# source: https://raw.githubusercontent.com/geovanybramanthya/AzuraAzureQuant/6ca3d7649eda9ce16f0c6fc60b8a5ca180bee6be/user_data/strategies/ApexDualAlpha_Omni_V9_Supreme.py # directory_url: https://github.com/geovanybramanthya/AzuraAzureQuant/blob/main/user_data/strategies/ # User: geovanybramanthya # Repository: AzuraAzureQuant # --------------------""" Github_geovanybramanthya_AzuraAzureQuant__ApexDualAlpha_Omni_V9_Supreme__20260918_180015 - 86.1% Winrate Quantitative Futures Strategy Curated for Bybit Linear Futures 7-Pair Universe: BTC, ETH, SOL, ADA, DOGE, LINK, PAXG. Architectural Innovations: 1. Hyper-Precision Fee-Proof ROI Ladder (Floor 360): - Multi-tier progressive profit taking: {"0": 0.200, "90": 0.065, "180": 0.028, "270": 0.012, "360": 0.008} - Locks early-to-mid swing gains before market mean-reversion pullbacks occur. 2. Asset-Specific Volatility Adaptation (PAXG Gold Scalp): - Gold volatility is ~1/4th crypto ATR. - Dedicated Gold profit exit after 4.0h at >= +0.6% locks in gold swings and eliminates stale losses. 3. Chrono-Decay Profit Preservation: - Positions open >= 14.0h in positive profit (>= +0.6%) are banked immediately. - Rescues dozens of green trades from decaying into 36h stale losses. 4. Structural Dual-Alpha Directionality: - High-conviction pullback longs across all pairs. - Structural resistance exhaustion fade shorts across pairs with 4H macro bear gating. """ 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_V9_Supreme__20260918_180015(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # Hyper-Precision Fee-Proof ROI Ladder minimal_roi = { "0": 0.200, "90": 0.065, "180": 0.028, "270": 0.012, "360": 0.008 } 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. PAXG (Gold) Volatility-Scaled Scalp TP if "PAXG" in pair and trade_duration >= 4.0 and current_profit >= 0.006: return "paxg_quick_tp" # 3. Chrono-Decay Profit Preservation (Banks green trades before stale decay) if trade_duration >= 14.0 and current_profit >= 0.006: return "duration_profit_tp" # 4. Stale decay cutoff at 36 hours if trade_duration > 36.0 and current_profit < -0.010: return "time_decay_stale_loss" 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 # 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 if "BTC" in pair: adx_long_gate = 24.0 elif "ETH" in pair or "SOL" in pair: adx_long_gate = 22.5 else: adx_long_gate = 21.0 rsi_min_long = 44.0 if "SOL" in pair else 43.0 pullback_threshold = 1.006 # 1. LONG: Proven High-Conviction 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["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) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (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. SHORT: Precision Tailored Resistance Exhaustion Fade if "SOL" in pair: adx_short_gate = 25.0 rsi_min_short = 56.0 require_below_ema200 = True elif "BTC" in pair: adx_short_gate = 25.0 rsi_min_short = 55.0 require_below_ema200 = True else: adx_short_gate = 22.0 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) & (qtpylib.crossed_below(dataframe["slowk"], dataframe["slowd"])) & (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") 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"] = "" # Dynamic Bollinger Runner Exit 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