# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Discord_AlphaTrend.py import logging import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import pandas as pd import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy import ( IStrategy, DecimalParameter, stoploss_from_open, ) from datetime import datetime logger = logging.getLogger(__name__) class Github_remiotore_freqtrade__Discord_AlphaTrend__20260111_210550(IStrategy): INTERFACE_VERSION = 3 timeframe = "15m" minimal_roi = {"0": 100.0} stoploss = -0.99 trailing_stop = False trailing_stop_positive = 0.03 trailing_stop_positive_offset = 0.05 trailing_only_offset_is_reached = True startup_candle_count = 14 use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = True use_custom_stoploss = True can_short = True # default params buy_params = {"alpha_coeff": 1} sell_params = { "leverage_num": 1, # custom stop loss params "pHSL": -0.675, "pPF_1": 0.069, "pPF_2": 0.044, "pSL_1": 0.1, "pSL_2": 0.149, } alpha_coeff = DecimalParameter( low=0.5, high=1.5, default=buy_params["alpha_coeff"], space="buy", optimize=True, decimals=1, ) # leverage_num = IntParameter(1, 2, default=1, space="sell") # trailing stoploss trailing_optimize = True pHSL = DecimalParameter( -0.990, -0.040, default=-0.08, decimals=3, space="sell", optimize=trailing_optimize, ) pPF_1 = DecimalParameter( 0.008, 0.100, default=0.016, decimals=3, space="sell", optimize=trailing_optimize, ) pSL_1 = DecimalParameter( 0.01, 0.05, default=0.02, decimals=2, space="sell", optimize=trailing_optimize, ) pPF_2 = DecimalParameter( 0.04, 0.20, default=0.08, decimals=2, space="sell", optimize=trailing_optimize, ) pSL_2 = DecimalParameter( 0.04, 0.20, default=0.040, decimals=2, space="sell", optimize=trailing_optimize, ) def populate_indicators(self, df: pd.DataFrame, metadata: dict) -> pd.DataFrame: df["ema_200"] = ta.EMA(df, timeperiod=200) df["rsi_14"] = ta.RSI(df, 14) # MFI df["mfi_14"] = ta.MFI(df, 14) # Alpha Trend (Tradingview) df["TR"] = ta.TRANGE(df) df["ATR"] = ta.SMA(df["TR"], 14) for val in self.alpha_coeff.range: df[f"alpha_trend_{val}"] = alpha_trend( df, df["ATR"], coeff=val, mfi_threshold=50 ) return df def populate_entry_trend(self, df: pd.DataFrame, metadata: dict) -> pd.DataFrame: alpha_buy = qtpylib.crossed_above( df[f"alpha_trend_{self.alpha_coeff.value}"], df[f"alpha_trend_{self.alpha_coeff.value}"].shift(2), ) alpha_sell = qtpylib.crossed_below( df[f"alpha_trend_{self.alpha_coeff.value}"], df[f"alpha_trend_{self.alpha_coeff.value}"].shift(2), ) # Enter Long O1 = df.groupby( alpha_buy.shift().fillna(False).astype(int).eq(1).cumsum() ).cumcount() K2 = df.groupby(alpha_sell.astype(int).eq(1).cumsum()).cumcount() # Enter Short K1 = df.groupby(alpha_buy.astype(int).eq(1).cumsum()).cumcount() O2 = df.groupby( alpha_sell.shift().fillna(False).astype(int).eq(1).cumsum() ).cumcount() df.loc[ (df["close"] < df["ema_200"]) & (alpha_buy & (O1 > K2) & df["volume"] > 0), ["enter_long", "enter_tag"], ] = (1, "enter_long") df.loc[ (df["close"] > df["ema_200"]) & (alpha_sell & (O2 > K1) & df["volume"] > 0), ["enter_short", "enter_tag"], ] = (1, "enter_short") return df def populate_exit_trend(self, df: pd.DataFrame, metadata: dict) -> pd.DataFrame: alpha_buy = qtpylib.crossed_above( df[f"alpha_trend_{self.alpha_coeff.value}"], df[f"alpha_trend_{self.alpha_coeff.value}"].shift(2), ) alpha_sell = qtpylib.crossed_below( df[f"alpha_trend_{self.alpha_coeff.value}"], df[f"alpha_trend_{self.alpha_coeff.value}"].shift(2), ) ## Exit Long K1 = df.groupby(alpha_buy.astype(int).eq(1).cumsum()).cumcount() O2 = df.groupby( alpha_sell.shift().fillna(False).astype(int).eq(1).cumsum() ).cumcount() ## Exit Short O1 = df.groupby( alpha_buy.shift().fillna(False).astype(int).eq(1).cumsum() ).cumcount() K2 = df.groupby(alpha_sell.astype(int).eq(1).cumsum()).cumcount() df.loc[ (df["close"] > df["ema_200"]) & (alpha_sell & (O2 > K1) & df["volume"] > 0), ["exit_long", "exit_tag"], ] = (1, "exit_long") df.loc[ (df["close"] < df["ema_200"]) & (alpha_buy & (O1 > K2) & df["volume"] > 0), ["exit_short", "exit_tag"], ] = (1, "exit_short") return df def leverage( self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs, ) -> float: return 2 # self.leverage_num.value def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs, ) -> float: # hard stoploss profit HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used. if current_profit > PF_2: sl_profit = SL_2 + (current_profit - PF_2) elif current_profit > PF_1: sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL if self.can_short: if (-1 + ((1 - sl_profit) / (1 - current_profit))) <= 0: return 1 else: if (1 - ((1 + sl_profit) / (1 + current_profit))) <= 0: return 1 return stoploss_from_open(sl_profit, current_profit, is_short=trade.is_short) def alpha_trend( df: pd.DataFrame, ATR: pd.Series, coeff: float = 1.0, mfi_threshold: int = 50 ): upT = df["low"] - ATR * coeff downT = df["high"] + ATR * coeff new_values = [] previous_val = np.nan for idx in df.index: if df.loc[idx, "mfi_14"] >= mfi_threshold: if upT[idx] < previous_val: x = previous_val else: x = upT[idx] else: if downT[idx] > previous_val: x = previous_val else: x = downT[idx] new_values.append(x) previous_val = x return pd.Series(new_values, index=df.index)