# source: https://raw.githubusercontent.com/vaskosmihaylov/nfi-custom-strategies/a0e196e172735ed559754fa5a80759c30ce1c331/user_data/strategies/e0v1e/E0V1E_Shorts.py """ Github_vaskosmihaylov_nfi_custom_strategies__E0V1E_Shorts__20260127_103831 Strategy A shorts-only variant of the E0V1E strategy, designed to profit in bear markets and during overbought conditions. This strategy mirrors the successful long-only approach but inverts the logic for short positions. Original E0V1E Strategy (Longs): - Uses EWO (Elliott Wave Oscillator) for momentum divergence - Simple RSI and EMA-based entries - Fast 5m timeframe Strategy Concept: Uses Elliott Wave Oscillator (EWO) to identify momentum divergence for short entries. EWO measures the percentage difference between fast and slow EMAs. Entry Conditions (OR logic - either triggers entry): 1. Short EWO: Price above EMA during uptrend (inverted from long logic) 2. Short Buy_1: RSI and SMA-based mean reversion shorts Exit Conditions: - Signal: Price falls below EMA - ROI: 7% initial target (more conservative than longs' 10%) - Stop Loss: -18.9% (tighter than longs due to short squeeze risk) - Custom Stoploss: Dynamic trailing Key Differences from Long Strategy: - Tighter stop loss: -18.9% vs -99% (shorts are riskier) - Lower ROI targets: 7% vs 10% (faster profit-taking) - Inverted entry/exit logic - Max 4 short positions: Position limit enforcement via confirm_trade_entry Author: Derived from E0V1E Version: 1.0.0 """ from datetime import datetime, timedelta import logging from typing import Optional, Union import freqtrade.vendor.qtpylib.indicators as qtpylib 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 logger = logging.getLogger(__name__) def ewo(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif class Github_vaskosmihaylov_nfi_custom_strategies__E0V1E_Shorts__20260127_103831(IStrategy): """ Shorts-only Elliott Wave Oscillator strategy for bear markets. This strategy is designed to run in PARALLEL with E0V1E (longs) in separate containers to evaluate short performance independently. """ INTERFACE_VERSION = 3 can_short = True # More conservative ROI for shorts (30% lower than longs) minimal_roi = { "0": 0.07 # 7% profit target (vs 10% for longs) } timeframe = '5m' process_only_new_candles = True startup_candle_count = 20 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 stop loss for shorts (vs -0.99 for longs) stoploss = -0.99 # Very wide - will be tightened by custom_stoploss after 48h protection period = -0.189 # Custom stoploss use_custom_stoploss = True # Shorts-specific parameters (inverted from longs) is_optimize_ewo = True sell_rsi_fast = IntParameter(50, 65, default=55, space='buy', optimize=is_optimize_ewo) sell_rsi = IntParameter(65, 85, default=65, space='buy', optimize=is_optimize_ewo) sell_ewo = DecimalParameter(-5, 6.0, default=5.585, space='buy', optimize=is_optimize_ewo) sell_ema_low = DecimalParameter(1.01, 1.1, default=1.058, space='buy', optimize=is_optimize_ewo) sell_ema_high = DecimalParameter(0.8, 1.05, default=0.916, space='buy', optimize=is_optimize_ewo) is_optimize_32 = True sell_rsi_fast_32 = IntParameter(50, 80, default=54, space='buy', optimize=is_optimize_32) sell_rsi_32 = IntParameter(50, 85, default=81, space='buy', optimize=is_optimize_32) sell_sma15_32 = DecimalParameter(1.0, 1.1, default=1.058, decimals=3, space='buy', optimize=is_optimize_32) sell_cti_32 = DecimalParameter(0, 1, default=0.86, decimals=2, space='buy', optimize=is_optimize_32) is_optimize_deadfish = True cover_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='sell', optimize=is_optimize_deadfish) cover_deadfish_profit = DecimalParameter(0.05, 0.15, default=0.05, space='sell', optimize=is_optimize_deadfish) cover_deadfish_bb_factor = DecimalParameter(0.80, 1.10, default=1.0, space='sell', optimize=is_optimize_deadfish) cover_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, space='sell', optimize=is_optimize_deadfish) cover_fastx = IntParameter(0, 50, default=25, space='sell', optimize=True) def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: Optional[str], side: str, **kwargs) -> bool: """ Enforce maximum short position limit. This callback is executed before every entry to ensure we don't exceed max_short_trades positions. Essential for risk management in crypto shorts. Args: side: Trade direction ('long' or 'short') Returns: bool: True to confirm entry, False to reject """ # Only allow shorts in this strategy if side == "long": return False # Reject any long signals # Count current open short positions short_count = 0 trades = Trade.get_trades_proxy(is_open=True) for trade in trades: if trade.is_short: short_count += 1 # Check if we can open another short if short_count >= self.max_short_trades: return False # Already at max short positions return True # Confirm entry def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # buy_1 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) # ewo indicators dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['EWO'] = ewo(dataframe, 50, 200) # profit sell indicators stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # loss sell indicators bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] dataframe['bb_width'] = ( (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' # Inverted from long logic is_ewo_short = ( (dataframe['rsi_fast'] > self.sell_rsi_fast.value) & (dataframe['close'] > dataframe['ema_8'] * self.sell_ema_low.value) & (dataframe['EWO'] < self.sell_ewo.value) & (dataframe['close'] > dataframe['ema_16'] * self.sell_ema_high.value) & (dataframe['rsi'] > self.sell_rsi.value) ) # Inverted from buy_1 logic short_1 = ( (dataframe['rsi_slow'] > dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] > self.sell_rsi_fast_32.value) & (dataframe['rsi'] < self.sell_rsi_32.value) & (dataframe['close'] > dataframe['sma_15'] * self.sell_sma15_32.value) & (dataframe['cti'] > self.sell_cti_32.value) ) conditions.append(is_ewo_short) dataframe.loc[is_ewo_short, 'enter_tag'] += 'ewo_short' conditions.append(short_1) dataframe.loc[short_1, 'enter_tag'] += 'short_1' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'enter_short'] = 1 return dataframe def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: """ Indicator-based trailing stoploss for PROFITABLE shorts. 3-Layer Architecture: Layer 1: Base stoploss (-0.99) = Safety net Layer 2: custom_stoploss (THIS) = Indicator-based profit taking Layer 3: custom_exit = Time-based unclog + deadfish for losers Logic: - Hours 0-48: Don't tighten (return 1.0) - protection window - Hours 48+: Trail PROFITABLE shorts based on RSI/Stochastic indicators - Uses oversold levels to detect bottoms (time to take profit) - Tightens stops for EWO entries at high profit levels - Does NOT handle losing trades (custom_exit does that) Note: Indicator logic is INVERTED for shorts: - Profitable short = price went DOWN (RSI low, fastk low) - Low RSI/fastk = oversold = price bottom = SHORT should exit Args: pair: Trading pair trade: Trade object current_time: Current timestamp current_rate: Current price current_profit: Current profit/loss ratio **kwargs: Additional arguments Returns: float: Stoploss percentage (must be MORE NEGATIVE than base to tighten) """ # Calculate trade duration in hours trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600 # Phase 1: First 48 hours - PROTECTION WINDOW # Return 1.0 to keep base stoploss (-0.99) without tightening if trade_duration < 48: return 1.0 # Phase 2: After 48 hours - Trail ONLY profitable shorts # Losing/zombie trades are handled by custom_exit # Only apply indicator logic if trade is profitable if current_profit > 0.01: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() enter_tag = '' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: enter_tag = trade.enter_tag enter_tags = enter_tag.split() # Tight trailing for EWO entries at 5% profit if "ewo_short" in enter_tags and current_profit >= 0.05: return -0.01 # 1% stop # Exit on strong oversold when profitable (price might bounce back up) if current_candle["rsi"] < 15: return -0.01 # 1% stop if current_candle["fastk"] < self.cover_fastx.value: return -0.01 # 1% stop # Default: Return 1.0 to keep base stoploss active # Losing trades will be handled by custom_exit after 48h return 1.0 def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]: """ 3-Layer Exit System for Shorts: Layer 1: Base stoploss (-0.99) = Safety net (almost never hit) Layer 2: custom_stoploss = Indicator-based trailing for PROFITABLE shorts Layer 3: custom_exit (THIS) = Time-based unclog + deadfish for LOSING/ZOMBIE shorts Logic: - Hours 0-48: No forced exits, let position develop - After 48 hours: - If losing > 4%: Force exit ('unclog') - cut losses - If at breakeven (-0.5% to +0.5%): Force exit ('zombie') - free up capital - Check deadfish conditions (low volatility dead trade) - Otherwise: Let indicators handle it (custom_stoploss) Args: pair: Trading pair trade: Trade object current_time: Current timestamp current_rate: Current price current_profit: Current profit/loss ratio **kwargs: Additional arguments Returns: Optional[Union[str, bool]]: Exit reason string or None """ # Calculate trade duration in hours trade_duration_hours = (current_time - trade.open_date_utc).total_seconds() / 3600 # Phase 1: First 48 hours - NO forced exits if trade_duration_hours < 48: return None # Phase 2: After 48 hours - Unclog losing/zombie trades # Unclog: Force exit if losing > 4% (worst case scenario) if current_profit < -0.04: return 'unclog' # Zombie: Force exit if stuck at breakeven after 48h if -0.005 <= current_profit <= 0.005: return 'zombie' # Deadfish detection (low volatility dead trade) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() # stoploss - deadfish (inverted for shorts) if ((current_profit < self.cover_deadfish_profit.value) and (current_candle['bb_width'] < self.cover_deadfish_bb_width.value) and (current_candle['close'] < current_candle['bb_middleband2'] * self.cover_deadfish_bb_factor.value) and (current_candle['volume_mean_12'] < current_candle[ 'volume_mean_24'] * self.cover_deadfish_volume_factor.value)): logger.info(f"{pair} cover_stoploss_deadfish at {current_profit*100}") return "cover_stoploss_deadfish" # Profitable trades: Let custom_stoploss handle trailing return None def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: #dataframe.loc[(), ['exit_short', 'exit_tag']] = (0, 'short_out') return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: """ Fixed 3x leverage for all short trades. Returns: float: Leverage multiplier (3.0 = 3x) """ return 3.0