# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BSS2.py from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade import technical.indicators as ftt import math import logging logger = logging.getLogger(__name__) class Github_remiotore_freqtrade__BSS2__20260111_210550(IStrategy): INTERFACE_VERSION = 2 buy_params = { "base_nb_candles_buy": 12, "rsi_buy": 58, "ewo_high": 3.001, #3.001 "ewo_low": -10.289, #-10.289 "low_offset": 0.987, #0.987 "lambo2_ema_14_factor": 0.981, #0.981 "lambo2_enabled": True, "lambo2_rsi_14_limit": 39, #39 "lambo2_rsi_4_limit": 44, #44 "buy_adx": 20, "buy_fastd": 20, "buy_fastk": 22, "buy_ema_cofi": 0.98, "buy_ewo_high": 4.179 } sell_params = { "base_nb_candles_sell": 22, "high_offset": 1.07, "high_offset_2": 1.05 } @property def protections(self): return [ {"method": "CooldownPeriod", "stop_duration_candles": 5}, {"method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 20, "stop_duration_candles": 4, "max_allowed_drawdown": 0.2}, {"method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "only_per_pair": False}, {"method": "LowProfitPairs", "lookback_period_candles": 6, "trade_limit": 2, "stop_duration_candles": 60, "required_profit": 0.02}, {"method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "required_profit": 0.01} ] stoploss = -0.05 base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True) lambo2_enabled = bool(buy_params['lambo2_enabled']) lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True) lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True) lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True) fast_ewo = 60 slow_ewo = 220 ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter(3.0, 3.4, default=buy_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=False) trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.0135 trailing_only_offset_is_reached = True is_optimize_cofi = False buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97, optimize=is_optimize_cofi) buy_fastk = IntParameter(20, 30, default=20, optimize=is_optimize_cofi) buy_fastd = IntParameter(20, 30, default=20, optimize=is_optimize_cofi) buy_adx = IntParameter(20, 30, default=30, optimize=is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize=is_optimize_cofi) use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'} timeframe = '5m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 400 plot_config = {'main_plot': {'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}}} def custom_exit(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) rsi_value = dataframe['rsi'].iloc[-1] ema_value = dataframe['ema_14'].iloc[-1] price = current_rate if (current_time - trade.open_date_utc).total_seconds() / 3600 >= 16: if current_profit < -0.10 and rsi_value > 30 and price < ema_value: # Only exit if profit is less than -10% return 'market_based_exit' # Exit reason (optional) else: return False # Hold the trade (profitable even after 16 hours) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] if self.config['stake_currency'] in ['USDT', 'BUSD', 'USDC', 'DAI', 'TUSD', 'PAX', 'USD', 'EUR', 'GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" informative_pairs += [(btc_info_pair, self.timeframe), (btc_info_pair, self.inf_1h)] return informative_pairs def pump_dump_protection(self, dataframe, metadata): df36h = dataframe.copy().shift(432) df24h = dataframe.copy().shift(288) dataframe['volume_mean_short'] = dataframe['volume'].rolling(4).mean() dataframe['volume_mean_long'] = df24h['volume'].rolling(48).mean() dataframe['volume_mean_base'] = df36h['volume'].rolling(288).mean() dataframe['volume_change_percentage'] = (dataframe['volume_mean_long'] / dataframe['volume_mean_base']) dataframe['rsi_mean'] = dataframe['rsi'].rolling(48).mean() dataframe['pnd_volume_warn'] = np.where((dataframe['volume_mean_short'] / dataframe['volume_mean_long'] > 5.0), -1, 0) return dataframe def base_tf_btc_indicators(self, dataframe, metadata): dataframe['price_trend_long'] = (dataframe['close'].rolling(8).mean() / dataframe['close'].shift(8).rolling(144).mean()) ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) return dataframe def info_tf_btc_indicators(self, dataframe, metadata): dataframe['rsi_8'] = ta.RSI(dataframe, timeperiod=8) ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) return dataframe def EWO(self, dataframe, ema_length=5, ema2_length=3): ema1 = ta.EMA(dataframe, timeperiod=ema_length) ema2 = ta.EMA(dataframe, timeperiod=ema2_length) emadif = (ema1 - ema2) / ema2 * 100 return emadif def populate_indicators(self, dataframe, metadata): """ Calcula os indicadores técnicos utilizados pela estratégia. Argumentos: dataframe (pandas.DataFrame): DataFrame com dados históricos do mercado. metadata (dict): Dicionário com metadados sobre o par e a timeframe. Retorna: pandas.DataFrame: DataFrame com os indicadores técnicos adicionados. """ for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) dataframe['EWO'] = self.EWO(dataframe, self.fast_ewo, self.slow_ewo) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['zema_30'] = ftt.zema(dataframe, period=30) dataframe['zema_200'] = ftt.zema(dataframe, period=200) dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30'] stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['adx'] = ta.ADX(dataframe) dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe = self.pump_dump_protection(dataframe, metadata) if self.config['stake_currency'] in ['USDT', 'BUSD']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" btc_info_tf = self.dp.get_pair_dataframe(btc_info_pair, self.inf_1h) btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.inf_1h, ffill=True) drop_columns = [f"{s}_{self.inf_1h}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) btc_base_tf = self.dp.get_pair_dataframe(btc_info_pair, self.timeframe) btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True) drop_columns = [f"{s}_{self.timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) dataframe['ema_long_term'] = ta.EMA(dataframe, timeperiod=200) dataframe['volume_avg'] = ta.SMA(dataframe['volume'], timeperiod=20) return dataframe def populate_entry_trend(self, dataframe, metadata): conditions = [] dataframe.loc[:, 'buy_tag'] = '' lambo2 = ( bool(self.lambo2_enabled) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2, 'buy_tag'] += 'lambo2_' conditions.append(lambo2) buy1ewo = ( (dataframe['rsi_fast'] < 35) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) dataframe.loc[buy1ewo, 'buy_tag'] += 'buy1eworsi_' conditions.append(buy1ewo) buy2ewo = ( (dataframe['rsi_fast'] < 35) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) dataframe.loc[buy2ewo, 'buy_tag'] += 'buy2ewo_' conditions.append(buy2ewo) is_cofi = ( (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) & (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) & (dataframe['fastk'] < self.buy_fastk.value) & (dataframe['fastd'] < self.buy_fastd.value) & (dataframe['adx'] > self.buy_adx.value) & (dataframe['EWO'] > self.buy_ewo_high.value) ) dataframe.loc[is_cofi, 'buy_tag'] += 'cofi_' conditions.append(is_cofi) is_entry_confirmed = ( (dataframe['close'] > dataframe['ema_long_term']) & (dataframe['volume'] > dataframe['volume_avg']) ) buy_signal = conditions[0] for condition in conditions[1:]: buy_signal = buy_signal | condition dataframe.loc[buy_signal, 'buy'] = 1 return dataframe def populate_exit_trend(self, dataframe, metadata): conditions = [] dataframe.loc[:, 'sell_tag'] = '' sell1 = ( (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) ) dataframe.loc[sell1, 'sell_tag'] += 'sell1_' conditions.append(sell1) sell2 = ( (dataframe['close'] > (dataframe['hma_50'] * 1.015)) & (dataframe['volume_mean_short'] / dataframe['volume_mean_long'] > 5.0) & (dataframe['volume_change_percentage'] > 2.0) & (dataframe['rsi_mean'] > 50) ) dataframe.loc[sell2, 'sell_tag'] += 'sell2_' conditions.append(sell2) dataframe['sell_tag'] = dataframe['sell_tag'].apply(lambda x: x[:-1] if x != '' else x) is_exit_confirmed = ( (dataframe['close'] < dataframe['ema_long_term']) & # Preço abaixo da EMA de longo prazo (dataframe['volume'] < dataframe['volume_avg']) & # Volume abaixo da média móvel do volume (dataframe['close'].rolling(20).std() > 0.02) & # Desvio padrão dos últimos 20 períodos de fechamento maior que 2% (qtpylib.crossed_above(dataframe['rsi'], 70)) # Cruzamento do preço abaixo do RSI ) sell_signal = conditions[0] for condition in conditions[1:]: sell_signal = sell_signal | condition dataframe.loc[sell_signal, 'sell'] = 1 return dataframe