# source: https://raw.githubusercontent.com/nicnl31/pyalgotrader/c22b45dc6744b8dc3ea5c9e73858c81274686684/frameworks/freqtrade/user_data/strategies/NotAnotherSMAOffsetStrategy.py # --- Do not remove these libs --- # --- Do not remove these libs --- 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 # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 14, "ewo_high": 2.327, "ewo_high_2": -2.327, "ewo_low": -20.988, "low_offset": 0.975, "low_offset_2": 0.955, "rsi_buy": 69 } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 24, "high_offset": 0.991, "high_offset_2": 0.997 } 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_nicnl31_pyalgotrader__NotAnotherSMAOffsetStrategy__20240902_122600(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 0.215, "40": 0.032, "87": 0.016, "201": 0 } # Stoploss: stoploss = -0.35 # SMAOffset base_nb_candles_buy = IntParameter( 5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter( 5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) 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=True) 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=True) # Protection fast_ewo = 50 slow_ewo = 200 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) ewo_high_2 = DecimalParameter( -6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) # Trailing stop: trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.01 ignore_roi_if_buy_signal = False # Optimal timeframe for the strategy timeframe = '5m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 200 plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } protections = [ # { # "method": "StoplossGuard", # "lookback_period_candles": 12, # "trade_limit": 1, # "stop_duration_candles": 6, # "only_per_pair": True # }, # { # "method": "StoplossGuard", # "lookback_period_candles": 12, # "trade_limit": 2, # "stop_duration_candles": 6, # "only_per_pair": False # }, { "method": "LowProfitPairs", "lookback_period_candles": 60, "trade_limit": 1, "stop_duration": 60, "required_profit": -0.05 }, { "method": "CooldownPeriod", "stop_duration_candles": 2 } ] def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if ((rate > last_candle['close'])) : return False return True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.config['runmode'].value == 'hyperopt': # 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) else: dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_buy.value) dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_sell.value) 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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' buy_1 = ( (dataframe['rsi_fast'] <35)& (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)) ) dataframe.loc[buy_1, 'buy_tag'] += 'ewo1 ' conditions.append(buy_1) buy_2 = ( (dataframe['rsi_fast'] <35)& (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) ) dataframe.loc[buy_2, 'buy_tag'] += 'ewo2 ' conditions.append(buy_2) buy_3 = ( (dataframe['rsi_fast'] < 35)& (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)) ) dataframe.loc[buy_3, 'buy_tag'] += 'ewolow ' conditions.append(buy_3) if conditions: dataframe.loc[:, 'buy'] = reduce(lambda x, y: x | y, conditions) return dataframe def populate_sell_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[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0)& (dataframe['rsi_fast']>dataframe['rsi_slow']) ) ) conditions.append( ( dataframe['hma_50']*1.149 <= dataframe['ema_100'] ) | ( dataframe['close'] >= dataframe['ema_100']*0.951 ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell' ]=1 return dataframe