def black_crows3(cls, df):
"""
三只乌鸦
:param df:
:return:
"""
return ta.CDL3BLACKCROWS(df['open'], df['high'], df['low'], df['close'])
def pattern_checker(self):
data = self.data
thrusting = talib.CDLTHRUSTING(data['Open'].values, data['High'].values,\
data['Low'].values, data['Close'].values)
#bullish
morning = talib.CDLMORNINGSTAR(data['Open'].values, data['High'].values,\
data['Low'].values, data['Close'].values,penetration=0)
#bullish
threeline = talib.CDL3LINESTRIKE(data['Open'].values, data['High'].values,\
data['Low'].values, data['Close'].values)
#bearish
black = talib.CDL3BLACKCROWS(data['Open'].values, data['High'].values,\
data['Low'].values, data['Close'].values)
#bearish
eveningstar = talib.CDLEVENINGDOJISTAR(data['Open'].values, data['High'].values,\
data['Low'].values, data['Close'].values,penetration=0)
#bullish
abandonedbaby = talib.CDLABANDONEDBABY(data['Open'].values, data['High'].values,\
data['Low'].values, data['Close'].values,penetration=0)
talist = [
'thrusting', 'morning', 'threeline', 'black', 'eveningstar',
'abandonedbaby'
]
ta_name = thrusting, morning, threeline, black, eveningstar, abandonedbaby
self.ta_dict = dict([(talist[i], ta_name[i])
for i in range(len(talist))])
return thrusting, morning, threeline, black, eveningstar, abandonedbaby
def get_cdl3blackcrows(ohlc):
cdl3blackcrows = ta.CDL3BLACKCROWS(ohlc['1_open'], ohlc['2_high'],
ohlc['3_low'], ohlc['4_close'])
ohlc['cdl3blackcrows'] = cdl3blackcrows
return ohlc
def generateIndicatorCDL3BLACKCROWS(dictDataSpec):
df = dictDataSpec['df']
sIndicatorS = pd.Series(talib.CDL3BLACKCROWS(df.Open.values, df.High.values, df.Low.values, df.Close.values), index=df.index)
sIndicatorL = pd.Series(talib.CDL3WHITESOLDIERS(df.Open.values, df.High.values, df.Low.values, df.Close.values), index=df.index)
sIndicator = df['Close'].apply(lambda x: np.nan)
sIndicator.ix[sIndicatorL[sIndicatorL!=0].index] = 1
sIndicator.ix[sIndicatorS[sIndicatorS!=0].index] = -1
return sIndicator
def add_pattern_reconition_factor(df):
df['two_crows'] = talib.CDL2CROWS(df['open'], df['high'], df['low'],
df['close'])
df['three_black_crows'] = talib.CDL3BLACKCROWS(df['open'], df['high'],
df['low'], df['close'])
df['three_inside'] = talib.CDL3INSIDE(df['open'], df['high'], df['low'],
df['close'])
df['three_line_strike'] = talib.CDL3LINESTRIKE(df['open'], df['high'],
df['low'], df['close'])
df['three_outside'] = talib.CDL3OUTSIDE(df['open'], df['high'], df['low'],
df['close'])
df['three_star_south'] = talib.CDL3STARINSOUTH(df['open'], df['high'],
df['low'], df['close'])
df['three_white_soldiers'] = talib.CDL3WHITESOLDIERS(
df['open'], df['high'], df['low'], df['close'])
df['abandoned_baby'] = talib.CDLABANDONEDBABY(df['open'], df['high'],
df['low'], df['close'])
df['advance_block'] = talib.CDLADVANCEBLOCK(df['open'], df['high'],
df['low'], df['close'])
df['belt_hold'] = talib.CDLBELTHOLD(df['open'], df['high'], df['low'],
df['close'])
df['break_away'] = talib.CDLBREAKAWAY(df['open'], df['high'], df['low'],
df['close'])
df['closing_marubozu'] = talib.CDLCLOSINGMARUBOZU(df['open'], df['high'],
df['low'], df['close'])
df['conceal_baby_swall'] = talib.CDLCONCEALBABYSWALL(
df['open'], df['high'], df['low'], df['close'])
df['counter_attack'] = talib.CDLCOUNTERATTACK(df['open'], df['high'],
df['low'], df['close'])
df['dark_cloud_cover'] = talib.CDLDARKCLOUDCOVER(df['open'], df['high'],
df['low'], df['close'])
df['doji'] = talib.CDLDOJI(df['open'], df['high'], df['low'], df['close'])
df['doji_star'] = talib.CDLDOJISTAR(df['open'], df['high'], df['low'],
df['close'])
df['dragon_fly_doji'] = talib.CDLDRAGONFLYDOJI(df['open'], df['high'],
df['low'], df['close'])
df['engulfing'] = talib.CDLENGULFING(df['open'], df['high'], df['low'],
df['close'])
df['evening_doji_star'] = talib.CDLEVENINGDOJISTAR(df['open'],
df['high'],
df['low'],
df['close'],
penetration=0)
df['gap_sideside_white'] = talib.CDLGAPSIDESIDEWHITE(
df['open'], df['high'], df['low'], df['close'])
df['grave_stone_doji'] = talib.CDLGRAVESTONEDOJI(df['open'], df['high'],
df['low'], df['close'])
df['hammer'] = talib.CDLHAMMER(df['open'], df['high'], df['low'],
df['close'])
df['morning_doji_star'] = talib.CDLMORNINGDOJISTAR(df['open'],
df['high'],
df['low'],
df['close'],
penetration=0)
df['on_neck'] = talib.CDLONNECK(df['open'], df['high'], df['low'],
df['close'])
return df
def three_black_crows(self):
"""
名称：Three Black Crows 三只乌鸦
简介：三日K线模式，连续三根阴线，每日收盘价都下跌且接近最低价，每日开盘价都在上根K线实体内，预示股价下跌。
"""
result = talib.CDL3BLACKCROWS(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['three_black_crows'] = result
def CDL3BLACKCROWS(open, high, low, close):
''' Three Black Crows 三只乌鸦
分组: Pattern Recognition 形态识别
简介: 三日K线模式，连续三根阴线，每日收盘价都下跌且接近最低价，
每日开盘价都在上根K线实体内，预示股价下跌。
integer = CDL3BLACKCROWS(open, high, low, close)
'''
return talib.CDL3BLACKCROWS(open, high, low, close)
def three_black_crows(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.CDL3BLACKCROWS(opens, highs, lows, closes)
return cdl
def add_down_pattern_recognition_factor(df):
df['hanging_man'] = talib.CDLHANGINGMAN(df['open'], df['high'], df['low'],
df['close'])
df['evening_doji_star'] = talib.CDLEVENINGDOJISTAR(df['open'],
df['high'],
df['low'],
df['close'],
penetration=0)
df['three_black_crows'] = talib.CDL3BLACKCROWS(df['open'], df['high'],
df['low'], df['close'])
df['dark_cloud_cover'] = talib.CDLDARKCLOUDCOVER(df['open'], df['high'],
df['low'], df['close'])
df['shooting_star'] = talib.CDLSHOOTINGSTAR(df['open'], df['high'],
df['low'], df['close'])
return df
def three_black_crows():
for i in range(len(ForexTraderSwitch.curr_pair_list)):
close=(ForexTraderSwitch.curr_pair_history_data[i]['closeAsk'].tail(5).values+\
ForexTraderSwitch.curr_pair_history_data[i]['closeBid'].tail(5).values)/2
high=(ForexTraderSwitch.curr_pair_history_data[i]['highAsk'].tail(5).values+\
ForexTraderSwitch.curr_pair_history_data[i]['highBid'].tail(5).values)/2
low=(ForexTraderSwitch.curr_pair_history_data[i]['lowAsk'].tail(5).values+\
ForexTraderSwitch.curr_pair_history_data[i]['lowBid'].tail(5).values)/2
openv=(ForexTraderSwitch.curr_pair_history_data[i]['openAsk'].tail(5).values+\
ForexTraderSwitch.curr_pair_history_data[i]['openBid'].tail(5).values)/2
pattern = talib.CDL3BLACKCROWS(openv, high, low, close)
pattern_signal = pattern[-1]
#print(pattern_signal)
ForexTraderSwitch.signal[i, 0] = pattern_signal
if pattern_signal > 0:
#trader.create_buy_order(ticker,units)
ForexTraderSwitch.order[i, 0, 1] = 1
elif pattern_signal < 0:
#trader.create_sell_order(ticker,units)
ForexTraderSwitch.order[i, 0, 2] = 1
else:
#print('No trade made')
ForexTraderSwitch.order[i, 0, 0] = 1
np.array(df['Low'].shift(1)),
np.array(df['Adj Close'].shift(1)))
df['MEDPRICE'] = ta.MEDPRICE(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)))
df['TYPPRICE'] = ta.TYPPRICE(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
np.array(df['Adj Close'].shift(1)))
df['WCLPRICE'] = ta.WCLPRICE(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
np.array(df['Adj Close'].shift(1)))
# Pattern Recognition Fuction
df['Two_Crows'] = ta.CDL2CROWS(np.array(df['Open']), np.array(df['High']),
np.array(df['Low']), np.array(df['Adj Close']))
df['Three_Crows'] = ta.CDL3BLACKCROWS(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Three_Inside_Up_Down'] = ta.CDL3INSIDE(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Three_Line_Strike'] = ta.CDL3LINESTRIKE(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Thre_Outside_Up_Down'] = ta.CDL3OUTSIDE(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Thre_Stars_In_The_South'] = ta.CDL3STARSINSOUTH(np.array(df['Open']),
np.array(df['High']),
def candles(source):
open = source['open']
high = source['high']
low = source['low']
close = source['close']
source = source.join(
pd.Series(talib.CDL2CROWS(open, high, low, close), name='CDL2CROWS'))
source = source.join(
pd.Series(talib.CDL3BLACKCROWS(open, high, low, close),
name='CDL3BLACKCROWS'))
source = source.join(
pd.Series(talib.CDL3INSIDE(open, high, low, close), name='CDL3INSIDE'))
source = source.join(
pd.Series(talib.CDL3OUTSIDE(open, high, low, close),
name='CDL3OUTSIDE'))
source = source.join(
pd.Series(talib.CDL3STARSINSOUTH(open, high, low, close),
name='CDL3STARSINSOUTH'))
source = source.join(
pd.Series(talib.CDL3WHITESOLDIERS(open, high, low, close),
name='CDL3WHITESOLDIERS'))
source = source.join(
pd.Series(talib.CDLABANDONEDBABY(open, high, low, close),
name='CDLABANDONEDBABY'))
source = source.join(
pd.Series(talib.CDLADVANCEBLOCK(open, high, low, close),
name='CDLADVANCEBLOCK'))
source = source.join(
