def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) -
1) / (numpy.exp(2 * rsi) + 1)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
return dataframe
def populate_indicators(self, dataframe, metadata):
stoch = ta.STOCH(dataframe)
rsi = ta.RSI(dataframe)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe))
dataframe["slowk"] = stoch["slowk"]
dataframe["rsi"] = rsi
rsi = 0.1 * (rsi - 50)
dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
dataframe["bb_lowerband"] = bollinger["lower"]
dataframe["sar"] = ta.SAR(dataframe)
dataframe["CDLHAMMER"] = ta.CDLHAMMER(dataframe)
return dataframe
def scan_cdl_bearish(self):
try:
cdl = []
cdl.append(abstract.CDLENGULFING(self.df))
cdl.append(abstract.CDLPIERCING(self.df))
cdl.append(abstract.CDLHARAMI(self.df))
cdl.append(abstract.CDLHAMMER(self.df))
cdl.append(abstract.CDLINVERTEDHAMMER(self.df))
cdl.append(abstract.CDLMORNINGDOJISTAR(self.df))
cdl.append(abstract.CDLMORNINGSTAR(self.df))
cdl.append(abstract.CDLABANDONEDBABY(self.df))
cdl.append(abstract.CDLKICKING(self.df))
for i in range(0, 9):
for a in range(0, 2):
if cdl[i][a] == 100:
print("Found")
print(i)
print(self.df.index[i])
except:
print("Error")
return False
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['sar'] = ta.SAR(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2,
nbdevdn=2)['lowerband']
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mom'] = ta.MOM(dataframe)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ao'] = awesome_oscillator(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# add volatility indicators
dataframe['natr'] = ta.NATR(dataframe)
# add volume indicators
dataframe['obv'] = ta.OBV(dataframe)
# add more momentum indicators
dataframe['rocp'] = ta.ROCP(dataframe)
# add some pattern recognition
dataframe['CDL2CROWS'] = ta.CDL2CROWS(dataframe)
dataframe['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(dataframe)
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe)
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe)
dataframe['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(dataframe)
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe)
dataframe['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(dataframe)
dataframe['CDLBELTHOLD'] = ta.CDLBELTHOLD(dataframe)
dataframe['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(dataframe)
dataframe['CDLDOJI'] = ta.CDLDOJI(dataframe)
dataframe['CDLDOJISTAR'] = ta.CDLDOJISTAR(dataframe)
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe)
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
dataframe['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(dataframe)
dataframe['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(dataframe)
# enter categorical time
hour = datetime.strptime(str(dataframe['date'][len(dataframe) - 1]),
"%Y-%m-%d %H:%M:%S").hour
for h in range(24):
dataframe['hour_{0:02}'.format(h)] = int(h == hour)
return dataframe
def gen_ta(path):
df = pd.read_csv('h/%s.csv' % path)
df = df.dropna()
df = df.iloc[::-1]
df = df.reset_index(drop=True)
#生成技術指標
sma5 = indicator(talib.MA(df['close'], 5), 'SMA_5')
sma10 = indicator(talib.MA(df['close'], 10), 'SMA_10')
sma50 = indicator(talib.MA(df['close'], 50), 'SMA_50')
rsi = indicator(talib.RSI(df['close']), 'RSI_14')
