# source: https://raw.githubusercontent.com/geovanybramanthya/AzuraAzureQuant/6ca3d7649eda9ce16f0c6fc60b8a5ca180bee6be/user_data/strategies/ApexDualAlpha_Omni_V12_Active.py # directory_url: https://github.com/geovanybramanthya/AzuraAzureQuant/blob/main/user_data/strategies/ # User: geovanybramanthya # Repository: AzuraAzureQuant # --------------------""" Github_geovanybramanthya_AzuraAzureQuant__ApexDualAlpha_Omni_V12_Active__20260918_180015 - Pullback Paradox Resolution & Chop-Resilient Calibration Curated for Bybit Linear Futures 7-Pair Universe: BTC, ETH, SOL, ADA, DOGE, LINK, PAXG. Key Structural Advancements: 1. Resolves the Pullback Paradox: - Macro trend orientation is defined structurally: EMA_50_4h > EMA_200_4h (macro bull) - Pullback tolerance: Price is permitted to test dynamic 4H EMA50 support zone (close >= EMA50_4h * 0.985) 2. 2-Candle Crossover Window: - Captures entries even if confirmation occurs 1 candle after instantaneous cross. 3. Adaptive ADX Regime: - Calibrated to > 14.0 (BTC > 15.0) to operate through consolidation compressions. 4. Preserves High-Yield ROI & Asymmetric Payoff: - Same Take Profit ladder, trailing stop, and dynamic leverage (7x BTC, 3x Altcoins). """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__ApexDualAlpha_Omni_V12_Active__20260918_180015(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # High-Yield Precision Profit Ladder minimal_roi = { "0": 0.526, "467": 0.161, "688": 0.090, "1173": 0.011 } stoploss = -0.297 trailing_stop = True trailing_stop_positive = 0.217 trailing_stop_positive_offset = 0.249 trailing_only_offset_is_reached = False order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False } order_time_in_force = { "entry": "GTC", "exit": "GTC" } process_only_new_candles = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False startup_candle_count: int = 200 def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: if "BTC" in pair: return 7.0 return 3.0 def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600 # 1. Fast profit taking for Shorts on sudden explosive dumps (> +4.5% profit) if trade.is_short and current_profit > 0.045: return "short_explosive_dump_tp" # 2. Surgical Volatility Scalp for PAXG (Gold bullion low-volatility cycle) if "PAXG" in pair and trade_duration >= 4.0 and current_profit >= 0.0065: return "paxg_volatility_tp" # 3. Structural Profit Banking for ETH (protecting against macro ETH/BTC bleed) if "ETH" in pair and trade_duration >= 16.0 and current_profit >= 0.0080: return "eth_profit_bank_tp" # 4. Stale decay cutoff at 36 hours if trade_duration > 36.0 and current_profit < -0.010: return "time_decay_stale_loss" # 5. Micro-profit closure at 48 hours if trade_duration > 48.0 and current_profit < 0.005: return "time_decay_48h_cutoff" return None def informative_pairs(self): pairs = self.dp.current_whitelist() if hasattr(self, "dp") and self.dp else [] return [(pair, self.informative_timeframe) for pair in pairs] def informative_4h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) # Structural macro trend orientation: decoupled from immediate close position dataframe["macro_bull_4h"] = dataframe["ema_50"] > dataframe["ema_200"] dataframe["macro_bear_4h"] = dataframe["ema_50"] < dataframe["ema_200"] return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if hasattr(self, "dp") and self.dp: inf_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) if not inf_4h.empty: inf_4h = self.informative_4h_indicators(inf_4h, metadata) dataframe = merge_informative_pair(dataframe, inf_4h, self.timeframe, self.informative_timeframe, ffill=True) for col in [f"macro_bull_4h_{self.informative_timeframe}", f"macro_bear_4h_{self.informative_timeframe}"]: if col not in dataframe.columns: dataframe[col] = True if "bull" in col else False if f"adx_{self.informative_timeframe}" not