# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Squeeze001.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 from user_data.strategies import Config class Github_remiotore_freqtrade__Squeeze001__20260111_210550(IStrategy): """ Strategy based on LazyBear Squeeze Momentum Indicator (on TradingView.com) How to use it? > python3 ./freqtrade/main.py -s Github_remiotore_freqtrade__Squeeze001__20260111_210550 """ buy_params = { "buy_adx": 11.0, "buy_adx_enabled": False, "buy_bb_enabled": True, "buy_bb_gain": 0.02, "buy_ema_enabled": True, "buy_period": 36, "buy_sqz_band": 0.004, } buy_period = IntParameter(3, 50, default=36, space="buy") buy_adx = DecimalParameter(10, 50, decimals=0, default=11, space="buy") buy_sqz_band = DecimalParameter(0.002, 0.02, decimals=4, default=0.004, 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_ema_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_adx_enabled = CategoricalParameter([True, False], default=False, space="buy") sell_hold_enabled = CategoricalParameter([True, False], default=True, space="sell") startup_candle_count = max(buy_period.value, 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['sma'] = ta.SMA(dataframe, timeperiod=self.buy_period.value) dataframe['ema'] = ta.EMA(dataframe, timeperiod=self.buy_period.value) dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['adx'] = ta.ADX(dataframe) dataframe['dm_plus'] = ta.PLUS_DM(dataframe) dataframe['dm_minus'] = ta.MINUS_DM(dataframe) dataframe['sar'] = ta.SAR(dataframe) 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_upperband'] = bollinger['upper'] dataframe['bb_mid'] = bollinger['mid'] dataframe['bb_lowerband'] = bollinger['lower'] dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"]) keltner = qtpylib.keltner_channel(dataframe) dataframe["kc_upper"] = keltner["upper"] dataframe["kc_lower"] = keltner["lower"] dataframe["kc_middle"] = keltner["mid"] 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['dc_mid'] = ta.EMA(((dataframe['dc_upper'] + dataframe['dc_lower']) / 2), timeperiod=self.buy_period.value) dataframe['sqz_upper'] = (dataframe['bb_upperband'] - dataframe["kc_upper"]) dataframe['sqz_lower'] = (dataframe['bb_lowerband'] - dataframe["kc_lower"]) dataframe['sqz_on'] = ((dataframe['sqz_upper'] < 0) & (dataframe['sqz_lower'] > 0)) dataframe['sqz_off'] = ((dataframe['sqz_upper'] > 0) & (dataframe['sqz_lower'] < 0)) dataframe['sqz_ave'] = ta.EMA(((dataframe['dc_mid'] + dataframe['ema']) / 2), timeperiod=self.buy_period.value) dataframe['sqz_delta'] = ta.EMA((dataframe['close'] - dataframe['sqz_ave']), timeperiod=self.buy_period.value) dataframe['sqz_val'] = ta.LINEARREG(dataframe['sqz_delta'], timeperiod=self.buy_period.value) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append(dataframe['volume'] > 0) conditions.append(dataframe['sqz_upper'].notnull()) if self.buy_adx_enabled.value: conditions.append( (dataframe['adx'] > self.buy_adx.value) ) if self.buy_ema_enabled.value: conditions.append(dataframe['close'] < dataframe['ema']) if self.buy_bb_enabled.value: conditions.append(dataframe['bb_gain'] >= self.buy_bb_gain.value) conditions.append( (dataframe['sqz_val'] < -self.buy_sqz_band.value) & (dataframe['sqz_val'] > dataframe['sqz_val'].shift(1)) & (dataframe['sqz_val'].shift(1) <= dataframe['sqz_val'].shift(2)) ) 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_enabled.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 else: conditions = [] conditions.append(dataframe['volume'] > 0) conditions.append(dataframe['sqz_upper'].notnull()) conditions.append( (dataframe['sqz_val'] > self.buy_sqz_band.value) & (dataframe['sqz_val'] < dataframe['sqz_val'].shift(1)) & (dataframe['sqz_val'].shift(1) >= dataframe['sqz_val'].shift(2)) ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe