# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/DCBBBounce.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_freqtrade__DCBBBounce__20260111_210550(IStrategy): """ Simple strategy based on Contrarian Donchian Channels crossing Bollinger Bands How to use it? > python3 ./freqtrade/main.py -s Github_remiotore_freqtrade__DCBBBounce__20260111_210550.py """ buy_params = { "buy_adx": 25.0, "buy_adx_enabled": True, "buy_ema_enabled": False, "buy_period": 52, "buy_sar_enabled": True, "buy_sma_enabled": False, } buy_period = IntParameter(10, 120, default=52, space="buy") buy_adx = DecimalParameter(1, 99, decimals=0, default=25, space="buy") buy_sma_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_ema_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_adx_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_sar_enabled = CategoricalParameter([True, False], default=True, space="buy") sell_hold = CategoricalParameter([True, False], default=True, space="sell") startup_candle_count = buy_period.value if sell_hold.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": 0.261, "40": 0.087, "95": 0.023, "192": 0 } stoploss = -0.33 trailing_stop = True trailing_stop_positive = 0.168 trailing_stop_positive_offset = 0.253 trailing_only_offset_is_reached = False timeframe = '5m' process_only_new_candles = False use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = True order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': True } 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. """ bollinger = qtpylib.weighted_bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_lowerband'] = bollinger['lower'] dataframe['dc_upper'] = ta.MAX(dataframe['high'], timeperiod=self.buy_period.value) dataframe['dc_lower'] = ta.MIN(dataframe['low'], timeperiod=self.buy_period.value) dataframe["dcbb_diff_upper"] = (dataframe["dc_upper"] - dataframe['bb_upperband']) dataframe["dcbb_diff_lower"] = (dataframe["dc_lower"] - dataframe['bb_lowerband']) dataframe['adx'] = ta.ADX(dataframe) dataframe['dm_plus'] = ta.PLUS_DM(dataframe) dataframe['dm_minus'] = ta.MINUS_DM(dataframe) dataframe['mfi'] = ta.MFI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] 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) 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=200) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append(dataframe['dc_upper'].notnull()) if self.buy_sar_enabled.value: conditions.append(dataframe['sar'].notnull()) conditions.append(dataframe['close'] < dataframe['sar']) if self.buy_sma_enabled.value: conditions.append(dataframe['sma'].notnull()) conditions.append(dataframe['close'] > dataframe['sma']) if self.buy_ema_enabled.value: conditions.append(dataframe['ema50'].notnull()) conditions.append(dataframe['close'] > dataframe['ema50']) if self.buy_adx_enabled.value: conditions.append( (dataframe['adx'] > self.buy_adx.value) & (dataframe['dm_plus'] >= dataframe['dm_minus']) ) conditions.append( (dataframe['dcbb_diff_lower'].notnull()) & (dataframe['close'] >= dataframe['open']) & (qtpylib.crossed_above(dataframe['dcbb_diff_lower'], 0)) ) 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 """ if self.sell_hold.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 else: conditions = [] conditions.append( (dataframe['dcbb_diff_upper'].notnull()) & (qtpylib.crossed_below(dataframe['dcbb_diff_upper'], 0)) ) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe