# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ElliotV8_IF3_futlo_02.py import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame, Series from typing import Optional, Union from datetime import datetime from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, stoploss_from_open, informative) from freqtrade.persistence import Trade import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas_ta as pta from warnings import simplefilter simplefilter(action="ignore", category=pd.errors.PerformanceWarning) class Github_remiotore_freqtrade__ElliotV8_IF3_futlo_02__20260111_210550(IStrategy): """ This is a sample strategy to inspire you. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_entry_trend, populate_exit_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ INTERFACE_VERSION = 3 def version(self) -> str: return "v0.2" can_short = True timeframe = '5m' use_custom_stoploss = False process_only_new_candles = True use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False startup_candle_count: int = 400 """ PASTE OUTPUT FROM HYPEROPT HERE Can be overridden for specific sub-strategies (stake currencies) at the bottom. """ entry_params = { "leverage": 10, "base_nb_candles_buy": 14, "ewo_high": 2.327, "ewo_low": -19.988, "low_offset": 0.975, "rsi_buy": 69 } exit_params = { "pHSL": -9.99, "pPF_1": 0.0026, "pSL_1": 0.0016, "pPF_2": 0.0132, "pSL_2": 0.0112, "base_nb_candles_sell": 24, "high_offset": 0.991, "high_offset_2": 0.997 } minimal_roi = { "0": 0.215, "40": 0.032, "87": 0.016, "201": 0 } stoploss = -0.32 trailing_stop = True trailing_stop_positive = 0.001 * self.lev.value trailing_stop_positive_offset = 0.02 * self.lev.value trailing_only_offset_is_reached = True """ END HYPEROPT """ """ BEGIN hyperspace params """ lev = IntParameter(0, 50, default=entry_params['leverage'], space='buy', optimize=False) base_nb_candles_buy = IntParameter(5, 80, default=entry_params['base_nb_candles_buy'], space='buy', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=entry_params['low_offset'], space='buy', optimize=True) ewo_low = DecimalParameter(-20.0, -8.0,default=entry_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=entry_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=entry_params['rsi_buy'], space='buy', optimize=True) pHSL = DecimalParameter(-0.500, -0.020, default=exit_params['pHSL'], decimals=3, space='sell', optimize=False) pPF_1 = DecimalParameter(0.008, 0.020, default=exit_params['pPF_1'], decimals=3, space='sell', optimize=False) pSL_1 = DecimalParameter(0.008, 0.020, default=exit_params['pSL_1'], decimals=3, space='sell', optimize=False) pPF_2 = DecimalParameter(0.040, 0.100, default=exit_params['pPF_2'], decimals=3, space='sell', optimize=False) pSL_2 = DecimalParameter(0.020, 0.070, default=exit_params['pSL_2'], decimals=3, space='sell', optimize=False) base_nb_candles_sell = IntParameter(5, 80, default=exit_params['base_nb_candles_sell'], space='sell', optimize=True) high_offset = DecimalParameter(0.95, 1.1, default=exit_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=exit_params['high_offset_2'], space='sell', optimize=True) """ END hyperspace params """ def leverage(self, pair: str, current_time: 'datetime', current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: """ Customize leverage for each new trade. :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 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.lev.value def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: HSL = self.pHSL.value PF_1 = self.pPF_1.value * self.lev.value SL_1 = self.pSL_1.value * self.lev.value PF_2 = self.pPF_2.value * self.lev.value SL_2 = self.pSL_2.value * self.lev.value 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 (current_profit > PF_1): return stoploss_from_open(sl_profit, current_profit, is_short=trade.is_short) else: return HSL def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] @informative('1h') @informative('30m') @informative('15m') @informative('5m') def populate_indicators_inf(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to define informative indicators. Example usage: @informative('1h') def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe :param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe. :param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use current pair. :param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not specified, defaults to: * {base}_{quote}_{column}_{timeframe} if asset is specified. * {column}_{timeframe} if asset is not specified. Format string supports these format variables: * {asset} - full name of the asset, for example 'BTC/USDT'. * {base} - base currency in lower case, for example 'eth'. * {BASE} - same as {base}, except in upper case. * {quote} - quote currency in lower case, for example 'usdt'. * {QUOTE} - same as {quote}, except in upper case. * {column} - name of dataframe column. * {timeframe} - timeframe of informative dataframe. :param ffill: ffill dataframe after merging informative pair. :param candle_type: '', mark, index, premiumIndex, or funding_rate """ return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ 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) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['EWO'] = EWO(dataframe, 50, 200) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ dataframe.loc[:, 'enter_tag'] = '' dataframe.loc[:, 'enter_long'] = 0 dataframe.loc[:, 'enter_short'] = 0 dataframe.loc[ ( (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['rsi_fast'] < 35) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag']] += (1, 'enter_long_1;') dataframe.loc[ ( (dataframe['rsi_fast'] < 35) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag']] += (1, 'enter_long_2;') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ dataframe.loc[:, 'exit_tag'] = '' dataframe.loc[:, 'exit_long'] = 0 dataframe.loc[:, 'exit_short'] = 0 dataframe.loc[ ( (dataframe['rsi'] > 50) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) & (dataframe['close'] > dataframe['hma_50']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (dataframe['volume'] > 0) ), ['exit_long', 'exit_tag']] += (1, 'exit_long_1;') dataframe.loc[ ( (dataframe['rsi_fast'] > dataframe['rsi_slow']) & (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0) ), ['exit_long', 'exit_tag']] += (1, 'exit_long_2;') return dataframe def EWO(dataframe, ema_length=5, ema2_length=35): 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