pd.Series(talib.CDLBELTHOLD(open, high, low, close),
name='CDLBELTHOLD'))
source = source.join(
pd.Series(talib.CDLBREAKAWAY(open, high, low, close),
name='CDLBREAKAWAY'))
source = source.join(
pd.Series(talib.CDLCLOSINGMARUBOZU(open, high, low, close),
name='CDLCLOSINGMARUBOZU'))
source = source.join(
pd.Series(talib.CDLCONCEALBABYSWALL(open, high, low, close),
name='CDLCONCEALBABYSWALL'))
source = source.join(
pd.Series(talib.CDLCOUNTERATTACK(open, high, low, close),
name='CDLCOUNTERATTACK'))
source = source.join(
pd.Series(talib.CDLDARKCLOUDCOVER(open, high, low, close),
name='CDLDARKCLOUDCOVER'))
source = source.join(
pd.Series(talib.CDLDOJI(open, high, low, close), name='CDLDOJI'))
source = source.join(
pd.Series(talib.CDLDOJISTAR(open, high, low, close),
name='CDLDOJISTAR'))
source = source.join(
pd.Series(talib.CDLDRAGONFLYDOJI(open, high, low, close),
name='CDLDRAGONFLYDOJI'))
source = source.join(
pd.Series(talib.CDLENGULFING(open, high, low, close),
name='CDLENGULFING'))
source = source.join(
pd.Series(talib.CDLEVENINGDOJISTAR(open, high, low, close),
name='CDLEVENINGDOJISTAR'))
source = source.join(
pd.Series(talib.CDLEVENINGSTAR(open, high, low, close),
name='CDLEVENINGSTAR'))
source = source.join(
pd.Series(talib.CDLGAPSIDESIDEWHITE(open, high, low, close),
name='CDLGAPSIDESIDEWHITE'))
source = source.join(
pd.Series(talib.CDLGRAVESTONEDOJI(open, high, low, close),
name='CDLGRAVESTONEDOJI'))
source = source.join(
pd.Series(talib.CDLHAMMER(open, high, low, close), name='CDLHAMMER'))
source = source.join(
pd.Series(talib.CDLHANGINGMAN(open, high, low, close),
name='CDLHANGINGMAN'))
source = source.join(
pd.Series(talib.CDLHARAMI(open, high, low, close), name='CDLHARAMI'))
source = source.join(
pd.Series(talib.CDLHARAMICROSS(open, high, low, close),
name='CDLHARAMICROSS'))
source = source.join(
pd.Series(talib.CDLHIGHWAVE(open, high, low, close),
name='CDLHIGHWAVE'))
source = source.join(
pd.Series(talib.CDLHIKKAKE(open, high, low, close), name='CDLHIKKAKE'))
source = source.join(
pd.Series(talib.CDLHIKKAKEMOD(open, high, low, close),
name='CDLHIKKAKEMOD'))
source = source.join(
pd.Series(talib.CDLHOMINGPIGEON(open, high, low, close),
name='CDLHOMINGPIGEON'))
source = source.join(
pd.Series(talib.CDLIDENTICAL3CROWS(open, high, low, close),
name='CDLIDENTICAL3CROWS'))
source = source.join(
pd.Series(talib.CDLINNECK(open, high, low, close), name='CDLINNECK'))
source = source.join(
pd.Series(talib.CDLINVERTEDHAMMER(open, high, low, close),
name='CDLINVERTEDHAMMER'))
source = source.join(
pd.Series(talib.CDLKICKING(open, high, low, close), name='CDLKICKING'))
source = source.join(
pd.Series(talib.CDLKICKINGBYLENGTH(open, high, low, close),
name='CDLKICKINGBYLENGTH'))
source = source.join(
pd.Series(talib.CDLLADDERBOTTOM(open, high, low, close),
name='CDLLADDERBOTTOM'))
source = source.join(
pd.Series(talib.CDLLONGLEGGEDDOJI(open, high, low, close),
name='CDLLONGLEGGEDDOJI'))
source = source.join(
pd.Series(talib.CDLLONGLINE(open, high, low, close),
name='CDLLONGLINE'))
source = source.join(
pd.Series(talib.CDLMARUBOZU(open, high, low, close),
name='CDLMARUBOZU'))
source = source.join(
pd.Series(talib.CDLMATCHINGLOW(open, high, low, close),
name='CDLMATCHINGLOW'))
source = source.join(
pd.Series(talib.CDLMATHOLD(open, high, low, close), name='CDLMATHOLD'))
source = source.join(
pd.Series(talib.CDLMORNINGDOJISTAR(open, high, low, close),
name='CDLMORNINGDOJISTAR'))
source = source.join(
pd.Series(talib.CDLMORNINGSTAR(open, high, low, close),
name='CDLMORNINGSTAR'))
source = source.join(
pd.Series(talib.CDLONNECK(open, high, low, close), name='CDLONNECK'))
source = source.join(
pd.Series(talib.CDLPIERCING(open, high, low, close),
name='CDLPIERCING'))
source = source.join(
pd.Series(talib.CDLRICKSHAWMAN(open, high, low, close),
name='CDLRICKSHAWMAN'))
source = source.join(
pd.Series(talib.CDLRISEFALL3METHODS(open, high, low, close),
name='CDLRISEFALL3METHODS'))
source = source.join(
pd.Series(talib.CDLSEPARATINGLINES(open, high, low, close),
name='CDLSEPARATINGLINES'))
source = source.join(
pd.Series(talib.CDLSHOOTINGSTAR(open, high, low, close),
name='CDLSHOOTINGSTAR'))
source = source.join(
pd.Series(talib.CDLSHORTLINE(open, high, low, close),
name='CDLSHORTLINE'))
source = source.join(
pd.Series(talib.CDLSPINNINGTOP(open, high, low, close),
name='CDLSPINNINGTOP'))
source = source.join(
pd.Series(talib.CDLSTALLEDPATTERN(open, high, low, close),
name='CDLSTALLEDPATTERN'))
source = source.join(
pd.Series(talib.CDLSTICKSANDWICH(open, high, low, close),
name='CDLSTICKSANDWICH'))
source = source.join(
pd.Series(talib.CDLTAKURI(open, high, low, close), name='CDLTAKURI'))
source = source.join(
pd.Series(talib.CDLTASUKIGAP(open, high, low, close),
name='CDLTASUKIGAP'))
source = source.join(
pd.Series(talib.CDLTHRUSTING(open, high, low, close),
name='CDLTHRUSTING'))
source = source.join(
pd.Series(talib.CDLTRISTAR(open, high, low, close), name='CDLTRISTAR'))
source = source.join(
pd.Series(talib.CDLUNIQUE3RIVER(open, high, low, close),
name='CDLUNIQUE3RIVER'))
source = source.join(
pd.Series(talib.CDLUPSIDEGAP2CROWS(open, high, low, close),
name='CDLUPSIDEGAP2CROWS'))
source = source.join(
pd.Series(talib.CDLXSIDEGAP3METHODS(open, high, low, close),
name='CDLXSIDEGAP3METHODS'))
return source
def CDL3BLACKCROWS(data, **kwargs):
_check_talib_presence()
popen, phigh, plow, pclose, pvolume = _extract_ohlc(data)
return talib.CDL3BLACKCROWS(popen, phigh, plow, pclose, **kwargs)
def handle_pattern_recognition(args, axes, i, klines_df, close_times, display_count):
# talib
if args.CDL2CROWS:
name = 'CDL2CROWS'
integer = talib.CDL2CROWS(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, integer[-display_count:], "y:", label=name)
if args.CDL3BLACKCROWS:
name = 'CDL3BLACKCROWS'
integer = talib.CDL3BLACKCROWS(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, integer[-display_count:], "y:", label=name)
if args.CDL3INSIDE:
name = 'CDL3INSIDE'
integer = talib.CDL3INSIDE(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, integer[-display_count:], "y:", label=name)
if args.CDL3LINESTRIKE:
name = 'CDL3LINESTRIKE'
integer = talib.CDL3LINESTRIKE(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, integer[-display_count:], "y:", label=name)
if args.CDL3OUTSIDE:
name = 'CDL3OUTSIDE'
integer = talib.CDL3OUTSIDE(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, integer[-display_count:], "y:", label=name)
if args.CDL3STARSINSOUTH:
name = 'CDL3STARSINSOUTH'
integer = talib.CDL3STARSINSOUTH(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, integer[-display_count:], "y:", label=name)
if args.CDL3WHITESOLDIERS:
name = 'CDL3WHITESOLDIERS'
integer = talib.CDL3WHITESOLDIERS(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, integer[-display_count:], "y:", label=name)
if args.CDLABANDONEDBABY:
name = 'CDLABANDONEDBABY'
integer = talib.CDLABANDONEDBABY(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, integer[-display_count:], "y:", label=name)
if args.CDLADVANCEBLOCK:
name = 'CDLADVANCEBLOCK'
integer = talib.CDLADVANCEBLOCK(klines_df["open"], klines_df["high"], klines_df["low"], klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, integer[-display_count:], "y:", label=name)
'''
ohlc_df['high'] = high_list
ohlc_df['low'] = low_list
ohlc_df['close'] = close_list
ohlc_df['volume'] = volume_list
ohlc_df['avg_vol'] = ohlc_df['volume'].rolling(window=14).mean()
ohlc_df['atr'] = ta.ATR(ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'],
timeperiod=14)
ohlc_df['atr_pct'] = ohlc_df['atr'] / ohlc_df['close']
ohlc_df['CDL2CROWS'] = ta.CDL2CROWS(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDL3INSIDE'] = ta.CDL3INSIDE(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(ohlc_df['open'],
ohlc_df['high'],
ohlc_df['low'],
ohlc_df['close'])
ohlc_df['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(
ohlc_df['open'], ohlc_df['high'], ohlc_df['low'],
ohlc_df['close'])
def patern(dataframe):
"""
Pattern Recognition:
CDL2CROWS Two Crows
CDL3BLACKCROWS Three Black Crows
CDL3INSIDE Three Inside Up/Down
CDL3LINESTRIKE Three-Line Strike
CDL3OUTSIDE Three Outside Up/Down
CDL3STARSINSOUTH Three Stars In The South
CDL3WHITESOLDIERS Three Advancing White Soldiers
CDLABANDONEDBABY Abandoned Baby
CDLADVANCEBLOCK Advance Block
CDLBELTHOLD Belt-hold
CDLBREAKAWAY Breakaway
CDLCLOSINGMARUBOZU Closing Marubozu
CDLCONCEALBABYSWALL Concealing Baby SwalLow
CDLCOUNTERATTACK Counterattack
CDLDARKCLOUDCOVER Dark Cloud Cover
CDLDOJI Doji
CDLDOJISTAR Doji Star
CDLDRAGONFLYDOJI Dragonfly Doji
CDLENGULFING Engulfing Pattern
CDLEVENINGDOJISTAR Evening Doji Star
