# source: https://raw.githubusercontent.com/acsvalentinacs/HOPE-Minibot/65e927a2984b68dfce48fafe50b7a35813bd1102/vps_sync/strategies/HopeHyperopt.py # -*- coding: utf-8 -*- """ HOPE Hyperopt Strategy V2 ========================= Optimized for scalping on 5m timeframe. Realistic parameter ranges based on actual market data. Created: 2026-02-07 """ from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, BooleanParameter from freqtrade.strategy import merge_informative_pair from freqtrade.persistence import Trade from pandas import DataFrame import talib.abstract as ta from datetime import datetime, timezone from typing import Optional import logging log = logging.getLogger(__name__) class Github_acsvalentinacs_HOPE_Minibot__HopeHyperopt__20260218_115300(IStrategy): """ HOPE Hyperopt V2 - realistic scalping parameters. No custom_stoploss to let hyperopt optimize stoploss/trailing freely. """ # ================================================================ # HYPEROPT PARAMETER SPACES (realistic ranges) # ================================================================ # --- BUY --- buy_rsi_max = IntParameter(25, 55, default=42, space="buy", optimize=True) buy_ema_short = IntParameter(3, 12, default=5, space="buy", optimize=True) buy_ema_long = IntParameter(13, 30, default=21, space="buy", optimize=True) buy_volume_factor = DecimalParameter(1.0, 2.0, default=1.2, decimals=1, space="buy", optimize=True) buy_mom_threshold = DecimalParameter(-0.3, 0.3, default=0.0, decimals=2, space="buy", optimize=True) buy_require_btc_uptrend = BooleanParameter(default=True, space="buy", optimize=True) buy_bb_lower = BooleanParameter(default=False, space="buy", optimize=True) buy_macd_positive = BooleanParameter(default=False, space="buy", optimize=True) # --- SELL --- sell_rsi_min = IntParameter(60, 82, default=70, space="sell", optimize=True) sell_ema_cross = BooleanParameter(default=True, space="sell", optimize=True) # --- ROI (realistic for scalping) --- minimal_roi = { "0": 0.015, "5": 0.01, "10": 0.005, "20": 0.003 } # --- STOPLOSS --- stoploss = -0.02 # --- TRAILING --- trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.01 trailing_only_offset_is_reached = True # --- SETTINGS --- timeframe = "5m" process_only_new_candles = True startup_candle_count = 50 use_exit_signal = True exit_profit_only = False use_custom_stoploss = False # Let hyperopt optimize stoploss freely order_types = { "entry": "market", "exit": "market", "stoploss": "market", "stoploss_on_exchange": False, } def informative_pairs(self): return [("BTC/USDT", "1h")] # ================================================================ # INDICATORS # ================================================================ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # BTC 1h trend if self.dp: try: btc_1h = self.dp.get_pair_dataframe("BTC/USDT", "1h") if len(btc_1h) > 5: btc_1h["btc_trend"] = (btc_1h["close"] > btc_1h["close"].shift(4)).astype(int) btc_1h["btc_rsi"] = ta.RSI(btc_1h, timeperiod=14) dataframe = merge_informative_pair( dataframe, btc_1h[["date", "btc_trend", "btc_rsi"]], self.timeframe, "1h", ffill=True ) else: dataframe["btc_trend_1h"] = 1 dataframe["btc_rsi_1h"] = 50 except Exception: dataframe["btc_trend_1h"] = 1 dataframe["btc_rsi_1h"] = 50 else: dataframe["btc_trend_1h"] = 1 dataframe["btc_rsi_1h"] = 50 # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # EMAs (all periods hyperopt might need) for p in [3, 5, 7, 8, 10, 12, 13, 15, 18, 20, 21, 25, 26, 30]: dataframe[f"ema_{p}"] = ta.EMA(dataframe, timeperiod=p) # Volume dataframe["volume_sma"] = ta.SMA(dataframe["volume"], timeperiod=10) # Momentum dataframe["mom"] = ta.MOM(dataframe, timeperiod=5) # Bollinger Bands bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe["bb_upper"] = bb["upperband"] dataframe["bb_lower"] = bb["lowerband"] dataframe["bb_mid"] = bb["middleband"] # MACD macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) dataframe["macd"] = macd["macd"] dataframe["macd_signal"] = macd["macdsignal"] dataframe["macd_hist"] = macd["macdhist"] return dataframe # ================================================================ # ENTRY # ================================================================ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: ema_s = f"ema_{self.buy_ema_short.value}" ema_l = f"ema_{self.buy_ema_long.value}" if ema_s not in dataframe.columns: dataframe[ema_s] = ta.EMA(dataframe, timeperiod=self.buy_ema_short.value) if ema_l not in dataframe.columns: dataframe[ema_l] = ta.EMA(dataframe, timeperiod=self.buy_ema_long.value) conditions = ( (dataframe["rsi"] < self.buy_rsi_max.value) & (dataframe[ema_s] > dataframe[ema_l]) & (dataframe["volume"] > dataframe["volume_sma"] * self.buy_volume_factor.value) & (dataframe["mom"] > self.buy_mom_threshold.value) & (dataframe["volume"] > 0) ) if self.buy_require_btc_uptrend.value: conditions = conditions & (dataframe["btc_trend_1h"] == 1) if self.buy_bb_lower.value: conditions = conditions & (dataframe["close"] < dataframe["bb_mid"]) if self.buy_macd_positive.value: conditions = conditions & (dataframe["macd_hist"] > 0) dataframe.loc[conditions, "enter_long"] = 1 return dataframe # ================================================================ # EXIT # ================================================================ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: ema_s = f"ema_{self.buy_ema_short.value}" ema_l = f"ema_{self.buy_ema_long.value}" if ema_s not in dataframe.columns: dataframe[ema_s] = ta.EMA(dataframe, timeperiod=self.buy_ema_short.value) if ema_l not in dataframe.columns: dataframe[ema_l] = ta.EMA(dataframe, timeperiod=self.buy_ema_long.value) exit_cond = (dataframe["rsi"] > self.sell_rsi_min.value) if self.sell_ema_cross.value: exit_cond = exit_cond | (dataframe[ema_s] < dataframe[ema_l]) dataframe.loc[exit_cond, "exit_long"] = 1 return dataframe # ================================================================ # CONFIRM TRADE EXIT (min hold) # ================================================================ def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: hold_min = (current_time - trade.open_date_utc).total_seconds() / 60 profit_pct = trade.calc_profit_ratio(rate) * 100 # Emergency: always exit on big loss if profit_pct <= -2.5: return True # Block instant exits < 3 min (except stoploss) if exit_reason == "exit_signal" and hold_min < 3: return False return True