# source: https://raw.githubusercontent.com/zxjason/Freqtrade_Strategies/87e28f8f32c36dd18f1264eecdbbc3cf2da478b0/ElliotV5_SMA/ElliotV5_SMA_mod1.py ############################################################################################################# ## ElliotV5_SMA Strategy mod 1 by @zxjason & DS ## ## https://github.com/zxjason/Freqtrade_Strategies ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ############################################################################################################# ## GENERAL RECOMMENDATIONS ## ## For optimal performance, suggested to use only BTC and ETH. ## ############################################################################################################# ## DONATIONS ## ## usdc: 0xedB85Ce16ba8268F32925f6fF48190Cea97a9b81 ## ## REFERRAL LINKS ## ############################################################################################################# from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame import talib.abstract as ta import numpy as np from datetime import datetime import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter # Updated buy and sell parameters with narrowed ranges buy_params = { "base_nb_candles_buy": 17, "ewo_high": 3.34, "ewo_low": -17.457, "low_offset": 0.978, "rsi_buy": 65, "atr_mult": 2.0 # Added ATR multiplier for dynamic stoploss } sell_params = { "base_nb_candles_sell": 49, "high_offset": 1.019, "rsi_sell": 70 # Added RSI sell threshold } def EWO(dataframe, ema_length=50, ema2_length=200): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif class Github_zxjason_Freqtrade_Strategies__ElliotV5_SMA_mod1__20250429_023230(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.215, "40": 0.132, "87": 0.086, "201": 0.03 } stoploss = -0.15 # Adjusted to -15% (will be overridden by dynamic ATR) use_custom_stoploss = True # Enable custom stoploss # Narrowed parameter ranges for efficiency base_nb_candles_buy = IntParameter(10, 30, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter(20, 60, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter(0.95, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(1.0, 1.05, default=sell_params['high_offset'], space='sell', optimize=True) ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) rsi_sell = IntParameter(65, 90, default=sell_params['rsi_sell'], space='sell', optimize=True) atr_mult = DecimalParameter(1.5, 3.0, default=buy_params['atr_mult'], space='buy', optimize=True) trailing_stop = True trailing_stop_positive = 0.01 # Increased from 0.005 trailing_stop_positive_offset = 0.05 # Increased from 0.03 trailing_only_offset_is_reached = True timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True startup_candle_count = 200 def informative_pairs(self): pairs = self.dp.current_whitelist() return [(pair, self.informative_timeframe) for pair in pairs] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate EMAs for buy and sell signals for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate EWO with EMA dataframe['EWO'] = EWO(dataframe) # Add RSI and ATR for dynamic stoploss dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) # Merge informative timeframe (1h) data informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) informative['ema_200'] = ta.EMA(informative, timeperiod=200) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Ensure 1h trend is bullish bullish_1h = dataframe[f"ema_200_{self.informative_timeframe}"] < dataframe['close'] # Entry condition 1: EWO high with RSI conditions.append( (bullish_1h) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) ) # Entry condition 2: EWO low conditions.append( (bullish_1h) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) ) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'buy'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [ (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['rsi'] > self.rsi_sell.value) & (dataframe['volume'] > 0) ] dataframe.loc[reduce(lambda x, y: x | y, conditions), 'sell'] = 1 return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Dynamic stoploss using 2*ATR dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] atr_stop = last_candle['atr'] * self.atr_mult.value return current_rate - atr_stop