# source: https://raw.githubusercontent.com/aldrinmagno/FreqtradeStrategies/3d9c1b176faceca3c950f99c3c55c8a7abe77275/user_data/strategies/NostalgiaForInfinityX6.py """ Github_aldrinmagno_FreqtradeStrategies__NostalgiaForInfinityX6__20260317_150802 — Institutional-Grade Multi-Signal System (Simplified) Source: https://github.com/iterativv/NostalgiaForInfinity (simplified adaptation) Timeframe: 5m (informative: 15m, 1h, 1d) Description: Faithful simplified version of the NostalgiaForInfinityX architecture. Features 8 distinct buy conditions, 5 sell conditions, derisking system, and multi-timeframe indicator analysis. Blacklisted leveraged tokens: *UP/USDT, *DOWN/USDT, *BULL/USDT, *BEAR/USDT, *2L/USDT, *2S/USDT, *3L/USDT, *3S/USDT, *4L/USDT, *4S/USDT, *5L/USDT, *5S/USDT These should be excluded via pair_blacklist in config. """ from functools import reduce from freqtrade.strategy import ( IStrategy, BooleanParameter, DecimalParameter, IntParameter, merge_informative_pair, ) from pandas import DataFrame import numpy as np import pandas_ta as pta class Github_aldrinmagno_FreqtradeStrategies__NostalgiaForInfinityX6__20260317_150802(IStrategy): INTERFACE_VERSION: int = 3 timeframe: str = "5m" # Fully signal-driven minimal_roi: dict = {"0": 100} stoploss: float = -0.99 use_exit_signal: bool = True exit_profit_only: bool = False ignore_roi_if_entry_signal: bool = True # Informative timeframes inf_timeframes: list = ["15m", "1h", "1d"] # ----------------------------------------------------------------------- # Buy condition toggles # ----------------------------------------------------------------------- buy_cond_1_enabled = BooleanParameter(default=True, space="buy") buy_cond_2_enabled = BooleanParameter(default=True, space="buy") buy_cond_3_enabled = BooleanParameter(default=True, space="buy") buy_cond_4_enabled = BooleanParameter(default=True, space="buy") buy_cond_5_enabled = BooleanParameter(default=True, space="buy") buy_cond_6_enabled = BooleanParameter(default=True, space="buy") buy_cond_7_enabled = BooleanParameter(default=True, space="buy") buy_cond_8_enabled = BooleanParameter(default=True, space="buy") # ----------------------------------------------------------------------- # Buy hyperopt parameters — per condition # ----------------------------------------------------------------------- # Condition 1 — EMA crossover + RSI buy_1_rsi = IntParameter(25, 50, default=40, space="buy") # Condition 2 — BB lower band bounce + volume buy_2_rsi = IntParameter(20, 40, default=30, space="buy") buy_2_mfi = IntParameter(15, 40, default=30, space="buy") # Condition 3 — Williams %R oversold buy_3_willr = IntParameter(-99, -70, default=-85, space="buy") buy_3_rsi = IntParameter(20, 45, default=35, space="buy") # Condition 4 — VWAP dip buy_4_vwap_factor = DecimalParameter(0.96, 1.0, default=0.99, space="buy") buy_4_rsi = IntParameter(20, 45, default=35, space="buy") buy_4_rsi_1h_min = IntParameter(30, 55, default=40, space="buy") # Condition 5 — 1h StochRSI oversold buy_5_stochrsi_k = IntParameter(10, 35, default=20, space="buy") buy_5_stochrsi_d = IntParameter(10, 35, default=20, space="buy") buy_5_rsi = IntParameter(20, 45, default=35, space="buy") # Condition 6 — EMA 50/200 golden zone buy_6_rsi = IntParameter(15, 40, default=30, space="buy") buy_6_willr_1h = IntParameter(-99, -60, default=-75, space="buy") # Condition 7 — BB squeeze buy_7_bb_width = DecimalParameter(0.02, 0.08, default=0.04, space="buy") buy_7_rsi = IntParameter(20, 42, default=32, space="buy") buy_7_rsi_1h_min = IntParameter(35, 60, default=45, space="buy") # Condition 8 — Multi-TF RSI alignment buy_8_rsi = IntParameter(15, 38, default=28, space="buy") buy_8_rsi_15m = IntParameter(20, 45, default=35, space="buy") buy_8_rsi_1h = IntParameter(30, 55, default=45, space="buy") # ----------------------------------------------------------------------- # Sell hyperopt parameters # ----------------------------------------------------------------------- # Condition 1 — RSI overbought sell_1_rsi = IntParameter(60, 85, default=70, space="sell") # Condition 2 — Williams %R overbought sell_2_willr = IntParameter(-20, -3, default=-10, space="sell") sell_2_rsi = IntParameter(55, 80, default=65, space="sell") # Condition 3 — EMA bearish crossover sell_3_rsi = IntParameter(50, 75, default=60, space="sell") # Condition 4 — MFI overbought sell_4_mfi = IntParameter(70, 95, default=80, space="sell") sell_4_bb_factor = DecimalParameter(0.97, 1.01, default=0.99, space="sell") # Condition 5 — 1h StochRSI overbought sell_5_stochrsi = IntParameter(65, 95, default=80, space="sell") sell_5_rsi = IntParameter(55, 80, default=65, space="sell") # Derisking threshold derisk_profit_threshold = DecimalParameter(-0.08, -0.01, default=-0.03, space="sell") def informative_pairs(self) -> list: pairs = self.dp.current_whitelist() informative_pairs = [] for tf in self.inf_timeframes: informative_pairs.extend([(pair, tf) for pair in pairs]) return informative_pairs def _stochastic_rsi( self, dataframe: DataFrame, rsi_length: int = 14, stoch_length: int = 14, smooth_k: int = 3, smooth_d: int = 3, ) -> tuple: """Stochastic RSI indicator.""" rsi = pta.rsi(dataframe["close"], length=rsi_length) stoch_rsi_k = ( (rsi - rsi.rolling(window=stoch_length).min()) / (rsi.rolling(window=stoch_length).max() - rsi.rolling(window=stoch_length).min()) ) * 100 stoch_rsi_k = stoch_rsi_k.rolling(window=smooth_k).mean() stoch_rsi_d = stoch_rsi_k.rolling(window=smooth_d).mean() return stoch_rsi_k, stoch_rsi_d def _williams_r(self, dataframe: DataFrame, length: int = 14) -> "Series": """Williams %R indicator.""" highest_high = dataframe["high"].rolling(window=length).max() lowest_low = dataframe["low"].rolling(window=length).min() wr = ((highest_high - dataframe["close"]) / (highest_high - lowest_low)) * -100 return wr def _compute_vwap(self, dataframe: DataFrame) -> "Series": """Compute VWAP (Volume Weighted Average Price).""" typical_price = (dataframe["high"] + dataframe["low"] + dataframe["close"]) / 3 cumulative_tp_vol = (typical_price * dataframe["volume"]).cumsum() cumulative_vol = dataframe["volume"].cumsum() return cumulative_tp_vol / cumulative_vol def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # ===================================================================== # 5m indicators # ===================================================================== # EMAs for length in [5, 8, 13, 21, 50, 200]: dataframe[f"ema_{length}"] = pta.ema(dataframe["close"], length=length) # RSI dataframe["rsi_14"] = pta.rsi(dataframe["close"], length=14) # Bollinger Bands bb = pta.bbands(dataframe["close"], length=20, std=2.0) dataframe["bb_lowerband"] = bb["BBL_20_2.0"] dataframe["bb_middleband"] = bb["BBM_20_2.0"] dataframe["bb_upperband"] = bb["BBU_20_2.0"] dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] # Williams %R dataframe["willr_14"] = self._williams_r(dataframe, length=14) # VWAP dataframe["vwap"] = self._compute_vwap(dataframe) # MFI dataframe["mfi_14"] = pta.mfi( dataframe["high"], dataframe["low"], dataframe["close"], dataframe["volume"], length=14 ) # SMA 200 for trend dataframe["sma_200"] = pta.sma(dataframe["close"], length=200) # ===================================================================== # 15m informative # ===================================================================== inf_15m = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="15m") inf_15m["rsi_14_15m"] = pta.rsi(inf_15m["close"], length=14) dataframe = merge_informative_pair(dataframe, inf_15m, self.timeframe, "15m", ffill=True) # ===================================================================== # 1h informative # ===================================================================== inf_1h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="1h") inf_1h["rsi_14_1h"] = pta.rsi(inf_1h["close"], length=14) for