# source: https://raw.githubusercontent.com/webclinic017/freqtrade-strategies-1/749324e245f1d051eb0babdb642faa68f2350e89/user_data/strategies/ElliotV7.py # --- 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_low": -19.988, "low_offset": 0.975, "rsi_buy": 69 } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 24, "high_offset": 0.991, "high_offset_2": 0.997 } class github_webclinic017_freqtrade_strategies_1__ElliotV7__20210909_203443(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 0.051, "10": 0.031, "22": 0.018, "66": 0 } # Stoploss: stoploss = -0.32 # 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) 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) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) # Trailing stop: trailing_stop = True trailing_stop_positive = 0.005 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 ## Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } # Optimal timeframe for the strategy timeframe = '5m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 39 plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } use_custom_stoploss = False def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if current_profit < -0.1 and current_time - timedelta(minutes=720) > trade.open_date_utc: return -0.01 return -0.99 def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [(pair, '1h') 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) informative_1h['ema_fast'] = ta.EMA(informative_1h, timeperiod=20) informative_1h['ema_slow'] = ta.EMA(informative_1h, timeperiod=25) informative_1h['uptrend'] = ( (informative_1h['ema_fast'] > informative_1h['ema_slow']) ).astype('int') informative_1h['rsi_100'] = ta.RSI(informative_1h, timeperiod=100) return informative_1h def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) # Calculate all ma_buy values for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_lowerband'] = bollinger['lower'] # 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['hma_50']=hmao dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['rsi_100'] = ta.RSI(dataframe, timeperiod=100) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( (dataframe['uptrend_1h'] > 0)& (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)) ) ) conditions.append( ( (dataframe['uptrend_1h'] > 0)& (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)) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ]=1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( (dataframe['sma_9']>dataframe['hma_50'])& (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['sma_9'] (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), 'sell' ]=1 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