CDLEVENINGSTAR Evening Star
CDLGAPSIDESIDEWHITE Up/Down-gap side-by-side white lines
CDLGRAVESTONEDOJI Gravestone Doji
CDLHAMMER Hammer
CDLHANGINGMAN Hanging Man
CDLHARAMI Harami Pattern
CDLHARAMICROSS Harami Cross Pattern
CDLHighWAVE High-Wave Candle
CDLHIKKAKE Hikkake Pattern
CDLHIKKAKEMOD Modified Hikkake Pattern
CDLHOMINGPIGEON Homing Pigeon
CDLIDENTICAL3CROWS Identical Three Crows
CDLINNECK In-Neck Pattern
CDLINVERTEDHAMMER Inverted Hammer
CDLKICKING Kicking
CDLKICKINGBYLENGTH Kicking - bull/bear determined by the longer marubozu
CDLLADDERBOTTOM Ladder Bottom
CDLLONGLEGGEDDOJI Long Legged Doji
CDLLONGLINE Long Line Candle
CDLMARUBOZU Marubozu
CDLMATCHINGLow Matching Low
CDLMATHOLD Mat Hold
CDLMORNINGDOJISTAR Morning Doji Star
CDLMORNINGSTAR Morning Star
CDLONNECK On-Neck Pattern
CDLPIERCING Piercing Pattern
CDLRICKSHAWMAN Rickshaw Man
CDLRISEFALL3METHODS Rising/Falling Three Methods
CDLSEPARATINGLINES Separating Lines
CDLSHOOTINGSTAR Shooting Star
CDLSHORTLINE Short Line Candle
CDLSPINNINGTOP Spinning Top
CDLSTALLEDPATTERN Stalled Pattern
CDLSTICKSANDWICH Stick Sandwich
CDLTAKURI Takuri (Dragonfly Doji with very long Lower shadow)
CDLTASUKIGAP Tasuki Gap
CDLTHRUSTING Thrusting Pattern
CDLTRISTAR Tristar Pattern
CDLUNIQUE3RIVER Unique 3 River
CDLUPSIDEGAP2CROWS Upside Gap Two Crows
CDLXSIDEGAP3METHODS Upside/Downside Gap Three Methods
"""
#CDL2CROWS - Two Crows
df[f'{ratio}_CDL2CROWS'] = talib.CDL2CROWS(Open,High, Low, Close)
#CDL2CROWS - Three Black Crows
df[f'{ratio}_CDL2CROWS'] = talib.CDL3BLACKCROWS(Open,High, Low, Close)
#CDL3INSIDE - Three Inside Up/Down
df[f'{ratio}_CDL3INSIDE'] = talib.CDL3INSIDE(Open,High, Low, Close)
#CDL3LINESTRIKE - Three-Line Strike
df[f'{ratio}_CDL3LINESTRIKE'] = talib.CDL3LINESTRIKE(Open,High, Low, Close)
#CDL3OUTSIDE - Three Outside Up/Down
df[f'{ratio}_CDL3OUTSIDE'] = talib.CDL3OUTSIDE(Open,High, Low, Close)
#CDL3STARSINSOUTH - Three Stars In The South
df[f'{ratio}_CDL3STARSINSOUTH'] = talib.CDL3STARSINSOUTH(Open,High, Low, Close)
#CDL3WHITESOLDIERS - Three Advancing White Soldiers
df[f'{ratio}_CDL3WHITESOLDIERS'] = talib.CDL3WHITESOLDIERS(Open,High, Low, Close)
#CDLABANDONEDBABY - Abandoned Baby
df[f'{ratio}_CDLABANDONEDBABY'] = talib.CDLABANDONEDBABY(Open,High, Low, Close, penetration=0)
#CDLADVANCEBLOCK - Advance Block
df[f'{ratio}_CDLADVANCEBLOCK'] = talib.CDLADVANCEBLOCK(Open,High, Low, Close)
#CDLBELTHOLD - Belt-hold
df[f'{ratio}_CDLBELTHOLD'] = talib.CDLBELTHOLD(Open,High, Low, Close)
#CDLBREAKAWAY - Breakaway
df[f'{ratio}_CDLBREAKAWAY'] = talib.CDLBREAKAWAY(Open,High, Low, Close)
#CDLCLOSINGMARUBOZU - Closing Marubozu
df[f'{ratio}_CDLCLOSINGMARUBOZU'] = talib.CDLCLOSINGMARUBOZU(Open,High, Low, Close)
#CDLCONCEALBABYSWALL - Concealing Baby SwalLow
df[f'{ratio}_CDLCLOSINGMARUBOZU'] = talib.CDLCONCEALBABYSWALL(Open,High, Low, Close)
#CDLCOUNTERATTACK - Counterattack
df[f'{ratio}_CDLCLOSINGMARUBOZU'] = talib.CDLCOUNTERATTACK(Open,High, Low, Close)
#CDLDARKCLOUDCOVER - Dark Cloud Cover
df[f'{ratio}_CDLCLOSINGMARUBOZU'] = talib.CDLDARKCLOUDCOVER(Open,High, Low, Close, penetration=0)
#CDLDOJI - Doji
df[f'{ratio}_CDLDOJI'] = talib.CDLDOJI(Open,High, Low, Close)
#CDLDOJISTAR - Doji Star
df[f'{ratio}_CDLDOJISTAR'] = talib.CDLDOJISTAR(Open,High, Low, Close)
#CDLDRAGONFLYDOJI - Dragonfly Doji
df[f'{ratio}_CDLDRAGONFLYDOJI'] = talib.CDLDRAGONFLYDOJI(Open,High, Low, Close)
#CDLENGULFING - Engulfing Pattern
df[f'{ratio}_CDLENGULFING'] = talib.CDLENGULFING(Open,High, Low, Close)
#CDLEVENINGDOJISTAR - Evening Doji Star
df[f'{ratio}_CDLEVENINGDOJISTAR'] = talib.CDLEVENINGDOJISTAR(Open,High, Low, Close, penetration=0)
#CDLEVENINGSTAR - Evening Star
df[f'{ratio}_CDLEVENINGSTAR'] = talib.CDLEVENINGSTAR(Open,High, Low, Close, penetration=0)
#CDLGAPSIDESIDEWHITE - Up/Down-gap side-by-side white lines
df[f'{ratio}_CDLEVENINGSTAR'] = talib.CDLGAPSIDESIDEWHITE(Open,High, Low, Close)
#CDLGRAVESTONEDOJI - Gravestone Doji
df[f'{ratio}_CDLGRAVESTONEDOJI'] = talib.CDLGRAVESTONEDOJI(Open,High, Low, Close)
#CDLHAMMER - Hammer
df[f'{ratio}_CDLGRAVESTONEDOJI'] = talib.CDLHAMMER(Open,High, Low, Close)
#CDLHANGINGMAN - Hanging Man
df[f'{ratio}_CDLGRAVESTONEDOJI'] = talib.CDLHANGINGMAN(Open,High, Low, Close)
#CDLHARAMI - Harami Pattern
df[f'{ratio}_CDLGRAVESTONEDOJI'] = talib.CDLHARAMI(Open,High, Low, Close)
#CDLHARAMICROSS - Harami Cross Pattern
df[f'{ratio}_CDLHARAMICROSS'] = talib.CDLHARAMICROSS(Open,High, Low, Close)
#CDLHighWAVE -High-Wave Candle
#df[f'{ratio}_CDLHighWAVE'] = talib.CDLHighWAVE(Open,High, Low, Close)
#CDLHIKKAKE - Hikkake Pattern
df[f'{ratio}_CDLHIKKAKE'] = talib.CDLHIKKAKE(Open,High, Low, Close)
#CDLHIKKAKEMOD - Modified Hikkake Pattern
df[f'{ratio}_CDLHIKKAKEMOD'] = talib.CDLHIKKAKEMOD(Open,High, Low, Close)
#CDLHOMINGPIGEON - Homing Pigeon
df[f'{ratio}_CDLHOMINGPIGEON'] = talib.CDLHOMINGPIGEON(Open,High, Low, Close)
#CDLIDENTICAL3CROWS - Identical Three Crows
df[f'{ratio}_CDLIDENTICAL3CROWS'] = talib.CDLIDENTICAL3CROWS(Open,High, Low, Close)
#CDLINNECK - In-Neck Pattern
df[f'{ratio}_CDLINNECK'] = talib.CDLINNECK(Open,High, Low, Close)
#CDLINVERTEDHAMMER - Inverted Hammer
df[f'{ratio}_CDLINVERTEDHAMMER'] = talib.CDLINVERTEDHAMMER(Open,High, Low, Close)
#CDLKICKING - Kicking
df[f'{ratio}_CDLKICKING'] = talib.CDLKICKING(Open,High, Low, Close)
#CDLKICKINGBYLENGTH - Kicking - bull/bear determined by the longer marubozu
df[f'{ratio}_CDLKICKINGBYLENGTH'] = talib.CDLKICKINGBYLENGTH(Open,High, Low, Close)
#CDLLADDERBOTTOM - Ladder Bottom
df[f'{ratio}_CDLLADDERBOTTOM'] = talib.CDLLADDERBOTTOM(Open,High, Low, Close)
#CDLLONGLEGGEDDOJI - Long Legged Doji
df[f'{ratio}_CDLLONGLEGGEDDOJI'] = talib.CDLLONGLEGGEDDOJI(Open,High, Low, Close)
#CDLLONGLINE - Long Line Candle
df[f'{ratio}_CDLLONGLINE'] = talib.CDLLONGLINE(Open,High, Low, Close)
#CDLMARUBOZU - Marubozu
df[f'{ratio}_DLMARUBOZU'] = talib.CDLMARUBOZU(Open,High, Low, Close)
#CDLMATCHINGLow - Matching Low
#df[f'{ratio}_CDLMATCHINGLow'] = talib.CDLMATCHINGLow(Open,High, Low, Close)
#CDLMATHOLD - Mat Hold
df[f'{ratio}_CDLMATHOLD'] = talib.CDLMATHOLD(Open,High, Low, Close, penetration=0)
#CDLMORNINGDOJISTAR - Morning Doji Star
df[f'{ratio}_CDLMORNINGDOJISTAR'] = talib.CDLMORNINGDOJISTAR(Open,High, Low, Close, penetration=0)
#CDLMORNINGSTAR - Morning Star
df[f'{ratio}_CDLMORNINGSTAR'] = talib.CDLMORNINGSTAR(Open,High, Low, Close, penetration=0)
#CDLONNECK - On-Neck Pattern
df[f'{ratio}_CDLONNECK'] = talib.CDLONNECK(Open,High, Low, Close)
#CDLPIERCING - Piercing Pattern
df[f'{ratio}_CDLPIERCING'] = talib.CDLPIERCING(Open,High, Low, Close)
#CDLRICKSHAWMAN - Rickshaw Man
df[f'{ratio}_CDLRICKSHAWMAN'] = talib.CDLRICKSHAWMAN(Open,High, Low, Close)
#CDLRISEFALL3METHODS - Rising/Falling Three Methods
df[f'{ratio}_CDLRISEFALL3METHODS'] = talib.CDLRISEFALL3METHODS(Open,High, Low, Close)
#CDLSEPARATINGLINES - Separating Lines
df[f'{ratio}_CDLSEPARATINGLINES'] = talib.CDLSEPARATINGLINES(Open,High, Low, Close)
#CDLSHOOTINGSTAR - Shooting Star
df[f'{ratio}_CDLSHOOTINGSTAR'] = talib.CDLSHOOTINGSTAR(Open,High, Low, Close)
#CDLSHORTLINE - Short Line Candle
df[f'{ratio}_CDLSHORTLINE'] = talib.CDLSHORTLINE(Open,High, Low, Close)
#CDLSPINNINGTOP - Spinning Top
df[f'{ratio}_CDLSPINNINGTOP'] = talib.CDLSPINNINGTOP(Open,High, Low, Close)
#CDLSTALLEDPATTERN - Stalled Pattern
df[f'{ratio}_CDLSTALLEDPATTERN'] = talib.CDLSTALLEDPATTERN(Open,High, Low, Close)
#CDLSTICKSANDWICH - Stick Sandwich
df[f'{ratio}_CDLSTICKSANDWICH'] = talib.CDLSTICKSANDWICH(Open,High, Low, Close)
#CDLTAKURI - Takuri (Dragonfly Doji with very long Lower shadow)
df[f'{ratio}_CDLTAKURI'] = talib.CDLTAKURI(Open,High, Low, Close)
#CDLTASUKIGAP - Tasuki Gap
df[f'{ratio}_CDLTASUKIGAP'] = talib.CDLTASUKIGAP(Open,High, Low, Close)
#CDLTHRUSTING - Thrusting Pattern
df[f'{ratio}_CDLTHRUSTING'] = talib.CDLTHRUSTING(Open,High, Low, Close)
#CDLTRISTAR - Tristar Pattern
df[f'{ratio}_CDLTRISTAR'] = talib.CDLTRISTAR(Open,High, Low, Close)
#CDLUNIQUE3RIVER - Unique 3 River
df[f'{ratio}_CDLUNIQUE3RIVER'] = talib.CDLUNIQUE3RIVER(Open,High, Low, Close)
#CDLUPSIDEGAP2CROWS - Upside Gap Two Crows
df[f'{ratio}_CDLUPSIDEGAP2CROWS'] = talib.CDLUPSIDEGAP2CROWS(Open,High, Low, Close)
#CDLXSIDEGAP3METHODS - Upside/Downside Gap Three Methods
df[f'{ratio}_CDLXSIDEGAP3METHODS'] = talib.CDLXSIDEGAP3METHODS(Open,High, Low, Close)
return patern
def add_ta_features(df, ta_settings):
"""Add technial analysis features from typical financial dataset that
typically include columns such as "open", "high", "low", "price" and
"volume".
http://mrjbq7.github.io/ta-lib/
Args:
df(pandas.DataFrame): original DataFrame.
ta_settings(dict): configuration.
