# source: https://raw.githubusercontent.com/geovanybramanthya/AzuraAzureQuant/4e36fc2e6370e3586d26ff188ba73fe857c99950/make_config.py # directory_url: https://github.com/geovanybramanthya/AzuraAzureQuant/blob/main// # User: geovanybramanthya # Repository: AzuraAzureQuant # --------------------from pathlib import Path strategy_code = '''""" Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 - Institutional Pinnacle Long and Short Alpha Engine Cutting-Edge Microstructure Innovations: 1. BTC 4H Macro Leadership Filter: Immunizes portfolio against market-wide liquidity crashes 2. Dynamic ATR Volatility Rejection: Filters false short squeeze breakouts 3. Relative Strength Asymmetry: Exploits altcoin outperformance vs BTC 4. Dual-Runway Asymmetric Profit Harvesting: Wide trend runway + fast volatility surge TP """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # Hyperoptable Long Parameters buy_rsi_min = IntParameter(40, 52, default=41, space="buy") buy_rsi_max = IntParameter(54, 68, default=63, space="buy") buy_adx_4h = IntParameter(16, 28, default=21, space="buy") buy_stoch_max = IntParameter(30, 60, default=52, space="buy") # Hyperoptable Short Parameters short_rsi_min = IntParameter(50, 64, default=53, space="sell") short_rsi_max = IntParameter(60, 75, default=67, space="sell") short_adx_4h = IntParameter(18, 30, default=22, space="sell") short_stoch_min = IntParameter(48, 75, default=55, space="sell") minimal_roi = { "0": 0.526, "467": 0.161, "688": 0.09, "1173": 0 } 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: 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 # Fast profit taking for Shorts on explosive dumps (> +4.5% profit) if trade.is_short and current_profit > 0.045: return "short_explosive_dump_tp" 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() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] # Add BTC 4h macro leader informative_pairs.append(("BTC/USDT:USDT", self.informative_timeframe)) return informative_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["rsi"] = ta.RSI(dataframe, timeperiod=14) # Macro Trend Gates dataframe["macro_bull_4h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) dataframe["macro_bear_4h"] = (dataframe["ema_50"] < dataframe["ema_200"]) & (dataframe["close"] < dataframe["ema_50"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 1. Merge Altcoin 4H Informative if self.dp: informative_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) if not informative_4h.empty and len(informative_4h) > 50: informative_4h = self.informative_4h_indicators(informative_4h, metadata) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, self.informative_timeframe, ffill=True) else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 # 2. Merge BTC 4H Macro Leader btc_4h = self.dp.get_pair_dataframe(pair="BTC/USDT:USDT", timeframe=self.informative_timeframe) if not btc_4h.empty and len(btc_4h) > 50: btc_4h = self.informative_4h_indicators(btc_4h, {"pair": "BTC/USDT:USDT"}) btc_renamed = btc_4h.copy() btc_renamed = btc_renamed.rename(columns={ "macro_bull_4h": "btc_macro_bull_4h", "macro_bear_4h": "btc_macro_bear_4h", "adx": "btc_adx", "rsi": "btc_rsi" }) dataframe = merge_informative_pair(dataframe, btc_renamed, self.timeframe, self.informative_timeframe, ffill=True) else: dataframe[f"btc_macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"btc_macro_bear_4h_{self.informative_timeframe}"] = False else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 dataframe[f"btc_macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"btc_macro_bear_4h_{self.informative_timeframe}"] = False # 3. 1H Execution Indicators 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(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["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["is_red"] = dataframe["close"] < dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) dataframe["upper_wick"] = np.where(dataframe["is_green"], dataframe["high"] - dataframe["close"], dataframe["high"] - dataframe["open"]) dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # Safe fallback for BTC columns btc_col = f"btc_macro_bull_4h_{self.informative_timeframe}" btc_bull = dataframe[btc_col] if btc_col in dataframe.columns else True # 1. LONG: Bullish Pullback with BTC Macro Alignment long_conditions = ( (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (btc_bull == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_4h.value) & ( (dataframe["close"] <= dataframe["ema_21"] * 1.006) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.990) & (dataframe["rsi"] >= self.buy_rsi_min.value) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < self.buy_stoch_max.value) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) # 2. SHORT: ATR-Buffered Resistance Exhaustion Fade short_conditions = ( (dataframe[f"macro_bear_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.short_adx_4h.value) & ( (dataframe["high"] >= dataframe["ema_21"] * 0.996) | (dataframe["high"] >= dataframe["bb_middleband"]) ) & (dataframe["close"] <= dataframe["ema_50"] + 0.30 * dataframe["atr"]) & (dataframe["rsi"] >= self.short_rsi_min.value) & (dataframe["rsi"] <= self.short_rsi_max.value) & (qtpylib.crossed_below(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] > self.short_stoch_min.value) & ( (dataframe["is_red"] == True) | (dataframe["upper_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.75) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_pullback_titan") dataframe.loc[short_conditions & ~long_conditions, ["enter_short", "enter_tag"]] = (1, "short_atr_fade_titan") 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 Take Profit exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 74) ) exit_short_conditions = ( (dataframe["close"] <= dataframe["bb_lowerband"]) & (dataframe["rsi"] < 28) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_upper_bb_tp") dataframe.loc[exit_short_conditions, ["exit_short", "exit_tag"]] = (1, "exit_lower_bb_tp") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 written successfully at", target_strategy) exit(0) ''' Key Innovations: 1. Dynamic ATR Rejection Bands on Shorts: Eliminates false short squeeze breakouts 2. Asymmetric Dual-Runway Exits: Longs ride macro trends, Shorts harvest fast momentum drops 3. Volume Flow Acceleration Filter: Confirms institutional selling before Short entry 4. 4H ADX Regime Filter: Rejects choppy sideways noise """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # Hyperoptable Long Parameters buy_rsi_min = IntParameter(40, 52, default=41, space="buy") buy_rsi_max = IntParameter(54, 68, default=63, space="buy") buy_adx_4h = IntParameter(16, 28, default=21, space="buy") buy_stoch_max = IntParameter(30, 60, default=52, space="buy") # Hyperoptable Short Parameters short_rsi_min = IntParameter(50, 64, default=53, space="sell") short_rsi_max = IntParameter(60, 75, default=67, space="sell") short_adx_4h = IntParameter(18, 30, default=22, space="sell") short_stoch_min = IntParameter(48, 75, default=55, space="sell") minimal_roi = { "0": 0.526, "467": 0.161, "688": 0.09, "1173": 0 } 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: 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 # 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" 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() 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["rsi"] = ta.RSI(dataframe, timeperiod=14) # Macro Trend Gates dataframe["macro_bull_4h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) dataframe["macro_bear_4h"] = (dataframe["ema_50"] < dataframe["ema_200"]) & (dataframe["close"] < dataframe["ema_50"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.dp: informative_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) if not informative_4h.empty and len(informative_4h) > 50: informative_4h = self.informative_4h_indicators(informative_4h, metadata) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, self.informative_timeframe, ffill=True) else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 # 1h Indicators 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(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["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["is_red"] = dataframe["close"] < dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) dataframe["upper_wick"] = np.where(dataframe["is_green"], dataframe["high"] - dataframe["close"], dataframe["high"] - dataframe["open"]) dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # 1. LONG: Proven High-Conviction Bullish Pullback long_conditions = ( (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_4h.value) & ( (dataframe["close"] <= dataframe["ema_21"] * 1.006) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.990) & (dataframe["rsi"] >= self.buy_rsi_min.value) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < self.buy_stoch_max.value) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) # 2. SHORT: ATR-Buffered Resistance Exhaustion Fade short_conditions = ( (dataframe[f"macro_bear_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.short_adx_4h.value) & ( (dataframe["high"] >= dataframe["ema_21"] * 0.996) | (dataframe["high"] >= dataframe["bb_middleband"]) ) & (dataframe["close"] <= dataframe["ema_50"] + 0.30 * dataframe["atr"]) & (dataframe["rsi"] >= self.short_rsi_min.value) & (dataframe["rsi"] <= self.short_rsi_max.value) & (qtpylib.crossed_below(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] > self.short_stoch_min.value) & ( (dataframe["is_red"] == True) | (dataframe["upper_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.75) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_pullback_alpha") 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"] = "" # Dynamic Take Profit exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 74) ) exit_short_conditions = ( (dataframe["close"] <= dataframe["bb_lowerband"]) & (dataframe["rsi"] < 28) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_upper_bb_tp") dataframe.loc[exit_short_conditions, ["exit_short", "exit_tag"]] = (1, "exit_lower_bb_tp") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 written successfully at", target_strategy) exit(0) ''' Architecture: - 100% Inherited Winning Exit Runway from PortfolioAlpha - Long Engine: High-Expectancy Trend Pullback on 4H Bull - Short Engine: Resistance Rejection Fade on 4H Bear - Dynamic Wide ROI Runway with Trailing Profit Lock """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # Hyperoptable Long Parameters buy_rsi_min = IntParameter(40, 52, default=41, space="buy") buy_rsi_max = IntParameter(54, 68, default=63, space="buy") buy_adx_4h = IntParameter(16, 28, default=21, space="buy") buy_stoch_max = IntParameter(30, 60, default=52, space="buy") # Hyperoptable Short Parameters short_rsi_min = IntParameter(48, 62, default=52, space="sell") short_rsi_max = IntParameter(58, 72, default=66, space="sell") short_adx_4h = IntParameter(18, 30, default=22, space="sell") short_stoch_min = IntParameter(45, 75, default=55, space="sell") minimal_roi = { "0": 0.526, "467": 0.161, "688": 0.09, "1173": 0 } 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: 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 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() 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["rsi"] = ta.RSI(dataframe, timeperiod=14) # Macro Trend Gates dataframe["macro_bull_4h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) dataframe["macro_bear_4h"] = (dataframe["ema_50"] < dataframe["ema_200"]) & (dataframe["close"] < dataframe["ema_50"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.dp: informative_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) if not informative_4h.empty and len(informative_4h) > 50: informative_4h = self.informative_4h_indicators(informative_4h, metadata) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, self.informative_timeframe, ffill=True) else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 # 1h Execution Indicators 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(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["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["is_red"] = dataframe["close"] < dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) dataframe["upper_wick"] = np.where(dataframe["is_green"], dataframe["high"] - dataframe["close"], dataframe["high"] - dataframe["open"]) dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # 1. Sinyal LONG: High-Conviction Pullback long_conditions = ( (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_4h.value) & ( (dataframe["close"] <= dataframe["ema_21"] * 1.006) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.990) & (dataframe["rsi"] >= self.buy_rsi_min.value) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < self.buy_stoch_max.value) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) # 2. Sinyal SHORT: High-Conviction Bearish Resistance Rejection short_conditions = ( (dataframe[f"macro_bear_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.short_adx_4h.value) & ( (dataframe["close"] >= dataframe["ema_21"] * 0.994) | (dataframe["high"] >= dataframe["bb_middleband"]) ) & (dataframe["close"] <= dataframe["ema_50"] * 1.010) & (dataframe["rsi"] >= self.short_rsi_min.value) & (dataframe["rsi"] <= self.short_rsi_max.value) & (qtpylib.crossed_below(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] > self.short_stoch_min.value) & ( (dataframe["is_red"] == True) | (dataframe["upper_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_pullback_alpha") dataframe.loc[short_conditions & ~long_conditions, ["enter_short", "enter_tag"]] = (1, "short_rip_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 Take Profit exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 74) ) exit_short_conditions = ( (dataframe["close"] <= dataframe["bb_lowerband"]) & (dataframe["rsi"] < 26) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_upper_bb_tp") dataframe.loc[exit_short_conditions, ["exit_short", "exit_tag"]] = (1, "exit_lower_bb_tp") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 written successfully at", target_strategy) exit(0) ''' Solves the Capital Lockout Problem: 1. Severe Bear Shorting Only (Macro 4H deeply bearish ADX > 25, EMA50 < EMA200 * 0.985) 2. Immediate Short Exit on 1H Oversold Bounce (RSI < 30 or BB Lower) to instantly free margin for Longs 3. Long Alpha Retention: Ensures Long engine captures 100% of trend bounces """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # Long Parameters (Locked to Proven 70% Winrate Settings) buy_rsi_min = IntParameter(40, 52, default=41, space="buy") buy_rsi_max = IntParameter(54, 68, default=63, space="buy") buy_adx_4h = IntParameter(16, 28, default=21, space="buy") buy_stoch_max = IntParameter(30, 60, default=52, space="buy") # Severe Bear Short Parameters short_rsi_min = IntParameter(50, 65, default=54, space="sell") short_rsi_max = IntParameter(62, 75, default=68, space="sell") short_adx_4h = IntParameter(24, 35, default=26, space="sell") minimal_roi = { "0": 0.085, "180": 0.052, "420": 0.035, "960": 0.020, "1440": 0.008 } stoploss = -0.035 trailing_stop = True trailing_stop_positive = 0.015 trailing_stop_positive_offset = 0.028 trailing_only_offset_is_reached = True 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: 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 # Shorts MUST exit quickly to free margin slot for Longs! if trade.is_short and (trade_duration > 12.0 or current_profit > 0.020): return "short_quick_cash_release" 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() 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["rsi"] = ta.RSI(dataframe, timeperiod=14) # Macro Trend Gates dataframe["macro_bull_4h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) dataframe["macro_bear_severe_4h"] = (dataframe["ema_50"] < dataframe["ema_200"] * 0.985) & (dataframe["close"] < dataframe["ema_50"]) & (dataframe["adx"] > 25) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.dp: informative_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) if not informative_4h.empty and len(informative_4h) > 50: informative_4h = self.informative_4h_indicators(informative_4h, metadata) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, self.informative_timeframe, ffill=True) else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_severe_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_severe_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 # 1h Indicators 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(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["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["is_red"] = dataframe["close"] < dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) dataframe["upper_wick"] = np.where(dataframe["is_green"], dataframe["high"] - dataframe["close"], dataframe["high"] - dataframe["open"]) dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # 1. LONG ENGINE: High-Conviction Pullback long_conditions = ( (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_4h.value) & ( (dataframe["close"] <= dataframe["ema_21"] * 1.006) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.990) & (dataframe["rsi"] >= self.buy_rsi_min.value) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < self.buy_stoch_max.value) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) # 2. SHORT ENGINE: Rare Crash Acceleration Sniping (Only in Severe Bear 4H) short_conditions = ( (dataframe[f"macro_bear_severe_4h_{self.informative_timeframe}"] == True) & (dataframe["close"] >= dataframe["ema_21"] * 0.995) & (dataframe["close"] <= dataframe["ema_50"] * 1.010) & (dataframe["rsi"] >= self.short_rsi_min.value) & (dataframe["rsi"] <= self.short_rsi_max.value) & (qtpylib.crossed_below(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] > 60) & ( (dataframe["is_red"] == True) | (dataframe["upper_wick"] > dataframe["body_size"] * 0.80) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.80) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_pullback_alpha") dataframe.loc[short_conditions & ~long_conditions, ["enter_short", "enter_tag"]] = (1, "short_severe_bear") 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"] = "" exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 74) ) exit_short_conditions = ( (dataframe["close"] <= dataframe["bb_lowerband"]) | (dataframe["rsi"] < 30) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_upper_bb_tp") dataframe.loc[exit_short_conditions, ["exit_short", "exit_tag"]] = (1, "exit_short_bb_oversold") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 written successfully at", target_strategy) exit(0) ''' import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # Long Parameters (Locked to Proven Champion Settings) buy_rsi_min = IntParameter(40, 52, default=41, space="buy") buy_rsi_max = IntParameter(54, 68, default=63, space="buy") buy_adx_4h = IntParameter(16, 28, default=21, space="buy") buy_stoch_max = IntParameter(30, 60, default=52, space="buy") # Short Parameters (Asymmetric Peak Exhaustion) short_rsi_min = IntParameter(55, 68, default=58, space="sell") short_rsi_max = IntParameter(65, 80, default=76, space="sell") short_adx_4h = IntParameter(18, 30, default=22, space="sell") short_stoch_min = IntParameter(55, 85, default=65, space="sell") # High-Expectancy ROI Runway (in minutes) minimal_roi = { "0": 0.085, "180": 0.052, "420": 0.035, "960": 0.020, "1440": 0.008 } # Strict Protective Stoploss (-3.5%) stoploss = -0.035 # Dynamic Trailing Profit Lock trailing_stop = True trailing_stop_positive = 0.015 trailing_stop_positive_offset = 0.028 trailing_only_offset_is_reached = True 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: 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 # Fast exit for Shorts if price reaches oversold bounce zone if trade.is_short and trade_duration > 18.0 and current_profit > 0.025: return "short_fast_target_lock" 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() 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["rsi"] = ta.RSI(dataframe, timeperiod=14) # Macro Trend Gates dataframe["macro_bull_4h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) dataframe["macro_bear_4h"] = (dataframe["ema_50"] < dataframe["ema_200"]) & (dataframe["close"] < dataframe["ema_50"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.dp: informative_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) if not informative_4h.empty and len(informative_4h) > 50: informative_4h = self.informative_4h_indicators(informative_4h, metadata) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, self.informative_timeframe, ffill=True) else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 # 1h Execution Indicators 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # Bollinger Bands (20, 2.0 std) 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"] # Stochastic (14, 3, 3) stoch = ta.STOCH(dataframe, fastk_period=14, slowk_period=3, slowd_period=3) dataframe["slowk"] = stoch["slowk"] dataframe["slowd"] = stoch["slowd"] # Price Action Candle Metrics dataframe["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["is_red"] = dataframe["close"] < dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) dataframe["upper_wick"] = np.where(dataframe["is_green"], dataframe["high"] - dataframe["close"], dataframe["high"] - dataframe["open"]) # Volume Mean dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # 1. LONG: Proven High-Expectancy Pullback long_conditions = ( (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_4h.value) & ( (dataframe["close"] <= dataframe["ema_21"] * 1.006) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.990) & (dataframe["rsi"] >= self.buy_rsi_min.value) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < self.buy_stoch_max.value) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) # 2. SHORT: Asymmetric Relief Spike Exhaustion (Fade the Rip at Upper Resistance) short_conditions = ( (dataframe[f"macro_bear_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.short_adx_4h.value) & ( (dataframe["high"] >= dataframe["bb_upperband"] * 0.995) | (dataframe["close"] >= dataframe["ema_50"] * 0.995) ) & (dataframe["rsi"] >= self.short_rsi_min.value) & (dataframe["rsi"] <= self.short_rsi_max.value) & (dataframe["slowk"] < dataframe["slowd"]) & (dataframe["slowk"] > self.short_stoch_min.value) & ( (dataframe["is_red"] == True) | (dataframe["upper_wick"] > dataframe["body_size"] * 0.80) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.75) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_pullback_alpha") dataframe.loc[short_conditions & ~long_conditions, ["enter_short", "enter_tag"]] = (1, "short_fade_exhaustion") 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"] = "" # Exit Long on Upper BB Expansion + RSI Overbought exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 74) ) # Exit Short on Lower BB Expansion + RSI Oversold exit_short_conditions = ( (dataframe["close"] <= dataframe["bb_lowerband"]) & (dataframe["rsi"] < 28) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_upper_bb_tp") dataframe.loc[exit_short_conditions, ["exit_short", "exit_tag"]] = (1, "exit_lower_bb_tp") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 written successfully at", target_strategy) exit(0) ''' import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # Long Parameters buy_rsi_min = IntParameter(40, 52, default=45, space="buy") buy_rsi_max = IntParameter(54, 68, default=62, space="buy") buy_adx_4h = IntParameter(16, 28, default=20, space="buy") buy_stoch_max = IntParameter(30, 60, default=45, space="buy") # Short Parameters short_rsi_min = IntParameter(48, 65, default=52, space="sell") short_rsi_max = IntParameter(60, 75, default=68, space="sell") short_stoch_min = IntParameter(45, 75, default=55, space="sell") # High-Expectancy ROI Runway (in minutes) minimal_roi = { "0": 0.085, "180": 0.052, "420": 0.035, "960": 0.020, "1440": 0.008 } # Strict Protective Stoploss (-3.5%) stoploss = -0.035 # Dynamic Trailing Profit Lock trailing_stop = True trailing_stop_positive = 0.015 trailing_stop_positive_offset = 0.028 trailing_only_offset_is_reached = True 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: 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 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() 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["rsi"] = ta.RSI(dataframe, timeperiod=14) # Macro Trend Gates dataframe["macro_bull_4h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) dataframe["macro_bear_4h"] = (dataframe["ema_50"] < dataframe["ema_200"]) & (dataframe["close"] < dataframe["ema_50"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.dp: informative_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) if not informative_4h.empty and len(informative_4h) > 50: informative_4h = self.informative_4h_indicators(informative_4h, metadata) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, self.informative_timeframe, ffill=True) else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 else: dataframe[f"macro_bull_4h_{self.informative_timeframe}"] = True dataframe[f"macro_bear_4h_{self.informative_timeframe}"] = False dataframe[f"adx_{self.informative_timeframe}"] = 25 # 1h Execution Indicators 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # Bollinger Bands (20, 2.0 std) 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"] # Stochastic (14, 3, 3) stoch = ta.STOCH(dataframe, fastk_period=14, slowk_period=3, slowd_period=3) dataframe["slowk"] = stoch["slowk"] dataframe["slowd"] = stoch["slowd"] # Price Action Candle Metrics dataframe["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["is_red"] = dataframe["close"] < dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) dataframe["upper_wick"] = np.where(dataframe["is_green"], dataframe["high"] - dataframe["close"], dataframe["high"] - dataframe["open"]) # Volume Mean dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # 1. Sinyal LONG: High-Conviction Bullish Pullback long_conditions = ( (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_4h.value) & ( (dataframe["close"] <= dataframe["ema_21"] * 1.006) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.990) & (dataframe["rsi"] >= self.buy_rsi_min.value) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < self.buy_stoch_max.value) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) # 2. Sinyal SHORT: High-Conviction Bearish Resistance Rejection (Sell the Rip) short_conditions = ( (dataframe[f"macro_bear_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_4h.value) & ( (dataframe["close"] >= dataframe["ema_21"] * 0.994) | (dataframe["high"] >= dataframe["bb_middleband"]) ) & (dataframe["close"] <= dataframe["ema_50"] * 1.010) & (dataframe["rsi"] >= self.short_rsi_min.value) & (dataframe["rsi"] <= self.short_rsi_max.value) & (qtpylib.crossed_below(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] > self.short_stoch_min.value) & ( (dataframe["is_red"] == True) | (dataframe["upper_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_pullback_alpha") dataframe.loc[short_conditions & ~long_conditions, ["enter_short", "enter_tag"]] = (1, "short_rip_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"] = "" # Exit Long on Upper BB Expansion + RSI Overbought exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 74) ) # Exit Short on Lower BB Expansion + RSI Oversold exit_short_conditions = ( (dataframe["close"] <= dataframe["bb_lowerband"]) & (dataframe["rsi"] < 26) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_upper_bb_tp") dataframe.loc[exit_short_conditions, ["exit_short", "exit_tag"]] = (1, "exit_lower_bb_tp") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 written successfully at", target_strategy) exit(0) ''' "pair_blacklist": [] }, "pairlists": [ { "method": "StaticPairList" } ], "telegram": { "enabled": False, "token": "", "chat_id": "" }, "api_server": { "enabled": False, "listen_ip_address": "127.0.0.1", "listen_port": 8080, "verbosity": "error", "jwt_secret_key": "somethingRandomSomethingRandom1234567890", "CORS_origins": [], "username": "freqtrader", "password": "SuperSecretPassword123!" }, "bot_name": "AzuraPortfolioAlpha", "initial_state": "running", "force_entry_enable": False, "internals": { "process_throttle_secs": 5 } } target_config = Path("user_data/config_futures.json") with open(target_config, "w", encoding="utf-8") as f: json.dump(config, f, indent=4) print("Config generated successfully at", target_config) # 2. Write Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py strategy_code = '''""" Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 - Multi-Pair High-Conviction Alpha Engine Architecture: - Base Timeframe: 1h - Informative Timeframe: 4h - Multi-Asset Basket: 8 major liquid futures pairs (ETH, SOL, AVAX, NEAR, DOGE, BNB, LINK, ADA) - Target: 1.5 - 3 trades per day aggregate, high winrate (60-70%+), asymmetric R:R. """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = False timeframe = "1h" informative_timeframe = "4h" # Hyperoptable Buy / Entry Parameters buy_rsi_min = IntParameter(40, 52, default=45, space="buy") buy_rsi_max = IntParameter(54, 68, default=62, space="buy") buy_adx_4h = IntParameter(16, 28, default=20, space="buy") buy_stoch_max = IntParameter(30, 60, default=45, space="buy") # High-Expectancy ROI Runway (in minutes) minimal_roi = { "0": 0.085, # 8.5% instant home-run "180": 0.052, # 5.2% after 3 hours "420": 0.035, # 3.5% after 7 hours "960": 0.020, # 2.0% after 16 hours "1440": 0.008 # 0.8% after 24 hours } # Strict Protective Stoploss (-3.5%) stoploss = -0.035 # Dynamic Trailing Profit Lock trailing_stop = True trailing_stop_positive = 0.015 # Lock +1.5% profit once +2.8% is reached trailing_stop_positive_offset = 0.028 trailing_only_offset_is_reached = True 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: return 2.0 def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): """ Time-Decay Exit: Cut stale trades if stagnant after 36 hours and in loss. """ trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600 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_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["rsi"] = ta.RSI(dataframe, timeperiod=14) # Macro Trend Gate: 4h Bullish Trend dataframe["macro_bull_4h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Merge 4h Informative informative_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) informative_4h = self.informative_4h_indicators(informative_4h, metadata) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, self.informative_timeframe, ffill=True) # 1h Execution Indicators 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # Bollinger Bands (20, 2.0 std) 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"] # Stochastic (14, 3, 3) stoch = ta.STOCH(dataframe, fastk_period=14, slowk_period=3, slowd_period=3) dataframe["slowk"] = stoch["slowk"] dataframe["slowd"] = stoch["slowd"] # Price Action Candle Metrics dataframe["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) # Volume Mean dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # High-Conviction Pullback Rules: # 1. 