# source: https://raw.githubusercontent.com/marcosrioj/cryptotrader/3760b3457dcc3a323c182526d85d4dfa32d85b54/user_data/strategies/BollingerSqueezeScalpStrategy.py # Bollinger Bands Squeeze Scalping Strategy # Scalping strategy based on Bollinger Bands and volatility squeeze # Optimized to capture fast movements after low volatility import talib.abstract as ta from freqtrade.strategy import IStrategy, DecimalParameter, IntParameter from pandas import DataFrame import numpy as np class Github_marcosrioj_cryptotrader__BollingerSqueezeScalpStrategy__20251031_064108(IStrategy): """ Bollinger Bands Squeeze Scalping Strategy This strategy uses: - Bollinger Bands to identify volatility squeeze - RSI for momentum - Volume for breakout confirmation - ADX for trend strength Buy signals: - Bollinger Bands in squeeze (low volatility) - Price breaks above upper band - RSI > 50 (upward momentum) - Volume spike (2x average) - ADX > 20 (trend forming) Sell signals: - Price breaks below lower band - RSI < 50 (downward momentum) - Or stop loss/take profit """ # Basic strategy configuration INTERFACE_VERSION = 3 # Risk settings for scalping stoploss = -0.025 # 2.5% stop loss # Timeframe for scalping timeframe = '5m' # 5 minutes for less noise than 1m # Trailing stop trailing_stop = True trailing_stop_positive = 0.008 # 0.8% trailing_stop_positive_offset = 0.015 # 1.5% trailing_only_offset_is_reached = True # ROI optimized for scalping minimal_roi = { "0": 0.04, # 4% imediato "5": 0.025, # 2.5% após 5 minutos "10": 0.02, # 2% após 10 minutos "20": 0.015, # 1.5% após 20 minutos "30": 0.01 # 1% após 30 minutos } # Optimizable parameters bb_period = IntParameter(15, 25, default=20, space="buy") bb_std = DecimalParameter(1.8, 2.2, default=2.0, space="buy") rsi_period = IntParameter(10, 16, default=14, space="buy") rsi_buy_threshold = IntParameter(45, 55, default=50, space="buy") rsi_sell_threshold = IntParameter(45, 55, default=50, space="sell") volume_spike = DecimalParameter(1.8, 3.0, default=2.0, space="buy") adx_period = IntParameter(12, 18, default=14, space="buy") adx_threshold = IntParameter(18, 25, default=20, space="buy") squeeze_threshold = DecimalParameter(0.01, 0.03, default=0.02, space="buy") # Configurações de proteção use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False startup_candle_count: int = 40 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adiciona indicadores técnicos para squeeze scalping """ # Bollinger Bands bollinger = ta.BBANDS( dataframe, timeperiod=self.bb_period.value, nbdevup=self.bb_std.value, nbdevdn=self.bb_std.value ) dataframe['bb_lowerband'] = bollinger['lowerband'] dataframe['bb_middleband'] = bollinger['middleband'] dataframe['bb_upperband'] = bollinger['upperband'] # Bollinger Band Width (para detectar squeeze) dataframe['bb_width'] = ( (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband'] ) # Squeeze detection dataframe['bb_squeeze'] = dataframe['bb_width'] < self.squeeze_threshold.value # Bollinger Band position dataframe['bb_percent'] = ( (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband']) ) # Breakouts dataframe['bb_break_up'] = ( (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) <= dataframe['bb_upperband'].shift(1)) ) dataframe['bb_break_down'] = ( (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['close'].shift(1) >= dataframe['bb_lowerband'].shift(1)) ) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=self.rsi_period.value) # Volume analysis dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20) dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma'] # ADX para força da tendência dataframe['adx'] = ta.ADX(dataframe, timeperiod=self.adx_period.value) # MACD para momentum adicional macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # EMA rápida para direção dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=9) # Keltner Channels (para comparar com BB) dataframe['kc_upper'] = dataframe['bb_middleband'] + (ta.ATR(dataframe, timeperiod=20) * 2) dataframe['kc_lower'] = dataframe['bb_middleband'] - (ta.ATR(dataframe, timeperiod=20) * 2) # True squeeze (BB dentro de Keltner) dataframe['true_squeeze'] = ( (dataframe['bb_upperband'] < dataframe['kc_upper']) & (dataframe['bb_lowerband'] > dataframe['kc_lower']) ) # ATR para stop loss dinâmico dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Entry signals based on squeeze and breakout """ conditions = [] # Condição 1: Squeeze recente (nas últimas 3 velas) dataframe['recent_squeeze'] = ( dataframe['bb_squeeze'] | dataframe['bb_squeeze'].shift(1) | dataframe['bb_squeeze'].shift(2) | dataframe['true_squeeze'] | dataframe['true_squeeze'].shift(1) ) conditions.append(dataframe['recent_squeeze']) # Condição 2: Breakout para cima conditions.append(dataframe['bb_break_up']) # Condição 3: RSI com momentum de alta conditions.append(dataframe['rsi'] > self.rsi_buy_threshold.value) # Condição 4: Volume spike confirmando breakout conditions.append(dataframe['volume_ratio'] > self.volume_spike.value) # Condição 5: ADX mostrando força na tendência conditions.append(dataframe['adx'] > self.adx_threshold.value) # Condição 6: MACD positivo conditions.append(dataframe['macdhist'] > 0) # Condição 7: Preço acima da EMA rápida conditions.append(dataframe['close'] > dataframe['ema_fast']) # Condição 8: Não em zona de sobrecompra extrema conditions.append(dataframe['bb_percent'] < 0.95) if conditions: dataframe.loc[ np.logical_and.reduce(conditions), 'enter_long' ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Exit signals for squeeze scalping """ conditions = [] # Condição principal: Breakout para baixo conditions.append(dataframe['bb_break_down']) # Condições alternativas alt_conditions = [] # RSI perdendo momentum alt_conditions.append(dataframe['rsi'] < self.rsi_sell_threshold.value) # Preço volta para dentro das bandas após extensão alt_conditions.append( (dataframe['bb_percent'] > 0.8) & (dataframe['close'] < dataframe['bb_upperband']) ) # MACD virando negativo alt_conditions.append( (dataframe['macd'] < dataframe['macdsignal']) & (dataframe['macdhist'] < 0) ) # Volume diminuindo após spike alt_conditions.append( (dataframe['volume_ratio'] < 1.0) & (dataframe['volume_ratio'].shift(1) > 2.0) ) # Preço abaixo da EMA rápida alt_conditions.append(dataframe['close'] < dataframe['ema_fast']) # Combinar condições main_exit = np.logical_and.reduce(conditions) if conditions else False alt_exit = np.logical_and.reduce(alt_conditions) if alt_conditions else False dataframe.loc[main_exit | alt_exit, 'exit_long'] = 1 return dataframe def custom_stoploss(self, pair: str, trade, current_time, current_rate, current_profit, **kwargs): """ Stop loss baseado em ATR """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # Stop loss baseado em ATR atr_multiplier = 2.0 atr_stop = atr_multiplier * last_candle['atr'] / current_rate return max(-atr_stop, self.stoploss) def custom_exit(self, pair: str, trade, current_time, current_rate, current_profit, **kwargs): """ Custom exits for squeeze scalping """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # Saída rápida com lucro alto if current_profit > 0.03: return 'high_profit_exit' # Saída se squeeze voltou (fim do movimento) if current_profit > 0.01 and last_candle['bb_squeeze']: return 'squeeze_return_exit' # Saída se RSI extremo if current_profit > 0.008 and last_candle['rsi'] > 75: return 'rsi_extreme_exit' return None def leverage(self, pair: str, current_time, current_rate, proposed_leverage: float, max_leverage: float, entry_tag, side: str, **kwargs) -> float: """ Alavancagem para scalping """ return 10.0 # Alavancagem 10x (configurar manualmente na Bybit)