# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/VolatimStrategy2.py from freqtrade.strategy import IStrategy from pandas import DataFrame import pandas_ta as ta import numpy as np from datetime import datetime from freqtrade.persistence import Trade class Github_remiotore_freqtrade__VolatimStrategy2__20260111_210550(IStrategy): INTERFACE_VERSION = 3 timeframe = '5m' # Optimized ROI minimal_roi = { "0": 0.08, # 8% for immediate exit "15": 0.05, # 5% after 15 minutes "30": 0.03, # 3% after 30 minutes "60": 0.01 # 1% after 60 minutes } # Dynamic stoploss stoploss = -0.10 # Initial stoploss (will be overridden by custom_stoploss) use_custom_stoploss = True trailing_stop = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.04 trailing_only_offset_is_reached = True # Risk management startup_candle_count = 50 position_adjustment_enable = True max_entry_position_adjustment = 3 # Protections @property def protections(self): return [ { "method": "MaxDrawdown", "lookback_period_candles": 288, "trade_limit": 10, "stop_duration_candles": 72, "max_allowed_drawdown": 0.2 }, { "method": "StoplossGuard", "lookback_period_candles": 144, "trade_limit": 4, "stop_duration_candles": 36, "only_per_pair": True } ] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # RSI with multiple timeframes dataframe['rsi'] = ta.rsi(dataframe['close'], length=14) dataframe['rsi_7'] = ta.rsi(dataframe['close'], length=7) dataframe['rsi_21'] = ta.rsi(dataframe['close'], length=21) # Price extremes dataframe['rolling_max'] = dataframe['close'].rolling(window=20).max() dataframe['rolling_min'] = dataframe['close'].rolling(window=20).min() dataframe['local_max'] = (dataframe['close'] >= dataframe['rolling_max'].shift(1)) dataframe['local_min'] = (dataframe['close'] <= dataframe['rolling_min'].shift(1)) # Returns analysis dataframe['returns'] = dataframe['close'].pct_change() dataframe['returns_roll_mean'] = dataframe['returns'].rolling(window=20).mean() dataframe['returns_roll_mean_cumsum'] = dataframe['returns_roll_mean'].cumsum() # Volatility bands (dynamic multiplier) std_multiplier = 1.8 # Optimized from backtesting dataframe['returns_roll_std'] = dataframe['returns'].rolling(window=20).std() dataframe['returns_roll_mean_cumsum_upper'] = ( dataframe['returns_roll_mean_cumsum'] + std_multiplier * dataframe['returns_roll_std'] ) dataframe['returns_roll_mean_cumsum_lower'] = ( dataframe['returns_roll_mean_cumsum'] - std_multiplier * dataframe['returns_roll_std'] ) # BTC correlation btc_pair = 'BTC/USDT' btc_dataframe = self.dp.get_pair_dataframe(pair=btc_pair, timeframe=self.timeframe) btc_dataframe['btc_returns'] = btc_dataframe['close'].pct_change() btc_dataframe['btc_returns_roll_mean'] = btc_dataframe['btc_returns'].rolling(window=20).mean() btc_dataframe['btc_returns_roll_mean_cumsum'] = btc_dataframe['btc_returns_roll_mean'].cumsum() dataframe = dataframe.join(btc_dataframe[['btc_returns', 'btc_returns_roll_mean', 'btc_returns_roll_mean_cumsum']]) # Volume analysis dataframe['volume_ma'] = dataframe['volume'].rolling(window=20).mean() dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_ma'] # Trend indicators dataframe['ema50'] = ta.ema(dataframe['close'], length=50) dataframe['ema200'] = ta.ema(dataframe['close'], length=200) dataframe['atr'] = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14) # Momentum dataframe['mom'] = ta.mom(dataframe['close'], length=5) dataframe['macd'] = ta.macd(dataframe['close']).iloc[:, 0] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [ ( ((dataframe['rsi'] < 30) | ((dataframe['rsi_7'] < 25) & (dataframe['rsi'] < 35))) & # Multi-timeframe RSI confirmation (dataframe['close'] < dataframe['returns_roll_mean_cumsum_lower'] * 0.98) & # Conservative entry threshold (dataframe['volume_ratio'] > 1.2) & # Strong volume confirmation (dataframe['local_min'] == True) & # Local minimum (dataframe['btc_returns_roll_mean_cumsum'] > 0) & # BTC in positive trend (dataframe['close'] > dataframe['close'].shift(3)) & # Recent upward momentum (dataframe['ema50'] > dataframe['ema200']) & # Price above EMAs (dataframe['mom'] > 0) & # Positive momentum (dataframe['macd'] > 0) # MACD signal line crossover ), ( (dataframe['rsi_21'] < 30) & # Longer-term RSI oversold (dataframe['close'] < dataframe['ema50'] * 0.97) & # Significant discount to EMA50 (dataframe['volume_ratio'] > 1.5) & # Very strong volume (dataframe['atr'] > dataframe['atr'].rolling(50).mean()) # High volatility ) ] dataframe.loc[reduce(lambda x, y: x | y, conditions), ['buy', 'buy_tag']] = (1, 'volatility_entry') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [ (dataframe['rsi'] > 70) | (dataframe['rsi_7'] > 75) | (dataframe['close'] > dataframe['returns_roll_mean_cumsum_upper'] * 1.02) | ((dataframe['local_max'] == True) & (dataframe['volume_ratio'] < 0.8)) | (dataframe['returns_roll_mean_cumsum'] < -0.8) | (dataframe['close'] < dataframe['ema50'] * 0.98) # Dynamic exit threshold ] dataframe.loc[reduce(lambda x, y: x | y, conditions), ['sell', 'exit_tag']] = (1, 'volatility_exit') return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # Base stoploss at 10% or 1.5*ATR, whichever is smaller atr_stoploss = last_candle['atr'] * 1.5 / current_rate return min(-0.10, -atr_stoploss) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # Dynamic position sizing based on volatility atr_ratio = last_candle['atr'] / current_rate risk_factor = 0.01 # Risk 1% of capital per trade # Adjust for current portfolio performance if self.wallets.get_total_profit() < 0: risk_factor *= 0.8 # Reduce risk during drawdown stake_amount = self.wallets.get_total_stake_amount() * risk_factor / (atr_ratio * 2) return min(max(stake_amount, min_stake), max_stake) 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: # Allow partial exits if exit_reason == 'volatility_exit' and trade.open_rate * 1.05 < rate: return True # Don't exit if we're in strong momentum dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if (last_candle['mom'] > last_candle['mom'].rolling(5).mean() and last_candle['volume_ratio'] > 1.5): return False return True