# source: https://raw.githubusercontent.com/vaskosmihaylov/nfi-custom-strategies/91c1f22df4c5b0ac540cf09a447fe0dd78d9d5cd/user_data/strategies/El/Auto_EI_t4c0s.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from typing import Optional 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 import math import logging logger = logging.getLogger(__name__) # @Rallipanos # changes by IcHiAT def EWO(dataframe, ema_length=5, ema2_length=3): 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 class Github_vaskosmihaylov_nfi_custom_strategies__Auto_EI_t4c0s__20260513_073522(IStrategy): weak_entry_tags = {"ewo_mean_dn", "ewo_zero_dn"} ''' ______ __ __ __ __ ______ __ __ __ __ __ ______ / \ / | _/ | / | / | / \ / \ / | / | / | / | / \ /$$$$$$ |$$ |____ / $$ | _______ $$ | /$$/ /$$$$$$ |$$ \ $$ | _$$ |_ $$ | $$ | _______ /$$$$$$ | _______ $$ | $$/ $$ \ $$$$ | / |$$ |/$$/ $$ ___$$ |$$$ \$$ | / $$ | $$ |__$$ | / |$$$ \$$ | / | $$ | $$$$$$$ | $$ | /$$$$$$$/ $$ $$< / $$< $$$$ $$ | $$$$$$/ $$ $$ |/$$$$$$$/ $$$$ $$ |/$$$$$$$/ $$ | __ $$ | $$ | $$ | $$ | $$$$$ \ _$$$$$ |$$ $$ $$ | $$ | __$$$$$$$$ |$$ | $$ $$ $$ |$$ \ $$ \__/ |$$ | $$ | _$$ |_ $$ \_____ $$ |$$ \ / \__$$ |$$ |$$$$ | $$ |/ | $$ |$$ \_____ $$ \$$$$ | $$$$$$ | $$ $$/ $$ | $$ |/ $$ |$$ |$$ | $$ |$$ $$/ $$ | $$$ |______$$ $$/ $$ |$$ |$$ $$$/ / $$/ $$$$$$/ $$/ $$/ $$$$$$/ $$$$$$$/ $$/ $$/ $$$$$$/ $$/ $$// |$$$$/ $$/ $$$$$$$/ $$$$$$/ $$$$$$$/ $$$$$$/ ''' ### 3-13-2023 ### # WIP - V1 # No hyper-opt using just defaults from orig EI3 strat, you need to try and hyper-opt this file - # Will suggest adding in an roi table. ### # Buy hyperspace params: buy_params = { "atr_length": 23, "b01": 0.78, "b02": 0.24, "b03": 0.55, "b04": 0.55, "b05": 0.55, "b06": 0.9, "base_nb_candles_buy": 14, "buy_adx": 27, "buy_ema_cofi": 0.972, "buy_ewo_high": 11.346, "buy_fastd": 30, "buy_fastk": 26, "fib_dn": 7.6, "fib_up": 5.0, "increment": 1.0007, "lambo2_ema_14_factor": 0.82, "lambo2_rsi_14_limit": 23, "lambo2_rsi_4_limit": 16, "low_offset": 0.992, "mean_dn": 6.5, "mean_up": 7.0, "rsi_buy": 42, "window": 36, "x01": 3.9, "x02": 2.1, "x03": 4.6, "x04": 1.3, "x05": 3.7, "z01": 2.1, "zero_dn": 5.2, "zero_up": 6.9, } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 14, "day1": 1, "day2": 4, "day3": 5, "day4": 8, "fib_dns": 2.3, "fib_ups": 1.6, "high_offset": 1.012, "high_offset_2": 1.014, "mean_dns": 2.2, "mean_ups": 5.4, "moon": 0.004, "s01": 0.5, "s02": 0.5, "s03": 0.5, "s04": 0.5, "s05": 0.5, "s06": 0.5, "unclog1": 0.15, "unclog2": 0.16, "unclog3": 0.12, "unclog4": 0.18, "y01": 3.6, "y02": 3.4, "y03": 2.6, "z02": 2.5, "zero_dns": 7.3, "zero_ups": 1.8, } # ROI table: minimal_roi = { "0": 0.243, "46": 0.035, "127": 0.015, "237": 0 } @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 5 }, { "method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 20, "stop_duration_candles": 4, "max_allowed_drawdown": 0.2 }, { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "only_per_pair": False }, { "method": "LowProfitPairs", "lookback_period_candles": 6, "trade_limit": 2, "stop_duration_candles": 60, "required_profit": 0.02 }, { "method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "required_profit": 0.01 } ] # Stoploss - Unclog stoploss = -0.99 moon = DecimalParameter(0.001, 0.004, default=sell_params['moon'], decimals=3, space='sell', optimize=True) unclog1 = DecimalParameter(0.1, 0.2, default=sell_params['unclog1'], decimals=2, space='sell', optimize=True) unclog2 = DecimalParameter(0.08, 0.2, default=sell_params['unclog2'], decimals=2, space='sell', optimize=True) unclog3 = DecimalParameter(0.10, 0.2, default=sell_params['unclog3'], decimals=2, space='sell', optimize=True) unclog4 = DecimalParameter(0.14, 0.2, default=sell_params['unclog4'], decimals=2, space='sell', optimize=True) day1 = IntParameter(1, 4, default=sell_params['day1'], space='sell', optimize=True) day2 = IntParameter(2, 5, default=sell_params['day2'], space='sell', optimize=True) day3 = IntParameter(3, 6, default=sell_params['day3'], space='sell', optimize=True) day4 = IntParameter(4, 10, default=sell_params['day4'], space='sell', optimize=True) # SMAOffset base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True) # lambo2 lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True) lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True) lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 locked_stoploss = {} def _notify_runtime(self, message: str) -> None: if self.dp and self.dp.runmode.value in ("live", "dry_run"): self.dp.send_msg(message) logger.info(message) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) window = IntParameter(12, 70, default=buy_params['window'], space='buy', optimize=True) #cofi is_optimize_cofi = True buy_ema_cofi = DecimalParameter(0.96, 0.98, default=buy_params['buy_ema_cofi'] , optimize = is_optimize_cofi) buy_fastk = IntParameter(20, 30, default=buy_params['buy_fastk'], optimize = is_optimize_cofi) buy_fastd = IntParameter(20, 30, default=buy_params['buy_fastd'], optimize = is_optimize_cofi) buy_adx = IntParameter(20, 30, default=buy_params['buy_adx'], optimize = is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=buy_params['buy_ewo_high'], optimize = is_optimize_cofi) atr_length = IntParameter(10, 30, default=buy_params['atr_length'], space='buy', optimize=True) increment = DecimalParameter(low=1.0005, high=1.001, default=buy_params['increment'], decimals=4 ,space='buy', optimize=True, load=True) ### Buy Weight Mulitpliers ### x01 = DecimalParameter(1.0, 5.0, default=buy_params['x01'], decimals=1, space='buy', optimize=True) x02 = DecimalParameter(1.0, 5.0, default=buy_params['x02'], decimals=1, space='buy', optimize=True) x03 = DecimalParameter(1.0, 5.0, default=buy_params['x03'], decimals=1, space='buy', optimize=True) x04 = DecimalParameter(1.0, 5.0, default=buy_params['x04'], decimals=1, space='buy', optimize=True) x05 = DecimalParameter(1.0, 5.0, default=buy_params['x05'], decimals=1, space='buy', optimize=True) ### Sell Weight Mulitpliers ### y01 = DecimalParameter(1.0, 5.0, default=sell_params['y01'], decimals=1, space='sell', optimize=True) y02 = DecimalParameter(1.0, 5.0, default=sell_params['y02'], decimals=1, space='sell', optimize=True) y03 = DecimalParameter(1.0, 5.0, default=sell_params['y03'], decimals=1, space='sell', optimize=True) ### General Weight Mulitpliers ### z01 = DecimalParameter(1.0, 5.0, default=buy_params['z01'], decimals=1, space='buy', optimize=True) z02 = DecimalParameter(1.0, 5.0, default=sell_params['z02'], decimals=1, space='sell', optimize=True) ### Entry / Exit Thresholds ### b01 = DecimalParameter(0.0, 1.10, default=buy_params['b01'], decimals=2, space='buy', optimize=True) b02 = DecimalParameter(0.0, 1.10, default=buy_params['b02'], decimals=2, space='buy', optimize=True) b03 = DecimalParameter(0.0, 1.10, default=buy_params['b03'], decimals=2, space='buy', optimize=True) b04 = DecimalParameter(0.0, 1.10, default=buy_params['b04'], decimals=2, space='buy', optimize=True) b05 = DecimalParameter(0.0, 1.10, default=buy_params['b05'], decimals=2, space='buy', optimize=True) b06 = DecimalParameter(0.0, 1.10, default=buy_params['b06'], decimals=2, space='buy', optimize=True) s01 = DecimalParameter(0.0, 1.10, default=sell_params['s01'], decimals=2, space='sell', optimize=True) s02 = DecimalParameter(0.0, 1.10, default=sell_params['s02'], decimals=2, space='sell', optimize=True) s03 = DecimalParameter(0.0, 1.10, default=sell_params['s03'], decimals=2, space='sell', optimize=True) s04 = DecimalParameter(0.0, 1.10, default=sell_params['s04'], decimals=2, space='sell', optimize=True) s05 = DecimalParameter(0.0, 1.10, default=sell_params['s05'], decimals=2, space='sell', optimize=True) s06 = DecimalParameter(0.0, 1.10, default=sell_params['s06'], decimals=2, space='sell', optimize=True) fib_dn = DecimalParameter(1.0, 10.0, default=buy_params['fib_dn'], decimals=1, space='buy', optimize=True) mean_dn = DecimalParameter(1.0, 10.0, default=buy_params['mean_dn'], decimals=1, space='buy', optimize=True) zero_dn = DecimalParameter(1.0, 10.0, default=buy_params['zero_dn'], decimals=1, space='buy', optimize=True) zero_up = DecimalParameter(1.0, 10.0, default=buy_params['zero_up'], decimals=1, space='buy', optimize=True) mean_up = DecimalParameter(1.0, 10.0, default=buy_params['mean_up'], decimals=1, space='buy', optimize=True) fib_up = DecimalParameter(1.0, 10.0, default=buy_params['fib_up'], decimals=1, space='buy', optimize=True) fib_dns = DecimalParameter(1.0, 10.0, default=sell_params['fib_dns'], decimals=1, space='sell', optimize=True) mean_dns = DecimalParameter(1.0, 10.0, default=sell_params['mean_dns'], decimals=1, space='sell', optimize=True) zero_dns = DecimalParameter(1.0, 10.0, default=sell_params['zero_dns'], decimals=1, space='sell', optimize=True) zero_ups = DecimalParameter(1.0, 10.0, default=sell_params['zero_ups'], decimals=1, space='sell', optimize=True) mean_ups = DecimalParameter(1.0, 10.0, default=sell_params['mean_ups'], decimals=1, space='sell', optimize=True) fib_ups = DecimalParameter(1.0, 10.0, default=sell_params['fib_ups'], decimals=1, space='sell', optimize=True) use_custom_stoploss = True process_only_new_candles = True # Custom Entry last_entry_price = None ### Trailing Stop ### def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() SLT1 = current_candle['move_mean'] SL1 = current_candle['move_mean'] * 0.5 SLT2 = current_candle['move_mean_x'] SL2 = current_candle['move_mean_x'] - current_candle['move_mean'] display_profit = current_profit * 100 slt1 = SLT1 * 100 sl1 = SL1 * 100 slt2 = SLT2 * 100 sl2 = SL2 * 100 # if len(self.locked_stoploss) > 0: # print(self.locked_stoploss) if current_candle['max_l'] != 0: # ignore stoploss if setting new highs if pair not in self.locked_stoploss: # No locked stoploss for this pair yet if SLT2 is not None and current_profit > SLT2: self.locked_stoploss[pair] = SL2 self._notify_runtime(f'*** {pair} *** Profit {display_profit:.3f}% - {slt2:.3f}%/{sl2:.3f}% activated') return SL2 elif SLT1 is not None and current_profit > SLT1: self.locked_stoploss[pair] = SL1 self._notify_runtime(f'*** {pair} *** Profit {display_profit:.3f}% - {slt1:.3f}%/{sl1:.3f}% activated') return SL1 else: return self.stoploss else: # Stoploss has been locked for this pair self._notify_runtime(f'*** {pair} *** Profit {display_profit:.3f}% stoploss locked at {self.locked_stoploss[pair]:.4f}') return self.locked_stoploss[pair] if current_profit < -.01: if pair in self.locked_stoploss: del self.locked_stoploss[pair] self._notify_runtime(f'*** {pair} *** Stoploss reset.') return self.stoploss def custom_entry_price(self, pair: str, trade: Optional['Trade'], current_time: datetime, proposed_rate: float, entry_tag: Optional[str], side: str, **kwargs) -> float: dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) entry_price = (dataframe['close'].iat[-1] + dataframe['open'].iat[-1] + proposed_rate + proposed_rate) / 4 if self.dp and self.dp.runmode.value in ("live", "dry_run"): logger.info(f"{pair} Using Entry Price: {entry_price} | close: {dataframe['close'].iat[-1]} open: {dataframe['open'].iat[-1]} proposed_rate: {proposed_rate}") # Check if there is a stored last entry price and if it matches the proposed entry price if self.last_entry_price is not None and abs(entry_price - self.last_entry_price) < 0.0001: # Tolerance for floating-point comparison entry_price *= self.increment.value # Increment by 0.2% if self.dp and self.dp.runmode.value in ("live", "dry_run"): logger.info(f"{pair} Incremented entry price: {entry_price} based on previous entry price : {self.last_entry_price}.") # Update the last entry price self.last_entry_price = entry_price return entry_price 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].squeeze() if exit_reason == 'roi' and (last_candle['max_l'] < 0.0015): return False # Handle freak events if exit_reason == 'roi' and trade.calc_profit_ratio(rate) < 0.001: self._notify_runtime(f'{trade.pair} ROI is below 0') return False if exit_reason == 'partial_exit' and trade.calc_profit_ratio(rate) < 0: self._notify_runtime(f'{trade.pair} partial exit is below 0') return False if exit_reason == 'trailing_stop_loss' and trade.calc_profit_ratio(rate) < 0: self._notify_runtime(f'{trade.pair} trailing stop price is below 0') return False return True def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): held_days = (current_time - trade.open_date_utc).days enter_tag = (trade.enter_tag or "").strip() # Weak dip entries should not be allowed to drift into the broad unclog ladder. if enter_tag in self.weak_entry_tags: if current_profit < -0.12 and held_days >= 3: return 'unclog weak 3' if current_profit < -0.10 and held_days >= 2: return 'unclog weak 2' if current_profit < -0.08 and held_days >= 1: return 'unclog weak 1' # Sell any positions at a loss if they are held for more than X days. if current_profit < -self.unclog4.value and held_days >= self.day4.value: return 'unclog 4' if current_profit < -self.unclog3.value and held_days >= self.day3.value: return 'unclog 3' if current_profit < -self.unclog2.value and held_days >= self.day2.value: return 'unclog 2' if current_profit < -self.unclog1.value and held_days >= self.day1.value: return 'unclog 1' # Sell signal use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False ## Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'} # Optimal timeframe for the strategy timeframe = '5m' position_adjustment_enable = False process_only_new_candles = True startup_candle_count = 200 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['ma_lo'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * (self.low_offset.value) dataframe['ma_hi'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * (self.high_offset.value) dataframe['ma_hi_2'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * (self.high_offset_2.value) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) # HMA-BUY