# source: https://raw.githubusercontent.com/spritepete/freqstrat/d49fd53143a1d7030ac54548324206160970cf16/AdaptiveRenkoStrategy1.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from functools import reduce from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) # -------------------------------- # Add your lib to import here import ta import pandas_ta as pda class Github_spritepete_freqstrat__AdaptiveRenkoStrategy1__20250102_122353(IStrategy): """ Advanced Supertrend Strategy with hyperoptable parameters for multiple timeframes and multipliers. This allows finding optimal parameters through hyperopt. """ INTERFACE_VERSION = 2 # Minimal ROI designed for the strategy minimal_roi = { "0": 100 # inactive } # Optimal stoploss designed for the strategy stoploss = -0.99 # inactive # Trailing stoploss trailing_stop = False # Hyperoptable parameters buy_m1 = IntParameter(1, 8, default=3, space='buy', optimize=True) buy_m2 = IntParameter(1, 10, default=4, space='buy', optimize=True) buy_m3 = IntParameter(1, 14, default=8, space='buy', optimize=True) buy_p1 = IntParameter(7, 51, default=20, space='buy', optimize=True) buy_p2 = IntParameter(7, 101, default=20, space='buy', optimize=True) buy_p3 = IntParameter(7, 151, default=40, space='buy', optimize=True) buy_ema = IntParameter(30, 180, default=90, space='buy', optimize=True) sell_m1 = IntParameter(1, 7, default=4, space='sell', optimize=True) sell_m2 = IntParameter(1, 7, default=4, space='sell', optimize=True) sell_m3 = IntParameter(1, 7, default=4, space='sell', optimize=True) sell_p1 = IntParameter(7, 21, default=14, space='sell', optimize=True) sell_p2 = IntParameter(7, 21, default=14, space='sell', optimize=True) sell_p3 = IntParameter(7, 21, default=14, space='sell', optimize=True) sell_ema = IntParameter(30, 180, default=90, space='sell', optimize=True) # Additional parameters buy_stoch_rsi = DecimalParameter(0.5, 1, decimals=3, default=0.8, space="buy") sell_stoch_rsi = DecimalParameter(0, 0.5, decimals=3, default=0.2, space="sell") # Strategy parameters timeframe = '1h' process_only_new_candles = False use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False startup_candle_count: int = 150 # Increased due to longer lookback periods # Order settings order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } def calculate_supertrend(self, dataframe: DataFrame, period: int, multiplier: float) -> pd.Series: """ Calculate Supertrend indicator with proper error handling """ try: supertrend = pda.supertrend( high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], length=period, multiplier=multiplier ) direction_col = f'SUPERTd_{period}_{multiplier:.1f}' if direction_col in supertrend: return supertrend[direction_col] # Fallback if column name doesn't match direction_cols = [col for col in supertrend.columns if col.startswith('SUPERTd_')] if direction_cols: return supertrend[direction_cols[0]] return pd.Series(0, index=dataframe.index) # Default fallback except Exception as e: print(f"Error calculating Supertrend for period={period}, multiplier={multiplier}: {str(e)}") return pd.Series(0, index=dataframe.index) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame. Calculates all default indicators to ensure correct column names in dataframe. """ # Get default values buy_ema_val = 90 # default value from initialization sell_ema_val = 90 # default value from initialization buy_m1_val = 3 # default values from initialization buy_m2_val = 4 buy_m3_val = 8 buy_p1_val = 20 buy_p2_val = 20 buy_p3_val = 40 sell_m1_val = 4 sell_m2_val = 4 sell_m3_val = 4 sell_p1_val = 14 sell_p2_val = 14 sell_p3_val = 14 # Stochastic RSI dataframe['stoch_rsi'] = ta.momentum.stochrsi(dataframe['close']) # EMAs with default values dataframe[f'ema_{buy_ema_val}'] = ta.trend.ema_indicator(dataframe['close'], buy_ema_val) dataframe[f'ema_{sell_ema_val}'] = ta.trend.ema_indicator(dataframe['close'], sell_ema_val) # Buy Supertrends with default values supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=buy_p1_val, multiplier=buy_m1_val) dataframe[f'supertrend_1_{buy_m1_val}_{buy_p1_val}'] = supertrend[f'SUPERTd_{buy_p1_val}_{float(buy_m1_val):.1f}'] supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=buy_p2_val, multiplier=buy_m2_val) dataframe[f'supertrend_2_{buy_m2_val}_{buy_p2_val}'] = supertrend[f'SUPERTd_{buy_p2_val}_{float(buy_m2_val):.1f}'] supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=buy_p3_val, multiplier=buy_m3_val) dataframe[f'supertrend_3_{buy_m3_val}_{buy_p3_val}'] = supertrend[f'SUPERTd_{buy_p3_val}_{float(buy_m3_val):.1f}'] # Sell