From 5e654620b791b3f1b218a40ec1b699a492f41ae5 Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 13 Sep 2019 19:49:13 +0200 Subject: [PATCH 1/3] Use available indicators in tests where possible --- tests/optimize/test_backtesting.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/optimize/test_backtesting.py b/tests/optimize/test_backtesting.py index 770f6c4ba..99fb30cbd 100644 --- a/tests/optimize/test_backtesting.py +++ b/tests/optimize/test_backtesting.py @@ -603,7 +603,7 @@ def test_processed(default_conf, mocker, testdatadir) -> None: cols = dataframe.columns # assert the dataframe got some of the indicator columns for col in ['close', 'high', 'low', 'open', 'date', - 'ema50', 'ao', 'macd', 'plus_dm']: + 'ema10', 'rsi', 'fastd', 'plus_di']: assert col in cols From 01357845891ba0629f8352115a8725b07e965d5a Mon Sep 17 00:00:00 2001 From: Matthias Date: Fri, 13 Sep 2019 19:49:34 +0200 Subject: [PATCH 2/3] remove unused indicators from default_strategy --- freqtrade/strategy/default_strategy.py | 38 ++++++++++++++------------ 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/freqtrade/strategy/default_strategy.py b/freqtrade/strategy/default_strategy.py index 4907f20ed..caf1bb82d 100644 --- a/freqtrade/strategy/default_strategy.py +++ b/freqtrade/strategy/default_strategy.py @@ -4,7 +4,6 @@ import talib.abstract as ta from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib -from freqtrade.indicator_helpers import fishers_inverse from freqtrade.strategy.interface import IStrategy @@ -75,7 +74,8 @@ class DefaultStrategy(IStrategy): dataframe['adx'] = ta.ADX(dataframe) # Awesome oscillator - dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) + # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) + """ # Commodity Channel Index: values Oversold:<-100, Overbought:>100 dataframe['cci'] = ta.CCI(dataframe) @@ -87,16 +87,15 @@ class DefaultStrategy(IStrategy): dataframe['macdhist'] = macd['macdhist'] # MFI - dataframe['mfi'] = ta.MFI(dataframe) + # dataframe['mfi'] = ta.MFI(dataframe) # Minus Directional Indicator / Movement - dataframe['minus_dm'] = ta.MINUS_DM(dataframe) + # 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_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) - dataframe['minus_di'] = ta.MINUS_DI(dataframe) """ # ROC @@ -106,15 +105,15 @@ class DefaultStrategy(IStrategy): dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) - dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi']) + # dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi']) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) - dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) + # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stoch - stoch = ta.STOCH(dataframe) - dataframe['slowd'] = stoch['slowd'] - dataframe['slowk'] = stoch['slowk'] + # stoch = ta.STOCH(dataframe) + # dataframe['slowd'] = stoch['slowd'] + # dataframe['slowk'] = stoch['slowk'] # Stoch fast stoch_fast = ta.STOCHF(dataframe) @@ -134,37 +133,39 @@ class DefaultStrategy(IStrategy): # 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'] + # dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband'] # 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'] - + """ # 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) + """ + dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) # SAR Parabol - dataframe['sar'] = ta.SAR(dataframe) + # 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) + # 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 # ------------------------------------ """ @@ -216,6 +217,7 @@ class DefaultStrategy(IStrategy): dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] """ + """ # Chart type # ------------------------------------ # Heikinashi stategy @@ -224,7 +226,7 @@ class DefaultStrategy(IStrategy): dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] - + """ return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: From 2cf045c53ea9cec1ee8216730d87a3c2eab7d7d9 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 14 Sep 2019 10:00:32 +0200 Subject: [PATCH 3/3] Remove commented indicators from DefaultStrategy --- freqtrade/strategy/default_strategy.py | 131 ++----------------------- 1 file changed, 6 insertions(+), 125 deletions(-) diff --git a/freqtrade/strategy/default_strategy.py b/freqtrade/strategy/default_strategy.py index caf1bb82d..b839a9618 100644 --- a/freqtrade/strategy/default_strategy.py +++ b/freqtrade/strategy/default_strategy.py @@ -10,7 +10,10 @@ from freqtrade.strategy.interface import IStrategy class DefaultStrategy(IStrategy): """ Default Strategy provided by freqtrade bot. - You can override it with your own strategy + Please do not modify this strategy, it's intended for internal use only. + Please look at the SampleStrategy in the user_data/strategy directory + or strategy repository https://github.com/freqtrade/freqtrade-strategies + for samples and inspiration. """ INTERFACE_VERSION = 2 @@ -73,160 +76,38 @@ class DefaultStrategy(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) - """ # 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) - # dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi']) - - # 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 - # ------------------------------------ - - # 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 = 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['ema3'] = ta.EMA(dataframe, timeperiod=3) - dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) - dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) - dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) - """ - dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) - # SAR Parabol - # dataframe['sar'] = ta.SAR(dataframe) + # EMA - Exponential Moving Average + dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) # 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'] - """ - # 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 def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: