# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/Saturn5.py from datetime import timedelta from functools import reduce import freqtrade.vendor.qtpylib.indicators as qtpylib import talib.abstract as ta from freqtrade.strategy import IStrategy from pandas import DataFrame def to_minutes(**timdelta_kwargs): return int(timedelta(**timdelta_kwargs).total_seconds() / 60) class Github_DerSalvador_freqtrade_helm_chart__Saturn5__20260115_122204(IStrategy): INTERFACE_VERSION = 3 timeframe = '15m' # Stoploss stoploss = -0.2 startup_candle_count: int = 480 trailing_stop = False use_custom_stoploss = False use_exit_signal = False # signal controls entry_signal_1 = True entry_signal_2 = True entry_signal_3 = True # ROI table: minimal_roi = {'0': 0.05} # Indicator values: # Signal 1 s1_ema_xs = 3 s1_ema_sm = 5 s1_ema_md = 10 s1_ema_xl = 50 s1_ema_xxl = 240 # Signal 2 s2_ema_input = 50 s2_ema_offset_input = -1 s2_bb_sma_length = 49 s2_bb_std_dev_length = 64 s2_bb_lower_offset = 3 s2_fib_sma_len = 50 s2_fib_atr_len = 14 s2_fib_lower_value = 4.236 @property def protections(self): # Don't enter a trade right after exiting a trade. # Stop trading if max-drawdown is reached. # Considering all pairs that have a minimum of 20 trades # If max-drawdown is > 20% this will activate # Stop trading if a certain amount of stoploss occurred within a certain time window. # Considering all pairs that have a minimum of 4 trades # Looks at all pairs # Lock pairs with low profits # Considering all pairs that have a minimum of 2 trades # If profit < 2% this will activate for a pair # Lock pairs with low profits # Considering all pairs that have a minimum of 4 trades # If profit < 1% this will activate for a pair return [{'method': 'CooldownPeriod', 'stop_duration': to_minutes(hours=1, minutes=15)}, {'method': 'MaxDrawdown', 'lookback_period': to_minutes(hours=12), 'trade_limit': 20, 'stop_duration': to_minutes(hours=1), 'max_allowed_drawdown': 0.2}, {'method': 'StoplossGuard', 'lookback_period': to_minutes(hours=6), 'trade_limit': 4, 'stop_duration': to_minutes(minutes=30), 'only_per_pair': False}, {'method': 'LowProfitPairs', 'lookback_period': to_minutes(hours=1, minutes=30), 'trade_limit': 2, 'stop_duration': to_minutes(hours=15), 'required_profit': 0.02}, {'method': 'LowProfitPairs', 'lookback_period': to_minutes(hours=6), 'trade_limit': 4, 'stop_duration': to_minutes(minutes=30), 'required_profit': 0.01}] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Adding EMA's into the dataframe dataframe['s1_ema_xs'] = ta.EMA(dataframe, timeperiod=self.s1_ema_xs) dataframe['s1_ema_sm'] = ta.EMA(dataframe, timeperiod=self.s1_ema_sm) dataframe['s1_ema_md'] = ta.EMA(dataframe, timeperiod=self.s1_ema_md) dataframe['s1_ema_xl'] = ta.EMA(dataframe, timeperiod=self.s1_ema_xl) dataframe['s1_ema_xxl'] = ta.EMA(dataframe, timeperiod=self.s1_ema_xxl) s2_ema_value = ta.EMA(dataframe, timeperiod=self.s2_ema_input) s2_ema_xxl_value = ta.EMA(dataframe, timeperiod=200) dataframe['s2_ema'] = s2_ema_value - s2_ema_value * self.s2_ema_offset_input dataframe['s2_ema_xxl_off'] = s2_ema_xxl_value - s2_ema_xxl_value * self.s2_fib_lower_value dataframe['s2_ema_xxl'] = ta.EMA(dataframe, timeperiod=200) s2_bb_sma_value = ta.SMA(dataframe, timeperiod=self.s2_bb_sma_length) s2_bb_std_dev_value = ta.STDDEV(dataframe, self.s2_bb_std_dev_length) dataframe['s2_bb_std_dev_value'] = s2_bb_std_dev_value dataframe['s2_bb_lower_band'] = s2_bb_sma_value - s2_bb_std_dev_value * self.s2_bb_lower_offset s2_fib_atr_value = ta.ATR(dataframe, timeframe=self.s2_fib_atr_len) s2_fib_sma_value = ta.SMA(dataframe, timeperiod=self.s2_fib_sma_len) dataframe['s2_fib_lower_band'] = s2_fib_sma_value - s2_fib_atr_value * self.s2_fib_lower_value s3_bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['s3_bb_lowerband'] = s3_bollinger['lower'] # Volume weighted MACD dataframe['fastMA'] = ta.EMA(dataframe['volume'] * dataframe['close'], 12) / ta.EMA(dataframe['volume'], 12) dataframe['slowMA'] = ta.EMA(dataframe['volume'] * dataframe['close'], 26) / ta.EMA(dataframe['volume'], 26) dataframe['vwmacd'] = dataframe['fastMA'] - dataframe['slowMA'] dataframe['signal'] = ta.EMA(dataframe['vwmacd'], 9) dataframe['hist'] = dataframe['vwmacd'] - dataframe['signal'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # basic entry methods to keep the strategy simple if self.entry_signal_1: conditions = [dataframe['vwmacd'] < dataframe['signal'], dataframe['low'] < dataframe['s1_ema_xxl'], dataframe['close'] < dataframe['s1_ema_xxl'], qtpylib.crossed_above(dataframe['s1_ema_sm'], dataframe['s1_ema_md']), dataframe['s1_ema_xs'] < dataframe['s1_ema_xl'], dataframe['volume'] > 0] dataframe.loc[reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag']] = (1, 'entry_signal_1') if self.entry_signal_2: conditions = [qtpylib.crossed_above(dataframe['s2_fib_lower_band'], dataframe['s2_bb_lower_band']), dataframe['close'] < dataframe['s2_ema'], dataframe['volume'] > 0] dataframe.loc[reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag']] = (1, 'entry_signal_2') if self.entry_signal_3: conditions = [dataframe['low'] < dataframe['s3_bb_lowerband'], dataframe['low'] > dataframe['s1_ema_xxl'], dataframe['volume'] > 0] dataframe.loc[reduce(lambda x, y: x & y, conditions), ['enter_long', 'enter_tag']] = (1, 'entry_signal_3') if not all([self.entry_signal_1, self.entry_signal_2, self.entry_signal_3]): dataframe.loc[(), 'enter_long'] = 0 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # This is essentailly ignored as we're using strict ROI / Stoploss / TTP sale scenarios dataframe.loc[(), 'exit_long'] = 0 return dataframe