# source: https://raw.githubusercontent.com/ExaByt3s/strategies/056a7e4f99f428796a7b3145708dd4de0b441a1c/archived/Patterns2.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter import Config class github_ExaByt3s_strategies__Patterns2__20221107_180033(IStrategy): """ Trades based on detection of the 3 White Soldiers candlestick pattern How to use it? > python3 ./freqtrade/main.py -s github_ExaByt3s_strategies__Patterns2__20221107_180033 """ # Buy hyperspace params: buy_params = { "buy_bb_enabled": False, "buy_bb_gain": 0.01, "buy_mfi": 19.0, "buy_mfi_enabled": True, "buy_rsi": 4.0, "buy_rsi_enabled": False, "buy_sma_enabled": True, } pattern_strength = 90 buy_rsi = DecimalParameter(1, 50, decimals=0, default=31, space="buy") buy_mfi = DecimalParameter(1, 50, decimals=0, default=50, space="buy") buy_bb_gain = DecimalParameter(0.01, 0.10, decimals=2, default=0.02, space="buy") buy_bb_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_sma_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_rsi_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_mfi_enabled = CategoricalParameter([True, False], default=True, space="buy") # # Buy hyperspace params: # buy_params = { # "buy_CDL3INSIDE_enabled": True, # "buy_CDL3LINESTRIKE_enabled": False, # "buy_CDL3OUTSIDE_enabled": False, # "buy_CDL3WHITESOLDIERS_enabled": False, # "buy_CDLDRAGONFLYDOJI_enabled": True, # "buy_CDLENGULFING_enabled": False, # "buy_CDLHAMMER_enabled": True, # "buy_CDLHARAMI_enabled": False, # "buy_CDLINVERTEDHAMMER_enabled": True, # "buy_CDLMORNINGSTAR_enabled": False, # "buy_CDLPIERCING_enabled": True, # "buy_CDLSPINNINGTOP_enabled": False, # } # buy_CDLHAMMER_enabled = True buy_CDLINVERTEDHAMMER_enabled = True buy_CDLDRAGONFLYDOJI_enabled = True buy_CDLPIERCING_enabled = True buy_CDLMORNINGSTAR_enabled = False buy_CDL3WHITESOLDIERS_enabled = False buy_CDL3LINESTRIKE_enabled = False buy_CDLSPINNINGTOP_enabled = False buy_CDLENGULFING_enabled = False buy_CDLHARAMI_enabled = False buy_CDL3OUTSIDE_enabled = False buy_CDL3INSIDE_enabled = True # set the startup candles count to the longest average used (EMA, EMA etc) startup_candle_count = 20 # set common parameters minimal_roi = Config.minimal_roi trailing_stop = Config.trailing_stop trailing_stop_positive = Config.trailing_stop_positive trailing_stop_positive_offset = Config.trailing_stop_positive_offset trailing_only_offset_is_reached = Config.trailing_only_offset_is_reached stoploss = Config.stoploss timeframe = Config.timeframe process_only_new_candles = Config.process_only_new_candles use_sell_signal = Config.use_sell_signal sell_profit_only = Config.sell_profit_only ignore_roi_if_buy_signal = Config.ignore_roi_if_buy_signal order_types = Config.order_types def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> 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. """ # MFI dataframe['mfi'] = ta.MFI(dataframe) # # Stoch fast # stoch_fast = ta.STOCHF(dataframe) # dataframe['fastd'] = stoch_fast['fastd'] # dataframe['fastk'] = stoch_fast['fastk'] # 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) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_upperband'] = bollinger['lower'] dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"]) # # 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) # # # SAR Parabol # dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) # 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] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ gconditions = [] # GUARDS AND TRENDS if self.buy_rsi_enabled.value: gconditions.append( (dataframe['rsi'] <= self.buy_rsi.value) & (dataframe['rsi'] > 0) ) if self.buy_sma_enabled.value: gconditions.append(dataframe['close'] < dataframe['sma']) if self.buy_mfi_enabled.value: gconditions.append(dataframe['mfi'] <= self.buy_mfi.value) # potential gain > goal if self.buy_bb_enabled.value: gconditions.append(dataframe['bb_gain'] >= self.buy_bb_gain.value) tconditions = [] # look for 3 White Soldiers pattern if self.buy_CDL3WHITESOLDIERS_enabled: tconditions.append(dataframe['CDL3WHITESOLDIERS'] >= self.pattern_strength) if self.buy_CDLMORNINGSTAR_enabled: tconditions.append(dataframe['CDLMORNINGSTAR'] >= self.pattern_strength) if self.buy_CDL3LINESTRIKE_enabled: tconditions.append(dataframe['CDL3LINESTRIKE'] >= self.pattern_strength) if self.buy_CDL3OUTSIDE_enabled: tconditions.append(dataframe['CDL3OUTSIDE'] >= self.pattern_strength) if self.buy_CDLHAMMER_enabled: tconditions.append(dataframe['CDLHAMMER'] >= self.pattern_strength) if self.buy_CDLINVERTEDHAMMER_enabled: tconditions.append(dataframe['CDLINVERTEDHAMMER'] >= self.pattern_strength) if self.buy_CDLDRAGONFLYDOJI_enabled: tconditions.append(dataframe['CDLDRAGONFLYDOJI'] >= self.pattern_strength) if self.buy_CDLPIERCING_enabled: tconditions.append(dataframe['CDLPIERCING'] >= self.pattern_strength) if self.buy_CDLSPINNINGTOP_enabled: tconditions.append(dataframe['CDLSPINNINGTOP'] >= self.pattern_strength) if self.buy_CDLENGULFING_enabled: tconditions.append(dataframe['CDLENGULFING'] >= self.pattern_strength) if self.buy_CDLHARAMI_enabled: tconditions.append(dataframe['CDLHARAMI'] >= self.pattern_strength) if self.buy_CDL3INSIDE_enabled: tconditions.append(dataframe['CDL3INSIDE'] >= self.pattern_strength) # build the dataframe using the guard and pattern results gr = False pr = False if gconditions: gr = reduce(lambda x, y: x & y, gconditions) if tconditions: pr = reduce(lambda x, y: x | y, tconditions) dataframe.loc[(gr & pr), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ conditions = [] # don't set a sell signal (just use ROI) dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 return dataframe