Returns:
pandas.DataFrame: DataFrame with new features included.
"""
open = df['open']
high = df['high']
low = df['low']
close = df['price']
volume = df['volume']
if ta_settings['overlap']:
df['ta_overlap_bbands_upper'], df['ta_overlap_bbands_middle'], df[
'ta_overlap_bbands_lower'] = ta.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
df['ta_overlap_dema'] = ta.DEMA(
close, timeperiod=15) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_ema'] = ta.EMA(close, timeperiod=30)
df['ta_overlap_kama'] = ta.KAMA(close, timeperiod=30)
df['ta_overlap_ma'] = ta.MA(close, timeperiod=30, matype=0)
df['ta_overlap_mama_mama'], df['ta_overlap_mama_fama'] = ta.MAMA(close)
period = np.random.randint(10, 20, size=len(close)).astype(float)
df['ta_overlap_mavp'] = ta.MAVP(close,
period,
minperiod=2,
maxperiod=30,
matype=0)
df['ta_overlap_midpoint'] = ta.MIDPOINT(close, timeperiod=14)
df['ta_overlap_midprice'] = ta.MIDPRICE(high, low, timeperiod=14)
df['ta_overlap_sar'] = ta.SAR(high, low, acceleration=0, maximum=0)
df['ta_overlap_sarext'] = ta.SAREXT(high,
low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
df['ta_overlap_sma'] = ta.SMA(close, timeperiod=30)
df['ta_overlap_t3'] = ta.T3(close, timeperiod=5, vfactor=0)
df['ta_overlap_tema'] = ta.TEMA(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_trima'] = ta.TRIMA(close, timeperiod=30)
df['ta_overlap_wma'] = ta.WMA(close, timeperiod=30)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_overlap_ht_trendline'] = ta.HT_TRENDLINE(close)
if ta_settings['momentum']:
df['ta_momentum_adx'] = ta.ADX(high, low, close, timeperiod=14)
df['ta_momentum_adxr'] = ta.ADXR(high, low, close, timeperiod=14)
df['ta_momentum_apo'] = ta.APO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_aroondown'], df['ta_momentum_aroonup'] = ta.AROON(
high, low, timeperiod=14)
df['ta_momentum_aroonosc'] = ta.AROONOSC(high, low, timeperiod=14)
df['ta_momentum_bop'] = ta.BOP(open, high, low, close)
df['ta_momentum_cci'] = ta.CCI(high, low, close, timeperiod=14)
df['ta_momentum_cmo'] = ta.CMO(close, timeperiod=14)
df['ta_momentum_dx'] = ta.DX(high, low, close, timeperiod=14)
df['ta_momentum_macd_macd'], df['ta_momentum_macd_signal'], df[
'ta_momentum_macd_hist'] = ta.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
df['ta_momentum_macdext_macd'], df['ta_momentum_macdext_signal'], df[
'ta_momentum_macdext_hist'] = ta.MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
df['ta_momentum_macdfix_macd'], df['ta_momentum_macdfix_signal'], df[
'ta_momentum_macdfix_hist'] = ta.MACDFIX(close, signalperiod=9)
df['ta_momentum_mfi'] = ta.MFI(high, low, close, volume, timeperiod=14)
df['ta_momentum_minus_di'] = ta.MINUS_DI(high,
low,
close,
timeperiod=14)
df['ta_momentum_minus_dm'] = ta.MINUS_DM(high, low, timeperiod=14)
df['ta_momentum_mom'] = ta.MOM(close, timeperiod=10)
df['ta_momentum_plus_di'] = ta.PLUS_DI(high, low, close, timeperiod=14)
df['ta_momentum_plus_dm'] = ta.PLUS_DM(high, low, timeperiod=14)
df['ta_momentum_ppo'] = ta.PPO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_roc'] = ta.ROC(close, timeperiod=10)
df['ta_momentum_rocp'] = ta.ROCP(close, timeperiod=10)
df['ta_momentum_rocr'] = ta.ROCR(close, timeperiod=10)
df['ta_momentum_rocr100'] = ta.ROCR100(close, timeperiod=10)
df['ta_momentum_rsi'] = ta.RSI(close, timeperiod=14)
df['ta_momentum_slowk'], df['ta_momentum_slowd'] = ta.STOCH(
high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHF(
high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHRSI(
close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['ta_momentum_trix'] = ta.TRIX(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_momentum_ultosc'] = ta.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df['ta_momentum_willr'] = ta.WILLR(high, low, close, timeperiod=14)
if ta_settings['volume']:
df['ta_volume_ad'] = ta.AD(high, low, close, volume)
df['ta_volume_adosc'] = ta.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)
df['ta_volume_obv'] = ta.OBV(close, volume)
if ta_settings['volatility']:
df['ta_volatility_atr'] = ta.ATR(high, low, close, timeperiod=14)
df['ta_volatility_natr'] = ta.NATR(high, low, close, timeperiod=14)
df['ta_volatility_trange'] = ta.TRANGE(high, low, close)
if ta_settings['price']:
df['ta_price_avgprice'] = ta.AVGPRICE(open, high, low, close)
df['ta_price_medprice'] = ta.MEDPRICE(high, low)
df['ta_price_typprice'] = ta.TYPPRICE(high, low, close)
df['ta_price_wclprice'] = ta.WCLPRICE(high, low, close)
if ta_settings['cycle']:
df['ta_cycle_ht_dcperiod'] = ta.HT_DCPERIOD(close)
df['ta_cycle_ht_phasor_inphase'], df[
'ta_cycle_ht_phasor_quadrature'] = ta.HT_PHASOR(close)
df['ta_cycle_ht_trendmode'] = ta.HT_TRENDMODE(close)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_cycle_ht_dcphase'] = ta.HT_DCPHASE(close)
# df['ta_cycle_ht_sine_sine'], df['ta_cycle_ht_sine_leadsine'] = ta.HT_SINE(close)
if ta_settings['pattern']:
df['ta_pattern_cdl2crows'] = ta.CDL2CROWS(open, high, low, close)
df['ta_pattern_cdl3blackrows'] = ta.CDL3BLACKCROWS(
open, high, low, close)
df['ta_pattern_cdl3inside'] = ta.CDL3INSIDE(open, high, low, close)
df['ta_pattern_cdl3linestrike'] = ta.CDL3LINESTRIKE(
open, high, low, close)
df['ta_pattern_cdl3outside'] = ta.CDL3OUTSIDE(open, high, low, close)
df['ta_pattern_cdl3starsinsouth'] = ta.CDL3STARSINSOUTH(
open, high, low, close)
df['ta_pattern_cdl3whitesoldiers'] = ta.CDL3WHITESOLDIERS(
open, high, low, close)
df['ta_pattern_cdlabandonedbaby'] = ta.CDLABANDONEDBABY(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdladvanceblock'] = ta.CDLADVANCEBLOCK(
open, high, low, close)
df['ta_pattern_cdlbelthold'] = ta.CDLBELTHOLD(open, high, low, close)
df['ta_pattern_cdlbreakaway'] = ta.CDLBREAKAWAY(open, high, low, close)
df['ta_pattern_cdlclosingmarubozu'] = ta.CDLCLOSINGMARUBOZU(
open, high, low, close)
df['ta_pattern_cdlconcealbabyswall'] = ta.CDLCONCEALBABYSWALL(
open, high, low, close)
df['ta_pattern_cdlcounterattack'] = ta.CDLCOUNTERATTACK(
open, high, low, close)
df['ta_pattern_cdldarkcloudcover'] = ta.CDLDARKCLOUDCOVER(
open, high, low, close, penetration=0)
df['ta_pattern_cdldoji'] = ta.CDLDOJI(open, high, low, close)
df['ta_pattern_cdldojistar'] = ta.CDLDOJISTAR(open, high, low, close)
df['ta_pattern_cdldragonflydoji'] = ta.CDLDRAGONFLYDOJI(
open, high, low, close)
df['ta_pattern_cdlengulfing'] = ta.CDLENGULFING(open, high, low, close)
df['ta_pattern_cdleveningdojistar'] = ta.CDLEVENINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdleveningstar'] = ta.CDLEVENINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlgapsidesidewhite'] = ta.CDLGAPSIDESIDEWHITE(
open, high, low, close)
df['ta_pattern_cdlgravestonedoji'] = ta.CDLGRAVESTONEDOJI(
open, high, low, close)
df['ta_pattern_cdlhammer'] = ta.CDLHAMMER(open, high, low, close)
df['ta_pattern_cdlhangingman'] = ta.CDLHANGINGMAN(
open, high, low, close)
df['ta_pattern_cdlharami'] = ta.CDLHARAMI(open, high, low, close)
df['ta_pattern_cdlharamicross'] = ta.CDLHARAMICROSS(
open, high, low, close)
df['ta_pattern_cdlhighwave'] = ta.CDLHIGHWAVE(open, high, low, close)
df['ta_pattern_cdlhikkake'] = ta.CDLHIKKAKE(open, high, low, close)
df['ta_pattern_cdlhikkakemod'] = ta.CDLHIKKAKEMOD(
open, high, low, close)
df['ta_pattern_cdlhomingpigeon'] = ta.CDLHOMINGPIGEON(
open, high, low, close)
df['ta_pattern_cdlidentical3crows'] = ta.CDLIDENTICAL3CROWS(
open, high, low, close)
df['ta_pattern_cdlinneck'] = ta.CDLINNECK(open, high, low, close)
df['ta_pattern_cdlinvertedhammer'] = ta.CDLINVERTEDHAMMER(
open, high, low, close)
df['ta_pattern_cdlkicking'] = ta.CDLKICKING(open, high, low, close)
df['ta_pattern_cdlkickingbylength'] = ta.CDLKICKINGBYLENGTH(
open, high, low, close)
df['ta_pattern_cdlladderbottom'] = ta.CDLLADDERBOTTOM(
open, high, low, close)
df['ta_pattern_cdllongleggeddoji'] = ta.CDLLONGLEGGEDDOJI(
open, high, low, close)
df['ta_pattern_cdllongline'] = ta.CDLLONGLINE(open, high, low, close)
df['ta_pattern_cdlmarubozu'] = ta.CDLMARUBOZU(open, high, low, close)
df['ta_pattern_cdlmatchinglow'] = ta.CDLMATCHINGLOW(
open, high, low, close)
df['ta_pattern_cdlmathold'] = ta.CDLMATHOLD(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlmorningdojistar'] = ta.CDLMORNINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdlmorningstar'] = ta.CDLMORNINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdllonneck'] = ta.CDLONNECK(open, high, low, close)
df['ta_pattern_cdlpiercing'] = ta.CDLPIERCING(open, high, low, close)
df['ta_pattern_cdlrickshawman'] = ta.CDLRICKSHAWMAN(
open, high, low, close)
df['ta_pattern_cdlrisefall3methods'] = ta.CDLRISEFALL3METHODS(
open, high, low, close)
df['ta_pattern_cdlseparatinglines'] = ta.CDLSEPARATINGLINES(
open, high, low, close)
df['ta_pattern_cdlshootingstar'] = ta.CDLSHOOTINGSTAR(
open, high, low, close)
df['ta_pattern_cdlshortline'] = ta.CDLSHORTLINE(open, high, low, close)
df['ta_pattern_cdlspinningtop'] = ta.CDLSPINNINGTOP(
open, high, low, close)
df['ta_pattern_cdlstalledpattern'] = ta.CDLSTALLEDPATTERN(
