# source: https://raw.githubusercontent.com/AlexCryptoKing/freqtrade/4238f4a087fd5f2b6ebcf97f50c5fb4565e9603d/Strategies/old_AlexSuperMultiV6.py # --- Do not remove these libs --- from freqtrade.strategy import IStrategy, CategoricalParameter, DecimalParameter, IntParameter from freqtrade.persistence import Trade from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from functools import reduce import datetime import logging logger = logging.getLogger(__name__) class Github_AlexCryptoKing_freqtrade__old_AlexSuperMultiV6__20241012_105537(IStrategy): INTERFACE_VERSION = 3 # ROI parameters for hyperopt roi_0 = DecimalParameter(0.001, 0.02, default=0.001, space='roi') roi_10 = DecimalParameter(0.001, 0.02, default=0.003, space='roi') roi_30 = DecimalParameter(0.001, 0.02, default=0.005, space='roi') roi_60 = DecimalParameter(0.001, 0.02, default=0.01, space='roi') # Stoploss parameter for hyperopt stoploss_opt = DecimalParameter(-0.2, -0.01, default=-0.1, space='stoploss') # Trailing stop parameters for hyperopt trailing_stop_opt = CategoricalParameter([True, False], default=False, space='trailing') trailing_stop_positive_opt = DecimalParameter(0.001, 0.02, default=0.01, space='trailing') trailing_stop_positive_offset_opt = DecimalParameter(0.001, 0.05, default=0.02, space='trailing') trailing_only_offset_is_reached_opt = CategoricalParameter([True, False], default=True, space='trailing') # Timeframes: timeframe = '5m' timeframe_support = '5m' timeframe_main = '5m' # Other settings: use_exit_signal = True exit_profit_only = True ignore_roi_if_entry_signal = True ignore_buying_expired_candle_after = 1 startup_candle_count: int = 20 can_short = False # Custom parameters for indicators jma_length = IntParameter(5, 30, default=10, space='buy') jma_phase = IntParameter(-100, 100, default=15, space='buy') jma_power = IntParameter(1, 5, default=2, space='buy') range_filter_sampling_period = IntParameter(10, 50, default=20, space='buy') range_multiplier = DecimalParameter(1.0, 2.0, default=1.2, space='buy') adx_length = IntParameter(5, 30, default=5, space='buy') adx_threshold = IntParameter(20, 40, default=25, space='buy') sar_start = DecimalParameter(0.02, 0.5, default=0.25, space='buy') sar_increment = DecimalParameter(0.01, 0.2, default=0.02, space='buy') sar_maximum = DecimalParameter(0.1, 0.5, default=0.2, space='buy') rsi_length = IntParameter(5, 30, default=10, space='buy') rsi_centerline = IntParameter(30, 70, default=50, space='buy') macd_fast_length = IntParameter(5, 15, default=5, space='buy') macd_slow_length = IntParameter(5, 30, default=8, space='buy') macd_signal_smoothing = IntParameter(3, 10, default=5, space='buy') volume_factor = DecimalParameter(1.0, 2.0, default=1.5, space='buy') sma_volume_length = IntParameter(5, 30, default=10, space='buy') # Parameters for sell space sell_rsi_centerline = IntParameter(30, 70, default=50, space='sell') # Protection parameters max_drawdown = DecimalParameter(-0.3, -0.05, default=-0.1, space='protection') stop_consecutive_losses = IntParameter(1, 5, default=3, space='protection') cooldown_period = IntParameter(5, 60, default=30, space='protection') max_daily_trades = IntParameter(1, 20, default=5, space='protection') def __init__(self, config: dict) -> None: super().