From be4a4180ae9fcaca3d95db72d561c98d0bcd954d Mon Sep 17 00:00:00 2001 From: Matthias Date: Thu, 21 Nov 2019 06:40:30 +0100 Subject: [PATCH] Use single line comments for samples --- freqtrade/templates/base_strategy.py.j2 | 186 +++++++++++------------ freqtrade/templates/sample_strategy.py | 189 +++++++++++------------- 2 files changed, 178 insertions(+), 197 deletions(-) diff --git a/freqtrade/templates/base_strategy.py.j2 b/freqtrade/templates/base_strategy.py.j2 index 46c118383..174c801ee 100644 --- a/freqtrade/templates/base_strategy.py.j2 +++ b/freqtrade/templates/base_strategy.py.j2 @@ -109,58 +109,61 @@ class {{ strategy }}(IStrategy): # ADX dataframe['adx'] = ta.ADX(dataframe) - """ - # Awesome oscillator - dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) - # Commodity Channel Index: values Oversold:<-100, Overbought:>100 - dataframe['cci'] = ta.CCI(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) + + # # Commodity Channel Index: values Oversold:<-100, Overbought:>100 + # dataframe['cci'] = ta.CCI(dataframe) + # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] - # MFI - dataframe['mfi'] = ta.MFI(dataframe) + # # MFI + # dataframe['mfi'] = ta.MFI(dataframe) - """ - # Minus Directional Indicator / Movement - dataframe['minus_dm'] = ta.MINUS_DM(dataframe) - dataframe['minus_di'] = ta.MINUS_DI(dataframe) + # # Minus Directional Indicator / Movement + # dataframe['minus_dm'] = ta.MINUS_DM(dataframe) + # dataframe['minus_di'] = ta.MINUS_DI(dataframe) - # Plus Directional Indicator / Movement - dataframe['plus_dm'] = ta.PLUS_DM(dataframe) - dataframe['plus_di'] = ta.PLUS_DI(dataframe) - dataframe['minus_di'] = ta.MINUS_DI(dataframe) + # # Plus Directional Indicator / Movement + # dataframe['plus_dm'] = ta.PLUS_DM(dataframe) + # dataframe['plus_di'] = ta.PLUS_DI(dataframe) + # dataframe['minus_di'] = ta.MINUS_DI(dataframe) - # ROC - dataframe['roc'] = ta.ROC(dataframe) + # # ROC + # dataframe['roc'] = ta.ROC(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, 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, value [0.0, 100.0] (https://goo.gl/2JGGoy) - dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) + # # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) + # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) + + # # Stoch + # stoch = ta.STOCH(dataframe) + # dataframe['slowd'] = stoch['slowd'] + # dataframe['slowk'] = stoch['slowk'] - # Stoch - stoch = ta.STOCH(dataframe) - dataframe['slowd'] = stoch['slowd'] - dataframe['slowk'] = stoch['slowk'] - """ # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] - """ - # Stoch RSI - stoch_rsi = ta.STOCHRSI(dataframe) - dataframe['fastd_rsi'] = stoch_rsi['fastd'] - dataframe['fastk_rsi'] = stoch_rsi['fastk'] - """ + # # Stoch RSI + # stoch_rsi = ta.STOCHRSI(dataframe) + # dataframe['fastd_rsi'] = stoch_rsi['fastd'] + # dataframe['fastk_rsi'] = stoch_rsi['fastk'] # Overlap Studies # ------------------------------------ @@ -171,17 +174,16 @@ class {{ strategy }}(IStrategy): dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] - """ - # 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['ema50'] = ta.EMA(dataframe, timeperiod=50) - dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) + # # 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['ema50'] = ta.EMA(dataframe, timeperiod=50) + # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) + + # # SMA - Simple Moving Average + # dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) - # SMA - Simple Moving Average - dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) - """ # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) @@ -197,65 +199,57 @@ class {{ strategy }}(IStrategy): # 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] - """ + # # 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) - """ + # # 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] - """ + # # 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 - # ------------------------------------ - """ - # Heikinashi stategy - heikinashi = qtpylib.heikinashi(dataframe) - dataframe['ha_open'] = heikinashi['open'] - dataframe['ha_close'] = heikinashi['close'] - dataframe['ha_high'] = heikinashi['high'] - dataframe['ha_low'] = heikinashi['low'] - """ + # # Chart type + # # ------------------------------------ + # # Heikinashi stategy + # 