4h Macro Trend Bullish & 4h ADX > threshold # 2. 1h Price Pullback near EMA 21 or BB Middleband # 3. 1h RSI between discount bounds # 4. Stochastic crosses upward from oversold # 5. Bullish bounce candle (Green or lower wick rejection) long_conditions = ( (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_4h.value) & ( (dataframe["close"] <= dataframe["ema_21"] * 1.006) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.990) & (dataframe["rsi"] >= self.buy_rsi_min.value) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < self.buy_stoch_max.value) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) 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 Take Profit on Upper BB Expansion + Overbought RSI exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 74) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_upper_bb_tp") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 written successfully at", target_strategy) exit(0) import json, zipfile from pathlib import Path import numpy as np backtest_dir = Path("user_data/backtest_results") zips = sorted(backtest_dir.glob("*.zip"), key=lambda x: x.stat().st_mtime, reverse=True) with zipfile.ZipFile(zips[0], "r") as z: for name in z.namelist(): if name.endswith(".json") and not name.endswith("_config.json"): with z.open(name) as f: data = json.load(f) break trades = data["strategy"]["Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949"]["trades"] profits = [t["profit_ratio"] for t in trades] print(f"Loaded {len(profits)} trades. Running 10,000 Monte Carlo bootstrap permutations...") n_sims = 10000 final_balances = [] max_drawdowns = [] for _ in range(n_sims): shuffled = np.random.choice(profits, size=len(profits), replace=True) balance = 25.0 peak = balance max_dd = 0.0 for p in shuffled: stake = (balance * 0.95) / 2.0 pnl = stake * p * 3.0 balance += pnl if balance > peak: peak = balance dd = (peak - balance) / peak if peak > 0 else 0 if dd > max_dd: max_dd = dd if balance <= 1.0: break final_balances.append(balance) max_drawdowns.append(max_dd) final_balances = np.array(final_balances) max_drawdowns = np.array(max_drawdowns) print("\n================ MONTE CARLO ANALYSIS RESULTS (10,000 RUNS) ================") print(f"1. Probability of Ruin (Saldo Hancur <= $5): {np.mean(final_balances <= 5.0)*100:.2f}%") print(f"2. Median Expected Balance: ${np.median(final_balances):.2f} (Pertumbuhan +{(np.median(final_balances)/25.0 - 1)*100:.1f}%)") print(f"3. 5th Percentile (Kasus 5% Terburuk): ${np.percentile(final_balances, 5):.2f}") print(f"4. 95th Percentile (Kasus 5% Terbaik): ${np.percentile(final_balances, 95):.2f}") print(f"5. Median Max Drawdown: {np.median(max_drawdowns)*100:.2f}%") print(f"6. 95% Confidence Value at Risk (VaR): {np.percentile(max_drawdowns, 95)*100:.2f}%") print("============================================================================\n") exit(0) import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = False timeframe = "15m" informative_timeframe = "1h" # Hyperoptable Parameters trend_buy_rsi_min = IntParameter(40, 52, default=44, space="buy") trend_buy_rsi_max = IntParameter(54, 66, default=60, space="buy") range_buy_rsi_max = IntParameter(28, 42, default=36, space="buy") adx_regime_threshold = IntParameter(18, 26, default=22, space="buy") # High-Expectancy ROI Runway (in minutes) minimal_roi = { "0": 0.052, # 5.2% instant target "60": 0.034, # 3.4% after 1 hour "180": 0.022, # 2.2% after 3 hours "360": 0.014, # 1.4% after 6 hours "720": 0.006 # 0.6% after 12 hours } # Strict Protective Hard Stoploss (-2.8%) stoploss = -0.028 # Dynamic Trailing Profit Lock trailing_stop = True trailing_stop_positive = 0.010 # Lock +1.0% once +2.0% is reached trailing_stop_positive_offset = 0.020 trailing_only_offset_is_reached = True 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: return 2.0 def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): """ Cut stagnant trades after 18 hours to keep capital rotating for fresh setups. """ trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600 if trade_duration > 18.0 and current_profit < 0.003: return "stale_trade_18h_cutoff" return None def informative_1h_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["rsi"] = ta.RSI(dataframe, timeperiod=14) # 1h Macro Classification dataframe["macro_bull_1h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Merge 1h Informative informative_1h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) informative_1h = self.informative_1h_indicators(informative_1h, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.informative_timeframe, ffill=True) # 15m Indicators 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # Bollinger Bands (20, 2.0 std) 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"] # Donchian 24-Period S/R Channel (6-hour Support/Resistance) dataframe["support_6h"] = dataframe["low"].rolling(window=24).min() dataframe["resistance_6h"] = dataframe["high"].rolling(window=24).max() # Stochastic (14, 3, 3) stoch = ta.STOCH(dataframe, fastk_period=14, slowk_period=3, slowd_period=3) dataframe["slowk"] = stoch["slowk"] dataframe["slowd"] = stoch["slowd"] # Price Action Candle Metrics dataframe["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) # Volume Mean dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # Sinyal 1: Trend-Pullback Engine (Saat 1H Trending & ADX Kuat) trend_conditions = ( (dataframe[f"macro_bull_1h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] >= self.adx_regime_threshold.value) & ( (dataframe["close"] <= dataframe["ema_21"] * 1.004) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.992) & (dataframe["rsi"] >= self.trend_buy_rsi_min.value) & (dataframe["rsi"] <= self.trend_buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < 50) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.60) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) # Sinyal 2: Support-Bounce Engine (Saat 1H Sideways / Konsolidasi) range_conditions = ( (dataframe[f"adx_{self.informative_timeframe}"] < self.adx_regime_threshold.value) & ( (dataframe["close"] <= dataframe["bb_lowerband"] * 1.003) | (dataframe["low"] <= dataframe["support_6h"] * 1.002) ) & (dataframe["rsi"] <= self.range_buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < 40) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.75) ) dataframe.loc[trend_conditions, ["enter_long", "enter_tag"]] = (1, "trend_pullback") dataframe.loc[range_conditions & ~trend_conditions, ["enter_long", "enter_tag"]] = (1, "range_support_bounce") 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 Take Profit on Upper BB Expansion + Overbought RSI exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 72) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_upper_bb_tp") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 written successfully at", target_strategy) exit(0) backtest_dir = Path("user_data/backtest_results") zips = sorted(backtest_dir.glob("*.zip"), key=lambda x: x.stat().st_mtime, reverse=True) latest_zip = zips[0] with zipfile.ZipFile(latest_zip, "r") as z: trade_files = [f for f in z.namelist() if f.endswith(".json") and not f.endswith(".meta.json")] with z.open(trade_files[0]) as f: data = json.load(f) strat = list(data["strategy"].keys())[0] results = data["strategy"][strat]["results_per_pair"] print(f"\nPAIR BREAKDOWN FOR {strat}:") print("-" * 80) for r in results: key = r.get("key", "") trades = r.get("trades", 0) profit_pct = r.get("profit_mean_pct", 0) profit_tot = r.get("profit_total_abs", 0) wins = r.get("wins", 0) win_pct = (wins / trades * 100) if trades > 0 else 0 print(f"{key:<16} | Trades: {trades:<4} | Wins: {wins:<3} | Winrate: {win_pct:>5.1f}% | Tot Profit: {profit_tot:>8.2f} USDT") trades = data["strategy"][strat]["trades"] df_trades = pd.DataFrame(trades) df_trades["close_date"] = pd.to_datetime(df_trades["close_date"]) df_trades = df_trades.sort_values("close_date") df_trades["profit_ratio"] = df_trades["profit_ratio"].astype(float) daily_returns = df_trades.set_index("close_date")["profit_ratio"].resample("D").sum().fillna(0.0) report_path = Path("user_data/quantstats_tearsheet.html") qs.reports.html(daily_returns, output=str(report_path), title=f"Azura Portfolio Alpha ({strat}) - QuantStats Tearsheet") print(f"\nQuantStats Tearsheet successfully updated at: {report_path}") # 2. Write Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py strategy_code = '''""" Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 - Active Intraday Trend & Pullback Engine Architecture: - Base: 15m (Active Intraday Execution) - Informative: 4h (Macro Trend Gate) - Asymmetric R:R: Targets +3.5% to +6.0% with tight -2.5% stop to eliminate fee drag. - Frequency: ~1-2 trades per day with high winrate. """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = False timeframe = "15m" informative_timeframe = "4h" # Hyperoptable Buy / Entry Parameters buy_rsi_min = IntParameter(36, 48, default=42, space="buy") buy_rsi_max = IntParameter(50, 64, default=58, space="buy") buy_stoch_max = IntParameter(25, 55, default=40, space="buy") buy_adx_4h = IntParameter(14, 26, default=18, space="buy") # High-Expectancy ROI Runway (in minutes) minimal_roi = { "0": 0.055, # 5.5% instant target "60": 0.038, # 3.8% after 1 hour "180": 0.024, # 2.4% after 3 hours "360": 0.015, # 1.5% after 6 hours "720": 0.008 # 0.8% after 12 hours } # Strict Protective Hard Stoploss stoploss = -0.026 # Dynamic Trailing Profit Lock trailing_stop = True trailing_stop_positive = 0.012 # Lock +1.2% once +2.2% is reached trailing_stop_positive_offset = 0.022 trailing_only_offset_is_reached = True 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: return 2.0 def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): """ Auto-cut trades held > 12 hours if stagnant to recycle margin. """ trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600 if trade_duration > 12.0 and current_profit < 0.004: return "intraday_time_cutoff_12h" return None 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["rsi"] = ta.RSI(dataframe, timeperiod=14) # Macro Trend Gate: 4h Bullish dataframe["macro_bull_4h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Merge 4h Informative informative_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) informative_4h = self.informative_4h_indicators(informative_4h, metadata) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, self.informative_timeframe, ffill=True) # 15m Indicators 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # Bollinger Bands (20, 2.0 std) 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"] # Stochastic (14, 3, 3) stoch = ta.STOCH(dataframe, fastk_period=14, slowk_period=3, slowd_period=3) dataframe["slowk"] = stoch["slowk"] dataframe["slowd"] = stoch["slowd"] # Price Action Candle Metrics dataframe["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) # Volume Mean dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # 15m Pullback Dip Rules: # 1. 4h Macro Trend is Bullish & 4h ADX > threshold # 2. 15m Price pulled back to EMA 21 or BB Middleband # 3. 15m RSI between min and max (discount zone) # 4. Stochastic crosses upward from oversold # 5. Bullish bounce candle (Green OR lower wick rejection) long_conditions = ( (dataframe[f"macro_bull_4h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_4h.value) & ( (dataframe["close"] <= dataframe["ema_21"] * 1.004) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.992) & (dataframe["rsi"] >= self.buy_rsi_min.value) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < self.buy_stoch_max.value) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.75) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_intraday_dip") 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 Take Profit on Upper Bollinger Expansion + Overbought RSI exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 72) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_upper_bb_tp") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 written successfully at", target_strategy) # 2. Write Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py strategy_code = '''""" Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 - High-Frequency Mean-Reversion Scalper Architecture: - Base Timeframe: 5m (Precision Dip Execution) - Informative Timeframe: 1h (Macro Trend Gate) - Logic: Buys oversold RSI & Bollinger Band extremes during 1h uptrend. - Target Frequency: 1-3 trades per day with fast profit taking. """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = False timeframe = "5m" informative_timeframe = "1h" # Hyperoptable Buy / Entry Parameters buy_rsi_max = IntParameter(28, 42, default=35, space="buy") buy_stoch_max = IntParameter(20, 48, default=35, space="buy") buy_adx_1h = IntParameter(14, 28, default=18, space="buy") # Fast Scalp ROI Table (in minutes) minimal_roi = { "0": 0.022, # 2.2% instant target "20": 0.015, # 1.5% after 20 mins "45": 0.010, # 1.0% after 45 mins "90": 0.006, # 0.6% after 1.5 hours "180": 0.003 # 0.3% after 3 hours } # Strict Protective Stoploss (-2.0% max loss per scalp) stoploss = -0.020 # Dynamic Trailing Profit Lock trailing_stop = True trailing_stop_positive = 0.006 # Lock +0.6% profit once +1.0% is reached trailing_stop_positive_offset = 0.010 trailing_only_offset_is_reached = True 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: return 2.0 def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): """ Auto-cut scalps if stagnant after 4 hours to free margin for fresh dips. """ trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600 if trade_duration > 4.0 and current_profit < 0.002: return "scalp_time_cutoff_4h" return None def informative_1h_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["rsi"] = ta.RSI(dataframe, timeperiod=14) # Macro Trend Gate: 1h Bullish dataframe["macro_bull_1h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Merge 1h Informative informative_1h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) informative_1h = self.informative_1h_indicators(informative_1h, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.informative_timeframe, ffill=True) # 5m Indicators 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # Bollinger Bands (20, 2.0 std) 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"] # Stochastic (14, 3, 3) stoch = ta.STOCH(dataframe, fastk_period=14, slowk_period=3, slowd_period=3) dataframe["slowk"] = stoch["slowk"] dataframe["slowd"] = stoch["slowd"] # Price Action Candle Metrics dataframe["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) # Volume Mean dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # 5m Dip Scalp Rules: # 1. 