SQUEEZE dataframe['HMA_SQZ'] = (((dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] - dataframe['hma_50']) / dataframe[f'ma_buy_{self.base_nb_candles_buy.value}']) * 100) dataframe['zero'] = 0 # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) dataframe.loc[dataframe['EWO'] > 0, "EWO_UP"] = dataframe['EWO'] dataframe.loc[dataframe['EWO'] < 0, "EWO_DN"] = dataframe['EWO'] dataframe['EWO_UP'].ffill() dataframe['EWO_DN'].ffill() dataframe['EWO_MEAN_UP'] = dataframe['EWO_UP'].mean() dataframe['EWO_MEAN_DN'] = dataframe['EWO_DN'].mean() dataframe['EWO_UP_FIB'] = dataframe['EWO_MEAN_UP'] * 1.618 dataframe['EWO_DN_FIB'] = dataframe['EWO_MEAN_DN'] * 1.618 # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) #lambo2 dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) # Cofi stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['adx'] = ta.ADX(dataframe) dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['OHLC4'] = (dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4 # Check how far we are from min and max dataframe['max'] = dataframe['OHLC4'].rolling(4).max() / dataframe['OHLC4'] - 1 dataframe['min'] = abs(dataframe['OHLC4'].rolling(4).min() / dataframe['OHLC4'] - 1) dataframe['max_l'] = dataframe['OHLC4'].rolling(48).max() / dataframe['OHLC4'] - 1 dataframe['min_l'] = abs(dataframe['OHLC4'].rolling(48).min() / dataframe['OHLC4'] - 1) dataframe['max_x'] = dataframe['OHLC4'].rolling(336).max() / dataframe['OHLC4'] - 1 dataframe['min_x'] = abs(dataframe['OHLC4'].rolling(336).min() / dataframe['OHLC4'] - 1) # Apply rolling window operation to the 'OHLC4'column rolling_window = dataframe['OHLC4'].rolling(self.window.value) rolling_max = rolling_window.max() rolling_min = rolling_window.min() # Calculate the peak-to-peak value on the resulting rolling window data ptp_value = rolling_window.apply(lambda x: np.ptp(x)) # Assign the calculated peak-to-peak value to the DataFrame column dataframe['move'] = ptp_value / dataframe['OHLC4'] dataframe['move_mean'] = dataframe['move'].mean() dataframe['move_mean_x'] = dataframe['move'].mean() * 1.6 dataframe['exit_mean'] = rolling_min * (1 + dataframe['move_mean']) dataframe['exit_mean_x'] = rolling_min * (1 + dataframe['move_mean_x']) dataframe['enter_mean'] = rolling_max * (1 - dataframe['move_mean']) dataframe['enter_mean_x'] = rolling_max * (1 - dataframe['move_mean_x']) dataframe['atr_pcnt'] = (ta.ATR(dataframe, timeperiod=5) / dataframe['OHLC4']) # Apply rolling window operation to the 'OHLC4'column rolling_window_x = dataframe['OHLC4'].rolling(200) rolling_max_x = rolling_window_x.max() rolling_min_x = rolling_window_x.min() # Calculate the peak-to-peak value on the resulting rolling window data ptp_value_x = rolling_window_x.apply(lambda x: np.ptp(x)) # Assign the calculated peak-to-peak value to the DataFrame column dataframe['move_l'] = ptp_value_x / dataframe['OHLC4'] dataframe['move_mean_l'] = dataframe['move_l'].mean() dataframe['move_mean_xl'] = dataframe['move_l'].mean() * 1.6 dataframe['exit_mean_l'] = rolling_min_x * (1 + dataframe['move_mean_l']) dataframe['exit_mean_xl'] = rolling_min_x * (1 + dataframe['move_mean_xl']) dataframe['enter_mean_l'] = rolling_max_x * (1 - dataframe['move_mean_l']) dataframe['enter_mean_xL'] = rolling_max_x * (1 - dataframe['move_mean_xl']) ### Buying Weights & Signals ### dataframe.loc[(dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)), 'buy0'] = 1 dataframe.loc[(dataframe['close'] > (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)), 'buy0'] = 0 dataframe.loc[(dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)), 'buy1'] = 1 dataframe.loc[(dataframe['rsi_4'] > int(self.lambo2_rsi_4_limit.value)), 'buy1'] = 0 dataframe.loc[(dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)), 'buy2'] = 1 dataframe.loc[(dataframe['rsi_14'] > int(self.lambo2_rsi_14_limit.value)), 'buy2'] = 0 dataframe.loc[(dataframe['atr_pcnt'] > dataframe['min_l']), 'buy3'] = 1 dataframe.loc[(dataframe['atr_pcnt'] < dataframe['min_l']), 'buy3'] = 0 dataframe.loc[(dataframe['rsi']self.rsi_buy.value), 'buy4'] = 0 dataframe['lambo_weight'] = ( (dataframe['buy0']+dataframe['buy1']+dataframe['buy2']+dataframe['buy3']+dataframe['buy4'])/5) * self.x01.value dataframe.loc[(dataframe['rsi_fast'] < 35), 'buy10'] = 1 dataframe.loc[(dataframe['rsi_fast'] > 35), 'buy10'] = 0 dataframe.loc[(dataframe['close'] < dataframe['ma_lo']), 'buy11'] = 1 dataframe.loc[(dataframe['close'] > dataframe['ma_lo']), 'buy11'] = 0 dataframe.loc[(dataframe['close'] < dataframe['enter_mean_x']), 'buy12'] = 1 dataframe.loc[(dataframe['close'] > dataframe['enter_mean_x']), 'buy12'] = 0 dataframe.loc[(dataframe['close'].shift() < dataframe['enter_mean_x'].shift()), 'buy13'] = 1 dataframe.loc[(dataframe['close'].shift() > dataframe['enter_mean_x'].shift()), 'buy13'] = 0 dataframe.loc[(dataframe['rsi'] < self.rsi_buy.value), 'buy14'] = 1 dataframe.loc[(dataframe['rsi'] > self.rsi_buy.value), 'buy14'] = 0 dataframe.loc[(dataframe['atr_pcnt'] > dataframe['min']), 'buy15'] = 1 dataframe.loc[(dataframe['atr_pcnt'] < dataframe['min']), 'buy15'] = 0 dataframe.loc[(dataframe['EWO'] > dataframe['EWO_MEAN_UP']), 'buy16'] = 1 dataframe.loc[(dataframe['EWO'] < dataframe['EWO_MEAN_UP']), 'buy16'] = 0 dataframe['buy1ewo_weight'] = ( (dataframe['buy10']+dataframe['buy11']+dataframe['buy12']+dataframe['buy13'] +dataframe['buy14']+dataframe['buy15']+dataframe['buy16'])/7) * self.x02.value dataframe.loc[(dataframe['rsi_fast'] < 35), 'buy20'] = 1 dataframe.loc[(dataframe['rsi_fast'] > 35), 'buy20'] = 0 dataframe.loc[(dataframe['close'] < dataframe['ma_lo']), 'buy21'] = 1 dataframe.loc[(dataframe['close'] > dataframe['ma_lo']), 'buy21'] = 0 dataframe.loc[(dataframe['EWO'] < dataframe['EWO_DN_FIB']), 'buy22'] = 1 dataframe.loc[(dataframe['EWO'] > dataframe['EWO_DN_FIB']), 'buy22'] = 0 dataframe.loc[(dataframe['atr_pcnt'] > dataframe['min']), 'buy23'] = 1 dataframe.loc[(dataframe['atr_pcnt'] < dataframe['min']), 'buy23'] = 0 dataframe['buy2ewo_weight'] = ( (dataframe['buy20']+dataframe['buy21']+dataframe['buy22']+dataframe['buy23'])/4) * self.x03.value dataframe.loc[(dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value), 'buy30'] = 1 dataframe.loc[(dataframe['open'] > dataframe['ema_8'] * self.buy_ema_cofi.value), 'buy30'] = 0 dataframe['buy31'] = 0 dataframe.loc[qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']), 'buy31'] = 1 dataframe.loc[(dataframe['fastk'] < self.buy_fastk.value), 'buy32'] = 1 dataframe.loc[(dataframe['fastk'] > self.buy_fastk.value), 'buy32'] = 0 dataframe.loc[(dataframe['fastd'] < self.buy_fastd.value), 'buy33'] = 1 dataframe.loc[(dataframe['fastd'] > self.buy_fastd.value), 'buy33'] = 0 dataframe.loc[(dataframe['adx'] > self.buy_adx.value), 'buy34'] = 1 dataframe.loc[(dataframe['adx'] < self.buy_adx.value), 'buy34'] = 0 