From 00ab6f572a9b249f394ad8a4b887ba56731b72fd Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 5 Oct 2019 10:01:38 +0200 Subject: [PATCH] Cleanup legacy strategy it's just a test and does not need the commented elements --- tests/strategy/legacy_strategy.py | 158 +----------------------------- 1 file changed, 4 insertions(+), 154 deletions(-) diff --git a/tests/strategy/legacy_strategy.py b/tests/strategy/legacy_strategy.py index af1b617a6..89ce3f8cb 100644 --- a/tests/strategy/legacy_strategy.py +++ b/tests/strategy/legacy_strategy.py @@ -6,8 +6,6 @@ from pandas import DataFrame # Add your lib to import here import talib.abstract as ta -import freqtrade.vendor.qtpylib.indicators as qtpylib -import numpy # noqa # This class is a sample. Feel free to customize it. @@ -17,7 +15,6 @@ class TestStrategyLegacy(IStrategy): removed in a future update. Please do not use this as a template, but refer to user_data/strategy/sample_strategy.py for a uptodate version of this template. - """ # Minimal ROI designed for the strategy. @@ -51,156 +48,9 @@ class TestStrategyLegacy(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) - dataframe['minus_di'] = ta.MINUS_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 = 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) - - # 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'] - - # 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) -> DataFrame: @@ -212,8 +62,8 @@ class TestStrategyLegacy(IStrategy): dataframe.loc[ ( (dataframe['adx'] > 30) & - (dataframe['tema'] <= dataframe['bb_middleband']) & - (dataframe['tema'] > dataframe['tema'].shift(1)) + (dataframe['tema'] > dataframe['tema'].shift(1)) & + (dataframe['volume'] > 0) ), 'buy'] = 1 @@ -228,8 +78,8 @@ class TestStrategyLegacy(IStrategy): dataframe.loc[ ( (dataframe['adx'] > 70) & - (dataframe['tema'] > dataframe['bb_middleband']) & - (dataframe['tema'] < dataframe['tema'].shift(1)) + (dataframe['tema'] < dataframe['tema'].shift(1)) & + (dataframe['volume'] > 0) ), 'sell'] = 1 return dataframe