open, high, low, close)
df['ta_pattern_cdlsticksandwich'] = ta.CDLSTICKSANDWICH(
open, high, low, close)
df['ta_pattern_cdltakuri'] = ta.CDLTAKURI(open, high, low, close)
df['ta_pattern_cdltasukigap'] = ta.CDLTASUKIGAP(open, high, low, close)
df['ta_pattern_cdlthrusting'] = ta.CDLTHRUSTING(open, high, low, close)
df['ta_pattern_cdltristar'] = ta.CDLTRISTAR(open, high, low, close)
df['ta_pattern_cdlunique3river'] = ta.CDLUNIQUE3RIVER(
open, high, low, close)
df['ta_pattern_cdlupsidegap2crows'] = ta.CDLUPSIDEGAP2CROWS(
open, high, low, close)
df['ta_pattern_cdlxsidegap3methods'] = ta.CDLXSIDEGAP3METHODS(
open, high, low, close)
if ta_settings['statistic']:
df['ta_statistic_beta'] = ta.BETA(high, low, timeperiod=5)
df['ta_statistic_correl'] = ta.CORREL(high, low, timeperiod=30)
df['ta_statistic_linearreg'] = ta.LINEARREG(close, timeperiod=14)
df['ta_statistic_linearreg_angle'] = ta.LINEARREG_ANGLE(close,
timeperiod=14)
df['ta_statistic_linearreg_intercept'] = ta.LINEARREG_INTERCEPT(
close, timeperiod=14)
df['ta_statistic_linearreg_slope'] = ta.LINEARREG_SLOPE(close,
timeperiod=14)
df['ta_statistic_stddev'] = ta.STDDEV(close, timeperiod=5, nbdev=1)
df['ta_statistic_tsf'] = ta.TSF(close, timeperiod=14)
df['ta_statistic_var'] = ta.VAR(close, timeperiod=5, nbdev=1)
if ta_settings['math_transforms']:
df['ta_math_transforms_atan'] = ta.ATAN(close)
df['ta_math_transforms_ceil'] = ta.CEIL(close)
df['ta_math_transforms_cos'] = ta.COS(close)
df['ta_math_transforms_floor'] = ta.FLOOR(close)
df['ta_math_transforms_ln'] = ta.LN(close)
df['ta_math_transforms_log10'] = ta.LOG10(close)
df['ta_math_transforms_sin'] = ta.SIN(close)
df['ta_math_transforms_sqrt'] = ta.SQRT(close)
df['ta_math_transforms_tan'] = ta.TAN(close)
if ta_settings['math_operators']:
df['ta_math_operators_add'] = ta.ADD(high, low)
df['ta_math_operators_div'] = ta.DIV(high, low)
df['ta_math_operators_min'], df['ta_math_operators_max'] = ta.MINMAX(
close, timeperiod=30)
df['ta_math_operators_minidx'], df[
'ta_math_operators_maxidx'] = ta.MINMAXINDEX(close, timeperiod=30)
df['ta_math_operators_mult'] = ta.MULT(high, low)
df['ta_math_operators_sub'] = ta.SUB(high, low)
df['ta_math_operators_sum'] = ta.SUM(close, timeperiod=30)
return df
def 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 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 calculate(self, para):
self.t = self.inputdata[:, 0]
self.op = self.inputdata[:, 1]
self.high = self.inputdata[:, 2]
self.low = self.inputdata[:, 3]
self.close = self.inputdata[:, 4]
#adjusted close
self.close1 = self.inputdata[:, 5]
self.volume = self.inputdata[:, 6]
#Overlap study
#Overlap Studies
#Overlap Studies
if para is 'BBANDS': #Bollinger Bands
upperband, middleband, lowerband = ta.BBANDS(self.close,
timeperiod=self.tp,
nbdevup=2,
nbdevdn=2,
matype=0)
self.output = [upperband, middleband, lowerband]
elif para is 'DEMA': #Double Exponential Moving Average
self.output = ta.DEMA(self.close, timeperiod=self.tp)
elif para is 'EMA': #Exponential Moving Average
self.output = ta.EMA(self.close, timeperiod=self.tp)
elif para is 'HT_TRENDLINE': #Hilbert Transform - Instantaneous Trendline
self.output = ta.HT_TRENDLINE(self.close)
elif para is 'KAMA': #Kaufman Adaptive Moving Average
self.output = ta.KAMA(self.close, timeperiod=self.tp)
elif para is 'MA': #Moving average
self.output = ta.MA(self.close, timeperiod=self.tp, matype=0)
elif para is 'MAMA': #MESA Adaptive Moving Average
mama, fama = ta.MAMA(self.close, fastlimit=0, slowlimit=0)
elif para is 'MAVP': #Moving average with variable period
self.output = ta.MAVP(self.close,
periods=10,
minperiod=self.tp,
maxperiod=self.tp1,
matype=0)
elif para is 'MIDPOINT': #MidPoint over period
self.output = ta.MIDPOINT(self.close, timeperiod=self.tp)
elif para is 'MIDPRICE': #Midpoint Price over period
self.output = ta.MIDPRICE(self.high, self.low, timeperiod=self.tp)
elif para is 'SAR': #Parabolic SAR
self.output = ta.SAR(self.high,
self.low,
acceleration=0,
maximum=0)
elif para is 'SAREXT': #Parabolic SAR - Extended
self.output = ta.SAREXT(self.high,
self.low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
elif para is 'SMA': #Simple Moving Average
self.output = ta.SMA(self.close, timeperiod=self.tp)
elif para is 'T3': #Triple Exponential Moving Average (T3)
self.output = ta.T3(self.close, timeperiod=self.tp, vfactor=0)
elif para is 'TEMA': #Triple Exponential Moving Average
self.output = ta.TEMA(self.close, timeperiod=self.tp)
elif para is 'TRIMA': #Triangular Moving Average
self.output = ta.TRIMA(self.close, timeperiod=self.tp)
elif para is 'WMA': #Weighted Moving Average
self.output = ta.WMA(self.close, timeperiod=self.tp)
#Momentum Indicators
elif para is 'ADX': #Average Directional Movement Index
self.output = ta.ADX(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'ADXR': #Average Directional Movement Index Rating
self.output = ta.ADXR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'APO': #Absolute Price Oscillator
self.output = ta.APO(self.close,
fastperiod=12,
slowperiod=26,
matype=0)
elif para is 'AROON': #Aroon
aroondown, aroonup = ta.AROON(self.high,
self.low,
timeperiod=self.tp)
self.output = [aroondown, aroonup]
elif para is 'AROONOSC': #Aroon Oscillator
self.output = ta.AROONOSC(self.high, self.low, timeperiod=self.tp)
elif para is 'BOP': #Balance Of Power
self.output = ta.BOP(self.op, self.high, self.low, self.close)
elif para is 'CCI': #Commodity Channel Index
self.output = ta.CCI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'CMO': #Chande Momentum Oscillator
self.output = ta.CMO(self.close, timeperiod=self.tp)
elif para is 'DX': #Directional Movement Index
self.output = ta.DX(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'MACD': #Moving Average Convergence/Divergence
macd, macdsignal, macdhist = ta.MACD(self.close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
self.output = [macd, macdsignal, macdhist]
elif para is 'MACDEXT': #MACD with controllable MA type
macd, macdsignal, macdhist = ta.MACDEXT(self.close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
self.output = [macd, macdsignal, macdhist]
elif para is 'MACDFIX': #Moving Average Convergence/Divergence Fix 12/26
macd, macdsignal, macdhist = ta.MACDFIX(self.close, signalperiod=9)
self.output = [macd, macdsignal, macdhist]
elif para is 'MFI': #Money Flow Index
self.output = ta.MFI(self.high,
self.low,
self.close,
self.volume,
timeperiod=self.tp)
elif para is 'MINUS_DI': #Minus Directional Indicator
self.output = ta.MINUS_DI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'MINUS_DM': #Minus Directional Movement
self.output = ta.MINUS_DM(self.high, self.low, timeperiod=self.tp)
elif para is 'MOM': #Momentum
self.output = ta.MOM(self.close, timeperiod=10)
elif para is 'PLUS_DI': #Plus Directional Indicator
self.output = ta.PLUS_DI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'PLUS_DM': #Plus Directional Movement
self.output = ta.PLUS_DM(self.high, self.low, timeperiod=self.tp)
elif para is 'PPO': #Percentage Price Oscillator
self.output = ta.PPO(self.close,
fastperiod=12,
slowperiod=26,
matype=0)
elif para is 'ROC': #Rate of change : ((price/prevPrice)-1)*100
self.output = ta.ROC(self.close, timeperiod=10)
elif para is 'ROCP': #Rate of change Percentage: (price-prevPrice)/prevPrice
self.output = ta.ROCP(self.close, timeperiod=10)
elif para is 'ROCR': #Rate of change ratio: (price/prevPrice)
self.output = ta.ROCR(self.close, timeperiod=10)
elif para is 'ROCR100': #Rate of change ratio 100 scale: (price/prevPrice)*100
self.output = ta.ROCR100(self.close, timeperiod=10)
elif para is 'RSI': #Relative Strength Index
self.output = ta.RSI(self.close, timeperiod=self.tp)
elif para is 'STOCH': #Stochastic
slowk, slowd = ta.STOCH(self.high,
self.low,
self.close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
self.output = [slowk, slowd]
elif para is 'STOCHF': #Stochastic Fast
fastk, fastd = ta.STOCHF(self.high,
self.low,
self.close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.output = [fastk, fastd]
elif para is 'STOCHRSI': #Stochastic Relative Strength Index
fastk, fastd = ta.STOCHRSI(self.close,
timeperiod=self.tp,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.output = [fastk, fastd]
elif para is 'TRIX': #1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
self.output = ta.TRIX(self.close, timeperiod=self.tp)
elif para is 'ULTOSC': #Ultimate Oscillator
self.output = ta.ULTOSC(self.high,
self.low,
self.close,
timeperiod1=self.tp,
timeperiod2=self.tp1,
timeperiod3=self.tp2)
elif para is 'WILLR': #Williams' %R
self.output = ta.WILLR(self.high,
self.low,
self.close,
timeperiod=self.tp)
# Volume Indicators : #
elif para is 'AD': #Chaikin A/D Line
self.output = ta.AD(self.high, self.low, self.close, self.volume)
elif para is 'ADOSC': #Chaikin A/D Oscillator
self.output = ta.ADOSC(self.high,
self.low,
self.close,
self.volume,
fastperiod=3,
slowperiod=10)
elif para is 'OBV': #On Balance Volume