length in [5, 8, 13, 21, 50, 200]: inf_1h[f"ema_{length}_1h"] = pta.ema(inf_1h["close"], length=length) inf_1h["willr_14_1h"] = self._williams_r(inf_1h, length=14) stoch_k, stoch_d = self._stochastic_rsi(inf_1h) inf_1h["stochrsi_k_1h"] = stoch_k inf_1h["stochrsi_d_1h"] = stoch_d dataframe = merge_informative_pair(dataframe, inf_1h, self.timeframe, "1h", ffill=True) # ===================================================================== # 1d informative # ===================================================================== inf_1d = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="1d") inf_1d["ema_200_1d"] = pta.ema(inf_1d["close"], length=200) inf_1d["rsi_14_1d"] = pta.rsi(inf_1d["close"], length=14) dataframe = merge_informative_pair(dataframe, inf_1d, self.timeframe, "1d", ffill=True) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions: list = [] # ----------------------------------------------------------------- # Buy condition 1 — EMA crossover with RSI confirmation # ----------------------------------------------------------------- if self.buy_cond_1_enabled.value: buy_cond_1 = ( (dataframe["ema_5"] > dataframe["ema_13"]) & (dataframe["ema_13"] > dataframe["ema_21"]) & (dataframe["rsi_14"] < self.buy_1_rsi.value) & (dataframe["close"] < dataframe["bb_lowerband"]) & (dataframe["volume"] > 0) ) conditions.append(buy_cond_1) # ----------------------------------------------------------------- # Buy condition 2 — BB lower band bounce with volume # ----------------------------------------------------------------- if self.buy_cond_2_enabled.value: buy_cond_2 = ( (dataframe["close"] < dataframe["bb_lowerband"]) & (dataframe["rsi_14"] < self.buy_2_rsi.value) & (dataframe["mfi_14"] < self.buy_2_mfi.value) & (dataframe["volume"] > dataframe["volume"].shift(1)) ) conditions.append(buy_cond_2) # ----------------------------------------------------------------- # Buy condition 3 — Williams %R oversold with EMA support # ----------------------------------------------------------------- if self.buy_cond_3_enabled.value: buy_cond_3 = ( (dataframe["willr_14"] < self.buy_3_willr.value) & (dataframe["close"] > dataframe["ema_200"]) & (dataframe["rsi_14"] < self.buy_3_rsi.value) & (dataframe["volume"] > 0) ) conditions.append(buy_cond_3) # ----------------------------------------------------------------- # Buy condition 4 — VWAP dip buy # ----------------------------------------------------------------- if self.buy_cond_4_enabled.value: buy_cond_4 = ( (dataframe["close"] < dataframe["vwap"] * self.buy_4_vwap_factor.value) & (dataframe["rsi_14"] < self.buy_4_rsi.value) & (dataframe[f"rsi_14_1h_1h"] > self.buy_4_rsi_1h_min.value) & (dataframe["volume"] > 0) ) conditions.append(buy_cond_4) # ----------------------------------------------------------------- # Buy condition 5 — 1h Stochastic RSI oversold # ----------------------------------------------------------------- if self.buy_cond_5_enabled.value: buy_cond_5 = ( (dataframe[f"stochrsi_k_1h_1h"] < self.buy_5_stochrsi_k.value) & (dataframe[f"stochrsi_d_1h_1h"] < self.buy_5_stochrsi_d.value) & (dataframe["rsi_14"] < self.buy_5_rsi.value) & (dataframe["close"] < dataframe["ema_50"]) & (dataframe["volume"] > 0) ) conditions.append(buy_cond_5) # ----------------------------------------------------------------- # Buy condition 6 — EMA 50/200 golden zone with RSI # ----------------------------------------------------------------- if self.buy_cond_6_enabled.value: buy_cond_6 = ( (dataframe["close"] < dataframe["ema_50"]) & (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["rsi_14"] < self.buy_6_rsi.value) & (dataframe[f"willr_14_1h_1h"] < self.buy_6_willr_1h.value) & (dataframe["volume"] > 0) ) conditions.append(buy_cond_6) # ----------------------------------------------------------------- # Buy condition 7 — BB squeeze with 