# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Patterns.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 class Github_remiotore_ccxt_freqtrade__Patterns__20260111_210550(IStrategy): """ Trades based on detection of candlestick patterns How to use it? > python3 ./freqtrade/main.py -s Github_remiotore_ccxt_freqtrade__Patterns__20260111_210550 """ pattern_strength = 90 rsi_limit = 20 mfi_limit = 25 buy_CDLHAMMER_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLINVERTEDHAMMER_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLDRAGONFLYDOJI_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLPIERCING_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLMORNINGSTAR_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDL3WHITESOLDIERS_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_CDL3LINESTRIKE_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLSPINNINGTOP_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLENGULFING_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLHARAMI_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDL3OUTSIDE_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDL3INSIDE_enabled = CategoricalParameter([True, False], default=False, space="buy") sell_params = { "sell_CDL3INSIDE_enabled": True, "sell_CDL3LINESTRIKE_enabled": True, "sell_CDL3OUTSIDE_enabled": True, "sell_CDLDARKCLOUDCOVER_enabled": False, "sell_CDLENGULFING_enabled": False, "sell_CDLEVENINGDOJISTAR_enabled": True, "sell_CDLEVENINGSTAR_enabled": True, "sell_CDLGRAVESTONEDOJI_enabled": True, "sell_CDLHANGINGMAN_enabled": True, "sell_CDLHARAMI_enabled": True, "sell_CDLSHOOTINGSTAR_enabled": True, "sell_CDLSPINNINGTOP_enabled": True, "sell_hold_enabled": False, } sell_CDL3LINESTRIKE_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLSPINNINGTOP_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLENGULFING_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLHARAMI_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDL3OUTSIDE_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDL3INSIDE_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLHANGINGMAN_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLSHOOTINGSTAR_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLGRAVESTONEDOJI_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLDARKCLOUDCOVER_enabled = CategoricalParameter([True, False], default=False, space="sell") sell_CDLEVENINGDOJISTAR_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLEVENINGSTAR_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_hold_enabled = CategoricalParameter([True, False], default=True, space="sell") if sell_hold_enabled.value: minimal_roi = { "0": 0.278, "39": 0.087, "124": 0.038, "135": 0 } trailing_stop = True trailing_stop_positive = 0.172 trailing_stop_positive_offset = 0.212 trailing_only_offset_is_reached = False stoploss = -0.333 else: minimal_roi = { "0": 0.296, "26": 0.104, "36": 0.037, "65": 0 } stoploss = -0.284 trailing_stop = True trailing_stop_positive = 0.183 trailing_stop_positive_offset = 0.274 trailing_only_offset_is_reached = True timeframe = '5m' process_only_new_candles = False use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = False order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } 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) stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['rsi'] = ta.RSI(dataframe) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] 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['sar'] = ta.SAR(dataframe) 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_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 = [] gconditions.append( (dataframe['rsi'] < self.rsi_limit) & (dataframe['rsi'] > 0) ) gconditions.append(dataframe['close'] < dataframe['sma']) gconditions.append(dataframe['mfi'] <= self.mfi_limit) conditions = [] if self.buy_CDL3WHITESOLDIERS_enabled.value: conditions.append(dataframe['CDL3WHITESOLDIERS'] >= self.pattern_strength) if self.buy_CDLMORNINGSTAR_enabled.value: conditions.append(dataframe['CDLMORNINGSTAR'] >= self.pattern_strength) if self.buy_CDL3LINESTRIKE_enabled.value: conditions.append(dataframe['CDL3LINESTRIKE'] >= self.pattern_strength) if self.buy_CDL3OUTSIDE_enabled.value: conditions.append(dataframe['CDL3OUTSIDE'] >= self.pattern_strength) if self.buy_CDLHAMMER_enabled.value: conditions.append(dataframe['CDLHAMMER'] >= self.pattern_strength) if self.buy_CDLINVERTEDHAMMER_enabled.value: conditions.append(dataframe['CDLINVERTEDHAMMER'] >= self.pattern_strength) if self.buy_CDLDRAGONFLYDOJI_enabled.value: conditions.append(dataframe['CDLDRAGONFLYDOJI'] >= self.pattern_strength) if self.buy_CDLPIERCING_enabled.value: conditions.append(dataframe['CDLPIERCING'] >= self.pattern_strength) if self.buy_CDLSPINNINGTOP_enabled.value: conditions.append(dataframe['CDLSPINNINGTOP'] >= self.pattern_strength) if self.buy_CDLENGULFING_enabled.value: conditions.append(dataframe['CDLENGULFING'] >= self.pattern_strength) if self.buy_CDLHARAMI_enabled.value: conditions.append(dataframe['CDLHARAMI'] >= self.pattern_strength) if self.buy_CDL3INSIDE_enabled.value: conditions.append(dataframe['CDL3INSIDE'] >= self.pattern_strength) if gconditions: gr = reduce(lambda x, y: x & y, gconditions) pr = False if conditions: pr = reduce(lambda x, y: x | y, conditions) dataframe.loc[(gr & pr), 'buy'] = 1 else: if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), '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 = [] if self.sell_hold_enabled.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 else: if self.sell_CDLEVENINGSTAR_enabled.value: conditions.append(dataframe['CDLEVENINGSTAR'] >= self.pattern_strength) if self.sell_CDL3LINESTRIKE_enabled.value: conditions.append(dataframe['CDL3LINESTRIKE'] >= self.pattern_strength) if self.sell_CDLEVENINGDOJISTAR_enabled.value: conditions.append(dataframe['CDLEVENINGDOJISTAR'] >= self.pattern_strength) if self.sell_CDL3LINESTRIKE_enabled.value: conditions.append(dataframe['CDL3LINESTRIKE'] <= -self.pattern_strength) if self.sell_CDL3OUTSIDE_enabled.value: conditions.append(dataframe['CDL3OUTSIDE'] <= -self.pattern_strength) if self.sell_CDLHANGINGMAN_enabled.value: conditions.append(dataframe['CDLHANGINGMAN'] >= self.pattern_strength) if self.sell_CDLSHOOTINGSTAR_enabled.value: conditions.append(dataframe['CDLSHOOTINGSTAR'] >= self.pattern_strength) if self.sell_CDLGRAVESTONEDOJI_enabled.value: conditions.append(dataframe['CDLGRAVESTONEDOJI'] >= self.pattern_strength) if self.sell_CDLDARKCLOUDCOVER_enabled.value: conditions.append(dataframe['CDLDARKCLOUDCOVER'] >= self.pattern_strength) if self.sell_CDLSPINNINGTOP_enabled.value: conditions.append(dataframe['CDLSPINNINGTOP'] <= -self.pattern_strength) if self.sell_CDLENGULFING_enabled.value: conditions.append(dataframe['CDLENGULFING'] <= -self.pattern_strength) if self.sell_CDLHARAMI_enabled.value: conditions.append(dataframe['CDLHARAMI'] <= -self.pattern_strength) if self.sell_CDL3INSIDE_enabled.value: conditions.append(dataframe['CDL3INSIDE'] <= -self.pattern_strength) dataframe.loc[reduce(lambda x, y: x | y, conditions), 'sell'] = 1 return dataframe