# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Apollo11.py from datetime import datetime from datetime import timedelta from functools import reduce import freqtrade.vendor.qtpylib.indicators as qtpylib import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy import IStrategy from pandas import DataFrame def to_minutes(**timdelta_kwargs): return int(timedelta(**timdelta_kwargs).total_seconds() / 60) class Github_remiotore_freqtrade__Apollo11__20260111_210550(IStrategy): timeframe = "15m" stoploss = -0.16 startup_candle_count: int = 480 trailing_stop = False use_custom_stoploss = True use_sell_signal = False buy_signal_1 = True buy_signal_2 = True buy_signal_3 = True minimal_roi = { "0": 10, # This is 10000%, which basically disables ROI } s1_ema_xs = 3 s1_ema_sm = 5 s1_ema_md = 10 s1_ema_xl = 50 s1_ema_xxl = 240 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): return [ { "method": "CooldownPeriod", "stop_duration": to_minutes(minutes=0), }, { "method": "MaxDrawdown", "lookback_period": to_minutes(hours=12), "trade_limit": 20, # Considering all pairs that have a minimum of 20 trades "stop_duration": to_minutes(hours=1), "max_allowed_drawdown": 0.2, # If max-drawdown is > 20% this will activate }, { "method": "StoplossGuard", "lookback_period": to_minutes(hours=6), "trade_limit": 4, # Considering all pairs that have a minimum of 4 trades "stop_duration": to_minutes(minutes=30), "only_per_pair": False, # Looks at all pairs }, { "method": "LowProfitPairs", "lookback_period": to_minutes(hours=1, minutes=30), "trade_limit": 2, # Considering all pairs that have a minimum of 2 trades "stop_duration": to_minutes(hours=15), "required_profit": 0.02, # If profit < 2% this will activate for a pair }, { "method": "LowProfitPairs", "lookback_period": to_minutes(hours=6), "trade_limit": 4, # Considering all pairs that have a minimum of 4 trades "stop_duration": to_minutes(minutes=30), "required_profit": 0.01, # If profit < 1% this will activate for a pair }, ] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> 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"] 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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.buy_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), ["buy", "buy_tag"]] = (1, "buy_signal_1") if self.buy_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), ["buy", "buy_tag"]] = (1, "buy_signal_2") if self.buy_signal_3: conditions = [ dataframe["low"] < dataframe["s3_bb_lowerband"], dataframe["volume"] > 0, ] dataframe.loc[reduce(lambda x, y: x & y, conditions), ["buy", "buy_tag"]] = (1, "buy_signal_3") if not self.buy_signal_1 and not self.buy_signal_2 and not self.buy_signal_3: dataframe.loc[(), "buy"] = 0 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(), "sell"] = 0 return dataframe def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs ) -> float: if current_profit > 0.2: return 0.04 if current_profit > 0.1: return 0.03 if current_profit > 0.06: return 0.02 if current_profit > 0.03: return 0.01 if current_profit <= -0.10: if trade.open_date_utc + timedelta(hours=60) < current_time: return current_profit / 1.75 if current_profit <= -0.08: if trade.open_date_utc + timedelta(hours=120) < current_time: return current_profit / 1.70 return -1