1h RSI divergence # ----------------------------------------------------------------- if self.buy_cond_7_enabled.value: buy_cond_7 = ( (dataframe["bb_width"] < self.buy_7_bb_width.value) & (dataframe["close"] < dataframe["bb_lowerband"]) & (dataframe[f"rsi_14_1h_1h"] > self.buy_7_rsi_1h_min.value) & (dataframe["rsi_14"] < self.buy_7_rsi.value) & (dataframe["volume"] > 0) ) conditions.append(buy_cond_7) # ----------------------------------------------------------------- # Buy condition 8 — Multi-timeframe RSI alignment # ----------------------------------------------------------------- if self.buy_cond_8_enabled.value: buy_cond_8 = ( (dataframe["rsi_14"] < self.buy_8_rsi.value) & (dataframe[f"rsi_14_15m_15m"] < self.buy_8_rsi_15m.value) & (dataframe[f"rsi_14_1h_1h"] < self.buy_8_rsi_1h.value) & (dataframe["close"] < dataframe["sma_200"]) & (dataframe["close"] < dataframe["bb_middleband"]) & (dataframe["volume"] > 0) ) conditions.append(buy_cond_8) # OR all conditions together if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), "enter_long"] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions: list = [] # ----------------------------------------------------------------- # Sell condition 1 — RSI overbought # ----------------------------------------------------------------- sell_cond_1 = ( (dataframe["rsi_14"] > self.sell_1_rsi.value) & (dataframe["close"] > dataframe["bb_upperband"]) & (dataframe["volume"] > 0) ) conditions.append(sell_cond_1) # ----------------------------------------------------------------- # Sell condition 2 — Williams %R overbought # ----------------------------------------------------------------- sell_cond_2 = ( (dataframe["willr_14"] > self.sell_2_willr.value) & (dataframe["rsi_14"] > self.sell_2_rsi.value) & (dataframe["volume"] > 0) ) conditions.append(sell_cond_2) # ----------------------------------------------------------------- # Sell condition 3 — EMA bearish crossover # ----------------------------------------------------------------- sell_cond_3 = ( (dataframe["ema_5"] < dataframe["ema_13"]) & (dataframe["ema_13"] < dataframe["ema_21"]) & (dataframe["rsi_14"] > self.sell_3_rsi.value) & (dataframe["volume"] > 0) ) conditions.append(sell_cond_3) # ----------------------------------------------------------------- # Sell condition 4 — MFI overbought with BB upper band # ----------------------------------------------------------------- sell_cond_4 = ( (dataframe["mfi_14"] > self.sell_4_mfi.value) & (dataframe["close"] > dataframe["bb_upperband"] * self.sell_4_bb_factor.value) & (dataframe["volume"] > 0) ) conditions.append(sell_cond_4) # ----------------------------------------------------------------- # Sell condition 5 — 1h Stochastic RSI overbought # ----------------------------------------------------------------- sell_cond_5 = ( (dataframe[f"stochrsi_k_1h_1h"] > self.sell_5_stochrsi.value) & (dataframe[f"stochrsi_d_1h_1h"] > self.sell_5_stochrsi.value) & (dataframe["rsi_14"] > self.sell_5_rsi.value) & (dataframe["volume"] > 0) ) conditions.append(sell_cond_5) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), "exit_long"] = 1 return dataframe def custom_exit( self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, **kwargs, ) -> str | bool: """ Derisking system: if open profit drops below -3% AND the 1h EMA-21 < EMA-50 (downtrend), trigger a sell. """ if current_profit > self.derisk_profit_threshold.value: return False dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) < 1: return False last_candle = dataframe.iloc[-1] ema_21_1h = last_candle.get(f"ema_21_1h_1h", 0) ema_50_1h = last_candle.get(f"ema_50_1h_1h", 0) if ema_21_1h > 0 and ema_50_1h > 0 and ema_21_1h < ema_50_1h: return "derisk_1h_downtrend" return False def custom_stoploss( self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, **kwargs, ) -> float: """Exchange stoploss safety net at -0.12.""" return -0.12