sma5u = sma5.diff().rename(columns={'SMA_5': 'SMA_5_u'})
sma10u = sma10.diff().rename(columns={'SMA_10': 'SMA_10_u'})
sma50u = sma50.diff().rename(columns={'SMA_50': 'SMA_50_u'})
CDL2CROWS = indicator(abstract.CDL2CROWS(df), 'CDL2CROWS')
CDL3BLACKCROWS = indicator(abstract.CDL3BLACKCROWS(df), 'CDL3BLACKCROWS')
CDL3INSIDE = indicator(abstract.CDL3INSIDE(df), 'CDL3INSIDE')
CDL3LINESTRIKE = indicator(abstract.CDL3LINESTRIKE(df), 'CDL3LINESTRIKE')
CDL3OUTSIDE = indicator(abstract.CDL3OUTSIDE(df), 'CDL3OUTSIDE')
CDL3STARSINSOUTH = indicator(abstract.CDL3STARSINSOUTH(df),
'CDL3STARSINSOUTH')
CDL3WHITESOLDIERS = indicator(abstract.CDL3WHITESOLDIERS(df),
'CDL3WHITESOLDIERS')
CDLABANDONEDBABY = indicator(abstract.CDLABANDONEDBABY(df),
'CDLABANDONEDBABY')
CDLADVANCEBLOCK = indicator(abstract.CDLADVANCEBLOCK(df),
'CDLADVANCEBLOCK')
CDLBELTHOLD = indicator(abstract.CDLBELTHOLD(df), 'CDLBELTHOLD')
CDLBREAKAWAY = indicator(abstract.CDLBREAKAWAY(df), 'CDLBREAKAWAY')
CDLCLOSINGMARUBOZU = indicator(abstract.CDLCLOSINGMARUBOZU(df),
'CDLCLOSINGMARUBOZU')
CDLCONCEALBABYSWALL = indicator(abstract.CDLCONCEALBABYSWALL(df),
'CDLCONCEALBABYSWALL')
CDLCOUNTERATTACK = indicator(abstract.CDLCOUNTERATTACK(df),
'CDLCOUNTERATTACK')
CDLDARKCLOUDCOVER = indicator(abstract.CDLDARKCLOUDCOVER(df),
'CDLDARKCLOUDCOVER')
CDLDOJI = indicator(abstract.CDLDOJI(df), 'CDLDOJI')
CDLDOJISTAR = indicator(abstract.CDLDOJISTAR(df), 'CDLDOJISTAR')
CDLDRAGONFLYDOJI = indicator(abstract.CDLDRAGONFLYDOJI(df),
'CDLDRAGONFLYDOJI')
CDLENGULFING = indicator(abstract.CDLENGULFING(df), 'CDLENGULFING')
CDLEVENINGDOJISTAR = indicator(abstract.CDLEVENINGDOJISTAR(df),
'CDLEVENINGDOJISTAR')
CDLEVENINGSTAR = indicator(abstract.CDLEVENINGSTAR(df), 'CDLEVENINGSTAR')
CDLGAPSIDESIDEWHITE = indicator(abstract.CDLGAPSIDESIDEWHITE(df),
'CDLGAPSIDESIDEWHITE')
CDLGRAVESTONEDOJI = indicator(abstract.CDLGRAVESTONEDOJI(df),
'CDLGRAVESTONEDOJI')
CDLHAMMER = indicator(abstract.CDLHAMMER(df), 'CDLHAMMER')
CDLHANGINGMAN = indicator(abstract.CDLHANGINGMAN(df), 'CDLHANGINGMAN')
CDLHARAMI = indicator(abstract.CDLHARAMI(df), 'CDLHARAMI')
CDLHARAMICROSS = indicator(abstract.CDLHARAMICROSS(df), 'CDLHARAMICROSS')
CDLHIGHWAVE = indicator(abstract.CDLHIGHWAVE(df), 'CDLHIGHWAVE')
CDLHIKKAKE = indicator(abstract.CDLHIKKAKE(df), 'CDLHIKKAKE')
CDLHIKKAKEMOD = indicator(abstract.CDLHIKKAKEMOD(df), 'CDLHIKKAKEMOD')
CDLHOMINGPIGEON = indicator(abstract.CDLHOMINGPIGEON(df),
'CDLHOMINGPIGEON')
CDLIDENTICAL3CROWS = indicator(abstract.CDLIDENTICAL3CROWS(df),
'CDLIDENTICAL3CROWS')
CDLINNECK = indicator(abstract.CDLINNECK(df), 'CDLINNECK')
CDLINVERTEDHAMMER = indicator(abstract.CDLINVERTEDHAMMER(df),
'CDLINVERTEDHAMMER')
CDLKICKING = indicator(abstract.CDLKICKING(df), 'CDLKICKING')
CDLKICKINGBYLENGTH = indicator(abstract.CDLKICKINGBYLENGTH(df),
'CDLKICKINGBYLENGTH')
CDLLADDERBOTTOM = indicator(abstract.CDLLADDERBOTTOM(df),
'CDLLADDERBOTTOM')
CDLLONGLEGGEDDOJI = indicator(abstract.CDLLONGLEGGEDDOJI(df),
'CDLLONGLEGGEDDOJI')
CDLLONGLINE = indicator(abstract.CDLLONGLINE(df), 'CDLLONGLINE')
CDLMARUBOZU = indicator(abstract.CDLMARUBOZU(df), 'CDLMARUBOZU')
CDLMATCHINGLOW = indicator(abstract.CDLMATCHINGLOW(df), 'CDLMATCHINGLOW')
CDLMATHOLD = indicator(abstract.CDLMATHOLD(df), 'CDLMATHOLD')
CDLMORNINGDOJISTAR = indicator(abstract.CDLMORNINGDOJISTAR(df),
