# source: https://raw.githubusercontent.com/vaskosmihaylov/nfi-custom-strategies/91c1f22df4c5b0ac540cf09a447fe0dd78d9d5cd/user_data/strategies/Ai/el_extrema_RL.py import numpy import warnings import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from datetime import datetime from freqtrade.strategy.interface import IStrategy from typing import Dict from functools import reduce from pandas import DataFrame, errors from datetime import datetime import numpy from scipy.signal import argrelextrema from freqtrade.strategy import ( IStrategy, DecimalParameter, IntParameter, CategoricalParameter, BooleanParameter ) warnings.simplefilter(action="ignore", category=errors.PerformanceWarning) class Github_vaskosmihaylov_nfi_custom_strategies__el_extrema_RL__20260513_073522(IStrategy): INTERFACE_VERSION = 3 can_short = True entry_params = { 'base_nb_candles_entry': 12, 'ewo_high': 4.428, 'ewo_low': -12.383, 'low_offset': 0.915, 'rsi_entry': 44, } exit_params = { 'base_nb_candles_exit': 72, 'high_offset': 1.008, } minimal_roi = { '0': 0.5, '60': 0.45, '120': 0.4, '240': 0.3, '360': 0.25, '720': 0.2, '1440': 0.15, '2880': 0.1, '3600': 0.05, '7200': 0.02, } stoploss = -0.05 max_open_trades = 9 timeframe = '5m' informative_timeframe = '1h' process_only_new_candles = True use_custom_stoploss = False trailing_stop = False use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.01 ignore_roi_if_entry_signal = True base_nb_candles_entry = IntParameter(5, 80, default=entry_params['base_nb_candles_entry'], space='entry', optimize=True) base_nb_candles_exit = IntParameter(5, 80, default=exit_params['base_nb_candles_exit'], space='exit', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=entry_params['low_offset'], space='entry', optimize=True) high_offset = DecimalParameter(0.99, 1.1, default=exit_params['high_offset'], space='exit', optimize=True) fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=entry_params['ewo_low'], space='entry', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=entry_params['ewo_high'], space='entry', optimize=True) rsi_entry = IntParameter(30, 70, default=entry_params['rsi_entry'], space='entry', optimize=True) cooldown_lookback = IntParameter(2, 48, default=1, space='protection', optimize=True) stop_duration = IntParameter(12, 200, default=4, space='protection', optimize=True) use_stop_protection = BooleanParameter(default=True, space='protection', optimize=True) @property def protections(self): prot = [] prot.append( { 'method': 'CooldownPeriod', 'stop_duration_candles': self.cooldown_lookback.value } ) if self.use_stop_protection.value: prot.append( { 'method': 'StoplossGuard', 'lookback_period_candles': 24 * 3, 'trade_limit': 2, 'stop_duration_candles': self.stop_duration.value, 'only_per_pair': False, } ) return prot @property def plot_config(self): plot_config = {} plot_config['main_plot'] = { '%-obv' : {}, '&-s_close' : {} } plot_config['subplots'] = { 'RSI': { 'rsi': {} }, 'SMA' : { 'sma5' : {}, 'sma35' : {}, }, 'EWO' : { 'EWO' : {} } } return plot_config def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def get_informative_indicators(self, metadata: dict): dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) return dataframe def feature_engineering_expand_all( self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs ) -> DataFrame: dataframe['%-rsi-period'] = ta.RSI(dataframe, timeperiod=period) dataframe['%-mfi-period'] = ta.MFI(dataframe, timeperiod=period) dataframe['%-adx-period'] = ta.ADX(dataframe, timeperiod=period) dataframe['%-sma-period'] = ta.SMA(dataframe, timeperiod=period) dataframe['%-ema-period'] = ta.EMA(dataframe, timeperiod=period) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=period, stds=2.2) dataframe['bb_lowerband-period'] = bollinger['lower'] dataframe['bb_middleband-period'] = bollinger['mid'] dataframe['bb_upperband-period'] = bollinger['upper'] dataframe['%-bb_width-period'] = (dataframe['bb_upperband-period'] - dataframe['bb_lowerband-period']) / dataframe['bb_middleband-period'] dataframe['%-close-bb_lower-period'] = dataframe['close'] / dataframe['bb_lowerband-period'] dataframe['%-roc-period'] = ta.ROC(dataframe, timeperiod=period) dataframe['%-relative_volume-period'] = (dataframe['volume'] / dataframe['volume'].rolling(period).mean()) return dataframe def feature_engineering_expand_basic( self, dataframe: DataFrame, metadata: Dict, **kwargs ) -> DataFrame: dataframe['%-pct-change'] = dataframe['close'].pct_change() dataframe['%-raw_volume'] = dataframe['volume'] dataframe['%-raw_price'] = dataframe['close'] dataframe['%-obv'] = ta.OBV(dataframe) return dataframe def feature_engineering_standard( self, dataframe: DataFrame, metadata: Dict, **kwargs ) -> DataFrame: dataframe['%-day_of_week'] = dataframe['date'].dt.dayofweek dataframe['%-hour_of_day'] = dataframe['date'].dt.hour return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: dataframe['&-s_close'] = (dataframe['close'].shift(-self.freqai_info['feature_parameters']['label_period_candles']).rolling(self.freqai_info['feature_parameters']['label_period_candles']).mean()/ dataframe['close']- 1) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.freqai.start(dataframe, metadata, self) for val in self.base_nb_candles_entry.range: dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in self.base_nb_candles_exit.range: dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma35'] = ta.SMA(dataframe, timeperiod=35) dataframe['EWO'] = (dataframe['sma5'] - dataframe['sma35']) / dataframe['close'] * 100 dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['&s-extrema'] = 0 dataframe.loc[argrelextrema(dataframe['close'].values, numpy.less, order=5)[0], '&s-extrema'] = -1 dataframe.loc[argrelextrema(dataframe['close'].values, numpy.greater, order=5)[0], '&s-extrema'] = 1 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: entry_conditions = [ ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] > 0.01) & (dataframe['&s-extrema'] < 0) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) ), ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] > 0.01) & (dataframe['&s-extrema'] < 0) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) ) ] if entry_conditions: dataframe.loc[reduce(lambda x, y: x | y, entry_conditions), 'enter_long'] = 1 exit_conditions = [ ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] < -0.01) & (dataframe['&s-extrema'] > 0) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) ) ] if exit_conditions: dataframe.loc[reduce(lambda x, y: x | y, exit_conditions), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: exit_long_conditions = [ ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] < 0) & (dataframe['&s-extrema'] > 0) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) ) ] if exit_long_conditions: dataframe.loc[reduce(lambda x, y: x | y, exit_long_conditions), 'exit_long'] = 1 exit_short_conditions = [ ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] < 0) & (dataframe['&s-extrema'] < 0) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) ), ( (dataframe['do_predict'] == 1) & (dataframe['&-s_close'] < 0) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) ) ] if exit_short_conditions: dataframe.loc[reduce(lambda x, y: x | y, exit_short_conditions), 'exit_short'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag:str, side: str, **kwargs) -> float: return 10.0