dataframe.loc[(dataframe['EWO'] > dataframe['EWO_MEAN_UP']), 'buy35'] = 1 dataframe.loc[(dataframe['EWO'] < dataframe['EWO_MEAN_UP']), 'buy35'] = 0 dataframe.loc[(dataframe['atr_pcnt'] > dataframe['min']), 'buy36'] = 1 dataframe.loc[(dataframe['atr_pcnt'] < dataframe['min']), 'buy36'] = 0 dataframe['cofi_weight'] = ( (dataframe['buy30']+dataframe['buy31']+dataframe['buy32']+dataframe['buy33']+dataframe['buy34']+dataframe['buy35']+dataframe['buy36'])/7) * self.x04.value dataframe['buy_weight'] = ta.SMA(((dataframe['lambo_weight'] + dataframe['buy1ewo_weight'] + dataframe['buy2ewo_weight'] + dataframe['cofi_weight']) / 4) * self.x05.value, timeperiod=5) ### General Indicators ### dataframe.loc[(dataframe['fastk'] < self.buy_fastk.value), 'gen1'] = 1 dataframe.loc[(dataframe['fastk'] > self.buy_fastk.value), 'gen1'] = -1 dataframe.loc[(dataframe['fastd'] < self.buy_fastd.value), 'gen2'] = 1 dataframe.loc[(dataframe['fastd'] > self.buy_fastd.value), 'gen2'] = -1 dataframe.loc[(dataframe['adx'] > self.buy_adx.value), 'gen3'] = 1 dataframe.loc[(dataframe['adx'] < self.buy_adx.value), 'gen3'] = -1 dataframe['gen4'] = 0 dataframe.loc[qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']), 'gen4'] = 1 dataframe.loc[qtpylib.crossed_below(dataframe['fastk'], dataframe['fastd']), 'gen4'] = -1 # Distance from min and max summed for different ranges. Selling it will go negative. dataframe['gen5'] = (dataframe['max']+dataframe['max_l']+dataframe['max_x']) - (dataframe['min']+dataframe['min_l']+dataframe['min_x']) dataframe['gen6'] = 0 dataframe.loc[qtpylib.crossed_above(dataframe['ema_8'], dataframe['ema_14']), 'gen6'] = 1 dataframe.loc[qtpylib.crossed_below(dataframe['ema_8'], dataframe['ema_14']), 'gen6'] = -1 dataframe['general_weight'] = ((dataframe['gen1']+dataframe['gen2']+dataframe['gen3']+dataframe['gen4']+dataframe['gen5']+dataframe['gen6'])/6) dataframe.loc[(dataframe['general_weight'] > 0), 'gen_buy'] = dataframe['general_weight'] * (1 + dataframe['move']) dataframe.loc[(dataframe['general_weight'] < 0), 'gen_buy'] = 0 dataframe.loc[(dataframe['general_weight'] < 0), 'gen_sell'] = abs(dataframe['general_weight'] * (1 + dataframe['move'])) dataframe.loc[(dataframe['general_weight'] > 0), 'gen_sell'] = 0 ### SELLING Weights & Signals ### dataframe.loc[(dataframe['close'] > dataframe['hma_50']), 'sell0'] = 1 dataframe.loc[(dataframe['close'] < dataframe['hma_50']), 'sell0'] = 0 dataframe.loc[(dataframe['close'] > dataframe['ma_hi_2']), 'sell1'] = 1 dataframe.loc[(dataframe['close'] < dataframe['ma_hi_2']), 'sell1'] = 0 dataframe.loc[(dataframe['max_l'] != 0), 'sell2'] = 1 dataframe.loc[(dataframe['max_l'] == 0), 'sell2'] = 0 dataframe.loc[(dataframe['close'] > dataframe['exit_mean_x']), 'sell3'] = 1 dataframe.loc[(dataframe['close'] < dataframe['exit_mean_x']), 'sell3'] = 0 dataframe.loc[(dataframe['rsi'] > 50), 'sell4'] = 1 dataframe.loc[(dataframe['rsi'] < 50), 'sell4'] = 0 dataframe.loc[(dataframe['rsi_fast'] > dataframe['rsi_slow']), 'sell5'] = 1 dataframe.loc[(dataframe['rsi_fast'] < dataframe['rsi_slow']), 'sell5'] = 0 dataframe['hi2_weight'] = ( (dataframe['sell0']+dataframe['sell1']+dataframe['sell2']+dataframe['sell3']+dataframe['sell4']+dataframe['sell5'])/6) * self.y01.value dataframe.loc[(dataframe['close'] < dataframe['hma_50']), 'sell10'] = 1 dataframe.loc[(dataframe['close'] > dataframe['hma_50']), 'sell10'] = 0 dataframe.loc[(dataframe['close'] > dataframe['ma_hi']), 'sell11'] = 1 