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 # ------------------------------------ diff --git a/freqtrade/templates/sample_strategy.py b/freqtrade/templates/sample_strategy.py index d62e6120c..724e52156 100644 --- a/freqtrade/templates/sample_strategy.py +++ b/freqtrade/templates/sample_strategy.py @@ -111,64 +111,60 @@ class SampleStrategy(IStrategy): # ADX dataframe['adx'] = ta.ADX(dataframe) - """ - # Aroon, Aroon Oscillator - aroon = ta.AROON(dataframe) - dataframe['aroonup'] = aroon['aroonup'] - dataframe['aroondown'] = aroon['aroondown'] - dataframe['aroonosc'] = ta.AROONOSC(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) + # # Awesome oscillator + # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) + + # # Commodity Channel Index: values Oversold:<-100, Overbought:>100 + # dataframe['cci'] = ta.CCI(dataframe) - # Commodity Channel Index: values Oversold:<-100, Overbought:>100 - dataframe['cci'] = ta.CCI(dataframe) - """ # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] - # MFI - dataframe['mfi'] = ta.MFI(dataframe) + # # MFI + # dataframe['mfi'] = ta.MFI(dataframe) - """ - # Minus Directional Indicator / Movement - dataframe['minus_dm'] = ta.MINUS_DM(dataframe) - dataframe['minus_di'] = ta.MINUS_DI(dataframe) + # # Minus Directional Indicator / Movement + # dataframe['minus_dm'] = ta.MINUS_DM(dataframe) + # dataframe['minus_di'] = ta.MINUS_DI(dataframe) - # Plus Directional Indicator / Movement - dataframe['plus_dm'] = ta.PLUS_DM(dataframe) - dataframe['plus_di'] = ta.PLUS_DI(dataframe) - dataframe['minus_di'] = ta.MINUS_DI(dataframe) + # # Plus Directional Indicator / Movement + # dataframe['plus_dm'] = ta.PLUS_DM(dataframe) + # dataframe['plus_di'] = ta.PLUS_DI(dataframe) + # dataframe['minus_di'] = ta.MINUS_DI(dataframe) - # ROC - dataframe['roc'] = ta.ROC(dataframe) + # # ROC + # dataframe['roc'] = ta.ROC(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, 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, value [0.0, 100.0] (https://goo.gl/2JGGoy) - dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) + # # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) + # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) + + # # Stoch + # stoch = ta.STOCH(dataframe) + # dataframe['slowd'] = stoch['slowd'] + # dataframe['slowk'] = stoch['slowk'] - # Stoch - stoch = ta.STOCH(dataframe) - dataframe['slowd'] = stoch['slowd'] - dataframe['slowk'] = stoch['slowk'] - """ # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] - """ - # Stoch RSI - stoch_rsi = ta.STOCHRSI(dataframe) - dataframe['fastd_rsi'] = stoch_rsi['fastd'] - dataframe['fastk_rsi'] = stoch_rsi['fastk'] - """ + # # Stoch RSI + # stoch_rsi = ta.STOCHRSI(dataframe) + # dataframe['fastd_rsi'] = stoch_rsi['fastd'] + # dataframe['fastk_rsi'] = stoch_rsi['fastk'] # Overlap Studies # ------------------------------------ @@ -179,17 +175,16 @@ class SampleStrategy(IStrategy): dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] - """ - # 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['ema50'] = ta.EMA(dataframe, timeperiod=50) - dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) + # # 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['ema50'] = ta.EMA(dataframe, timeperiod=50) + # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) + + # # SMA - Simple Moving Average + # dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) - # SMA - Simple Moving Average - dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) - """ # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) @@ -205,65 +200,57 @@ class SampleStrategy(IStrategy): # 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] - """ + # # 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) - """ + # # 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] - """ + # # 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 - # ------------------------------------ - """ - # Heikinashi stategy - heikinashi = qtpylib.heikinashi(dataframe) - dataframe['ha_open'] = heikinashi['open'] - dataframe['ha_close'] = heikinashi['close'] - dataframe['ha_high'] = heikinashi['high'] - dataframe['ha_low'] = heikinashi['low'] - """ + # # Chart type + # # ------------------------------------ + # # Heikinashi stategy + # 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 # ------------------------------------