# source: https://raw.githubusercontent.com/rmallarapu-bc/brahma/9287745fc036c2f4c00c586e598290885c2883b9/archive/BetaFinder.py from pandas import DataFrame import talib.abstract as ta import numpy as np from freqtrade.strategy import (DecimalParameter, IStrategy, IntParameter) from datetime import datetime, timedelta # noqa from typing import Optional, Union # noqa # super trend performance # Training dataset: 2022 H1 # BACKTEST_TIMERANGE="20220101-" # (P) 233% - (M) -58% down - (D) 0.7% # BACKTEST_TIMERANGE="20200101-20210101" # (P) 144% - (M) 304% up - (D) 32% - Not enough historical data # BACKTEST_TIMERANGE="20210101-20220101" # (P) 646% - (M) 61% mixed (D) 32% # NOTE: Increasing leverage results in losses - do NOT use it. class Github_rmallarapu_bc_brahma__BetaFinder__20240229_213751(IStrategy): INTERFACE_VERSION: int = 3 can_short = True # Buy hyperspace params: buy_params = { "buy_m1": 4, "buy_m2": 7, "buy_m3": 1, "buy_p1": 8, "buy_p2": 9, "buy_p3": 8, } # Sell hyperspace params: sell_params = { "sell_m1": 1, "sell_m2": 3, "sell_m3": 6, "sell_p1": 16, "sell_p2": 18, "sell_p3": 18, } # ROI table: minimal_roi = {"0": 0.1, "30": 0.75, "60": 0.05, "120": 0.025} # Stoploss: stoploss = -0.265 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.1 trailing_only_offset_is_reached = False timeframe = "1m" startup_candle_count = 60 buy_m1 = IntParameter(1, 7, default=1) buy_m2 = IntParameter(1, 7, default=3) buy_m3 = IntParameter(1, 7, default=4) buy_p1 = IntParameter(7, 21, default=14) buy_p2 = IntParameter(7, 21, default=10) buy_p3 = IntParameter(7, 21, default=10) sell_m1 = IntParameter(1, 7, default=1) sell_m2 = IntParameter(1, 7, default=3) sell_m3 = IntParameter(1, 7, default=4) sell_p1 = IntParameter(7, 21, default=14) sell_p2 = IntParameter(7, 21, default=10) sell_p3 = IntParameter(7, 21, default=10) protect_optimize = True cooldown_lookback = IntParameter(1, 240, default=5, space="protection", optimize=protect_optimize) max_drawdown_lookback = IntParameter(1, 288, default=12, space="protection", optimize=protect_optimize) max_drawdown_trade_limit = IntParameter(1, 20, default=5, space="protection", optimize=protect_optimize) max_drawdown_stop_duration = IntParameter(1, 288, default=12, space="protection", optimize=protect_optimize) max_allowed_drawdown = DecimalParameter(0.10, 0.50, default=0.20, decimals=2, space="protection", optimize=protect_optimize) stoploss_guard_lookback = IntParameter(1, 288, default=12, space="protection", optimize=protect_optimize) stoploss_guard_trade_limit = IntParameter(1, 20, default=3, space="protection", optimize=protect_optimize) stoploss_guard_stop_duration = IntParameter(1, 288, default=12, space="protection", optimize=protect_optimize) leverage_optimize = True leverage_num = IntParameter(low=1, high=10, default=1, space='buy', optimize=leverage_optimize) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: for multiplier in self.buy_m1.range: for period in self.buy_p1.range: dataframe[f"supertrend_1_buy_{multiplier}_{period}"] = self.supertrend( dataframe, multiplier, period )["STX"] for multiplier in self.buy_m2.range: for period in self.buy_p2.range: dataframe[f"supertrend_2_buy_{multiplier}_{period}"] = self.supertrend( dataframe, multiplier, period )["STX"] for multiplier in self.buy_m3.range: for period in self.buy_p3.range: dataframe[f"supertrend_3_buy_{multiplier}_{period}"] = self.supertrend( dataframe, multiplier, period )["STX"] for multiplier in self.sell_m1.range: for period in self.sell_p1.range: dataframe[f"supertrend_1_sell_{multiplier}_{period}"] = self.supertrend( dataframe, multiplier, period )["STX"] for multiplier in self.sell_m2.range: for period in self.sell_p2.range: dataframe[f"supertrend_2_sell_{multiplier}_{period}"] = self.supertrend( dataframe, multiplier, period )["STX"] for multiplier in self.sell_m3.range: for period in self.sell_p3.range: dataframe[f"supertrend_3_sell_{multiplier}_{period}"] = self.supertrend( dataframe, multiplier, period )["STX"] dataframe["RSI"] = ta.RSI(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( dataframe[f"supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}"] == "up" ) & ( dataframe[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up" ) & ( dataframe[f"supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}"] == "up" ) & ( # The three indicators are 'up' for the current candle dataframe["volume"] > 0 ), "enter_long", ] = 1 dataframe.loc[ ( dataframe[ f"supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}" ] == "down" ) & ( dataframe[ f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}" ] == "down" ) & ( dataframe[ f"supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}" ] == "down" ) & ( # The three indicators are 'down' for the current candle dataframe["volume"] > 0 ), "enter_short", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( dataframe[ f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}" ] == "down" ), "exit_long", ] = 1 dataframe.loc[ ( dataframe[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up" ), "exit_short", ] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ return self.leverage_num.value """ Supertrend Indicator; adapted for freqtrade from: https://github.com/freqtrade/freqtrade-strategies/issues/30 """ def supertrend(self, dataframe: DataFrame, multiplier, period): df = dataframe.copy() df["TR"] = ta.TRANGE(df) df["ATR"] = ta.SMA(df["TR"], period) st = "ST_" + str(period) + "_" + str(multiplier) stx = "STX_" + str(period) + "_" + str(multiplier) # Compute basic upper and lower bands df["basic_ub"] = (df["high"] + df["low"]) / 2 + multiplier * df["ATR"] df["basic_lb"] = (df["high"] + df["low"]) / 2 - multiplier * df["ATR"] # Compute final upper and lower bands df["final_ub"] = 0.00 df["final_lb"] = 0.00 for i in range(period, len(df)): df["final_ub"].iat[i] = ( df["basic_ub"].iat[i] if df["basic_ub"].iat[i] < df["final_ub"].iat[i - 1] or df["close"].iat[i - 1] > df["final_ub"].iat[i - 1] else df["final_ub"].iat[i - 1] ) df["final_lb"].iat[i] = ( df["basic_lb"].iat[i] if df["basic_lb"].iat[i] > df["final_lb"].iat[i - 1] or df["close"].iat[i - 1] < df["final_lb"].iat[i - 1] else df["final_lb"].iat[i - 1] ) # Set the Supertrend value df[st] = 0.00 for i in range(period, len(df)): df[st].iat[i] = ( df["final_ub"].iat[i] if df[st].iat[i - 1] == df["final_ub"].iat[i - 1] and df["close"].iat[i] <= df["final_ub"].iat[i] else df["final_lb"].iat[i] if df[st].iat[i - 1] == df["final_ub"].iat[i - 1] and df["close"].iat[i] > df["final_ub"].iat[i] else df["final_lb"].iat[i] if df[st].iat[i - 1] == df["final_lb"].iat[i - 1] and df["close"].iat[i] >= df["final_lb"].iat[i] else df["final_ub"].iat[i] if df[st].iat[i - 1] == df["final_lb"].iat[i - 1] and df["close"].iat[i] < df["final_lb"].iat[i] else 0.00 ) # Mark the trend direction up/down df[stx] = np.where( (df[st] > 0.00), np.where((df["close"] < df[st]), "down", "up"), np.NaN ) # Remove basic and final bands from the columns df.drop(["basic_ub", "basic_lb", "final_ub", "final_lb"], inplace=True, axis=1) df.fillna(0, inplace=True) return DataFrame(index=df.index, data={"ST": df[st], "STX": df[stx]})