__init__(config) # Assign the hyperoptimized parameters to the strategy's ROI and stoploss settings self.minimal_roi = {'0': self.roi_0.value, '10': self.roi_10.value, '30': self.roi_30.value, '60': self.roi_60.value} self.stoploss = self.stoploss_opt.value self.trailing_stop = self.trailing_stop_opt.value self.trailing_stop_positive = self.trailing_stop_positive_opt.value self.trailing_stop_positive_offset = self.trailing_stop_positive_offset_opt.value self.trailing_only_offset_is_reached = self.trailing_only_offset_is_reached_opt.value def JMA(self, data, length, phase, power): phase_ratio = phase / 100.0 volty = ta.EMA(data, timeperiod=length) d_volty = data - volty jma = volty + d_volty * (1 - phase_ratio) ** power return jma def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['JMA'] = self.JMA(dataframe['close'], self.jma_length.value, self.jma_phase.value, self.jma_power.value) dataframe['Range_Filter'] = ta.MAX(dataframe['high'], timeperiod=self.range_filter_sampling_period.value) - ta.MIN(dataframe['low'], timeperiod=self.range_filter_sampling_period.value) dataframe['ADX'] = ta.ADX(dataframe, timeperiod=self.adx_length.value) dataframe['RSI'] = ta.RSI(dataframe, timeperiod=self.rsi_length.value) dataframe['SAR'] = ta.SAR(dataframe, acceleration=self.sar_start.value, maximum=self.sar_maximum.value) macd = ta.MACD(dataframe, fastperiod=self.macd_fast_length.value, slowperiod=self.macd_slow_length.value, signalperiod=self.macd_signal_smoothing.value) dataframe['MACD'] = macd['macd'] dataframe['MACD_signal'] = macd['macdsignal'] dataframe['MACD_hist'] = macd['macdhist'] dataframe['SMA_Volume'] = ta.SMA(dataframe['volume'], timeperiod=self.sma_volume_length.value) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_long = [dataframe['close'] > dataframe['JMA'], dataframe['ADX'] > self.adx_threshold.value, dataframe['RSI'] > self.rsi_centerline.value, dataframe['SAR'] < dataframe['close'], dataframe['MACD_hist'] > 0, dataframe['volume'] > self.volume_factor.value * dataframe['SMA_Volume']] # Combine long conditions if conditions_long: dataframe['enter_long'] = 0 conditions = reduce(lambda x, y: x & y, conditions_long) dataframe.loc[conditions, 'enter_long'] = 1 # Debugging logger.debug(f'Conditions met for entering long: {dataframe.loc[conditions, ['date', 'close', 'enter_long']]}') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_exit_long = [dataframe['close'] < dataframe['JMA'], dataframe['ADX'] < self.adx_threshold.value, dataframe['RSI'] < self.sell_rsi_centerline.value, dataframe['MACD'] < dataframe['MACD_signal']] # Combine exit long conditions if conditions_exit_long: dataframe.loc[reduce(lambda x, y: x & y, conditions_exit_long), 'exit_long'] = 1 return dataframe def leverage(self, pair: str, current_time: 'datetime', current_rate: float, proposed_leverage: float, **kwargs) -> float: return 1.0 # Protection logic def check_protections(self): # Max drawdown protection drawdown = self.wallet.get_drawdown() if drawdown < self.max_drawdown.value: print('Stopped trading due to max drawdown') return False # Stop trading # Stop trading on consecutive losses consecutive_losses = self.wallet.get_consecutive_losses() if consecutive_losses >= self.stop_consecutive_losses.value: print('Stopped trading due to consecutive losses') return False # Stop trading # Cooldown period after a loss last_trade = Trade.get_trades(query={'status': 'closed'}).order_by(Trade.id.desc()).first() if last_trade: last_loss_time = last_trade.close_date if last_trade.close_profit < 0 else None if last_loss_time and (datetime.datetime.now() - last_loss_time).seconds / 60 < self.cooldown_period.value: print('Cooldown period active') return False # Cooldown period active # Max daily trades protection trades_today = Trade.get_trades(query={'status': 'closed', 'open_date': {'$gte': datetime.datetime.now().date()}}).count() if trades_today >= self.max_daily_trades.value: print('Stopped trading due to max daily trades') return False # Stop trading return True # All protections passed def should_enter_trade(self, dataframe: DataFrame, metadata: dict) -> bool: can_enter = self.check_protections() if not can_enter: print('Cannot enter trade due to protections') else: print('Entering trade') return can_enter and super().should_enter_trade(dataframe, metadata)