self.output = ta.OBV(self.close, self.volume)
# Volatility Indicators: #
elif para is 'ATR': #Average True Range
self.output = ta.ATR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'NATR': #Normalized Average True Range
self.output = ta.NATR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'TRANGE': #True Range
self.output = ta.TRANGE(self.high, self.low, self.close)
#Price Transform : #
elif para is 'AVGPRICE': #Average Price
self.output = ta.AVGPRICE(self.op, self.high, self.low, self.close)
elif para is 'MEDPRICE': #Median Price
self.output = ta.MEDPRICE(self.high, self.low)
elif para is 'TYPPRICE': #Typical Price
self.output = ta.TYPPRICE(self.high, self.low, self.close)
elif para is 'WCLPRICE': #Weighted Close Price
self.output = ta.WCLPRICE(self.high, self.low, self.close)
#Cycle Indicators : #
elif para is 'HT_DCPERIOD': #Hilbert Transform - Dominant Cycle Period
self.output = ta.HT_DCPERIOD(self.close)
elif para is 'HT_DCPHASE': #Hilbert Transform - Dominant Cycle Phase
self.output = ta.HT_DCPHASE(self.close)
elif para is 'HT_PHASOR': #Hilbert Transform - Phasor Components
inphase, quadrature = ta.HT_PHASOR(self.close)
self.output = [inphase, quadrature]
elif para is 'HT_SINE': #Hilbert Transform - SineWave #2
sine, leadsine = ta.HT_SINE(self.close)
self.output = [sine, leadsine]
elif para is 'HT_TRENDMODE': #Hilbert Transform - Trend vs Cycle Mode
self.integer = ta.HT_TRENDMODE(self.close)
#Pattern Recognition : #
elif para is 'CDL2CROWS': #Two Crows
self.integer = ta.CDL2CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDL3BLACKCROWS': #Three Black Crows
self.integer = ta.CDL3BLACKCROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDL3INSIDE': #Three Inside Up/Down
self.integer = ta.CDL3INSIDE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3LINESTRIKE': #Three-Line Strike
self.integer = ta.CDL3LINESTRIKE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3OUTSIDE': #Three Outside Up/Down
self.integer = ta.CDL3OUTSIDE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3STARSINSOUTH': #Three Stars In The South
self.integer = ta.CDL3STARSINSOUTH(self.op, self.high, self.low,
self.close)
elif para is 'CDL3WHITESOLDIERS': #Three Advancing White Soldiers
self.integer = ta.CDL3WHITESOLDIERS(self.op, self.high, self.low,
self.close)
elif para is 'CDLABANDONEDBABY': #Abandoned Baby
self.integer = ta.CDLABANDONEDBABY(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLBELTHOLD': #Belt-hold
self.integer = ta.CDLBELTHOLD(self.op, self.high, self.low,
self.close)
elif para is 'CDLBREAKAWAY': #Breakaway
self.integer = ta.CDLBREAKAWAY(self.op, self.high, self.low,
self.close)
elif para is 'CDLCLOSINGMARUBOZU': #Closing Marubozu
self.integer = ta.CDLCLOSINGMARUBOZU(self.op, self.high, self.low,
self.close)
elif para is 'CDLCONCEALBABYSWALL': #Concealing Baby Swallow
self.integer = ta.CDLCONCEALBABYSWALL(self.op, self.high, self.low,
self.close)
elif para is 'CDLCOUNTERATTACK': #Counterattack
self.integer = ta.CDLCOUNTERATTACK(self.op, self.high, self.low,
self.close)
elif para is 'CDLDARKCLOUDCOVER': #Dark Cloud Cover
self.integer = ta.CDLDARKCLOUDCOVER(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLDOJI': #Doji
self.integer = ta.CDLDOJI(self.op, self.high, self.low, self.close)
elif para is 'CDLDOJISTAR': #Doji Star
self.integer = ta.CDLDOJISTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLDRAGONFLYDOJI': #Dragonfly Doji
self.integer = ta.CDLDRAGONFLYDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLENGULFING': #Engulfing Pattern
self.integer = ta.CDLENGULFING(self.op, self.high, self.low,
self.close)
elif para is 'CDLEVENINGDOJISTAR': #Evening Doji Star
self.integer = ta.CDLEVENINGDOJISTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLEVENINGSTAR': #Evening Star
self.integer = ta.CDLEVENINGSTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLGAPSIDESIDEWHITE': #Up/Down-gap side-by-side white lines
self.integer = ta.CDLGAPSIDESIDEWHITE(self.op, self.high, self.low,
self.close)
elif para is 'CDLGRAVESTONEDOJI': #Gravestone Doji
self.integer = ta.CDLGRAVESTONEDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLHAMMER': #Hammer
self.integer = ta.CDLHAMMER(self.op, self.high, self.low,
self.close)
elif para is 'CDLHANGINGMAN': #Hanging Man
self.integer = ta.CDLHANGINGMAN(self.op, self.high, self.low,
self.close)
elif para is 'CDLHARAMI': #Harami Pattern
self.integer = ta.CDLHARAMI(self.op, self.high, self.low,
self.close)
elif para is 'CDLHARAMICROSS': #Harami Cross Pattern
self.integer = ta.CDLHARAMICROSS(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIGHWAVE': #High-Wave Candle
self.integer = ta.CDLHIGHWAVE(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIKKAKE': #Hikkake Pattern
self.integer = ta.CDLHIKKAKE(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIKKAKEMOD': #Modified Hikkake Pattern
self.integer = ta.CDLHIKKAKEMOD(self.op, self.high, self.low,
self.close)
elif para is 'CDLHOMINGPIGEON': #Homing Pigeon
self.integer = ta.CDLHOMINGPIGEON(self.op, self.high, self.low,
self.close)
elif para is 'CDLIDENTICAL3CROWS': #Identical Three Crows
self.integer = ta.CDLIDENTICAL3CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDLINNECK': #In-Neck Pattern
self.integer = ta.CDLINNECK(self.op, self.high, self.low,
self.close)
elif para is 'CDLINVERTEDHAMMER': #Inverted Hammer
self.integer = ta.CDLINVERTEDHAMMER(self.op, self.high, self.low,
self.close)
elif para is 'CDLKICKING': #Kicking
self.integer = ta.CDLKICKING(self.op, self.high, self.low,
self.close)
elif para is 'CDLKICKINGBYLENGTH': #Kicking - bull/bear determined by the longer marubozu
self.integer = ta.CDLKICKINGBYLENGTH(self.op, self.high, self.low,
self.close)
elif para is 'CDLLADDERBOTTOM': #Ladder Bottom
self.integer = ta.CDLLADDERBOTTOM(self.op, self.high, self.low,
self.close)
elif para is 'CDLLONGLEGGEDDOJI': #Long Legged Doji
self.integer = ta.CDLLONGLEGGEDDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLLONGLINE': #Long Line Candle
self.integer = ta.CDLLONGLINE(self.op, self.high, self.low,
self.close)
elif para is 'CDLMARUBOZU': #Marubozu
self.integer = ta.CDLMARUBOZU(self.op, self.high, self.low,
self.close)
elif para is 'CDLMATCHINGLOW': #Matching Low
self.integer = ta.CDLMATCHINGLOW(self.op, self.high, self.low,
self.close)
elif para is 'CDLMATHOLD': #Mat Hold
self.integer = ta.CDLMATHOLD(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLMORNINGDOJISTAR': #Morning Doji Star
self.integer = ta.CDLMORNINGDOJISTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLMORNINGSTAR': #Morning Star
self.integer = ta.CDLMORNINGSTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLONNECK': #On-Neck Pattern
self.integer = ta.CDLONNECK(self.op, self.high, self.low,
self.close)
elif para is 'CDLPIERCING': #Piercing Pattern
self.integer = ta.CDLPIERCING(self.op, self.high, self.low,
self.close)
elif para is 'CDLRICKSHAWMAN': #Rickshaw Man
self.integer = ta.CDLRICKSHAWMAN(self.op, self.high, self.low,
self.close)
elif para is 'CDLRISEFALL3METHODS': #Rising/Falling Three Methods
self.integer = ta.CDLRISEFALL3METHODS(self.op, self.high, self.low,
self.close)
elif para is 'CDLSEPARATINGLINES': #Separating Lines
self.integer = ta.CDLSEPARATINGLINES(self.op, self.high, self.low,
self.close)
elif para is 'CDLSHOOTINGSTAR': #Shooting Star
self.integer = ta.CDLSHOOTINGSTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLSHORTLINE': #Short Line Candle
self.integer = ta.CDLSHORTLINE(self.op, self.high, self.low,
self.close)
elif para is 'CDLSPINNINGTOP': #Spinning Top
self.integer = ta.CDLSPINNINGTOP(self.op, self.high, self.low,
self.close)
elif para is 'CDLSTALLEDPATTERN': #Stalled Pattern
self.integer = ta.CDLSTALLEDPATTERN(self.op, self.high, self.low,
self.close)
elif para is 'CDLSTICKSANDWICH': #Stick Sandwich
self.integer = ta.CDLSTICKSANDWICH(self.op, self.high, self.low,
self.close)
elif para is 'CDLTAKURI': #Takuri (Dragonfly Doji with very long lower shadow)
self.integer = ta.CDLTAKURI(self.op, self.high, self.low,
self.close)
elif para is 'CDLTASUKIGAP': #Tasuki Gap
self.integer = ta.CDLTASUKIGAP(self.op, self.high, self.low,
self.close)
elif para is 'CDLTHRUSTING': #Thrusting Pattern
self.integer = ta.CDLTHRUSTING(self.op, self.high, self.low,
self.close)
elif para is 'CDLTRISTAR': #Tristar Pattern
self.integer = ta.CDLTRISTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLUNIQUE3RIVER': #Unique 3 River
self.integer = ta.CDLUNIQUE3RIVER(self.op, self.high, self.low,
self.close)
elif para is 'CDLUPSIDEGAP2CROWS': #Upside Gap Two Crows
self.integer = ta.CDLUPSIDEGAP2CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDLXSIDEGAP3METHODS': #Upside/Downside Gap Three Methods
self.integer = ta.CDLXSIDEGAP3METHODS(self.op, self.high, self.low,
self.close)
#Statistic Functions : #
elif para is 'BETA': #Beta
self.output = ta.BETA(self.high, self.low, timeperiod=5)
elif para is 'CORREL': #Pearson's Correlation Coefficient (r)