'CDLMORNINGDOJISTAR')
CDLMORNINGSTAR = indicator(abstract.CDLMORNINGSTAR(df), 'CDLMORNINGSTAR')
CDLONNECK = indicator(abstract.CDLONNECK(df), 'CDLONNECK')
CDLPIERCING = indicator(abstract.CDLPIERCING(df), 'CDLPIERCING')
CDLRICKSHAWMAN = indicator(abstract.CDLRICKSHAWMAN(df), 'CDLRICKSHAWMAN')
CDLRISEFALL3METHODS = indicator(abstract.CDLRISEFALL3METHODS(df),
'CDLRISEFALL3METHODS')
CDLSEPARATINGLINES = indicator(abstract.CDLSEPARATINGLINES(df),
'CDLSEPARATINGLINES')
CDLSHOOTINGSTAR = indicator(abstract.CDLSHOOTINGSTAR(df),
'CDLSHOOTINGSTAR')
CDLSHORTLINE = indicator(abstract.CDLSHORTLINE(df), 'CDLSHORTLINE')
CDLSPINNINGTOP = indicator(abstract.CDLSPINNINGTOP(df), 'CDLSPINNINGTOP')
CDLSTALLEDPATTERN = indicator(abstract.CDLSTALLEDPATTERN(df),
'CDLSTALLEDPATTERN')
CDLSTICKSANDWICH = indicator(abstract.CDLSTICKSANDWICH(df),
'CDLSTICKSANDWICH')
CDLTAKURI = indicator(abstract.CDLTAKURI(df), 'CDLTAKURI')
CDLTASUKIGAP = indicator(abstract.CDLTASUKIGAP(df), 'CDLTASUKIGAP')
CDLTHRUSTING = indicator(abstract.CDLTHRUSTING(df), 'CDLTHRUSTING')
CDLTRISTAR = indicator(abstract.CDLTRISTAR(df), 'CDLTRISTAR')
CDLUNIQUE3RIVER = indicator(abstract.CDLUNIQUE3RIVER(df),
'CDLUNIQUE3RIVER')
CDLUPSIDEGAP2CROWS = indicator(abstract.CDLUPSIDEGAP2CROWS(df),
'CDLUPSIDEGAP2CROWS')
CDLXSIDEGAP3METHODS = indicator(abstract.CDLXSIDEGAP3METHODS(df),
'CDLXSIDEGAP3METHODS')
macd, macdsignal, macdhist = talib.MACD(df['close'])
macd = indicator(macd, 'macd')
macdsignal = indicator(macdsignal, 'macdsignal')
macdhist = indicator(macdhist, 'macdhist')
df = pd.concat([
df, sma5, sma5u, sma10, sma10u, sma50, sma50u, rsi, CDL2CROWS,
CDL3BLACKCROWS, CDL3INSIDE, CDL3LINESTRIKE, CDL3OUTSIDE,
CDL3STARSINSOUTH, CDL3WHITESOLDIERS, CDLABANDONEDBABY, CDLADVANCEBLOCK,
CDLBELTHOLD, CDLBREAKAWAY, CDLCLOSINGMARUBOZU, CDLCONCEALBABYSWALL,
CDLCOUNTERATTACK, CDLDARKCLOUDCOVER, CDLDOJI, CDLDOJISTAR,
CDLDRAGONFLYDOJI, CDLENGULFING, CDLEVENINGDOJISTAR, CDLEVENINGSTAR,
CDLGAPSIDESIDEWHITE, CDLGRAVESTONEDOJI, CDLHAMMER, CDLHANGINGMAN,
CDLHARAMI, CDLHARAMICROSS, CDLHIGHWAVE, CDLHIKKAKE, CDLHIKKAKEMOD,
CDLHOMINGPIGEON, CDLIDENTICAL3CROWS, CDLINNECK, CDLINVERTEDHAMMER,
CDLKICKING, CDLKICKINGBYLENGTH, CDLLADDERBOTTOM, CDLLONGLEGGEDDOJI,
CDLLONGLINE, CDLMARUBOZU, CDLMATCHINGLOW, CDLMATHOLD,
CDLMORNINGDOJISTAR, CDLMORNINGSTAR, CDLONNECK, CDLPIERCING,
CDLRICKSHAWMAN, CDLRISEFALL3METHODS, CDLSEPARATINGLINES,
CDLSHOOTINGSTAR, CDLSHORTLINE, CDLSPINNINGTOP, CDLSTALLEDPATTERN,
CDLSTICKSANDWICH, CDLTAKURI, CDLTASUKIGAP, CDLTHRUSTING, CDLTRISTAR,
CDLUNIQUE3RIVER, CDLUPSIDEGAP2CROWS, CDLXSIDEGAP3METHODS, macd,
macdsignal, macdhist
],
axis=1)
df['next_open'] = df['open'].shift(-1)
df = df[:-1]
df = df[(df['SMA_50'] > 0)]
df = df.reset_index(drop=True)
return df
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Momentum Indicators
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# # Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Aroon, Aroon Oscillator
aroon = ta.AROON(dataframe)
dataframe['aroonup'] = aroon['aroonup']
dataframe['aroondown'] = aroon['aroondown']
dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# Awesome Oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# Keltner Channel
keltner = qtpylib.keltner_channel(dataframe)
dataframe["kc_upperband"] = keltner["upper"]
