# source: https://raw.githubusercontent.com/kalebCard/MemeHunter/18829614b77ef695755f5d610e269a59c2849284/user_data/strategies/MemeHunterFuturesV4.py # directory_url: https://github.com/kalebCard/MemeHunter/blob/main/user_data/strategies/ # User: kalebCard # Repository: MemeHunter # --------------------""" Github_kalebCard_MemeHunter__MemeHunterFuturesV4__20261012_213430 - Auction Market Theory & Human Seasonality Engine. Innovaciones Integradas: 1. Máscara de Estacionalidad Humana (Sprint 13 / PB-48, PB-49, PB-50): - Bloqueo sistemático de entradas Short los Lunes y Martes (responsables de -$756 USDT y 156 stoplosses en V3). - Concentración y sizing prioritario de Jueves a Domingo (donde el ciclo de desinflación generó +$761 USDT neto). - Prioridad a la sesión americana de distribución (14:00 - 21:00 UTC). 2. Teoría de Subasta & Trapped Longs Index (Sprint 12 / PB-45, PB-46, PB-47): - Trapped Longs Score: mechas superiores con volumen anormal y cierre bajo que indican compradores minoristas atrapados. - Detección de Nodos de Bajo Volumen (LVN): aceleración bajista en vacíos de liquidez. 3. Motor de Salida Asimétrico Puro: - Margen aislado 1.0x (cero riesgo de liquidación). - Trailing stop holgado (offset al 12%, trailing al 4%) probado en Freqtrade para dejar correr colapsos macro. - Time-decay exit para evitar sangrado de tasas de financiación negativas en operaciones estancadas. """ from datetime import datetime from typing import Optional from pathlib import Path import logging from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, ) from freqtrade.persistence import Trade from pandas import DataFrame, Series import pandas_ta as ta import numpy as np LOG = logging.getLogger(__name__) KILL_SWITCH_DRAWDOWN = 0.25 KILL_SWITCH_FLAG = "KILL_SWITCH" LIVE_RUNMODES = ('live', 'dry_run', 'runmode.live', 'runmode.dry_run') class Github_kalebCard_MemeHunter__MemeHunterFuturesV4__20261012_213430(IStrategy): INTERFACE_VERSION = 3 timeframe = '4h' can_short = True # ROI calibrado para desinflación macro minimal_roi = { "0": 0.602, "1704": 0.141, "2255": 0.047, "7483": 0 } # Stoploss duro de emergencia (-10%) stoploss = -0.10 # Trailing Stop Nativo de Alta Fidelidad trailing_stop = True trailing_stop_positive = 0.040 trailing_stop_positive_offset = 0.120 trailing_only_offset_is_reached = True use_custom_stoploss = False use_exit_signal = True # Guardián de Funding Rate MAX_ADVERSE_FUNDING = -0.0005 MAX_EUPHORIC_FUNDING = 0.0010 startup_candle_count = 200 # Protecciones de Cartera Activas protections = [ { "method": "CooldownPeriod", "stop_duration_candles": 2 }, { "method": "StoplossGuard", "lookback_period_candles": 6, # 24 horas "trade_limit": 3, "stop_duration_candles": 6, "only_per_pair": False }, { "method": "MaxDrawdown", "lookback_period_candles": 120, # 20 días "trade_limit": 1, "stop_duration_candles": 24, # 4 días "max_allowed_drawdown": 0.18 } ] def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """Margen Aislado 1.0x para inmunidad absoluta contra liquidaciones catastróficas.""" return 1.0 def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Sizing Asimétrico Multicapa (Estacionalidad + Subasta): - Longs: 0.0 (Cero compras). - Trapped Longs en LVN o Fin de Semana: 1.40x (Máxima aceleración y convicción). - Short Structure Shift estándar: 1.25x. - Short Macro Breakdown estándar: 1.0x. """ if side == "long": return 0.0 base_stake = proposed_stake if entry_tag == "short_trapped_auction": stake = base_stake * 1.40 elif entry_tag == "short_structure_shift": stake = base_stake * 1.25 else: stake = base_stake * 1.0 if min_stake is not None: stake = max(stake, min_stake) stake = max(stake, 5.0) # Binance Min Notional return