCEALBABYSWALL":
res = talib.CDLCONCEALBABYSWALL(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLCOUNTERATTACK":
res = talib.CDLCOUNTERATTACK(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLDARKCLOUDCOVER":
res = talib.CDLDARKCLOUDCOVER(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2],
penetration=penetration)
elif pattern_type == "CDLDOJI":
res = talib.CDLDOJI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLDOJISTAR":
res = talib.CDLDOJISTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLDRAGONFLYDOJI":
res = talib.CDLDRAGONFLYDOJI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLENGULFING":
res = talib.CDLENGULFING(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLEVENINGDOJISTAR":
res = talib.CDLEVENINGDOJISTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2],
penetration=penetration)
elif pattern_type == "CDLEVENINGSTAR":
res = talib.CDLEVENINGSTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2], penetration=penetration)
elif pattern_type == "CDLGAPSIDESIDEWHITE":
res = talib.CDLGAPSIDESIDEWHITE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLGRAVESTONEDOJI":
res = talib.CDLGRAVESTONEDOJI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHAMMER":
res = talib.CDLHAMMER(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHANGINGMAN":
res = talib.CDLHANGINGMAN(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHARAMI":
res = talib.CDLHARAMI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHARAMICROSS":
res = talib.CDLHARAMICROSS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHIGHWAVE":
res = talib.CDLHIGHWAVE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHIKKAKE":
res = talib.CDLHIKKAKE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHIKKAKEMOD":
res = talib.CDLHIKKAKEMOD(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLHOMINGPIGEON":
res = talib.CDLHOMINGPIGEON(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLIDENTICAL3CROWS":
res = talib.CDLIDENTICAL3CROWS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLINNECK":
res = talib.CDLINNECK(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLINVERTEDHAMMER":
res = talib.CDLINVERTEDHAMMER(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLKICKING":
res = talib.CDLKICKING(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLKICKINGBYLENGTH":
res = talib.CDLKICKINGBYLENGTH(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLLADDERBOTTOM":
res = talib.CDLLADDERBOTTOM(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLLONGLEGGEDDOJI":
res = talib.CDLLONGLEGGEDDOJI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLLONGLINE":
res = talib.CDLLONGLINE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLMARUBOZU":
res = talib.CDLMARUBOZU(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLMATCHINGLOW":
res = talib.CDLMATCHINGLOW(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLMATHOLD":
res = talib.CDLMATHOLD(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2], penetration=penetration)
elif pattern_type == "CDLMORNINGDOJISTAR":
res = talib.CDLMORNINGDOJISTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2],
penetration=penetration)
elif pattern_type == "CDLMORNINGSTAR":
res = talib.CDLMORNINGSTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2], penetration=penetration)
elif pattern_type == "CDLONNECK":
res = talib.CDLONNECK(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLPIERCING":
res = talib.CDLPIERCING(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLRICKSHAWMAN":
res = talib.CDLRICKSHAWMAN(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLRISEFALL3METHODS":
res = talib.CDLRISEFALL3METHODS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSEPARATINGLINES":
res = talib.CDLSEPARATINGLINES(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSHOOTINGSTAR":
res = talib.CDLSHOOTINGSTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSHORTLINE":
res = talib.CDLSHORTLINE(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSPINNINGTOP":
res = talib.CDLSPINNINGTOP(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSTALLEDPATTERN":
res = talib.CDLSTALLEDPATTERN(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLSTICKSANDWICH":
res = talib.CDLSTICKSANDWICH(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLTAKURI":
res = talib.CDLTAKURI(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLTASUKIGAP":
res = talib.CDLTASUKIGAP(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLTHRUSTING":
res = talib.CDLTHRUSTING(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLTRISTAR":
res = talib.CDLTRISTAR(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLUNIQUE3RIVER":
res = talib.CDLUNIQUE3RIVER(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLUPSIDEGAP2CROWS":
res = talib.CDLUPSIDEGAP2CROWS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
elif pattern_type == "CDLXSIDEGAP3METHODS":
res = talib.CDLXSIDEGAP3METHODS(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
else:
raise ValueError('pattern type string not recognised')
return res / 100 if sequential else res[-1] / 100
def 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
