def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Set Up Bollinger Bands
upper_bb1, mid_bb1, lower_bb1 = ta.BBANDS(dataframe['close'],
timeperiod=36)
upper_bb2, mid_bb2, lower_bb2 = ta.BBANDS(
qtpylib.typical_price(dataframe), timeperiod=12)
# Only putting some bands into dataframe as the others are not used elsewhere in the strategy
dataframe['lower-bb1'] = lower_bb1
dataframe['lower-bb2'] = lower_bb2
dataframe['mid-bb2'] = mid_bb2
dataframe['bb1-delta'] = (mid_bb1 - dataframe['lower-bb1']).abs()
dataframe['closedelta'] = (dataframe['close'] -
dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
# Additional indicators
dataframe['ema_fast'] = ta.EMA(dataframe['close'], timeperiod=6)
dataframe['ema_slow'] = ta.EMA(dataframe['close'], timeperiod=48)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(
window=24).mean()
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# 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)
# Informative Pair Indicators
coin, stake = metadata['pair'].split('/')
fiat = self.fiat
stake_fiat = f"{stake}/{self.fiat}"
coin_fiat = f"{coin}/{self.fiat}"
coin_fiat_inf = self.dp.get_pair_dataframe(pair=f"{coin}/{fiat}",
timeframe=self.timeframe)
dataframe['coin-fiat-adx'] = ta.ADX(coin_fiat_inf, timeperiod=21)
coin_aroon = ta.AROON(coin_fiat_inf, timeperiod=25)
dataframe['coin-fiat-aroon-down'] = coin_aroon['aroondown']
dataframe['coin-fiat-aroon-up'] = coin_aroon['aroonup']
stake_fiat_inf = self.dp.get_pair_dataframe(pair=f"{stake}/{fiat}",
timeframe=self.timeframe)
dataframe['stake-fiat-adx'] = ta.ADX(stake_fiat_inf, timeperiod=21)
stake_aroon = ta.AROON(stake_fiat_inf, timeperiod=25)
dataframe['stake-fiat-aroon-down'] = stake_aroon['aroondown']
dataframe['stake-fiat-aroon-up'] = stake_aroon['aroonup']
# These indicators are used to persist a buy signal in live trading only
# They dramatically slow backtesting down
if self.config['runmode'].value in ('live', 'dry_run'):
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
def AROON(self):
aroondown, aroonup = abstract.AROON(self.company_stock)
self.company_stock['AROON_U'] = aroonup
self.company_stock['AROON_D'] = aroondown
return aroondown, aroonup
def get_momentum_indicators(self):
# https://mrjbq7.github.io/ta-lib/func_groups/momentum_indicators.html
if self.verbose:
print self.ticker, 'get_momentum_indicators'
_a = ['open', 'high', 'low', 'close', 'volume']
inputs = {_a[i]: self.data[_a[i]].values for i in range(len(_a))}
# average directional movement index
self.data['mi_adx_14'] = abstract.ADX(inputs, timeperiod=14)
# average directional movement index rating
self.data['mi_adxr_14'] = abstract.ADX(inputs, timeperiod=14)
# absolute price oscillator
self.data['mi_apo'] = abstract.APO(inputs,
fastperiod=12,
slowperiod=26,
matype=0)
# aroon
self.data['mi_aroon_d'], self.data['mi_aroon_u'] = abstract.AROON(
inputs, timeperiod=14)
# aroon oscillator
self.data['mi_aroonosc'] = abstract.AROONOSC(inputs, timeperiod=14)
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=90) #90
aroon = ta.AROON(dataframe, timeperiod=60) #60
dataframe['aroon-down'] = aroon['aroondown']
dataframe['aroon-up'] = aroon['aroonup']
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# 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 __countAROON(self):
self.aroondown, self.aroonup = ta.AROON(self.stock.inputs)
self.aroonosc = ta.AROONOSC(self.stock.inputs)