1h Macro Trend is Bullish & 1h ADX > threshold # 2. 5m Price dips to or below Lower Bollinger Band # 3. 5m RSI in oversold zone (<= buy_rsi_max) # 4. Stochastic crosses upward from oversold # 5. Bullish bounce candle (Green OR lower wick rejection) long_conditions = ( (dataframe[f"macro_bull_1h_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_1h.value) & ( (dataframe["close"] <= dataframe["bb_lowerband"] * 1.002) | (dataframe["low"] <= dataframe["bb_lowerband"]) ) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < self.buy_stoch_max.value) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.70) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.70) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_dip_scalp") 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 Scalp Take Profit on Mean Reversion (Mid/Upper Bollinger + RSI > 65) exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_middleband"]) & (dataframe["rsi"] > 65) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_mean_reversion") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 written successfully at", target_strategy) # 2. Write Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py strategy_code = '''""" Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 - Institutional Multi-Timeframe Alpha Engine Features: 1. Multi-Timeframe Gating (1D Macro + 4H Regime + 1H Execution). 2. Anti-Chasing Dip/Pullback Entry: Only buys on EMA 21/50 pullbacks with Stochastic/RSI reversal & price action confirmation. 3. Strict Short Protection: Shorting permitted ONLY during confirmed 1D + 4H bear regimes. 4. Dynamic Break-Even Stop: Once trade hits +1.2% profit, stoploss is dynamically moved to +0.2% (Fee-Proof Break-Even). 5. Time-Decay Auto-Cut: Stale losing positions > 36h are closed to eliminate zombie drawdowns. """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) from freqtrade.persistence import Trade import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # Hyperoptable Buy / Dip Entry Parameters buy_rsi_min = IntParameter(34, 46, default=40, space="buy") buy_rsi_max = IntParameter(48, 62, default=56, space="buy") buy_stoch_max = IntParameter(30, 58, default=45, space="buy") buy_adx_4h = IntParameter(14, 26, default=18, space="buy") # Hyperoptable Sell / Rip Entry Parameters sell_rsi_min = IntParameter(38, 52, default=42, space="sell") sell_rsi_max = IntParameter(52, 66, default=58, space="sell") sell_stoch_min = IntParameter(42, 70, default=52, space="sell") sell_adx_4h = IntParameter(14, 26, default=18, space="sell") # High-Expectancy ROI Runway minimal_roi = { "0": 0.080, # 8% target "180": 0.050, # 5% after 3 hours "360": 0.035, # 3.5% after 6 hours "720": 0.022, # 2.2% after 12 hours "1440": 0.012 # 1.2% after 24 hours } # Strict Protective Hard Stoploss stoploss = -0.035 # Trailing Stop Configuration trailing_stop = False use_custom_stoploss = True 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: return 2.0 def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: """ Dynamic Break-Even & Trailing Profit Lock: - If profit >= +3.0%: Lock +1.8% profit - If profit >= +1.8%: Lock +0.8% profit - If profit >= +1.2%: Move stop to Break-Even (+0.2% to cover fees) - Otherwise: Maintain hard stoploss (-3.5%) """ if current_profit >= 0.030: return -0.012 elif current_profit >= 0.018: return -0.010 elif current_profit >= 0.012: return 0.002 return self.stoploss def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): """ Time-Decay Exit: Cut stale trades if they have not moved after 36 hours. """ trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600 if trade_duration > 36 and current_profit < -0.010: return "time_decay_stale_loss" if trade_duration > 48 and current_profit < 0.005: return "time_decay_48h_cutoff" return None def informative_1d_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["macro_bear_1d"] = dataframe["close"] < dataframe["ema_50"] dataframe["macro_bull_1d"] = dataframe["close"] > dataframe["ema_50"] return dataframe 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["macro_bull_4h"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_50"]) dataframe["macro_bear_4h"] = (dataframe["ema_50"] < dataframe["ema_200"]) & (dataframe["close"] < dataframe["ema_50"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1d = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="1d") informative_1d = self.informative_1d_indicators(informative_1d, metadata) dataframe = merge_informative_pair(dataframe, informative_1d, self.timeframe, "1d", ffill=True) informative_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="4h") informative_4h = self.informative_4h_indicators(informative_4h, metadata) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, "4h", ffill=True) 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(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["body_size"] = (dataframe["close"] - dataframe["open"]).abs() dataframe["is_green"] = dataframe["close"] > dataframe["open"] dataframe["is_red"] = dataframe["close"] < dataframe["open"] dataframe["lower_wick"] = np.where(dataframe["is_green"], dataframe["open"] - dataframe["low"], dataframe["close"] - dataframe["low"]) dataframe["upper_wick"] = np.where(dataframe["is_green"], dataframe["high"] - dataframe["close"], dataframe["high"] - dataframe["open"]) dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" long_conditions = ( (dataframe["macro_bull_4h_4h"] == True) & (dataframe["adx_4h"] > self.buy_adx_4h.value) & ( (dataframe["close"] <= dataframe["ema_21"] * 1.008) | (dataframe["low"] <= dataframe["bb_middleband"]) ) & (dataframe["close"] >= dataframe["ema_50"] * 0.990) & (dataframe["rsi"] >= self.buy_rsi_min.value) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < self.buy_stoch_max.value) & ( (dataframe["is_green"] == True) | (dataframe["lower_wick"] > dataframe["body_size"] * 0.75) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.75) ) short_conditions = ( (dataframe["macro_bear_1d_1d"] == True) & (dataframe["macro_bear_4h_4h"] == True) & (dataframe["adx_4h"] > self.sell_adx_4h.value) & ( (dataframe["close"] >= dataframe["ema_21"] * 0.992) | (dataframe["high"] >= dataframe["bb_middleband"]) ) & (dataframe["close"] <= dataframe["ema_50"] * 1.010) & (dataframe["rsi"] <= self.sell_rsi_max.value) & (dataframe["rsi"] >= self.sell_rsi_min.value) & (qtpylib.crossed_below(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] > self.sell_stoch_min.value) & ( (dataframe["is_red"] == True) | (dataframe["upper_wick"] > dataframe["body_size"] * 0.75) ) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.75) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_precision_dip") dataframe.loc[short_conditions, ["enter_short", "enter_tag"]] = (1, "short_precision_rip") 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"] = "" exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 74) ) exit_short_conditions = ( (dataframe["close"] <= dataframe["bb_lowerband"]) & (dataframe["rsi"] < 26) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_long_tp") dataframe.loc[exit_short_conditions, ["exit_short", "exit_tag"]] = (1, "exit_short_tp") return dataframe ''' strategy_code = '''""" Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 - Quantitative Multi-Timeframe Trend & Volatility Strategy Hyperoptable parameters for institutional parameter tuning Base Timeframe: 1h Informative Timeframe: 4h """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, BooleanParameter, merge_informative_pair, ) import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "1h" informative_timeframe = "4h" # Hyperoptable Buy / Entry Parameters buy_adx_4h = IntParameter(14, 30, default=18, space="buy") buy_adx_1h = IntParameter(12, 28, default=16, space="buy") buy_rsi_min = IntParameter(45, 58, default=50, space="buy") buy_rsi_max = IntParameter(60, 75, default=68, space="buy") # Hyperoptable Sell / Short Parameters sell_adx_4h = IntParameter(14, 30, default=18, space="sell") sell_adx_1h = IntParameter(12, 28, default=16, space="sell") sell_rsi_min = IntParameter(25, 40, default=32, space="sell") sell_rsi_max = IntParameter(42, 55, default=50, space="sell") # Dynamic Profit Harvesting minimal_roi = { "0": 0.12, "240": 0.08, "720": 0.05, "1440": 0.03, "2880": 0.015 } # Stoploss stoploss = -0.040 # Trailing Stop trailing_stop = True trailing_stop_positive = 0.020 trailing_stop_positive_offset = 0.035 trailing_only_offset_is_reached = True 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: return 2.0 def informative_4h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["macro_bull"] = (dataframe["ema_20"] > dataframe["ema_50"]) & (dataframe["close"] > dataframe["ema_200"]) dataframe["macro_bear"] = (dataframe["ema_20"] < dataframe["ema_50"]) & (dataframe["close"] < dataframe["ema_200"]) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_4h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) informative_4h = self.informative_4h_indicators(informative_4h, metadata) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, self.informative_timeframe, ffill=True) 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["donchian_high"] = dataframe["high"].rolling(window=20).max() dataframe["donchian_low"] = dataframe["low"].rolling(window=20).min() dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" long_conditions = ( (dataframe[f"macro_bull_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.buy_adx_4h.value) & ( (qtpylib.crossed_above(dataframe["ema_9"], dataframe["ema_21"])) | (dataframe["close"] > dataframe["donchian_high"].shift(1)) ) & (dataframe["rsi"] >= self.buy_rsi_min.value) & (dataframe["rsi"] <= self.buy_rsi_max.value) & (dataframe["adx"] > self.buy_adx_1h.value) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.95) ) short_conditions = ( (dataframe[f"macro_bear_{self.informative_timeframe}"] == True) & (dataframe[f"adx_{self.informative_timeframe}"] > self.sell_adx_4h.value) & ( (qtpylib.crossed_below(dataframe["ema_9"], dataframe["ema_21"])) | (dataframe["close"] < dataframe["donchian_low"].shift(1)) ) & (dataframe["rsi"] <= self.sell_rsi_max.value) & (dataframe["rsi"] >= self.sell_rsi_min.value) & (dataframe["adx"] > self.sell_adx_1h.value) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.95) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_alpha_trend") dataframe.loc[short_conditions, ["enter_short", "enter_tag"]] = (1, "short_alpha_trend") 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"] = "" exit_long_conditions = ( (qtpylib.crossed_below(dataframe["ema_9"], dataframe["ema_50"])) | (dataframe["rsi"] > 80) ) exit_short_conditions = ( (qtpylib.crossed_above(dataframe["ema_9"], dataframe["ema_50"])) | (dataframe["rsi"] < 20) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_long_reversal") dataframe.loc[exit_short_conditions, ["exit_short", "exit_tag"]] = (1, "exit_short_reversal") return dataframe """ Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949 - Quantitative High-Expectancy Pullback Strategy Designed with: 1. Confluence Filter: Macro 1h ADX > 25 & EMA 100/200 Trend 2. Sniper Entry: Deep Pullbacks into 15m BB lower/upper bands with volume exhaustion 3. Asymmetric Payoff: Positive expectancy (Reward:Risk >= 1.6:1), reduced trade churn """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from freqtrade.strategy import ( IStrategy, merge_informative_pair, ) import talib.abstract as ta from technical import qtpylib class Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949(IStrategy): INTERFACE_VERSION = 3 can_short: bool = True timeframe = "15m" informative_timeframe = "1h" # Positive Expectancy ROI Runway minimal_roi = { "0": 0.065, # 6.5% TP target "45": 0.045, # 4.5% TP after 45 mins "120": 0.030, # 3.0% TP after 2 hours "240": 0.020 # 2.0% TP after 4 hours } # Strict protective stoploss stoploss = -0.018 # -1.8% stoploss # Dynamic Trailing Profit Lock trailing_stop = True trailing_stop_positive = 0.015 trailing_stop_positive_offset = 0.025 trailing_only_offset_is_reached = True 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: return 2.0 def informative_1h_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["rsi"] = ta.RSI(dataframe, timeperiod=14) # High Quality Trend: EMA alignment + ADX > 24 dataframe["macro_bull"] = (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["adx"] > 24) dataframe["macro_bear"] = (dataframe["ema_50"] < dataframe["ema_200"]) & (dataframe["adx"] > 24) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) informative_1h = self.informative_1h_indicators(informative_1h, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.informative_timeframe, ffill=True) # 15m Indicators dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # Bollinger Bands (2.2 std dev for deep high-probability pullbacks) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2.2) dataframe["bb_lowerband"] = bollinger["lower"] dataframe["bb_middleband"] = bollinger["mid"] dataframe["bb_upperband"] = bollinger["upper"] # Stochastic Fast stoch = ta.STOCH(dataframe, fastk_period=14, slowk_period=3, slowd_period=3) dataframe["slowk"] = stoch["slowk"] dataframe["slowd"] = stoch["slowd"] # Volume Mean dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() 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"] = "" # Sniper Long: Macro 1h Bullish (ADX>24) + Deep 15m Oversold (Price <= BB Lower or RSI < 32) + Stoch cross long_conditions = ( (dataframe[f"macro_bull_{self.informative_timeframe}"] == True) & ( (dataframe["close"] <= dataframe["bb_lowerband"]) | (dataframe["rsi"] < 32) ) & (qtpylib.crossed_above(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] < 35) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.8) ) # Sniper Short: Macro 1h Bearish (ADX>24) + Deep 15m Overbought (Price >= BB Upper or RSI > 68) + Stoch cross short_conditions = ( (dataframe[f"macro_bear_{self.informative_timeframe}"] == True) & ( (dataframe["close"] >= dataframe["bb_upperband"]) | (dataframe["rsi"] > 68) ) & (qtpylib.crossed_below(dataframe["slowk"], dataframe["slowd"])) & (dataframe["slowk"] > 65) & (dataframe["volume"] > dataframe["volume_mean_20"] * 0.8) ) dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = (1, "long_sniper_dip") dataframe.loc[short_conditions, ["enter_short", "enter_tag"]] = (1, "short_sniper_rip") 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"] = "" # Exit Long: Price exceeds BB Upperband and RSI > 75 exit_long_conditions = ( (dataframe["close"] >= dataframe["bb_upperband"]) & (dataframe["rsi"] > 75) ) # Exit Short: Price falls below BB Lowerband and RSI < 25 exit_short_conditions = ( (dataframe["close"] <= dataframe["bb_lowerband"]) & (dataframe["rsi"] < 25) ) dataframe.loc[exit_long_conditions, ["exit_long", "exit_tag"]] = (1, "exit_long_tp") dataframe.loc[exit_short_conditions, ["exit_short", "exit_tag"]] = (1, "exit_short_tp") return dataframe ''' target_strategy = Path("user_data/strategies/Github_geovanybramanthya_AzuraAzureQuant__make_config__20260925_134949.py") with open(target_strategy, "w", encoding="utf-8") as f: f.write(strategy_code) print("Strategy regenerated successfully at", target_strategy)