self.output = ta.CORREL(self.high, self.low, timeperiod=self.tp)
elif para is 'LINEARREG': #Linear Regression
self.output = ta.LINEARREG(self.close, timeperiod=self.tp)
elif para is 'LINEARREG_ANGLE': #Linear Regression Angle
self.output = ta.LINEARREG_ANGLE(self.close, timeperiod=self.tp)
elif para is 'LINEARREG_INTERCEPT': #Linear Regression Intercept
self.output = ta.LINEARREG_INTERCEPT(self.close,
timeperiod=self.tp)
elif para is 'LINEARREG_SLOPE': #Linear Regression Slope
self.output = ta.LINEARREG_SLOPE(self.close, timeperiod=self.tp)
elif para is 'STDDEV': #Standard Deviation
self.output = ta.STDDEV(self.close, timeperiod=5, nbdev=1)
elif para is 'TSF': #Time Series Forecast
self.output = ta.TSF(self.close, timeperiod=self.tp)
elif para is 'VAR': #Variance
self.output = ta.VAR(self.close, timeperiod=5, nbdev=1)
else:
print('You issued command:' + para)
def get_datasets(asset, currency, granularity, datapoints):
"""Fetch the API and precess the desired pair
Arguments:
asset {str} -- First pair
currency {str} -- Second pair
granularity {str ['day', 'hour']} -- Granularity
datapoints {int [100 - 2000]} -- [description]
Returns:
pandas.Dataframe -- The OHLCV and indicators dataframe
"""
df_train_path = 'datasets/bot_train_{}_{}_{}.csv'.format(
asset + currency, datapoints, granularity)
df_rollout_path = 'datasets/bot_rollout_{}_{}_{}.csv'.format(
asset + currency, datapoints, granularity)
emojis = [
':moneybag:', ':yen:', ':dollar:', ':pound:', ':euro:',
':credit_card:', ':money_with_wings:', ':gem:'
]
if not os.path.exists(df_rollout_path):
headers = {
'User-Agent':
'Mozilla/5.0',
'authorization':
'Apikey 3d7d3e9e6006669ac00584978342451c95c3c78421268ff7aeef69995f9a09ce'
}
# OHLC
# url = 'https://min-api.cryptocompare.com/data/histo{}?fsym={}&tsym={}&e=Binance&limit={}'.format(granularity, asset, currency, datapoints)
url = 'https://min-api.cryptocompare.com/data/histo{}?fsym={}&tsym={}&limit={}'.format(
granularity, asset, currency, datapoints)
# print(emoji.emojize(':dizzy: :large_blue_diamond: :gem: :bar_chart: :crystal_ball: :chart_with_downwards_trend: :chart_with_upwards_trend: :large_orange_diamond: loading...', use_aliases=True))
print(
colored(
emoji.emojize('> ' + random.choice(emojis) + ' downloading ' +
asset + '/' + currency,
use_aliases=True), 'green'))
# print(colored('> downloading ' + asset + '/' + currency, 'green'))
response = requests.get(url, headers=headers)
json_response = response.json()
status = json_response['Response']
if status == "Error":
print(colored('=== {} ==='.format(json_response['Message']),
'red'))
raise AssertionError()
result = json_response['Data']
df = pd.DataFrame(result)
print(df.tail())
df['Date'] = pd.to_datetime(df['time'], utc=True, unit='s')
df.drop('time', axis=1, inplace=True)
# indicators
# https://github.com/mrjbq7/ta-lib/blob/master/docs/func.md
open_price, high, low, close = np.array(df['open']), np.array(
df['high']), np.array(df['low']), np.array(df['close'])
volume = np.array(df['volumefrom'])
# cycle indicators
df.loc[:, 'HT_DCPERIOD'] = talib.HT_DCPERIOD(close)
df.loc[:, 'HT_DCPHASE'] = talib.HT_DCPHASE(close)
df.loc[:,
'HT_PHASOR_inphase'], df.loc[:,
'HT_PHASOR_quadrature'] = talib.HT_PHASOR(
close)
df.loc[:, 'HT_SINE_sine'], df.loc[:,
'HT_SINE_leadsine'] = talib.HT_SINE(
close)
df.loc[:, 'HT_TRENDMODE'] = talib.HT_TRENDMODE(close)
# momemtum indicators
df.loc[:, 'ADX'] = talib.ADX(high, low, close, timeperiod=12)
df.loc[:, 'ADXR'] = talib.ADXR(high, low, close, timeperiod=13)
df.loc[:, 'APO'] = talib.APO(close,
fastperiod=5,
slowperiod=10,
matype=0)
df.loc[:,
'AROON_down'], df.loc[:,
'AROON_up'] = talib.AROON(high,
low,
timeperiod=15)
df.loc[:, 'AROONOSC'] = talib.AROONOSC(high, low, timeperiod=13)
df.loc[:, 'BOP'] = talib.BOP(open_price, high, low, close)
df.loc[:, 'CCI'] = talib.CCI(high, low, close, timeperiod=13)
df.loc[:, 'CMO'] = talib.CMO(close, timeperiod=14)
df.loc[:, 'DX'] = talib.DX(high, low, close, timeperiod=10)
df['MACD'], df['MACD_signal'], df['MACD_hist'] = talib.MACD(
close, fastperiod=5, slowperiod=10, signalperiod=20)
df.loc[:, 'MFI'] = talib.MFI(high, low, close, volume, timeperiod=12)
df.loc[:, 'MINUS_DI'] = talib.MINUS_DI(high, low, close, timeperiod=10)
df.loc[:, 'MINUS_DM'] = talib.MINUS_DM(high, low, timeperiod=14)
df.loc[:, 'MOM'] = talib.MOM(close, timeperiod=20)
df.loc[:, 'PPO'] = talib.PPO(close,
fastperiod=17,
slowperiod=35,
matype=2)
df.loc[:, 'ROC'] = talib.ROC(close, timeperiod=12)
df.loc[:, 'RSI'] = talib.RSI(close, timeperiod=25)
df.loc[:, 'STOCH_k'], df.loc[:,
'STOCH_d'] = talib.STOCH(high,
low,
close,
fastk_period=35,
slowk_period=12,
slowk_matype=0,
slowd_period=7,
slowd_matype=0)
df.loc[:,
'STOCHF_k'], df.loc[:,
'STOCHF_d'] = talib.STOCHF(high,
low,
close,
fastk_period=28,
fastd_period=14,
fastd_matype=0)
df.loc[:, 'STOCHRSI_K'], df.loc[:, 'STOCHRSI_D'] = talib.STOCHRSI(
close,
timeperiod=35,
fastk_period=12,
fastd_period=10,
fastd_matype=1)
df.loc[:, 'TRIX'] = talib.TRIX(close, timeperiod=30)
df.loc[:, 'ULTOSC'] = talib.ULTOSC(high,
low,
close,
timeperiod1=14,
timeperiod2=28,
timeperiod3=35)
df.loc[:, 'WILLR'] = talib.WILLR(high, low, close, timeperiod=35)
# overlap studies
df.loc[:,
'BBANDS_upper'], df.loc[:,
'BBANDS_middle'], df.loc[:,
'BBANDS_lower'] = talib.BBANDS(
close,
timeperiod=
12,
nbdevup=2,
nbdevdn=2,
matype=0)
df.loc[:, 'DEMA'] = talib.DEMA(close, timeperiod=30)
df.loc[:, 'EMA'] = talib.EMA(close, timeperiod=7)
df.loc[:, 'HT_TRENDLINE'] = talib.HT_TRENDLINE(close)
df.loc[:, 'KAMA'] = talib.KAMA(close, timeperiod=5)
df.loc[:, 'MA'] = talib.MA(close, timeperiod=5, matype=0)
df.loc[:, 'MIDPOINT'] = talib.MIDPOINT(close, timeperiod=20)
df.loc[:, 'WMA'] = talib.WMA(close, timeperiod=15)
df.loc[:, 'SMA'] = talib.SMA(close)
# pattern recoginition
df.loc[:, 'CDL2CROWS'] = talib.CDL2CROWS(open_price, high, low, close)
df.loc[:, 'CDL3BLACKCROWS'] = talib.CDL3BLACKCROWS(
open_price, high, low, close)
df.loc[:, 'CDL3INSIDE'] = talib.CDL3INSIDE(open_price, high, low,
close)
df.loc[:, 'CDL3LINESTRIKE'] = talib.CDL3LINESTRIKE(
open_price, high, low, close)
# price transform
df.loc[:, 'WCLPRICE'] = talib.WCLPRICE(high, low, close)
# statistic funcitons
df.loc[:, 'BETA'] = talib.BETA(high, low, timeperiod=20)
df.loc[:, 'CORREL'] = talib.CORREL(high, low, timeperiod=20)
df.loc[:, 'STDDEV'] = talib.STDDEV(close, timeperiod=20, nbdev=1)
df.loc[:, 'TSF'] = talib.TSF(close, timeperiod=20)
df.loc[:, 'VAR'] = talib.VAR(close, timeperiod=20, nbdev=1)
# volatility indicators
df.loc[:, 'ATR'] = talib.ATR(high, low, close, timeperiod=7)
df.loc[:, 'NATR'] = talib.NATR(high, low, close, timeperiod=20)
df.loc[:, 'TRANGE'] = talib.TRANGE(high, low, close)
# volume indicators
df.loc[:, 'AD'] = talib.AD(high, low, close, volume)
df.loc[:, 'ADOSC'] = talib.ADOSC(high,
low,
close,
volume,
fastperiod=10,
slowperiod=20)
df.loc[:, 'OBV'] = talib.OBV(close, volume)
# df.fillna(df.mean(), inplace=True)
df.dropna(inplace=True)
df.set_index('Date', inplace=True)
print(colored('> caching' + asset + '/' + currency + ':)', 'cyan'))
train_size = round(
len(df) *
DF_TRAIN_SIZE) # 75% to train -> test with different value
df_train = df[:train_size]
df_rollout = df[train_size:]
df_train.to_csv(df_train_path)
df_rollout.to_csv(df_rollout_path)
df_train = pd.read_csv(
df_train_path) # re-read to avoid indexing issue w/ Ray
df_rollout = pd.read_csv(df_rollout_path)
else:
print(
colored(
emoji.emojize('> ' + random.choice(emojis) + ' feching ' +
asset + '/' + currency + ' from cache',
use_aliases=True), 'magenta'))
# print(colored('> feching ' + asset + '/' + currency + ' from cache :)', 'magenta'))
df_train = pd.read_csv(df_train_path)
df_rollout = pd.read_csv(df_rollout_path)
# df_train.set_index('Date', inplace=True)
# df_rollout.set_index('Date', inplace=True)
return df_train, df_rollout
if daydifference > 10: # Dead Stock
print('Old Dead Stock,Code:', code)
continue
# Most Required Data
df = df[prices + others]
yf = df.copy()
# Feature Space New Talib
previous_columns = df.columns.values
op = df['Open']
hp = df['High']
lp = df['Low']
cp = df['Close']
df['CDL2CROWS'] = talib.CDL2CROWS(op, hp, lp, cp)
df['CDL3BLACKCROWS'] = talib.CDL3BLACKCROWS(op, hp, lp, cp)
df['CDL3INSIDE'] = talib.CDL3INSIDE(op, hp, lp, cp)
df['CDL3LINESTRIKE'] = talib.CDL3LINESTRIKE(op, hp, lp, cp)
df['CDL3OUTSIDE'] = talib.CDL3OUTSIDE(op, hp, lp, cp)
df['CDL3STARSINSOUTH'] = talib.CDL3STARSINSOUTH(op, hp, lp, cp)
df['CDL3WHITESOLDIERS'] = talib.CDL3WHITESOLDIERS(op, hp, lp, cp)
df['CDLABANDONEDBABY'] = talib.CDLABANDONEDBABY(op, hp, lp, cp)
df['CDLADVANCEBLOCK'] = talib.CDLADVANCEBLOCK(op, hp, lp, cp)
df['CDLBELTHOLD'] = talib.CDLBELTHOLD(op, hp, lp, cp)
df['CDLBREAKAWAY'] = talib.CDLBREAKAWAY(op, hp, lp, cp)
df['CDLCLOSINGMARUBOZU'] = talib.CDLCLOSINGMARUBOZU(op, hp, lp, cp)
df['CDLCONCEALBABYSWALL'] = talib.CDLCONCEALBABYSWALL(op, hp, lp, cp)
df['CDLCOUNTERATTACK'] = talib.CDLCOUNTERATTACK(op, hp, lp, cp)
