# source: https://raw.githubusercontent.com/TheoBrigitte/freqtrade/78e5c6ecf260465f9f57f9199ab95704da69a0c9/strategies/nasos/NASOSv5.py # --- Do not remove these libs --- # --- Do not remove these libs --- from logging import FATAL 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 import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter import technical.indicators as ftt # @Rallipanos # @pluxury # with help from @stash86 and @Perkmeister # Buy hyperspace params: # value loaded from strategy # value loaded from strategy # value loaded from strategy # value loaded from strategy # value loaded from strategy # value loaded from strategy # value loaded from strategy # value loaded from strategy # value loaded from strategy buy_params = {'low_offset': 0.981, 'base_nb_candles_buy': 8, 'ewo_high': 3.553, 'ewo_high_2': -5.585, 'ewo_low': -14.378, 'lookback_candles': 32, 'low_offset_2': 0.942, 'profit_threshold': 1.037, 'rsi_buy': 78, 'rsi_fast_buy': 37} # Sell hyperspace params: # value loaded from strategy # value loaded from strategy # value loaded from strategy sell_params = {'base_nb_candles_sell': 16, 'high_offset': 1.097, 'high_offset_2': 1.472} 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['low'] * 100 return emadif class Github_TheoBrigitte_freqtrade__NASOSv5__20250129_112953(IStrategy): INTERFACE_VERSION = 3 # ROI table: # "0": 0.283, # "40": 0.086, # "99": 0.036, minimal_roi = {'360': 0} # Stoploss: stoploss = -0.15 # SMAOffset base_nb_candles_buy = IntParameter(2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter(2, 25, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) low_offset_2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=False) high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=False) # Protection fast_ewo = 50 slow_ewo = 200 lookback_candles = IntParameter(1, 36, default=buy_params['lookback_candles'], space='buy', optimize=False) profit_threshold = DecimalParameter(0.99, 1.05, default=buy_params['profit_threshold'], space='buy', optimize=False) ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=False) ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=False) ewo_high_2 = DecimalParameter(-6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=False) rsi_buy = IntParameter(10, 80, default=buy_params['rsi_buy'], space='buy', optimize=False) rsi_fast_buy = IntParameter(10, 50, default=buy_params['rsi_fast_buy'], space='buy', optimize=False) # Trailing stop: trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.016 trailing_only_offset_is_reached = True # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False # Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'ioc'} # Optimal timeframe for the strategy timeframe = '5m' inf_15m = '15m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 200 use_custom_stoploss = True plot_config = {'main_plot': {'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}}, 'subplots': {'rsi': {'rsi': {'color': 'orange'}, 'rsi_fast': {'color': 'red'}, 'rsi_slow': {'color': 'green'}}, 'ewo': {'EWO': {'color': 'orange'}}}} slippage_protection = {'retries': 3, 'max_slippage': -0.02} def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if current_profit > 0.3: return 0.05 elif current_profit > 0.1: return 0.03 elif current_profit > 0.06: return 0.02 elif current_profit > 0.04: return 0.01 elif current_profit > 0.025: return 0.005 elif current_profit > 0.018: return 0.005 return 0.15 def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] if last_candle is not None: if exit_reason in ['exit_signal']: if last_candle['hma_50'] * 1.149 > last_candle['ema_100'] and last_candle['close'] < last_candle['ema_100'] * 0.951: # *1.2 return False # slippage try: state = self.slippage_protection['__pair_retries'] except KeyError: state = self.slippage_protection['__pair_retries'] = {} candle = dataframe.iloc[-1].squeeze() slippage = rate / candle['close'] - 1 if slippage < self.slippage_protection['max_slippage']: pair_retries = state.get(pair, 0) if pair_retries < self.slippage_protection['retries']: state[pair] = pair_retries + 1 return False state[pair] = 0 return True def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '15m') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, 'DataProvider is required for multiple timeframes.' # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # EMA # informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) # informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # # RSI # informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # informative_1h['bb_lowerband'] = bollinger['lower'] # informative_1h['bb_middleband'] = bollinger['mid'] # informative_1h['bb_upperband'] = bollinger['upper'] return informative_1h def informative_15m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, 'DataProvider is required for multiple timeframes.' # Get the informative pair informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_15m) # EMA # informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) # informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # # RSI # informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # informative_1h['bb_lowerband'] = bollinger['lower'] # informative_1h['bb_middleband'] = bollinger['mid'] # informative_1h['bb_upperband'] = bollinger['upper'] return informative_15m def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate all ma_buy values for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all ma_sell values 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['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # RSI 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_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # informative_1h = self.informative_1h_indicators(dataframe, metadata) informative_15m = self.informative_15m_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_15m, self.timeframe, self.inf_15m, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dont_buy_conditions = [] # don't buy if there isn't 3% profit to be made dont_buy_conditions.append(dataframe['close_15m'].rolling(self.lookback_candles.value).max() < dataframe['close'] * self.profit_threshold.value) dataframe.loc[(dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (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) & (dataframe['close'] < dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewo1') dataframe.loc[(dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (dataframe['close'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['rsi'] < 25), ['enter_long', 'enter_tag']] = (1, 'ewo2') dataframe.loc[(dataframe['rsi_fast'] < self.rsi_fast_buy.value) & (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) & (dataframe['close'] < dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewolow') if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'enter_long'] = 0 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append((dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (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) & (dataframe['rsi_fast'] > dataframe['rsi_slow'])) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1 return dataframe