in dataframe.columns: dataframe[f"adx_{self.informative_timeframe}"] = 25.0 if f"ema_50_{self.informative_timeframe}" not in dataframe.columns: dataframe[f"ema_50_{self.informative_timeframe}"] = dataframe["close"] # Streamlined 1H Indicators dataframe["ema_21"] = ta.EMA(dataframe, timeperiod=21) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2.0) dataframe["bb_lowerband"] = bollinger["lower"] dataframe["bb_middleband"] = bollinger["mid"] dataframe["bb_upperband"] = bollinger["upper"] stoch = ta.STOCH(dataframe, fastk_period=14, slowk_period=3, slowd_period=3) dataframe["slowk"] = stoch["slowk"] dataframe["slowd"] = stoch["slowd"] dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["is_red"] = dataframe["close"] < dataframe["open"] dataframe["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["lower_wick"] = dataframe[["open", "close"]].min(axis=1) - dataframe["low"] dataframe["upper_wick"] = dataframe["high"] - dataframe[["open", "close"]].max(axis=1) 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"] = "" pair = metadata.get("pair", "") # Long Configuration - Dynamic Chop Tolerance adx_long_gate = 15.0 if "BTC" in pair else 14.0 rsi_min_long = 42.0 if "SOL" in pair else 40.0 pullback_threshold = 1.008 # 2-candle crossover window stoch_cross_up = qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"]) cross_window_up = stoch_cross_up | ((dataframe["slowk"].shift(1) > dataframe["slowd"].shift(1)) & (dataframe["slowk"].shift(2) <= dataframe["slowd"].shift(2))) # 1. LONG: Paradox-Resolved Bullish Pullback long_conditions = ( (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > adx_long_gate) & (dataframe["close"] >= dataframe[f"ema_50_{self.informative_timeframe}"] * 0.985) & ( (dataframe["close"] <= dataframe["ema_21"] * pullback_threshold) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.988) & (dataframe["rsi"] >= rsi_min_long) & (dataframe["rsi"] <= 64.0) & cross_window_up & (dataframe["slowk"] < 58.0) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.65) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.65) ) # 2. SHORT: Resistance Exhaustion Fade if "BTC" not in pair: adx_short_gate = 16.0 if "SOL" in pair else 15.0 rsi_min_short = 54.0 if "SOL" in pair else 52.0 stoch_cross_down = qtpylib.crossed_below(dataframe["slowk"], dataframe["slowd"]) cross_window_down = stoch_cross_down | ((dataframe["slowk"].shift(1) < dataframe["slowd"].shift(1)) & (dataframe["slowk"].shift(2) >= dataframe["slowd"].shift(2))) short_base = ( (dataframe[f"macro_bear_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > adx_short_gate) & (dataframe["close"] <= dataframe[f"ema_50_{self.informative_timeframe}"] * 1.015) & ( (dataframe["high"] >= dataframe["ema_21"] * 0.996) | (dataframe["high"] >= dataframe["bb_middleband"] * 0.998) ) & (dataframe["close"] <= dataframe["ema_50"] + 0.35 * dataframe["atr"]) & (dataframe["rsi"] >= rsi_min_short) & (dataframe["rsi"] <= 68.0) & cross_window_down & (dataframe["slowk"] > 52.0) & ( (dataframe["is_red"] == True) | (dataframe["upper_wick"] > dataframe["body_size"] * 0.65) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.65) ) dataframe.loc[short_base & ~long_conditions, ["enter_short", "enter_tag"]] = (1, "short_atr_fade_alpha") dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_pullback_alpha") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, "exit_long"] = 0 dataframe.loc[:, "exit_short"] = 0 dataframe.loc[:, "exit_tag"] = "" long_exit = ( (dataframe["rsi"] >= 71) & (dataframe["close"] >= dataframe["bb_upperband"] * 0.998) ) short_exit = ( (dataframe["rsi"] <= 28) & (dataframe["close"] <= dataframe["bb_lowerband"] * 1.002) ) dataframe.loc[long_exit, ["exit_long", "exit_tag"]] = (1, "exit_upper_bb_tp") dataframe.loc[short_exit, ["exit_short", "exit_tag"]] = (1, "exit_lower_bb_tp") return dataframe