# source: https://raw.githubusercontent.com/geovanybramanthya/AzuraAzureQuant/6ca3d7649eda9ce16f0c6fc60b8a5ca180bee6be/user_data/strategies/ApexDualAlpha_Omni_V15_TriAlpha.py # directory_url: https://github.com/geovanybramanthya/AzuraAzureQuant/blob/main/user_data/strategies/ # User: geovanybramanthya # Repository: AzuraAzureQuant # --------------------""" Github_geovanybramanthya_AzuraAzureQuant__ApexDualAlpha_Omni_V15_TriAlpha__20260918_180015 - Preserved High-Yield Payoff with 2-Candle Window & Squeeze Alpha Curated for Bybit Linear Futures 7-Pair Universe: BTC, ETH, SOL, ADA, DOGE, LINK, PAXG. """ 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_V15_TriAlpha__20260918_180015(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # High-Yield Precision Profit Ladder (Identical to Champion V11 Option B) 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 if "PAXG" in pair and trade_duration >= 4.0 and current_profit >= 0.0065: return "paxg_volatility_tp" # 3. Structural Profit Banking for ETH 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) dataframe["macro_bull_4h"] = (dataframe["close"] > dataframe["ema_50"]) & (dataframe["ema_50"] > dataframe["ema_200"]) dataframe["macro_bear_4h"] = (dataframe["close"] < dataframe["ema_50"]) & (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 dataframe["ema_9"] = ta.EMA(dataframe, timeperiod=9) 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"] dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] stoch = ta.STOCH(dataframe, fastk_period=14, slowk_period=3, slowd_period=3) dataframe["slowk"] = stoch["slowk"] dataframe["slowd"] = stoch["slowd"] # 2-Candle Stochastic Inflection Window stoch_cross_up_0 = qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"]) stoch_cross_up_1 = qtpylib.crossed_above(dataframe["slowk"].shift(1), dataframe["slowd"].shift(1)) dataframe["k_cross_up_window"] = stoch_cross_up_0 | (stoch_cross_up_1 & (dataframe["slowk"] > dataframe["slowd"])) stoch_cross_down_0 = qtpylib.crossed_below(dataframe["slowk"], dataframe["slowd"]) stoch_cross_down_1 = qtpylib.crossed_below(dataframe["slowk"].shift(1), dataframe["slowd"].shift(1)) dataframe["k_cross_down_window"] = stoch_cross_down_0 | (stoch_cross_down_1 & (dataframe["slowk"] < dataframe["slowd"])) # Squeeze Detection: BB Width compression release dataframe["bb_width_q25"] = dataframe["bb_width"].rolling(50).quantile(0.25) dataframe["in_squeeze"] = dataframe["bb_width"] < dataframe["bb_width_q25"] dataframe["squeeze_released"] = (dataframe["in_squeeze"].shift(1) == True) & (dataframe["in_squeeze"] == False) 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 - Calibrated for Earlier Trend Onset if "BTC" in pair: adx_long_gate = 21.0 elif "ETH" in pair or "SOL" in pair: adx_long_gate = 20.0 else: adx_long_gate = 19.0 rsi_min_long = 44.0 if "SOL" in pair else 43.0 pullback_threshold = 1.006 # 1. LONG Archetype 1: Proven Bullish Pullback with 2-Candle Stochastic Inflection e1_pullback = ( (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > adx_long_gate) & ( (dataframe["close"] <= dataframe["ema_21"] * pullback_threshold) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.990) & (dataframe["rsi"] >= rsi_min_long) & (dataframe["rsi"] <= 63.0) & (dataframe["k_cross_up_window"] == True) & (dataframe["slowk"] < 54.0) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.68) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) # 2. LONG Archetype 2: Volatility Squeeze Expansion e2_squeeze = ( (dataframe["squeeze_released"] == True) & (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (dataframe["close"] > dataframe["bb_middleband"]) & (dataframe["close"] < dataframe["bb_upperband"] * 0.998) & (dataframe["close"] > dataframe["ema_9"]) & (dataframe["rsi"] >= 52.0) & (dataframe["rsi"] <= 68.0) & (dataframe["slowk"] > dataframe["slowd"]) & (dataframe["is_green"] == True) & (dataframe["volume"] > dataframe["volume_mean_20"] * 1.05) ) long_conditions = e1_pullback | e2_squeeze dataframe.loc[e1_pullback, ["enter_long", "enter_tag"]] = (1, "long_pullback_alpha") dataframe.loc[e2_squeeze & ~e1_pullback, ["enter_long", "enter_tag"]] = (1, "long_squeeze_expansion") # 3. SHORT Archetype: Precision Tailored Resistance Exhaustion Fade if "BTC" not in pair: if "SOL" in pair: adx_short_gate = 22.0 rsi_min_short = 56.0 require_below_ema200 = True else: adx_short_gate = 20.0 rsi_min_short = 53.0 require_below_ema200 = False short_base = ( (dataframe[f"macro_bear_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > adx_short_gate) & ( (dataframe["high"] >= dataframe["ema_21"] * 0.996) | (dataframe["high"] >= dataframe["bb_middleband"] * 0.998) ) & (dataframe["close"] <= dataframe["ema_50"] + 0.30 * dataframe["atr"]) & (dataframe["rsi"] >= rsi_min_short) & (dataframe["rsi"] <= 67.0) & (dataframe["k_cross_down_window"] == True) & (dataframe["slowk"] > 55.0) & ( (dataframe["is_red"] == True) | (dataframe["upper_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.72) ) if require_below_ema200: short_conditions = short_base & (dataframe["close"] < dataframe["ema_200"]) else: short_conditions = short_base dataframe.loc[short_conditions & ~long_conditions, ["enter_short", "enter_tag"]] = (1, "short_atr_fade_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