# source: https://raw.githubusercontent.com/webclinic017/test-1/a1afda90d5d3c09579a671697b5e3f48b6b4e63c/akiva6.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, # value loaded from strategy "high_offset": 1.054, # value loaded from strategy "high_offset_2": 1.018, # value loaded from strategy "high_offset_ema": 1.012, # value loaded from strategy "sell_custom_dec_profit_1": 0.05, # value loaded from strategy "sell_custom_dec_profit_2": 0.07, # value loaded from strategy "sell_custom_profit_0": 0.009, # value loaded from strategy "sell_custom_profit_1": 0.010, # value loaded from strategy "sell_custom_profit_2": 0.011, # value loaded from strategy "sell_custom_profit_3": 0.012, # value loaded from strategy "sell_custom_profit_4": 0.013, # value loaded from strategy "sell_custom_profit_under_rel_1": 0.020, # value loaded from strategy "sell_custom_profit_under_rsi_diff_1": 4.4, # value loaded from strategy "sell_custom_rsi_0": 33.0, # value loaded from strategy "sell_custom_rsi_1": 38.0, # value loaded from strategy "sell_custom_rsi_2": 43.0, # value loaded from strategy "sell_custom_rsi_3": 48.0, # value loaded from strategy "sell_custom_rsi_4": 50.0, # value loaded from strategy "sell_custom_stoploss_under_rel_1": 0.004, # value loaded from strategy "sell_custom_stoploss_under_rsi_diff_1": 8.0, # value loaded from strategy "sell_custom_under_profit_1": 0.01, # value loaded from strategy "sell_custom_under_profit_2": 0.02, # value loaded from strategy "sell_custom_under_profit_3": 0.3, # value loaded from strategy "sell_custom_under_rsi_1": 56.0, # value loaded from strategy "sell_custom_under_rsi_2": 60.0, # value loaded from strategy "sell_custom_under_rsi_3": 62.0, # value loaded from strategy "sell_trail_down_1": 0.18, # value loaded from strategy "sell_trail_down_2": 0.14, # value loaded from strategy "sell_trail_down_3": 0.01, # value loaded from strategy "sell_trail_profit_max_1": 0.46, # value loaded from strategy "sell_trail_profit_max_2": 0.12, # value loaded from strategy "sell_trail_profit_max_3": 0.1, # value loaded from strategy "sell_trail_profit_min_1": 0.15, # value loaded from strategy "sell_trail_profit_min_2": 0.01, # value loaded from strategy "sell_trail_profit_min_3": 0.05, # value loaded from strategy } 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__akiva6__20210905_191817(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { # "0": 0.283, # "40": 0.086, # "99": 0.036, "0": 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) sell_custom_profit_0 = DecimalParameter(0.001, 0.1, default=sell_params['sell_custom_profit_0'], space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=sell_params['sell_custom_rsi_0'], space='sell', decimals=3, optimize=False, load=True) sell_custom_profit_1 = DecimalParameter(0.005, 0.1, default=sell_params['sell_custom_profit_1'], space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=sell_params['sell_custom_rsi_1'], space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_2 = DecimalParameter(0.007, 0.1, default=sell_params['sell_custom_profit_2'], space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=sell_params['sell_custom_rsi_2'], space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_3 = DecimalParameter(0.009, 0.30, default=sell_params['sell_custom_profit_3'], space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=sell_params['sell_custom_rsi_3'], space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_4 = DecimalParameter(0.01, 0.6, default=sell_params['sell_custom_profit_4'], space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=sell_params['sell_custom_under_profit_1'], space='sell', decimals=2, optimize=False, load=True) sell_custom_under_profit_1 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_under_profit_1'], space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=sell_params['sell_custom_under_rsi_1'], space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_2 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_under_profit_2'], space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=sell_params['sell_custom_under_rsi_2'], space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_3 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_under_profit_3'], space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=sell_params['sell_custom_under_rsi_3'], space='sell', decimals=1, optimize=False, load=True) sell_custom_dec_profit_1 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_dec_profit_1'], space='sell', decimals=3, optimize=False, load=True) sell_custom_dec_profit_2 = DecimalParameter(0.05, 0.2, default=sell_params['sell_custom_dec_profit_2'], space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_1 = DecimalParameter(0.001, 0.25, default=sell_params['sell_trail_profit_min_1'], space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_1 = DecimalParameter(0.03, 0.5, default=sell_params['sell_trail_profit_max_1'], space='sell', decimals=2, optimize=False, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=sell_params['sell_trail_down_1'], space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_2 = DecimalParameter(0.004, 0.1, default=sell_params['sell_trail_profit_min_2'], space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=sell_params['sell_trail_profit_max_2'], space='sell', decimals=2, optimize=False, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=sell_params['sell_trail_down_2'], space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_3 = DecimalParameter(0.006, 0.1, default=sell_params['sell_trail_profit_min_3'], space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_3 = DecimalParameter(0.08, 0.16, default=sell_params['sell_trail_profit_max_3'], space='sell', decimals=2, optimize=False, load=True) sell_trail_down_3 = DecimalParameter(0.01, 0.04, default=sell_params['sell_trail_down_3'], space='sell', decimals=3, optimize=False, load=True) sell_custom_profit_under_rel_1 = DecimalParameter(0.01, 0.04, default=sell_params['sell_custom_profit_under_rel_1'], space='sell', optimize=False, load=True) sell_custom_profit_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=sell_params['sell_custom_profit_under_rsi_diff_1'], space='sell', optimize=False, load=True) sell_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=sell_params['sell_custom_stoploss_under_rel_1'], space='sell', optimize=False, load=True) sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=sell_params['sell_custom_stoploss_under_rsi_diff_1'], space='sell', optimize=False, load=True) # 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': -0.002 } buy_signals = {} # Custom Trailing Stoploss by Perkmeister 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 > 0.2): return 0.04 elif (current_profit > 0.1): return 0.03 elif (current_profit > 0.05): return 0.02 elif (current_profit > 0.02): return 0.01 return 0.99 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) last_candle = dataframe.iloc[-1].squeeze() max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) if (last_candle is not None): if (current_profit > self.sell_custom_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value): return 'signal_profit_4' elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value): return 'signal_profit_3' elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value): return 'signal_profit_2' elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value): return 'signal_profit_1' elif (current_profit > self.sell_custom_profit_0.value) & (last_candle['rsi'] < self.sell_custom_rsi_0.value): return 'signal_profit_0' elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_1.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_1' elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_2.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_2' elif (current_profit > self.sell_custom_under_profit_3.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_3.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_3' elif (current_profit > self.sell_custom_dec_profit_1.value) & (last_candle['sma_200_dec']): return 'signal_profit_d_1' elif (current_profit > self.sell_custom_dec_profit_2.value) & (last_candle['close'] < last_candle['ema_100']): return 'signal_profit_d_2' elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (max_profit > (current_profit + self.sell_trail_down_1.value)): return 'signal_profit_t_1' elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (max_profit > (current_profit + self.sell_trail_down_2.value)): return 'signal_profit_t_2' elif (last_candle['close'] < last_candle['ema_200']) & (current_profit > self.sell_trail_profit_min_3.value) & (current_profit < self.sell_trail_profit_max_3.value) & (max_profit > (current_profit + self.sell_trail_down_3.value)): return 'signal_profit_u_t_1' #elif (current_profit > 0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_profit_under_rsi_diff_1.value): #return 'signal_profit_u_e_1' #elif (current_profit < -0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_stoploss_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_stoploss_under_rsi_diff_1.value): #return 'signal_stoploss_u_1' 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 > 0.018)): # Reject sell signal when trailing stoplosses return False return True 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) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) 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 informative_1h 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['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) 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) informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) 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') dont_buy_conditions = [] dont_buy_conditions.append( ( (dataframe['close_1h'].rolling(24).max() < (dataframe['close'] * 1.03 )) # don't buy if there isn't 3% profit to be made ) ) if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'buy'] = 0 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 def EWO(dataframe, sma1_length=5, sma2_length=35): df = dataframe.copy() sma1 = ta.EMA(df, timeperiod=sma1_length) sma2 = ta.EMA(df, timeperiod=sma2_length) smadif = (sma1 - sma2) / df['close'] * 100 return smadif