# source: https://raw.githubusercontent.com/IISweetHeartII/freqtrade-strategies-lab/fee95ce63a145ad5d15d53c7c3af5d8acbebc3e2/user_data/strategies/E0V1E_LongShort.py """ Github_IISweetHeartII_freqtrade_strategies_lab__E0V1E_LongShort__20260216_081316 Strategy (Improved) ==================================== Based on E0V1E + E0V1EN backtest results analysis: - buy_new performed better than buy_1 (+15% vs -23% in 2024) - Added time-based exits from E0V1EN - Added 24h price change filter - Refined short entry conditions - Added CooldownPeriod protection Long Entry: RSI oversold + price below SMA (mean reversion) Short Entry: RSI overbought + price above SMA (mean reversion) """ from datetime import datetime, timedelta import talib.abstract as ta import pandas_ta as pta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from freqtrade.strategy import DecimalParameter, IntParameter from functools import reduce import warnings warnings.simplefilter(action="ignore", category=RuntimeWarning) class Github_IISweetHeartII_freqtrade_strategies_lab__E0V1E_LongShort__20260216_081316(IStrategy): minimal_roi = {"0": 1} timeframe = '5m' process_only_new_candles = True startup_candle_count = 300 # 288 for 24h + buffer can_short = True order_types = { 'entry': 'market', 'exit': 'market', 'emergency_exit': 'market', 'force_entry': 'market', 'force_exit': "market", 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_market_ratio': 0.99 } # Tighter stoploss than original -25% (reduces big losses) stoploss = -0.15 trailing_stop = True trailing_stop_positive = 0.002 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # ============ LONG PARAMETERS ============ # Based on buy_new success (better than buy_1) is_optimize_long = True buy_rsi_fast = IntParameter(20, 50, default=34, space='buy', optimize=is_optimize_long) buy_rsi_min = IntParameter(15, 40, default=28, space='buy', optimize=is_optimize_long) buy_sma15 = DecimalParameter(0.93, 0.99, default=0.96, decimals=3, space='buy', optimize=is_optimize_long) buy_cti = DecimalParameter(-1, 1, default=0.69, decimals=2, space='buy', optimize=is_optimize_long) # 24h price change filter (from E0V1EN) buy_24h_min_pct = DecimalParameter(-30.0, 0.0, default=-15.0, decimals=1, space='buy', optimize=True) buy_24h_max_pct = DecimalParameter(0.0, 100.0, default=50.0, decimals=1, space='buy', optimize=True) # ============ SHORT PARAMETERS ============ # Stricter conditions to only short in true overbought zones is_optimize_short = True sell_rsi_fast = IntParameter(65, 90, default=75, space='sell', optimize=is_optimize_short) sell_rsi_min = IntParameter(55, 80, default=65, space='sell', optimize=is_optimize_short) # Add RSI lower bound sell_rsi_max = IntParameter(70, 90, default=80, space='sell', optimize=is_optimize_short) sell_sma15 = DecimalParameter(1.02, 1.10, default=1.05, decimals=3, space='sell', optimize=is_optimize_short) sell_cti_min = DecimalParameter(0.0, 0.8, default=0.3, decimals=2, space='sell', optimize=is_optimize_short) # CTI positive range only # 24h price change filter for shorts sell_24h_min_pct = DecimalParameter(-50.0, 50.0, default=10.0, decimals=1, space='sell', optimize=True) sell_24h_max_pct = DecimalParameter(50.0, 200.0, default=100.0, decimals=1, space='sell', optimize=True) # ============ EXIT PARAMETERS ============ exit_fastx_long = IntParameter(50, 100, default=84, space='sell', optimize=True) exit_fastx_short = IntParameter(0, 50, default=16, space='sell', optimize=True) exit_cci_long = IntParameter(50, 150, default=80, space='sell', optimize=True) exit_cci_short = IntParameter(-150, -50, default=-80, space='sell', optimize=True) @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 96 # 8 hours cooldown } ] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # RSI indicators dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) # 24h price change (288 candles = 24h for 5m timeframe) dataframe['24h_change_pct'] = (dataframe['close'].pct_change(periods=288) * 100) # Exit indicators stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastk'] = stoch_fast['fastk'] dataframe['cci'] = ta.CCI(dataframe, timeperiod=20) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'enter_tag'] = '' # ============ LONG ENTRY ============ # Based on buy_new logic (performed better in backtest) long_conditions = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & # RSI declining (momentum weakening) (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & # Fast RSI oversold (dataframe['rsi'] > self.buy_rsi_min.value) & # But not too extreme (avoid falling knife) (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15.value) & # Price below SMA (dataframe['cti'] < self.buy_cti.value) & # CTI filter (dataframe['24h_change_pct'] > self.buy_24h_min_pct.value) & # 24h change filter (dataframe['24h_change_pct'] < self.buy_24h_max_pct.value) ) dataframe.loc[long_conditions, 'enter_long'] = 1 dataframe.loc[long_conditions, 'enter_tag'] = 'long_mean_reversion' # ============ SHORT ENTRY ============ # Stricter conditions: only short in true overbought zones short_conditions = ( (dataframe['rsi_slow'] > dataframe['rsi_slow'].shift(1)) & # RSI rising (momentum increasing) (dataframe['rsi_fast'] > self.sell_rsi_fast.value) & # Fast RSI overbought (75+) (dataframe['rsi'] > self.sell_rsi_min.value) & # RSI lower bound (65+) - confirm overbought (dataframe['rsi'] < self.sell_rsi_max.value) & # RSI upper bound (80-) - avoid extremes (dataframe['close'] > dataframe['sma_15'] * self.sell_sma15.value) & # Price above SMA (5%+) (dataframe['cti'] > self.sell_cti_min.value) & # CTI positive range only (0.3+) (dataframe['24h_change_pct'] > self.sell_24h_min_pct.value) & # 24h filter for shorts (dataframe['24h_change_pct'] < self.sell_24h_max_pct.value) ) dataframe.loc[short_conditions, 'enter_short'] = 1 dataframe.loc[short_conditions, 'enter_tag'] = 'short_mean_reversion' return dataframe def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() if trade.is_short: # ============ SHORT EXIT LOGIC ============ # Take profit when oversold (price likely to bounce) if current_profit > 0: if current_candle["fastk"] < self.exit_fastx_short.value: return "fastk_profit_exit_short" # CCI exit for shorts (oversold = time to cover) if current_profit > -0.03: if current_candle["cci"] < self.exit_cci_short.value: return "cci_exit_short" # Time-based exits for shorts (from E0V1EN) if current_time - timedelta(hours=7) > trade.open_date_utc: if current_profit >= -0.05: return "time_exit_short_7h" if current_time - timedelta(hours=10) > trade.open_date_utc: if current_profit >= -0.10: return "time_exit_short_10h" else: # ============ LONG EXIT LOGIC ============ # Take profit when overbought if current_profit > 0: if current_candle["fastk"] > self.exit_fastx_long.value: return "fastk_profit_exit_long" # CCI exit for longs (overbought = time to sell) if current_profit > -0.03: if current_candle["cci"] > self.exit_cci_long.value: return "cci_exit_long" # Time-based exits for longs (from E0V1EN) if current_time - timedelta(hours=7) > trade.open_date_utc: if current_profit >= -0.05: return "time_exit_long_7h" if current_time - timedelta(hours=10) > trade.open_date_utc: if current_profit >= -0.10: return "time_exit_long_10h" return None def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 dataframe.loc[:, 'exit_short'] = 0 return dataframe