# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/UpdatedStratBW.py from pandas_ta import trend from freqtrade.strategy import IStrategy import talib.abstract as ta import pandas as pd from pandas import DataFrame from freqtrade.persistence import Trade from datetime import datetime, timedelta, timezone import pandas_ta as pta class Github_remiotore_ccxt_freqtrade__UpdatedStratBW__20260111_210550(IStrategy): INTERFACE_VERSION = 3 timeframe = '5m' process_only_new_candles = True stake_currency = 'USDT' stake_amount = 'unlimited' use_exit_signal = True stoploss = -0.10 trailing_stop = True trailing_stop_positive = 0.10 # 0.5% profit lock trailing_stop_positive_offset = 0.15 # Offset to activate trailing stop ignore_roi_if_entry_signal = True use_custom_exit = True order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": True, "take_profit": "limit" } startup_candle_count = 800 minimal_roi = { "120": 0.07, "60": 0.05, "0": 0.12 } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['wma_20'] = ta.WMA(dataframe, timeperiod=20) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14) supertrend = pta.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=7, multiplier=3.0) dataframe['supertrend'] = supertrend['SUPERT_7_3.0'] dataframe['supertrend_direction'] = supertrend['SUPERTd_7_3.0'] dataframe['supertrend_long'] = supertrend['SUPERTl_7_3.0'] dataframe['supertrend_short'] = supertrend['SUPERTs_7_3.0'] adx = pta.adx(high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], length=14) dataframe['adx'] = adx['ADX_14'] dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['vwma'] = dataframe['close'].rolling(window=10).mean() dataframe['dynamic_stop'] = dataframe['atr'] * 1.5 dataframe['adx_scaled_stop'] = dataframe['adx'] / 10 bbands = ta.BBANDS(dataframe, length=20) dataframe['BBU_20_2.0'] = bbands['upperband'] # Upper Bollinger Band dataframe['BBL_20_2.0'] = bbands['lowerband'] # Lower Bollinger Band dataframe['BBM_20_2.0'] = bbands['middleband'] # Middle Bollinger Band return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['ema_50'] > dataframe['ema_100']) & (dataframe['macd'] > dataframe['macdsignal']) & (dataframe['rsi'] < 55) & (dataframe['mfi'] < 45) & (dataframe['wma_20'] > dataframe['ema_50']) & (dataframe['close'] > dataframe['supertrend_long']) & (dataframe['vwma'] > dataframe['vwma'].shift(1)) & (dataframe['atr'] > dataframe['atr'].rolling(14).mean() * 1.1)& (dataframe['close'] < dataframe['BBU_20_2.0']) ), ['enter_long', 'enter_tag'] ] = (1, 'bullish_crossover') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['ema_50'] < dataframe['ema_100']) & (dataframe['macd'] < dataframe['macdsignal']) & (dataframe['rsi'] > 65) & (dataframe['mfi'] > 65) & (dataframe['close'] > dataframe['supertrend_long'] * 1.08) & (dataframe['vwma'] < dataframe['vwma'].shift(1)) & # Weakening volume momentum (dataframe['atr'] < dataframe['atr'].rolling(14).mean() * 0.8) & (dataframe['close'] > dataframe['BBU_20_2.0']) ), ['exit_long', 'exit_tag'] ] = (1, 'bearish_reversal') # **New Adaptive Trailing Stop-Loss** - Only exit if close drops below the adjusted ATR-based trailing stop dataframe['trailing_stop'] = dataframe['close'] - (dataframe['atr'] * 2.0) dataframe.loc[ (dataframe['close'] < dataframe['trailing_stop']), ['exit_long', 'exit_tag'] ] = (1, 'trailing_stop_loss') return dataframe def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] atr = last_candle["atr"] avg_volatility = dataframe["atr"].rolling(50).mean().iloc[-1] adx = last_candle["adx"] # Default ROI Target roi_target = 0.10 # Dynamic ROI Logic if atr > avg_volatility * 1.2 and adx > 30: roi_target = 0.20 # High volatility + Strong trend → Hold longer elif atr < avg_volatility * 0.8 and adx < 20: roi_target = 0.05 # Low volatility + Weak trend → Exit early return current_profit >= roi_target # Callback: Custom Stoploss use_custom_stoploss = True def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) atr = dataframe.iloc[-1]["atr"] adx = dataframe.iloc[-1]["adx"] # ATR-Based Dynamic Stop dynamic_stop = max(-2.0 * atr, -0.10) # ADX-Based Stop (Strong trends allow more room) adx_stop = max(-0.5 * atr, -adx / 100) # Adaptive Stop-Loss Logic return min(dynamic_stop, adx_stop) # Callback: Confirm Trade Entry def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: str | None, side: str, **kwargs) -> bool: return True # Always allow trade entry # Callback: Confirm Trade Exit def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: return True # Always allow trade exit # Callback: Order Filled def order_filled(self, pair: str, trade: Trade, order, current_time: datetime, **kwargs) -> None: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1].squeeze() trade.set_custom_data(key="entry_candle_high", value=last_candle["high"]) # Time-Based Stop for Early Loss Cuts trade_duration = (current_time - trade.open_date_utc).days if trade_duration > 4 and current_profit < 0.00: return max(-0.5 * atr, current_profit) # Use dynamic ATR stop on losers # Break-Even Stop (Secures Small Gains) if current_profit >= 0.02: # Adjusted from 4% to 3.5% return max(0.00, current_profit * 0.5) # Move SL to secure partial profit return dynamic_stop # Default ATR-based stop # Callback: Confirm Trade Entry def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: str | None, side: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] # Avoid illiquid or highly volatile trades if last_candle["atr"] > last_candle["close"] * 0.05: # 5% ATR is too volatile return False return True # Only enter if conditions are favorable # Callback: Confirm Trade Exit def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: return True # Always allow trade exit # Callback: Order Filled def order_filled(self, pair: str, trade: Trade, order, current_time: datetime, **kwargs) -> None: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1].squeeze() trade.set_custom_data(key="entry_candle_high", value=last_candle["high"])