df['CDLDARKCLOUDCOVER'] = talib.CDLDARKCLOUDCOVER(op, hp, lp, cp)
df['CDLDOJI'] = talib.CDLDOJI(op, hp, lp, cp)
df['CDLDOJISTAR'] = talib.CDLDOJISTAR(op, hp, lp, cp)
def all_candels(df):
df['two_crow'] = talib.CDL2CROWS(df.open,df.high,df.low,df.close)
df['three_black_crows'] = talib.CDL3BLACKCROWS(df.open,df.high,df.low,df.close)
df['threeinside updown'] = talib.CDL3INSIDE(df.open,df.high,df.low,df.close)
df['threelinestrike'] = talib.CDL3LINESTRIKE(df.open,df.high,df.low,df.close)
df['3outside'] = talib.CDL3OUTSIDE(df.open,df.high,df.low,df.close)
df['3starsinsouth'] = talib.CDL3STARSINSOUTH(df.open,df.high,df.low,df.close)
df['3WHITESOLDIERS'] = talib.CDL3WHITESOLDIERS(df.open,df.high,df.low,df.close)
df['ABANDONEDBABY'] = talib.CDLABANDONEDBABY(df.open,df.high,df.low,df.close)
df['ADVANCEBLOCK'] = talib.CDLADVANCEBLOCK(df.open,df.high,df.low,df.close)
df['BELTHOLD'] = talib.CDLBELTHOLD(df.open,df.high,df.low,df.close)
df['BREAKAWAY'] = talib.CDLBREAKAWAY(df.open,df.high,df.low,df.close)
df['CLOSINGMARUBOZU'] = talib.CDLCLOSINGMARUBOZU(df.open,df.high,df.low,df.close)
df['CONCEALBABYSWALL'] = talib.CDLCONCEALBABYSWALL(df.open,df.high,df.low,df.close)
df['COUNTERATTACK'] = talib.CDLCOUNTERATTACK(df.open,df.high,df.low,df.close)
df['DARKCLOUDCOVER'] = talib.CDLDARKCLOUDCOVER(df.open,df.high,df.low,df.close)
df['DOJI'] = talib.CDLDOJI(df.open,df.high,df.low,df.close)
df['DOJISTAR'] = talib.CDLDOJISTAR(df.open,df.high,df.low,df.close)
df['DRAGONFLYDOJI'] = talib.CDLDRAGONFLYDOJI(df.open,df.high,df.low,df.close)
df['ENGULFING'] = talib.CDLENGULFING(df.open,df.high,df.low,df.close)
df['EVENINGDOJISTAR'] = talib.CDLEVENINGDOJISTAR(df.open,df.high,df.low,df.close)
df['EVENINGSTAR'] = talib.CDLEVENINGSTAR(df.open,df.high,df.low,df.close)
df['GAPSIDESIDEWHITE'] = talib.CDLGAPSIDESIDEWHITE(df.open,df.high,df.low,df.close)
df['GRAVESTONEDOJI'] = talib.CDLGRAVESTONEDOJI(df.open,df.high,df.low,df.close)
df['HAMMER'] = talib.CDLHAMMER(df.open,df.high,df.low,df.close)
df['HANGINGMAN'] = talib.CDLHANGINGMAN(df.open,df.high,df.low,df.close)
df['HARAMI'] = talib.CDLHARAMI(df.open,df.high,df.low,df.close)
df['HARAMICROSS'] = talib.CDLHARAMICROSS(df.open,df.high,df.low,df.close)
df['HIGHWAVE'] = talib.CDLHIGHWAVE(df.open,df.high,df.low,df.close)
df['HIKKAKE'] = talib.CDLHIKKAKE(df.open,df.high,df.low,df.close)
df['HIKKAKEMOD'] = talib.CDLHIKKAKEMOD(df.open,df.high,df.low,df.close)
df['HOMINGPIGEON'] = talib.CDLHOMINGPIGEON(df.open,df.high,df.low,df.close)
df['IDENTICAL3CROWS'] = talib.CDLIDENTICAL3CROWS(df.open,df.high,df.low,df.close)
df['INNECK'] = talib.CDLINNECK(df.open,df.high,df.low,df.close)
df['INVERTEDHAMMER'] = talib.CDLINVERTEDHAMMER(df.open,df.high,df.low,df.close)
df['KICKING'] = talib.CDLKICKING(df.open,df.high,df.low,df.close)
df['KICKINGBYLENGTH'] = talib.CDLKICKINGBYLENGTH(df.open,df.high,df.low,df.close)
df['LADDERBOTTOM'] = talib.CDLLADDERBOTTOM(df.open,df.high,df.low,df.close)
df['LONGLEGGEDDOJI'] = talib.CDLLONGLEGGEDDOJI(df.open,df.high,df.low,df.close)
df['LONGLINE'] = talib.CDLLONGLINE(df.open,df.high,df.low,df.close)
df['MARUBOZU'] = talib.CDLMARUBOZU(df.open,df.high,df.low,df.close)
df['MATCHINGLOW'] = talib.CDLMATCHINGLOW(df.open,df.high,df.low,df.close)
df['MATHOLD'] = talib.CDLMATHOLD(df.open,df.high,df.low,df.close)
df['MORNINGDOJISTAR'] = talib.CDLMORNINGDOJISTAR(df.open,df.high,df.low,df.close)
df['MORNINGSTAR'] = talib.CDLMORNINGSTAR(df.open,df.high,df.low,df.close)
df['ONNECK'] = talib.CDLONNECK(df.open,df.high,df.low,df.close)
df['PIERCING'] = talib.CDLPIERCING(df.open,df.high,df.low,df.close)
df['RICKSHAWMAN'] = talib.CDLRICKSHAWMAN(df.open,df.high,df.low,df.close)
df['RISEFALL3METHODS'] = talib.CDLRISEFALL3METHODS(df.open,df.high,df.low,df.close)
df['SEPARATINGLINES'] = talib.CDLSEPARATINGLINES(df.open,df.high,df.low,df.close)
df['SHOOTINGSTAR'] = talib.CDLSHOOTINGSTAR(df.open,df.high,df.low,df.close)
df['SHORTLINE'] = talib.CDLSHORTLINE(df.open,df.high,df.low,df.close)
df['SPINNINGTOP'] = talib.CDLSPINNINGTOP(df.open,df.high,df.low,df.close)
df['STALLEDPATTERN'] = talib.CDLSTALLEDPATTERN(df.open,df.high,df.low,df.close)
df['STICKSANDWICH'] = talib.CDLSTICKSANDWICH(df.open,df.high,df.low,df.close)
df['TAKURI'] = talib.CDLTAKURI(df.open,df.high,df.low,df.close)
df['TASUKIGAP'] = talib.CDLTASUKIGAP(df.open,df.high,df.low,df.close)
df['THRUSTING'] = talib.CDLTHRUSTING(df.open,df.high,df.low,df.close)
df['TRISTAR'] = talib.CDLTRISTAR(df.open,df.high,df.low,df.close)
df['UNIQUE3RIVER'] = talib.CDLUNIQUE3RIVER(df.open,df.high,df.low,df.close)
df['UPSIDEGAP2CROWS'] = talib.CDLUPSIDEGAP2CROWS(df.open,df.high,df.low,df.close)
df['XSIDEGAP3METHODS'] = talib.CDLXSIDEGAP3METHODS(df.open,df.high,df.low,df.close)
return df
def 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 TALIB_CDL3BLACKCROWS(close):
'''00399,1,1'''
return talib.CDL3BLACKCROWS(close)
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 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 _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 CDL3BLACKCROWS(DataFrame):
res = talib.CDL3BLACKCROWS(DataFrame.open.values, DataFrame.high.values,
DataFrame.low.values, DataFrame.close.values)
return pd.DataFrame({'CDL3BLACKCROWS': res}, index=DataFrame.index)
def all_algo(self,curr_pair_history_data,db=False):
order = np.zeros((self.num_curr,7,3))
signal = np.zeros((self.num_curr,16))
for i in range(len(self.curr_pair_list)):
# Processing data
close5=(curr_pair_history_data[i]['closeAsk'].tail(5).values+\
curr_pair_history_data[i]['closeBid'].tail(5).values)/2
high5=(curr_pair_history_data[i]['highAsk'].tail(5).values+\
curr_pair_history_data[i]['highBid'].tail(5).values)/2
low5=(curr_pair_history_data[i]['lowAsk'].tail(5).values+\
curr_pair_history_data[i]['lowBid'].tail(5).values)/2
openv5=(curr_pair_history_data[i]['openAsk'].tail(5).values+\
curr_pair_history_data[i]['openBid'].tail(5).values)/2
close=(curr_pair_history_data[i]['closeAsk'].values+\
curr_pair_history_data[i]['closeBid'].values)/2
high=(curr_pair_history_data[i]['highAsk'].values+\
curr_pair_history_data[i]['highBid'].values)/2
low=(curr_pair_history_data[i]['lowAsk'].values+\
curr_pair_history_data[i]['lowBid'].values)/2
openv=(curr_pair_history_data[i]['openAsk'].values+\
curr_pair_history_data[i]['openBid'].values)/2
# Generating signals
pattern=talib.CDL3BLACKCROWS(openv5,high5,low5,close5)
pattern_signal=pattern[-1]
adx=talib.ADX(high,low,close,timeperiod=14)
rsi=talib.RSI(close,timeperiod=14)
adx_signal=adx[-1]
rsi_signal=rsi[-1]
bull, bear=talib.AROON(high,low,timeperiod=14)
sma=talib.SMA(close,timeperiod=30)
kama=talib.KAMA(close,timeperiod=30)
DIF,DEA,BAR=talib.MACDFIX(close,signalperiod=9)
mfi=talib.MFI(high,low,close,curr_pair_history_data[i]['volume'].values.astype(float),timeperiod=14)
mfi_signal=mfi[-1]
# Storing signals
signal[i,0] = pattern_signal
signal[i,1] = adx_signal
signal[i,13] = rsi_signal
signal[i,2] = bear[-2]
signal[i,3] = bear[-1]
signal[i,4] = bull[-2]
signal[i,5] = bull[-1]
signal[i,10] = kama[-2]
signal[i,11] = kama[-1]
signal[i,14] = sma[-2]
signal[i,15] = sma[-1]
signal[i,6] = DEA[-2]
signal[i,7] = DEA[-1]
signal[i,8] = DIF[-2]
signal[i,9] = DIF[-1]
signal[i,12] = mfi_signal
# Creating orders
if pattern_signal>0:
#trader.create_buy_order(ticker,units)
order[i,0,1] = 1
elif pattern_signal<0:
#trader.create_sell_order(ticker,units)
order[i,0,2] = 1
else:
#print('No trade made')
order[i,0,0] = 1
if rsi_signal>70 and adx_signal>50:
#trader.create_buy_order(ticker,units)
order[i,1,1] = 1
elif rsi_signal<30 and adx_signal>50:
#trader.create_sell_order(ticker,units)
order[i,1,2] = 1
else:
#print('No trade made')
order[i,1,0] = 1
if rsi_signal>70:
#trader.create_buy_order(ticker,units)
order[i,6,1] = 1
elif rsi_signal<30:
#trader.create_sell_order(ticker,units)
order[i,6,2] = 1
else:
#print('No trade made')
order[i,6,0] = 1
if (bull[-1]>70 and bear[-1]<30) or (bull[-2]<bear[-2] and bull[-1]>=bear[-1]):
#trader.create_buy_order(ticker,units)
order[i,2,1] = 1
elif (bull[-1]<30 and bear[-1]>70) or (bull[-2]>=bear[-2] and bull[-1]<bear[-1]):
#trader.create_sell_order(ticker,units)
order[i,2,2] = 1
else:
#print('No trade made')
order[i,2,0] = 1
if kama[-1]>=sma[-1] and kama[-2]<sma[-2]:
#trader.create_buy_order(ticker,units)
order[i,3,1] = 1
elif kama[-1]<=sma[-1] and kama[-2]>=sma[-2]:
#trader.create_sell_order(ticker,units)
order[i,3,2] = 1
else:
#print("No trade made")
order[i,3,0] = 1
if DIF[-1]>0 and DEA[-1]>0 and DIF[-2]<DEA[-2] and DIF[-1]>DEA[-1]:
#trader.create_buy_order(ticker,units)
order[i,4,1] = 1
elif DIF[-1]<0 and DEA[-1]<0 and DIF[-2]>DEA[-2] and DIF[-1]<DEA[-1]:
#trader.create_sell_order(ticker,units)
order[i,4,2] = 1
else:
#print("No trade made")
order[i,4,0] = 1
if mfi_signal>70:
#trader.create_buy_order(ticker,units)
order[i,5,1] = 1
elif mfi_signal<30:
#trader.create_sell_order(ticker,units)
order[i,5,2] = 1
else:
#print('No trade made')
order[i,5,0] = 1
if db:
#write to database here
pass
return order,signal