# source: https://raw.githubusercontent.com/vigoferrel/qbtc-unified/ef7ffc8f45cc7ab041884cd0c08a2043acc1f0cb/freqtrade_qbtc/strategies/QBTCOptimalV2.py """ Github_vigoferrel_qbtc_unified__QBTCOptimalV2__20251009_202533 - Versión Mejorada Basada en Resultados Reales ============================================================ Filosofía: Solo mantener las señales que FUNCIONAN, eliminar las problemáticas. Optimizada en base a backtests que mostraron +0.04% ganancia, 53% win rate. CAMBIOS PRINCIPALES: - ELIMINADA: technical_breakdown (causaba la mayoría de pérdidas) - MEJORADA: Condiciones de entrada más selectivas - SIMPLIFICADA: Menos señales conflictivas - OPTIMIZADA: Para timeframe 5m, períodos cortos (10-15 días) Target: >60% win rate, >8% profit mensual, profit factor >1.5 Creado: 2025-09-10 Autor: QBTC Trading System - Optimal V2 (Evidence-Based) """ import talib.abstract as ta import pandas as pd import numpy as np from pandas import DataFrame from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import DecimalParameter, IntParameter import logging from datetime import datetime class Github_vigoferrel_qbtc_unified__QBTCOptimalV2__20251009_202533(IStrategy): # Metadatos optimizados basados en resultados INTERFACE_VERSION = 3 timeframe = '5m' # CONFIRMADO: Mejor timeframe can_short = False stoploss = -0.025 # Slightly tighter - less room for error # ROI más agresivo para capturar ganancias rápidamente minimal_roi = { "0": 0.08, # 8% inmediato para momentum muy fuerte "5": 0.05, # 5% después de 5min (más rápido) "15": 0.03, # 3% después de 15min "45": 0.02, # 2% después de 45min "120": 0.01 # 1% después de 2h } # Configuración conservadora para mayor consistencia max_open_trades = 4 # Reduced from 6 - better risk management stake_currency = 'USDT' stake_amount = 100 use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # CHANGED: Respect ROI for better profit taking # Orders optimized for speed and reliability order_types = { 'entry': 'market', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': True, 'stoploss_on_exchange_interval': 30 } order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' # Changed from IOC - more reliable fills } # PARÁMETROS OPTIMIZADOS - Más selectivos basados en evidencia # OVERSOLD BOUNCE - Más estricto rsi_oversold = IntParameter(20, 35, default=28, space="buy") # LOWER - more selective volume_spike_factor = DecimalParameter(1.8, 3.0, default=2.2, space="buy") # HIGHER - need strong volume # BREAKOUT STRATEGY - Balanced breakout_periods = IntParameter(15, 25, default=20, space="buy") breakout_volume_factor = DecimalParameter(1.5, 2.8, default=2.0, space="buy") # TREND FOLLOWING - More selective trend_strength = DecimalParameter(0.012, 0.025, default=0.018, space="buy") # HIGHER - stronger trends pullback_rsi_max = IntParameter(50, 65, default=58, space="buy") # LOWER - less overbought # EXIT PARAMETERS - Fine tuned rsi_overbought = IntParameter(78, 88, default=83, space="sell") volume_threshold_low = DecimalParameter(0.3, 0.6, default=0.4, space="sell") def __init__(self, config: dict) -> None: super().