dataframe["kc_lowerband"] = keltner["lower"]
dataframe["kc_middleband"] = keltner["mid"]
dataframe["kc_percent"] = (
(dataframe["close"] - dataframe["kc_lowerband"]) /
(dataframe["kc_upperband"] - dataframe["kc_lowerband"]))
dataframe["kc_width"] = (
(dataframe["kc_upperband"] - dataframe["kc_lowerband"]) /
dataframe["kc_middleband"])
# Ultimate Oscillator
dataframe['uo'] = ta.ULTOSC(dataframe)
# Commodity Channel Index: values [Oversold:-100, Overbought:100]
dataframe['cci'] = ta.CCI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stochastic RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # ROC
dataframe['roc'] = ta.ROC(dataframe)
# Overlap Studies
# ------------------------------------
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]))
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) /
dataframe["bb_middleband"])
# Bollinger Bands - Weighted (EMA based instead of SMA)
weighted_bollinger = qtpylib.weighted_bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe["wbb_upperband"] = weighted_bollinger["upper"]
dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
dataframe["wbb_middleband"] = weighted_bollinger["mid"]
dataframe["wbb_percent"] = (
(dataframe["close"] - dataframe["wbb_lowerband"]) /
(dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]))
dataframe["wbb_width"] = (
(dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
dataframe["wbb_middleband"])
# EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SMA - Simple Moving Average
dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(
dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(
dataframe) # values [0, 100]
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(
dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(
dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(
dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(
dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(
dataframe) # values [0, -100, 100]
# Chart type
# ------------------------------------
# Heikin Ashi Strategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
# Retrieve best bid and best ask from the orderbook
# ------------------------------------
"""
# first check if dataprovider is available
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
"""
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['adx'] = ta.ADX(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
bollinger = ta.BBANDS(dataframe,
timeperiod=20,
nbdevup=2.0,
nbdevdn=2.0)
dataframe['bb_lowerband'] = bollinger['lowerband']
dataframe['bb_middleband'] = bollinger['middleband']
dataframe['bb_upperband'] = bollinger['upperband']
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# Stoch
stoch = ta.STOCHF(dataframe, 5)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['fastk-previous'] = dataframe.fastk.shift(1)
dataframe['fastd-previous'] = dataframe.fastd.shift(1)
# Slow Stoch
slowstoch = ta.STOCHF(dataframe, 50)
dataframe['slowfastd'] = slowstoch['fastd']
dataframe['slowfastk'] = slowstoch['fastk']
dataframe['slowfastk-previous'] = dataframe.slowfastk.shift(1)
dataframe['slowfastd-previous'] = dataframe.slowfastd.shift(1)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) -
1) / (numpy.exp(2 * rsi) + 1)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
return dataframe