From 297166fcb92ac5a9f4822bfb6fcb70e0b6f6c366 Mon Sep 17 00:00:00 2001 From: Gerald Lonlas Date: Sat, 6 Jan 2018 01:11:01 -0800 Subject: [PATCH 1/2] Add 29 optional indicators populate_indicators() --- freqtrade/analyze.py | 189 +++++++++++++++++++++++++++++---- freqtrade/optimize/hyperopt.py | 2 +- 2 files changed, 169 insertions(+), 22 deletions(-) diff --git a/freqtrade/analyze.py b/freqtrade/analyze.py index 0abd473d7..8cc4c7b38 100644 --- a/freqtrade/analyze.py +++ b/freqtrade/analyze.py @@ -11,7 +11,7 @@ import talib.abstract as ta from pandas import DataFrame, to_datetime from freqtrade.exchange import get_ticker_history -from freqtrade.vendor.qtpylib.indicators import awesome_oscillator, crossed_above +from freqtrade.vendor.qtpylib.indicators import awesome_oscillator, PandasObject as qtpylib logger = logging.getLogger(__name__) @@ -40,34 +40,181 @@ def parse_ticker_dataframe(ticker: list) -> DataFrame: def populate_indicators(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. """ - dataframe['sar'] = ta.SAR(dataframe) + + # Momentum Indicator + # ------------------------------------ + + # ADX 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['rsi'] = ta.RSI(dataframe) - dataframe['cci'] = ta.CCI(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) + + # Awesome oscillator dataframe['ao'] = 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) + + # 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) + """ + # ROC + dataframe['roc'] = ta.ROC(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'] = (numpy.exp(2 * rsi) - 1) / (numpy.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) + + # 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'] + """ + + # Overlap Studies + # ------------------------------------ + + # Bollinger bands + bollinger = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2) + dataframe['bb_lowerband'] = bollinger['lowerband'] + """ + dataframe['bb_middleband'] = bollinger['middleband'] + dataframe['bb_upperband'] = bollinger['upperband'] + """ + # EMA - Exponential Moving Average + 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['ema200'] = ta.EMA(dataframe, timeperiod=200) + """ + # SAR Parabol + dataframe['sar'] = ta.SAR(dataframe) + + # SMA - Simple Moving Average + dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) + + # 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'] - dataframe['plus_dm'] = ta.PLUS_DM(dataframe) - dataframe['plus_di'] = ta.PLUS_DI(dataframe) - dataframe['minus_dm'] = ta.MINUS_DM(dataframe) - dataframe['minus_di'] = ta.MINUS_DI(dataframe) + + # 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 + # ------------------------------------ + """ + # 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'] + """ + return dataframe @@ -102,8 +249,8 @@ def populate_sell_trend(dataframe: DataFrame) -> DataFrame: dataframe.loc[ ( ( - (crossed_above(dataframe['rsi'], 70)) | - (crossed_above(dataframe['fastd'], 70)) + (qtpylib.crossed_above(dataframe['rsi'], 70)) | + (qtpylib.crossed_above(dataframe['fastd'], 70)) ) & (dataframe['adx'] > 10) & (dataframe['minus_di'] > 0) diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index d0d0916f8..fb86d0fd0 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -189,7 +189,7 @@ def buy_strategy_generator(params): # TRIGGERS triggers = { - 'lower_bb': dataframe['tema'] <= dataframe['blower'], + 'lower_bb': dataframe['tema'] <= dataframe['bb_lowerband'], 'faststoch10': (crossed_above(dataframe['fastd'], 10.0)), 'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)), 'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])), From 83a999d16ed200f10dcd045b86fd48d314f41bbc Mon Sep 17 00:00:00 2001 From: Gerald Lonlas Date: Sat, 6 Jan 2018 13:10:59 -0800 Subject: [PATCH 2/2] Change Bollinger bands for qtpylib.bollinger_bands --- freqtrade/analyze.py | 20 ++++++++++++-------- freqtrade/optimize/hyperopt.py | 2 +- 2 files changed, 13 insertions(+), 9 deletions(-) diff --git a/freqtrade/analyze.py b/freqtrade/analyze.py index 8cc4c7b38..ba29dd705 100644 --- a/freqtrade/analyze.py +++ b/freqtrade/analyze.py @@ -107,21 +107,25 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame: # Overlap Studies # ------------------------------------ + # Previous Bollinger bands + # Because ta.BBANDS implementation is broken with small numbers, it actually + # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands + # and use middle band instead. + dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband'] + """ # Bollinger bands - bollinger = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2) - dataframe['bb_lowerband'] = bollinger['lowerband'] - """ - dataframe['bb_middleband'] = bollinger['middleband'] - dataframe['bb_upperband'] = bollinger['upperband'] + 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'] """ + # EMA - Exponential Moving Average 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['ema200'] = ta.EMA(dataframe, timeperiod=200) - """ + # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index fb86d0fd0..d0d0916f8 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -189,7 +189,7 @@ def buy_strategy_generator(params): # TRIGGERS triggers = { - 'lower_bb': dataframe['tema'] <= dataframe['bb_lowerband'], + 'lower_bb': dataframe['tema'] <= dataframe['blower'], 'faststoch10': (crossed_above(dataframe['fastd'], 10.0)), 'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)), 'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),