__init__(config) self.logger = logging.getLogger(__name__) # Enhanced tracking self._strategy_stats = { 'oversold_trades': 0, 'breakout_trades': 0, 'trend_trades': 0, 'roi_exits': 0, 'signal_exits': 0, 'stop_exits': 0 } def informative_pairs(self): return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Indicadores esenciales - eliminamos complejidad innecesaria""" # Core indicators only dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=9) # Faster RSI # Essential EMAs dataframe['ema8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) # MACD for trend confirmation macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Volume analysis - key for crypto dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20) dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma'] # Bollinger Bands for volatility bollinger = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe['bb_lower'] = bollinger['lowerband'] dataframe['bb_middle'] = bollinger['middleband'] dataframe['bb_upper'] = bollinger['upperband'] dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lower']) / (dataframe['bb_upper'] - dataframe['bb_lower']) # Price action levels dataframe['high_20'] = dataframe['high'].rolling(window=self.breakout_periods.value).max() dataframe['low_20'] = dataframe['low'].rolling(window=self.breakout_periods.value).min() # Trend strength calculation dataframe['trend_strength'] = (dataframe['ema8'] - dataframe['ema50']) / dataframe['ema50'] # Market structure dataframe['higher_high'] = (dataframe['high'] > dataframe['high'].shift(1)) dataframe['higher_low'] = (dataframe['low'] > dataframe['low'].shift(1)) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ EVIDENCE-BASED ENTRY SYSTEM Only keep what works: oversold_bounce, breakout_momentum, trend_pullback """ pair = metadata['pair'] # Initialize dataframe.loc[:, 'enter_long'] = 0 dataframe.loc[:, 'enter_tag'] = '' # STRATEGY 1: OVERSOLD BOUNCE - Much more selective oversold_conditions = ( (dataframe['rsi'] <= self.rsi_oversold.value) & # Very oversold (dataframe['volume_ratio'] >= self.volume_spike_factor.value) & # Strong volume (dataframe['bb_percent'] <= 0.2) & # Deep in lower BB (dataframe['close'] > dataframe['open']) & # Green candle (bounce confirmation) (dataframe['close'] > dataframe['low'] * 1.01) & # Price recovering from low (dataframe['macd'] > dataframe['macdsignal'].shift(1)) & # MACD improving (dataframe['ema8'] > dataframe['ema8'].shift(2)) & # EMA8 starting to turn up (dataframe['higher_low']) # Market structure: higher low ) dataframe.loc[oversold_conditions, 'enter_long'] = 1 dataframe.loc[oversold_conditions, 'enter_tag'] = 'oversold_bounce' # STRATEGY 2: BREAKOUT MOMENTUM - Better confirmation breakout_conditions = ( (dataframe['close'] > dataframe['high_20'].shift(1)) & # Clean breakout (dataframe['volume_ratio'] >= self.breakout_volume_factor.value) & # Volume confirmation (dataframe['rsi'] > 50) & (dataframe['rsi'] < 75) & # Good RSI range (dataframe['macd'] > dataframe['macdsignal']) & # MACD positive (dataframe['ema8'] > dataframe['ema21']) & # Short term uptrend (dataframe['close'] > dataframe['open']) & # Green candle (dataframe['higher_high']) # Market structure: higher high ) # Don't overwrite oversold signals breakout_mask = breakout_conditions & (dataframe['enter_long'] == 0) dataframe.loc[breakout_mask, 'enter_long'] = 1 dataframe.loc[breakout_mask, 'enter_tag'] = 'breakout_momentum' # STRATEGY 3: TREND PULLBACK - More selective trend_conditions = ( (dataframe['trend_strength'] >= self.trend_strength.value) & # Strong uptrend (dataframe['close'] > dataframe['ema50']) & # Above major EMA (dataframe['close'] <= dataframe['ema21']) & # Pullback to EMA21 (dataframe['close'] > dataframe['ema21'] * 0.995) & # Not too deep below EMA21 (dataframe['rsi'] <= self.pullback_rsi_max.value) & # Not overbought (dataframe['rsi'] > 40) & # Not oversold either (dataframe['volume_ratio'] >= 1.0) & # Decent volume (dataframe['macd'] > dataframe['macdsignal']) & # MACD still positive (dataframe['bb_percent'] > 0.3) & (dataframe['bb_percent'] < 0.7) # Mid BB range ) # Don't overwrite other signals trend_mask = trend_conditions & (dataframe['enter_long'] == 0) dataframe.loc[trend_mask, 'enter_long'] = 1 dataframe.loc[trend_mask, 'enter_tag'] = 'trend_pullback' # Logging with better metrics if oversold_conditions.any(): self._strategy_stats['oversold_trades'] += 1 self.logger.info(f"🎯 OVERSOLD BOUNCE: {pair} | RSI={dataframe['rsi'].iloc[-1]:.1f} | Vol={dataframe['volume_ratio'].iloc[-1]:.1f}x | BB={dataframe['bb_percent'].iloc[-1]:.2f}") if breakout_mask.any(): self._strategy_stats['breakout_trades'] += 1 self.logger.info(f"🚀 BREAKOUT: {pair} | Price=${dataframe['close'].iloc[-1]:.4f} | High20=${dataframe['high_20'].iloc[-1]:.4f} | Vol={dataframe['volume_ratio'].iloc[-1]:.1f}x") if trend_mask.any(): self._strategy_stats['trend_trades'] += 1 self.logger.info(f"📈 TREND PULLBACK: {pair} | Strength={dataframe['trend_strength'].iloc[-1]:.3f} | RSI={dataframe['rsi'].iloc[-1]:.1f} | Price=${dataframe['close'].iloc[-1]:.4f}") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ SIMPLIFIED EXIT SYSTEM - Remove problematic technical_breakdown Keep only: overbought_exit, volume_collapse """ dataframe.loc[:, 'exit_long'] = 0 dataframe.loc[:, 'exit_tag'] = '' # EXIT 1: Overbought with momentum loss overbought_exit = ( (dataframe['rsi'] >= self.rsi_overbought.value) & # Strong overbought (dataframe['rsi_fast'] < dataframe['rsi_fast'].shift(1)) & # RSI declining (dataframe['volume_ratio'] < 1.2) & # Volume not supporting (dataframe['bb_percent'] > 0.8) # Near upper BB ) dataframe.loc[overbought_exit, 'exit_long'] = 1 dataframe.loc[overbought_exit, 'exit_tag'] = 'overbought_exit' # EXIT 2: Volume collapse - Strong distribution signal volume_collapse = ( (dataframe['volume_ratio'] < self.volume_threshold_low.value) & # Very low volume (dataframe['close'] < dataframe['close'].shift(1)) & # Price declining (dataframe['rsi'] > 70) & # Still high RSI (distribution) (dataframe['bb_percent'] > 0.75) & # High in BB range (dataframe['ema8'] < dataframe['ema8'].shift(1)) # EMA8 turning down ) volume_mask = volume_collapse & (dataframe['exit_long'] == 0) dataframe.loc[volume_mask, 'exit_long'] = 1 dataframe.loc[volume_mask, 'exit_tag'] = 'volume_collapse' # NOTE: technical_breakdown ELIMINATED - was causing most losses return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: """ CONSERVATIVE TRAILING STOP """ # Base stoploss stoploss_value = self.stoploss # After 3% profit, use trailing stop if current_profit > 0.03: # Trail at 1.5% distance trail_distance = 0.015 stoploss_value = current_profit - trail_distance return stoploss_value def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: str, side: str, **kwargs) -> bool: """ FINAL VALIDATION - Keep simple """ # Log performance stats periodically total_trades = sum(self._strategy_stats.values()) if total_trades > 0 and total_trades % 10 == 0: self.logger.info(f"📊 STATS - Total: {total_trades} | " f"Oversold: {self._strategy_stats['oversold_trades']} | " f"Breakout: {self._strategy_stats['breakout_trades']} | " f"Trend: {self._strategy_stats['trend_trades']}") return True def custom_exit(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> tuple: """ SIMPLE PROFIT MANAGEMENT """ # Take partial profits at 6% gain if current_profit >= 0.06: self.logger.info(f"💰 PARTIAL EXIT: {pair} at {current_profit:.1%} profit") return 0.4, 'partial_6pct' # Exit 40% of position return None