# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/TryEverything.py import numpy as np import pandas as pd import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from functools import reduce from freqtrade.constants import Config from freqtrade.optimize.space import SKDecimal from freqtrade.strategy import CategoricalParameter, BooleanParameter from freqtrade.strategy.interface import IStrategy from pandas import DataFrame def to_safe_numeric(series, fill_value): return pd.to_numeric(series, errors="coerce").fillna(fill_value) def is_rising(series, n): if not isinstance(n, int) or n <= 0: raise ValueError("'n' must be a positive integer.") if not pd.api.types.is_numeric_dtype(series): raise TypeError("Series must contain numeric data.") differences = series.diff() is_rising = differences > 0 rising_streaks = is_rising.rolling(window=n).sum() == n return rising_streaks.shift(-(n - 1)).fillna(False) def is_falling(series, n): if not isinstance(n, int) or n <= 0: raise ValueError("'n' must be a positive integer.") if not pd.api.types.is_numeric_dtype(series): raise TypeError("Series must contain numeric data.") differences = series.diff() is_falling = differences < 0 falling_streaks = is_falling.rolling(window=n).sum() == n return falling_streaks.shift(-(n - 1)).fillna(False) class Github_remiotore_freqtrade__TryEverything__20260111_210550(IStrategy): INTERFACE_VERSION = 3 RULES_SIZE = 2 minimal_roi = {} stoploss = -0.03 timeframe = "1h" use_custom_stoploss = True startup_candle_count = 100 # Define a dictionary of parameter spaces for hyperopt optimization class HyperOpt: @staticmethod def stoploss_space() -> list: return [SKDecimal(-0.30, -0.01, decimals=2, name="stoploss")] @staticmethod def roi_space() -> list: return [SKDecimal(0.02, 0.30, decimals=2, name="roi")] @staticmethod def generate_roi_table(params: dict) -> dict: return {0: params["roi"]} # Initialize parameters for buying and selling def __init__(self, config: Config): super().__init__(config) self.config = config self._initialize_parameters() def _initialize_parameters(self): self._define_parameters_for_side("buy", "bband_upper") self._define_parameters_for_side("sell", "bband_lower") def _define_parameters_for_side(self, side: str, default_indicator: str): indicators = [ # Multiple output indicators "bband_upper", "bband_middle", "bband_lower", "mama", "fama", "aroon_up", "aroon_down", "macd", "macd_signal", "macd_hist", "stoch_slowk", "stoch_slowd", "stochf_fastk", "stochf_fastd", "stochrsi_fastk", "stochrsi_fastd", # Single output indicators "adx", "adxr", "apo", "aroonosc", "bop", "cci", "cmo", "dema", "dx", "ema", "ht_trendline", "kama", "ma", "mfi", "minus_di", "minus_dm", "mom", "plus_di", "plus_dm", "ppo", "roc", "rocp", "rsi", "sar", "sarext", "sma", "tema", "trima", "trix", "ultosc", "willr", "wma", ] signal_ops = [ "disabled", "crossed_above", "crossed_below", "raising", "falling" ] price_type = [ "open", "high", "low", "close", "avgprice", "medprice", "typprice", "wclprice" ] indicator_shift = range(0, 10, 2) # np.arange(0, 10, 2) for i in range(Github_remiotore_freqtrade__TryEverything__20260111_210550.RULES_SIZE): self._create_categorical_parameter(f"{side}_indicator_{i}", indicators, default_indicator, side) self._create_categorical_parameter(f"{side}_signal_ops_{i}", signal_ops, "disabled", side) self._create_categorical_parameter(f"{side}_indicator_shift_{i}", indicator_shift, 0, side) self._create_categorical_parameter(f"{side}_price_type_{i}", price_type, "close", side) self._create_boolean_parameter(f"{side}_use_price_{i}", False, side) def _create_categorical_parameter(self, name, categories, default, space): setattr(self, name, CategoricalParameter(categories, default=default, space=space)) def _create_boolean_parameter(self, name, default, space): setattr(self, name, BooleanParameter(default=default, space=space)) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Multiple-output indicators multi_output_indicators = { "BBANDS": ("bband_upper", "bband_middle", "bband_lower"), "MAMA": ("mama", "fama"), "AROON": ("aroon_up", "aroon_down"), "MACD": ("macd", "macd_signal", "macd_hist"), "STOCH": ("stoch_slowk", "stoch_slowd"), "STOCHF": ("stochf_fastk", "stochf_fastd"), "STOCHRSI": ("stochrsi_fastk", "stochrsi_fastd"), } for indicator, column_names in multi_output_indicators.items(): indicator_results = getattr(ta, indicator)(dataframe) for i, column_name in enumerate(column_names): dataframe[column_name] = list(indicator_results)[i] # Single-output indicators single_output_indicators = [ "ADX", "ADXR", "APO", "AROONOSC", "BOP", "CCI", "CMO", "DEMA", "DX", "EMA", "HT_TRENDLINE", "KAMA", "MA", "MFI", "MINUS_DI", "MINUS_DM", "MOM", "PLUS_DI", "PLUS_DM", "PPO", "ROC", "ROCP", "RSI", "SAR", "SAREXT", "SMA", "TEMA", "TRIMA", "TRIX", "ULTOSC", "WILLR", "WMA", "AVGPRICE", "MEDPRICE", "TYPPRICE", "WCLPRICE", ] for func_name in single_output_indicators: dataframe[func_name.lower()] = getattr(ta, func_name)(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] for i in range(Github_remiotore_freqtrade__TryEverything__20260111_210550.RULES_SIZE): ops_val = getattr(self, f"buy_signal_ops_{i}").value if ops_val != "disabled": ind_val = getattr(self, f"buy_indicator_{i}").value price_type = getattr(self, f"buy_price_type_{i}").value shift_val = int( getattr(self, f"buy_indicator_shift_{i}").value) use_price = getattr(self, f"buy_use_price_{i}").value left_val = dataframe[price_type] if use_price else dataframe[ind_val] right_val = dataframe[ind_val].shift(shift_val) if ops_val == "crossed_above": conditions.append( qtpylib.crossed_above( to_safe_numeric(left_val, -np.inf), to_safe_numeric(right_val, -np.inf), )) elif ops_val == "crossed_below": conditions.append( qtpylib.crossed_below( to_safe_numeric(left_val, np.inf), to_safe_numeric(right_val, np.inf), )) elif ops_val == "raising": conditions.append(is_rising(left_val, shift_val + 1)) elif ops_val == "falling": conditions.append(is_falling(left_val, shift_val + 1)) if conditions: conditions.append(dataframe["volume"] > 0) 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 = [] for i in range(Github_remiotore_freqtrade__TryEverything__20260111_210550.RULES_SIZE): ops_val = getattr(self, f"sell_signal_ops_{i}").value if ops_val != "disabled": ind_val = getattr(self, f"sell_indicator_{i}").value price_type = getattr(self, f"sell_price_type_{i}").value shift_val = int( getattr(self, f"sell_indicator_shift_{i}").value) use_price = getattr(self, f"sell_use_price_{i}").value left_val = dataframe[price_type] if use_price else dataframe[ind_val] right_val = dataframe[ind_val].shift(shift_val) if ops_val == "crossed_above": conditions.append( qtpylib.crossed_above( to_safe_numeric(left_val, -np.inf), to_safe_numeric(right_val, -np.inf), )) elif ops_val == "crossed_below": conditions.append( qtpylib.crossed_below( to_safe_numeric(left_val, np.inf), to_safe_numeric(right_val, np.inf), )) elif ops_val == "raising": conditions.append(is_rising(left_val, shift_val + 1)) elif ops_val == "falling": conditions.append(is_falling(left_val, shift_val + 1)) if conditions: conditions.append(dataframe["volume"] > 0) dataframe.loc[reduce(lambda x, y: x & y, conditions), "exit_long"] = 1 return dataframe