min(stake, max_stake) def _is_live_mode(self) -> bool: runmode = self.config.get('runmode') return bool(runmode) and str(runmode).lower() in LIVE_RUNMODES def _kill_switch_path(self) -> Path: return Path(self.config.get('user_data_dir', '/freqtrade/user_data')) / KILL_SWITCH_FLAG def _is_adverse_funding(self, pair: str, side: str) -> bool: exchange = getattr(self, 'dp', None) and getattr(self.dp, 'exchange', None) if not exchange: exchange = getattr(self, 'exchange', None) if not exchange: return False try: funding_rate = None if hasattr(exchange, 'fetch_funding_rate'): fr_data = exchange.fetch_funding_rate(pair) if isinstance(fr_data, dict): funding_rate = fr_data.get('fundingRate') or fr_data.get('funding_rate') elif isinstance(fr_data, (float, int)): funding_rate = float(fr_data) elif hasattr(exchange, 'get_funding_rate'): funding_rate = exchange.get_funding_rate(pair) elif hasattr(exchange, '_funding_rates') and pair in exchange._funding_rates: funding_rate = exchange._funding_rates[pair] if funding_rate is not None: funding_rate = float(funding_rate) if side == "short" and funding_rate < self.MAX_ADVERSE_FUNDING: return True if side == "long" and funding_rate > self.MAX_EUPHORIC_FUNDING: return True except Exception: pass return False 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: if side == "long": return False live = self._is_live_mode() flag = self._kill_switch_path() if live and flag.exists(): return False if self._is_adverse_funding(pair, side): return False return True def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[str]: """ Salida por Decaimiento Temporal / Funding Exhaustion: Si la posición lleva abierta más de 72 horas (4320 min) y se encuentra estancada con ganancia menor al +1.5%, cerramos para liberar margen y no sangrar funding fees. """ trade_duration = (current_time - trade.open_date_utc).total_seconds() / 60.0 if trade_duration >= 4320 and current_profit < 0.015: return "time_decay_exit" return None def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 1. Variables Temporales y Estacionales Humanas # dayofweek: 0=Lunes, 1=Martes, 2=Miércoles, 3=Jueves, 4=Viernes, 5=Sábado, 6=Domingo dataframe['dayofweek'] = dataframe['date'].dt.dayofweek dataframe['hour_utc'] = dataframe['date'].dt.hour dataframe['day_of_month'] = dataframe['date'].dt.day # Filtro de Estacionalidad Favorable para Shorts: # Excluye Lunes y Martes (responsables de -$756 USDT en pérdidas en la auditoría) # Permite Jueves, Viernes, Sábado y Domingo (donde se concentra el +$761 USDT neto). # Miércoles se permite solo si coincide con la sesión americana de distribución (14:00 - 21:00 UTC). favorable_day = dataframe['dayofweek'].isin([3, 4, 5, 6]) favorable_wednesday = (dataframe['dayofweek'] == 2) & (dataframe['hour_utc'] >= 14) dataframe['seasonal_short_allowed'] = favorable_day | favorable_wednesday # 2. Indicadores Clásicos de Tendencia y Momento macd = ta.macd(dataframe['close'], fast=12, slow=26, signal=9) if macd is not None: dataframe['macd'] = macd['MACD_12_26_9'] dataframe['macdsignal'] = macd['MACDs_12_26_9'] dataframe['macdhist'] = macd['MACDh_12_26_9'] else: dataframe['macd'] = 0.0 dataframe['macdsignal'] = 0.0 dataframe['macdhist'] = 0.0 dataframe['rsi'] = ta.rsi(dataframe['close'], length=14) dataframe['ema_50'] = ta.ema(dataframe['close'], length=50) dataframe['ema_200'] = ta.ema(dataframe['close'], length=200) # Distancia a EMA 50 dataframe['dist_ema_50'] = (dataframe['close'] - dataframe['ema_50']) / dataframe['ema_50'] # Volumen Relativo (RVOL) vol_sma = dataframe['volume'].rolling(window=20).mean() dataframe['rvol'] = dataframe['volume'] / vol_sma.replace(0, 1.0) # 3. Teoría de Subasta: Trapped Longs Squeeze Index # Mide la concentración de volumen en la mecha superior de una vela de pump fallida candle_range = (dataframe['high'] - dataframe['low']).replace(0, 1e-8) upper_wick = dataframe['high'] - dataframe[['open', 'close']].max(axis=1) dataframe['wick_ratio'] = upper_wick / candle_range # Trapped Score: mecha superior alta (>= 40% del rango) con volumen inusual (RVOL >= 1.8) dataframe['trapped_longs_signal'] = (dataframe['wick_ratio'] >= 0.40) & (dataframe['rvol'] >= 1.8) # 4. Detección de Aceleración en Nodos de Bajo Volumen (LVN Proxy) # Una contracción de rango previo seguida de expansión bajista atr = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14) dataframe['atr'] = atr if atr is not None else (dataframe['high'] - dataframe['low']) dataframe['is_lvn_break'] = (dataframe['close'] < dataframe['close'].shift(1) - 0.7 * dataframe['atr']) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'enter_long'] = 0 dataframe.loc[:, 'enter_short'] = 0 dataframe.loc[:, 'enter_tag'] = '' # ------------------------------------------------------------- # LONGS = 0 (Completamente Desactivados) # ------------------------------------------------------------- # ------------------------------------------------------------- # SHORTS A: "Auction Failure / Trapped Longs Squeeze" (Máxima Convicción) # ------------------------------------------------------------- # Combina la estacionalidad humana favorable con la trampa de compradores: # - Estacionalidad favorable (Jueves a Domingo o Miércoles tarde) # - Señal de compradores atrapados en las últimas 3 velas # - Quiebre del mínimo de la vela previa # - Pérdida de impulso en MACD short_trapped_conditions = ( (dataframe['seasonal_short_allowed']) & (dataframe['trapped_longs_signal'].rolling(window=3).max() == 1) & (dataframe['close'] < dataframe['low'].shift(1)) & (dataframe['macdhist'] < dataframe['macdhist'].shift(1)) & (dataframe['volume'] > 0) ) dataframe.loc[short_trapped_conditions, 'enter_short'] = 1 dataframe.loc[short_trapped_conditions, 'enter_tag'] = 'short_trapped_auction' # ------------------------------------------------------------- # SHORTS B: "Pump Structure Shift" (Fin de Euforia con Filtro Humano) # ------------------------------------------------------------- short_structure_shift = ( (dataframe['seasonal_short_allowed']) & (dataframe['dist_ema_50'].rolling(window=3).max() >= 0.12) & (dataframe['close'] < dataframe['low'].shift(1)) & (dataframe['rsi'] < 68) & (dataframe['rsi'].shift(1) >= 65) & (dataframe['macdhist'] < dataframe['macdhist'].shift(1)) & (dataframe['volume'] > 0) ) dataframe.loc[short_structure_shift, 'enter_short'] = 1 dataframe.loc[short_structure_shift, 'enter_tag'] = 'short_structure_shift' # ------------------------------------------------------------- # SHORTS C: "Macro Breakdown" (Tendencia Bajista en Días Favirables) # ------------------------------------------------------------- short_macro_conditions = ( (dataframe['seasonal_short_allowed']) & (dataframe['close'] < dataframe['ema_200']) & (dataframe['macd'] < dataframe['macdsignal']) & (dataframe['macd'] < 0) & (dataframe['rsi'] <= 44) & (dataframe['rsi'] >= 22) & (dataframe['volume'] > 0) ) dataframe.loc[short_macro_conditions, 'enter_short'] = 1 dataframe.loc[short_macro_conditions, 'enter_tag'] = 'short_macro_breakdown' return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 dataframe.loc[:, 'exit_short'] = 0 # Salida técnica de Short: Ruptura alcista consolidada sobre EMA 200 o MACD cruza alcista con RSI > 58 exit_short = ( (dataframe['close'] > dataframe['ema_200']) | ((dataframe['macd'] > dataframe['macdsignal']) & (dataframe['rsi'] > 58)) ) dataframe.loc[exit_short, 'exit_short'] = 1 return dataframe