# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Patterns2_2.py 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_remiotore_ccxt_freqtrade__Patterns2_2__20260111_210550(IStrategy): """ Trades based on detection of the 3 White Soldiers candlestick pattern How to use it? > python3 ./freqtrade/main.py -s Patterns2 """ 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_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 startup_candle_count = 20 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. """ dataframe['mfi'] = ta.MFI(dataframe) dataframe['rsi'] = ta.RSI(dataframe) 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"]) dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] return dataframe def populate_entry_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 = [] 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) if self.buy_bb_enabled.value: gconditions.append(dataframe['bb_gain'] >= self.buy_bb_gain.value) tconditions = [] 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) 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_exit_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 = [] dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 return dataframe