# source: https://raw.githubusercontent.com/Hooker41/freqtrade/397b00855f5ac37b8f7a0bca30f55d83873c754d/freqtrade/strategy/default_strategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement 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 class github_Hooker41_freqtrade__default_strategy__20210601_222048(IStrategy): """ Default Strategy provided by freqtrade bot. You can override it with your own strategy """ # Minimal ROI designed for the strategy minimal_roi = { "40": 0.0, #in 40min "30": 0.01, #in 30min "20": 0.02, #in 20min "0": 0.04 } # Optimal stoploss designed for the strategy stoploss = -0.10 # Optimal ticker interval for the strategy timeframe = 5 # Slippage slippage = 0.01 def populate_indicators(self, 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. """ # Momentum Indicator # ------------------------------------ # 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) # 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['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: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ # dataframe.loc[ # ( # (dataframe['rsi'] < 35) & # (dataframe['fastd'] < 35) & # (dataframe['adx'] > 30) & # (dataframe['plus_di'] > 0.5) # ) | # ( # (dataframe['adx'] > 65) & # (dataframe['plus_di'] > 0.5) # ), # 'buy'] = 1 dataframe.loc[ (dataframe['rsi'] < 35), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ # dataframe.loc[ # ( # ( # (qtpylib.crossed_above(dataframe['rsi'], 70)) | # (qtpylib.crossed_above(dataframe['fastd'], 70)) # ) & # (dataframe['adx'] > 10) & # (dataframe['minus_di'] > 0) # ) | # ( # (dataframe['adx'] > 70) & # (dataframe['minus_di'] > 0.5) # ), # 'sell'] = 1 dataframe.loc[ (qtpylib.crossed_above(dataframe['rsi'], 70)), 'sell'] = 1 return dataframe