Supertrends with default values supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=sell_p1_val, multiplier=sell_m1_val) dataframe[f'supertrend_1_{sell_m1_val}_{sell_p1_val}'] = supertrend[f'SUPERTd_{sell_p1_val}_{float(sell_m1_val):.1f}'] supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=sell_p2_val, multiplier=sell_m2_val) dataframe[f'supertrend_2_{sell_m2_val}_{sell_p2_val}'] = supertrend[f'SUPERTd_{sell_p2_val}_{float(sell_m2_val):.1f}'] supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=sell_p3_val, multiplier=sell_m3_val) dataframe[f'supertrend_3_{sell_m3_val}_{sell_p3_val}'] = supertrend[f'SUPERTd_{sell_p3_val}_{float(sell_m3_val):.1f}'] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe """ # Calculate EMA if value is different from default if self.buy_ema.value != 90: # Check against default value dataframe[f'ema_{self.buy_ema.value}'] = ta.trend.ema_indicator(dataframe['close'], self.buy_ema.value) # Calculate Supertrends if values are different from defaults if (self.buy_m1.value != 3) or (self.buy_p1.value != 20): # Check against default values supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=self.buy_p1.value, multiplier=self.buy_m1.value) dataframe[f'supertrend_1_{self.buy_m1.value}_{self.buy_p1.value}'] = supertrend[f'SUPERTd_{self.buy_p1.value}_{float(self.buy_m1.value):.1f}'] if (self.buy_m2.value != 4) or (self.buy_p2.value != 20): # Check against default values supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=self.buy_p2.value, multiplier=self.buy_m2.value) dataframe[f'supertrend_2_{self.buy_m2.value}_{self.buy_p2.value}'] = supertrend[f'SUPERTd_{self.buy_p2.value}_{float(self.buy_m2.value):.1f}'] if (self.buy_m3.value != 8) or (self.buy_p3.value != 40): # Check against default values supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=self.buy_p3.value, multiplier=self.buy_m3.value) dataframe[f'supertrend_3_{self.buy_m3.value}_{self.buy_p3.value}'] = supertrend[f'SUPERTd_{self.buy_p3.value}_{float(self.buy_m3.value):.1f}'] conditions = [] conditions.append(dataframe['volume'] > 0) conditions.append(dataframe['close'] > dataframe[f'ema_{self.buy_ema.value}']) conditions.append(dataframe['stoch_rsi'] < self.buy_stoch_rsi.value) # Check supertrend signals conditions.append(dataframe[f'supertrend_1_{self.buy_m1.value}_{self.buy_p1.value}'] == 1) conditions.append(dataframe[f'supertrend_2_{self.buy_m2.value}_{self.buy_p2.value}'] == 1) conditions.append(dataframe[f'supertrend_3_{self.buy_m3.value}_{self.buy_p3.value}'] == 1) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe """ # Calculate EMA if value is different from default if self.sell_ema.value != 90: # Check against default value dataframe[f'ema_{self.sell_ema.value}'] = ta.trend.ema_indicator(dataframe['close'], self.sell_ema.value) # Calculate Supertrends if values are different from defaults if (self.sell_m1.value != 4) or (self.sell_p1.value != 14): # Check against default values supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=self.sell_p1.value, multiplier=self.sell_m1.value) dataframe[f'supertrend_1_{self.sell_m1.value}_{self.sell_p1.value}'] = supertrend[f'SUPERTd_{self.sell_p1.value}_{float(self.sell_m1.value):.1f}'] if (self.sell_m2.value != 4) or (self.sell_p2.value != 14): # Check against default values supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=self.sell_p2.value, multiplier=self.sell_m2.value) dataframe[f'supertrend_2_{self.sell_m2.value}_{self.sell_p2.value}'] = supertrend[f'SUPERTd_{self.sell_p2.value}_{float(self.sell_m2.value):.1f}'] if (self.sell_m3.value != 4) or (self.sell_p3.value != 14): # Check against default values supertrend = pda.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=self.sell_p3.value, multiplier=self.sell_m3.value) dataframe[f'supertrend_3_{self.sell_m3.value}_{self.sell_p3.value}'] = supertrend[f'SUPERTd_{self.sell_p3.value}_{float(self.sell_m3.value):.1f}'] conditions = [] conditions.append(dataframe['volume'] > 0) conditions.append(dataframe['close'] < dataframe[f'ema_{self.sell_ema.value}']) conditions.append(dataframe['stoch_rsi'] > self.sell_stoch_rsi.value) # Check supertrend signals conditions.append(dataframe[f'supertrend_1_{self.sell_m1.value}_{self.sell_p1.value}'] == -1) conditions.append(dataframe[f'supertrend_2_{self.sell_m2.value}_{self.sell_p2.value}'] == -1) conditions.append(dataframe[f'supertrend_3_{self.sell_m3.value}_{self.sell_p3.value}'] == -1) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe