# source: https://raw.githubusercontent.com/webclinic017/test-1/a1afda90d5d3c09579a671697b5e3f48b6b4e63c/NotAnotherSMAOffsetStrategyHOv3_akiva.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 from freqtrade.exchange import timeframe_to_prev_date # @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 # } # # Buy hyperspace params: # buy_params = { # "base_nb_candles_buy": 18, # "ewo_high": 3.422, # "ewo_high_2": -3.436, # "ewo_low": -8.562, # "low_offset": 0.966, # "low_offset_2": 0.959, # "rsi_buy": 66, # } # # # Sell hyperspace params: # # sell_params = { # # "base_nb_candles_sell": 17, # # "high_offset": 0.997, # # "high_offset_2": 1.01, # # } # # Sell hyperspace params: # sell_params = { # "base_nb_candles_sell": 7, # "high_offset": 1.014, # "high_offset_2": 0.995, # } # # Buy hyperspace params: # buy_params = { # "ewo_high_2": -5.642, # "low_offset_2": 0.951, # "rsi_buy": 54, # "base_nb_candles_buy": 16, # value loaded from strategy # "ewo_high": 3.422, # value loaded from strategy # "ewo_low": -8.562, # value loaded from strategy # "low_offset": 0.966, # value loaded from strategy # } # # Sell hyperspace params: # sell_params = { # "base_nb_candles_sell": 8, # "high_offset_2": 1.002, # "high_offset": 1.014, # value loaded from strategy # } # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 8, "ewo_high": 4.179, "ewo_low": -16.917, "ewo_high_2": -2.609, # value loaded from strategy "low_offset": 0.986, # value loaded from strategy "low_offset_2": 0.944, # value loaded from strategy "rsi_buy": 58, # value loaded from strategy } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 16, "high_offset": 1.054, "high_offset_2": 1.018, # value loaded from strategy "high_offset_ema": 1.004, } 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_webclinic017_test_1__NotAnotherSMAOffsetStrategyHOv3_akiva__20210905_191817(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { # "0": 0.283, # "40": 0.086, # "99": 0.036, "0": 10 } # Stoploss: stoploss = -0.04 # SMAOffset high_offset_ema = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=False) base_nb_candles_buy = IntParameter( 2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter( 10, 40, 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=False) 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=False) 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 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(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) # Trailing stop: trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.018 trailing_only_offset_is_reached = True # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 ignore_roi_if_buy_signal = False # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'ioc' } # Optimal timeframe for the strategy timeframe = '5m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 200 use_custom_stoploss = False plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } slippage_protection = { 'retries': 3, 'max_slippage': -0.02 } buy_signals = {} pHSL = DecimalParameter(-0.200, -0.040, default=-0.15, decimals=3, space='sell', optimize=False, load=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.020, default=0.018, decimals=3, space='sell', optimize=False, load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.013, decimals=3, space='sell', optimize=False, load=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', optimize=False, load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=False, load=True) # Custom Trailing Stoploss by Perkmeister def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # hard stoploss profit HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used. 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 return stoploss_from_open(sl_profit, current_profit) def get_ticker_indicator(self): return int(self.timeframe[:-1]) def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag else: trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) buy_signal = dataframe.loc[dataframe['date'] < trade_open_date] if not buy_signal.empty: buy_signal_candle = buy_signal.iloc[-1] buy_tag = buy_signal_candle['buy_tag'] if buy_signal_candle['buy_tag'] != '' else 'empty' buy_tags = buy_tag.split() last_candle = dataframe.iloc[-1].squeeze() if (last_candle['close'] > (last_candle['ema_offset_sell'])) : return 'sell signal (' + buy_tag +')' return None def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_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 (sell_reason in ['sell_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 current_profit = trade.calc_profit_ratio(rate) if (sell_reason.startswith('sell signal ') and (current_profit > self.pPF_1.value)): # Reject sell signal when trailing stoplosses return False return True def populate_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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (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)) ), ['buy', 'buy_tag']] = (1, 'ewo1') dataframe.loc[ ( (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) ), ['buy', 'buy_tag']] = (1, 'ewo2') dataframe.loc[ ( (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)) ), ['buy', 'buy_tag']] = (1, 'ewolow') 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['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']) ) ) dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) *self.high_offset_ema.value if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ]=1 return dataframe