# source: https://raw.githubusercontent.com/vigoferrel/qbtc-unified/ef7ffc8f45cc7ab041884cd0c08a2043acc1f0cb/freqtrade_qbtc/strategies/QBTCSimple.py """ Github_vigoferrel_qbtc_unified__QBTCSimple__20251009_202533 - Back to Basics: Solo lo que funciona ================================================= FILOSOFÍA SIMPLE: - Basado 100% en EVIDENCIA de backtests anteriores - QBTCOptimal mostró +0.04% en períodos cortos - ROI exits tenían 100% win rate - El problema son los stop losses agresivos CAMBIOS SIMPLES: ✅ Keep: ROI targets que funcionan (100% win rate) ✅ Keep: Señales oversold_bounce y trend_pullback que funcionaron ❌ Remove: technical_breakdown que causaba pérdidas 🔧 Fix: Stop loss menos agresivo (3% → 4%) 🔧 Fix: Condiciones más selectivas Target: >60% win rate, >5% profit mensual Creado: 2025-09-10 Autor: QBTC Trading System - Simple 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__QBTCSimple__20251009_202533(IStrategy): # Evidence-based settings INTERFACE_VERSION = 3 timeframe = '5m' # Proven best timeframe can_short = False stoploss = -0.04 # WIDER: 4% instead of 2.5% (less aggressive) # ROI from successful tests - KEEP WHAT WORKS minimal_roi = { "0": 0.06, # 6% immediate "10": 0.035, # 3.5% after 10min "30": 0.02, # 2% after 30min "90": 0.015, # 1.5% after 1.5h "240": 0.01 # 1% after 4h } # Conservative settings max_open_trades = 4 stake_currency = 'USDT' stake_amount = 100 use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # RESPECT ROI - they worked! # Simple orders order_types = { 'entry': 'market', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': True } order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } # SIMPLE PARAMETERS - Only the essentials rsi_oversold = IntParameter(25, 35, default=30, space="buy") volume_spike = DecimalParameter(1.5, 2.5, default=2.0, space="buy") trend_strength = DecimalParameter(0.01, 0.03, default=0.02, space="buy") # EXIT PARAMETERS rsi_overbought = IntParameter(75, 85, default=80, space="sell") volume_exhaustion = DecimalParameter(0.3, 0.6, default=0.45, space="sell") def __init__(self, config: dict) -> None: super().__init__(config) self.logger = logging.getLogger(__name__) self._stats = { 'oversold_entries': 0, 'trend_entries': 0, 'roi_exits': 0, 'signal_exits': 0 } def informative_pairs(self): return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """SIMPLE INDICATORS - Only what we need""" # Basic RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=9) # Simple EMAs dataframe['ema_21'] = ta.EMA(dataframe, timeperiod=21) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) # Volume dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20) dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma'] # MACD for trend macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] # Bollinger Bands bollinger = ta.BBANDS(dataframe, timeperiod=20) dataframe['bb_lower'] = bollinger['lowerband'] dataframe['bb_upper'] = bollinger['upperband'] dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lower']) / (dataframe['bb_upper'] - dataframe['bb_lower']) # Simple trend strength dataframe['trend_strength'] = (dataframe['ema_21'] - dataframe['ema_50']) / dataframe['ema_50'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ SIMPLE ENTRY LOGIC - Only proven signals Based on evidence from successful backtests """ pair = metadata['pair'] # Initialize dataframe.loc[:, 'enter_long'] = 0 dataframe.loc[:, 'enter_tag'] = '' # ENTRY 1: OVERSOLD BOUNCE - Proven to work oversold_bounce = ( (dataframe['rsi'] <= self.rsi_oversold.value) & # Oversold (dataframe['volume_ratio'] >= self.volume_spike.value) & # Volume spike (dataframe['bb_percent'] <= 0.25) & # Near BB lower (dataframe['close'] > dataframe['open']) & # Green candle (dataframe['macd'] > dataframe['macdsignal'].shift(1)) & # MACD improving (dataframe['close'] > dataframe['bb_lower'] * 1.002) # Above BB lower ) dataframe.loc[oversold_bounce, 'enter_long'] = 1 dataframe.loc[oversold_bounce, 'enter_tag'] = 'oversold_bounce' # ENTRY 2: TREND PULLBACK - Also proven to work trend_pullback = ( (dataframe['trend_strength'] >= self.trend_strength.value) & # Uptrend (dataframe['close'] > dataframe['ema_100']) & # Above major EMA (dataframe['close'] <= dataframe['ema_21']) & # Pullback to EMA21 (dataframe['close'] > dataframe['ema_21'] * 0.997) & # Not far below (dataframe['rsi'] > 40) & (dataframe['rsi'] < 65) & # Good RSI range (dataframe['volume_ratio'] >= 1.0) & # Decent volume (dataframe['macd'] > dataframe['macdsignal']) # MACD positive ) # Don't overwrite oversold signals pullback_mask = trend_pullback & (dataframe['enter_long'] == 0) dataframe.loc[pullback_mask, 'enter_long'] = 1 dataframe.loc[pullback_mask, 'enter_tag'] = 'trend_pullback' # Logging if oversold_bounce.any(): self._stats['oversold_entries'] += 1 self.logger.info(f"🎯 OVERSOLD BOUNCE: {pair} | RSI={dataframe['rsi'].iloc[-1]:.1f} | Vol={dataframe['volume_ratio'].iloc[-1]:.1f}x") if pullback_mask.any(): self._stats['trend_entries'] += 1 self.logger.info(f"📈 TREND PULLBACK: {pair} | Strength={dataframe['trend_strength'].iloc[-1]:.3f} | RSI={dataframe['rsi'].iloc[-1]:.1f}") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ SIMPLE EXIT LOGIC - Remove problematic exits Keep only what works reliably """ dataframe.loc[:, 'exit_long'] = 0 dataframe.loc[:, 'exit_tag'] = '' # EXIT 1: Overbought momentum loss overbought_exit = ( (dataframe['rsi'] >= self.rsi_overbought.value) & # Overbought (dataframe['rsi_fast'] < dataframe['rsi_fast'].shift(1)) & # RSI declining (dataframe['volume_ratio'] < 1.0) # Volume drying ) dataframe.loc[overbought_exit, 'exit_long'] = 1 dataframe.loc[overbought_exit, 'exit_tag'] = 'overbought_exit' # EXIT 2: Volume exhaustion - ONLY when really obvious volume_exhaustion = ( (dataframe['volume_ratio'] < self.volume_exhaustion.value) & # Very low volume (dataframe['bb_percent'] > 0.8) & # Near BB upper (dataframe['close'] < dataframe['close'].shift(1)) & # Price declining (dataframe['rsi'] > 70) # Still elevated RSI ) volume_mask = volume_exhaustion & (dataframe['exit_long'] == 0) dataframe.loc[volume_mask, 'exit_long'] = 1 dataframe.loc[volume_mask, 'exit_tag'] = 'volume_exhaustion' # NOTE: technical_breakdown REMOVED - it was causing losses return dataframe 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: """ Simple validation """ # Log stats periodically total_entries = self._stats['oversold_entries'] + self._stats['trend_entries'] if total_entries > 0 and total_entries % 15 == 0: oversold_rate = (self._stats['oversold_entries'] / total_entries) * 100 trend_rate = (self._stats['trend_entries'] / total_entries) * 100 self.logger.info(f"📊 ENTRY DISTRIBUTION: Oversold {oversold_rate:.1f}% | Trend {trend_rate:.1f}%") self.logger.info(f"✅ SIMPLE ENTRY: {pair} | {entry_tag} | ${rate:.4f}") return True