# --- Do not remove these libs --- import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np # -------------------------------- import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter from abc import ABC, abstractmethod from pandas import DataFrame from freqtrade.persistence import Trade from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_seconds from datetime import datetime, timedelta import math class CombinedBinHAndClucHyperV0(IStrategy): timeframe = '1m' use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # ---------------------------------------------------------------- # Hyper Params # # # Buy buy_a_bbdelta_rate = DecimalParameter(0.004, 0.016, default=0.016, decimals=3) buy_a_closedelta_rate = DecimalParameter(0.000, 0.010, default=0.0087, decimals=4) buy_a_tail_rate = DecimalParameter(0.12, 0.5, default=0.28, decimals=2) buy_a_time_window = IntParameter(40, 100, default=30) buy_a_min_sell_rate = DecimalParameter(1.004, 1.1, default=0.004, decimals=3) buy_b_close_rate = DecimalParameter(0.4, 1.8, default=0.979, decimals=3) buy_b_volume_mean_slow_window = IntParameter(100, 300, default=30) buy_b_ema_slow = IntParameter(40, 100, default=50) buy_b_time_window = IntParameter(100, 300, default=20) buy_b_volume_mean_slow_num = IntParameter(10, 100, default=20) # Sell sell_bb_middleband_window = IntParameter(50, 200, default=20) sell_trailing_stop_positive_offset = DecimalParameter(0.01, 0.03, default=0.008, decimals=3) sell_trailing_stop_positive = 0.001 # ---------------------------------------------------------------- # Buy hyperspace params: buy_params = { 'buy_a_bbdelta_rate': 0.0160, 'buy_a_closedelta_rate': 0.0088, 'buy_a_tail_rate': 0.9, 'buy_a_time_window': 21, 'buy_a_min_sell_rate': 1.03, 'buy_b_close_rate': 0.979, 'buy_b_time_window': 20, 'buy_b_ema_slow': 50, 'buy_b_volume_mean_slow_num': 20, 'buy_b_volume_mean_slow_window': 30, } # Sell hyperspace params: sell_params = { 'sell_bb_middleband_window': 91, "sell_trailing_stop_positive_offset": 0.008, } # ROI table: minimal_roi = { "0": 100 } # Stoploss: stoploss = -0.1 trailing_stop = False trailing_only_offset_is_reached = False use_custom_stoploss = True def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: sell_trailing_stop_positive_offset = self.sell_trailing_stop_positive_offset.value if isinstance(self.sell_trailing_stop_positive_offset, ABC) else self.sell_trailing_stop_positive_offset sell_trailing_stop_positive = self.sell_trailing_stop_positive.value if isinstance(self.sell_trailing_stop_positive, ABC) else self.sell_trailing_stop_positive dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if last_candle is None: return -1 trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc - timedelta(seconds=timeframe_to_seconds(self.timeframe))) trade_candle = dataframe.loc[dataframe['date'] == trade_date] if trade_candle.empty: return -1 trade_candle = trade_candle.squeeze() slippage_ratio = trade.open_rate / trade_candle['close'] - 1 slippage_ratio = slippage_ratio if slippage_ratio > 0 else 0 current_profit_comp = current_profit + slippage_ratio if current_profit_comp < sell_trailing_stop_positive_offset: return -1 else: return sell_trailing_stop_positive def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # strategy BinHV45 for x in self.buy_a_time_window.range if isinstance(self.buy_a_time_window, ABC) else [self.buy_a_time_window]: buy_bollinger = qtpylib.bollinger_bands(dataframe['close'], window=x, stds=2) dataframe[f'lower_{x}'] = buy_bollinger['lower'] dataframe[f'bbdelta_{x}'] = (buy_bollinger['mid'] - dataframe[f'lower_{x}']).abs() dataframe[f'closedelta_{x}'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe[f'tail_{x}'] = (dataframe['close'] - dataframe['low']).abs() # strategy ClucMay72018 for x in self.buy_b_time_window.range if isinstance(self.buy_b_time_window, ABC) else [self.buy_b_time_window]: bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=x, stds=2) dataframe[f'bb_lowerband_{x}'] = bollinger['lower'] for x in self.buy_b_ema_slow.range if isinstance(self.buy_b_ema_slow, ABC) else [self.buy_b_ema_slow]: dataframe[f'ema_slow_{x}'] = ta.EMA(dataframe, timeperiod=x) for x in self.buy_b_volume_mean_slow_window.range if isinstance(self.buy_b_volume_mean_slow_window, ABC) else [self.buy_b_volume_mean_slow_window]: dataframe[f'volume_mean_slow_{x}'] = dataframe['volume'].rolling(window=x).mean() for x in self.sell_bb_middleband_window.range if isinstance(self.sell_bb_middleband_window, ABC) else [self.sell_bb_middleband_window]: sell_bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=x, stds=2) dataframe[f'bb_middleband_{x}'] = sell_bollinger['mid'] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: buy_a_time_window = self.buy_a_time_window.value if isinstance(self.buy_a_time_window, ABC) else self.buy_a_time_window buy_a_bbdelta_rate = self.buy_a_bbdelta_rate.value if isinstance(self.buy_a_bbdelta_rate, ABC) else self.buy_a_bbdelta_rate buy_a_closedelta_rate = self.buy_a_closedelta_rate.value if isinstance(self.buy_a_closedelta_rate, ABC) else self.buy_a_closedelta_rate buy_a_tail_rate = self.buy_a_tail_rate.value if isinstance(self.buy_a_tail_rate, ABC) else self.buy_a_tail_rate buy_a_min_sell_rate = self.buy_a_min_sell_rate.value if isinstance(self.buy_a_min_sell_rate, ABC) else self.buy_a_min_sell_rate buy_b_ema_slow = self.buy_b_ema_slow.value if isinstance(self.buy_b_ema_slow, ABC) else self.buy_b_ema_slow buy_b_close_rate = self.buy_b_close_rate.value if isinstance(self.buy_b_close_rate, ABC) else self.buy_b_close_rate buy_b_time_window = self.buy_b_time_window.value if isinstance(self.buy_b_time_window, ABC) else self.buy_b_time_window buy_b_volume_mean_slow_window = self.buy_b_volume_mean_slow_window.value if isinstance(self.buy_b_volume_mean_slow_window, ABC) else self.buy_b_volume_mean_slow_window buy_b_volume_mean_slow_num = self.buy_b_volume_mean_slow_num.value if isinstance(self.buy_b_volume_mean_slow_num, ABC) else self.buy_b_volume_mean_slow_num sell_bb_middleband_window = self.sell_bb_middleband_window.value if isinstance(self.sell_bb_middleband_window, ABC) else self.sell_bb_middleband_window dataframe.loc[ ( # strategy BinHV45 dataframe[f'lower_{buy_a_time_window}'].shift().gt(0) & dataframe[f'bbdelta_{buy_a_time_window}'].gt(dataframe['close'] * buy_a_bbdelta_rate) & dataframe[f'closedelta_{buy_a_time_window}'].gt(dataframe['close'] * buy_a_closedelta_rate) & dataframe[f'tail_{buy_a_time_window}'].lt(dataframe[f'bbdelta_{buy_a_time_window}'] * buy_a_tail_rate) & dataframe['close'].lt(dataframe[f'lower_{buy_a_time_window}'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & dataframe[f'bb_middleband_{sell_bb_middleband_window}'].gt(dataframe['close'] * buy_a_min_sell_rate) ) | ( # strategy ClucMay72018 (dataframe['close'] < dataframe[f'ema_slow_{buy_b_ema_slow}']) & (dataframe['close'] < buy_b_close_rate * dataframe[f'bb_lowerband_{buy_b_time_window}']) & (dataframe['volume'] < (dataframe[f'volume_mean_slow_{buy_b_volume_mean_slow_window}'].shift(1) * buy_b_volume_mean_slow_num)) ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: sell_bb_middleband_window = self.sell_bb_middleband_window.value if isinstance(self.sell_bb_middleband_window, ABC) else self.sell_bb_middleband_window dataframe.loc[(dataframe['close'] > dataframe[f'bb_middleband_{sell_bb_middleband_window}']), 'sell'] = 1 return dataframe