dataframe.loc[(dataframe['close'] < dataframe['ma_hi']), 'sell11'] = 0 dataframe.loc[(dataframe['max_l'] != 0), 'sell12'] = 1 dataframe.loc[(dataframe['max_l'] == 0), 'sell12'] = 0 dataframe.loc[(dataframe['rsi_fast'] > dataframe['rsi_slow']), 'sell13'] = 1 dataframe.loc[(dataframe['rsi_fast'] < dataframe['rsi_slow']), 'sell13'] = 0 dataframe['hi_weight'] = ( (dataframe['sell10']+dataframe['sell11']+dataframe['sell12']+dataframe['sell13'])/4) * self.y02.value dataframe['sell_weight'] = ta.SMA(((dataframe['hi_weight'] + dataframe['hi2_weight']) / 2) * self.y03.value, timeperiod=5) dataframe['buy_decision'] = dataframe['buy_weight'] - dataframe['sell_weight'] dataframe['sell_decision'] = dataframe['sell_weight'] - dataframe['buy_weight'] dataframe = dataframe.copy() dataframe['Gen Buy Above'] = ( dataframe['gen_buy'] > min( self.b01.value, self.b02.value, self.b03.value, self.b04.value, self.b05.value, self.b06.value, ) ).astype(int) dataframe['Gen Sell Above'] = ( dataframe['gen_sell'] > min( self.s01.value, self.s02.value, self.s03.value, self.s04.value, self.s05.value, self.s06.value, ) ).astype(int) # Defragment before Freqtrade injects enter/exit tag columns. return dataframe.copy() def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = dataframe.copy() ewo_fib_dn = ( (dataframe['EWO'] < dataframe['EWO_DN_FIB']) & (dataframe['buy_decision'] > self.fib_dn.value) & (dataframe['gen_buy'] > self.b01.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_fib_dn, 'enter_long'] = 1 dataframe.loc[ewo_fib_dn, 'enter_tag'] = 'ewo_fib_dn' ewo_mean_dn = ( (dataframe['EWO'] > dataframe['EWO_DN_FIB']) & (dataframe['EWO'] < dataframe['EWO_MEAN_DN']) & (dataframe['buy_decision'] > self.mean_dn.value) & (dataframe['gen_buy'] > self.b02.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_mean_dn, 'enter_long'] = 1 dataframe.loc[ewo_mean_dn, 'enter_tag'] = 'ewo_mean_dn' ewo_zero_dn = ( (dataframe['EWO'] > dataframe['EWO_MEAN_DN']) & (dataframe['EWO'] < 0) & (dataframe['buy_decision'] > self.zero_dn.value) & (dataframe['gen_buy'] > self.b03.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_zero_dn, 'enter_long'] = 1 dataframe.loc[ewo_zero_dn, 'enter_tag'] = 'ewo_zero_dn' ewo_fib_up = ( (dataframe['EWO'] > dataframe['EWO_UP_FIB']) & (dataframe['buy_decision'] > self.fib_up.value) & (dataframe['gen_buy'] > self.b04.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_fib_up, 'enter_long'] = 1 dataframe.loc[ewo_fib_up, 'enter_tag'] = 'ewo_fib_up' ewo_mean_up = ( (dataframe['EWO'] < dataframe['EWO_UP_FIB']) & (dataframe['EWO'] > dataframe['EWO_MEAN_UP']) & (dataframe['buy_decision'] > self.mean_up.value) & (dataframe['gen_buy'] > self.b05.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_mean_up, 'enter_long'] = 1 dataframe.loc[ewo_mean_up, 'enter_tag'] = 'ewo_mean_up' ewo_zero_up = ( (dataframe['EWO'] < dataframe['EWO_MEAN_UP']) & (dataframe['EWO'] > 0) & (dataframe['buy_decision'] > self.zero_up.value) & (dataframe['gen_buy'] > self.b06.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_zero_up, 'enter_long'] = 1 dataframe.loc[ewo_zero_up, 'enter_tag'] = 'ewo_zero_up' return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = dataframe.copy() ewo_fib_dns = ( (dataframe['EWO'] < dataframe['EWO_DN_FIB']) & (dataframe['sell_decision'] > self.fib_dns.value) & (dataframe['gen_sell'] > self.s01.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_fib_dns, 'exit_long'] = 1 dataframe.loc[ewo_fib_dns, 'exit_tag'] = 'ewo_fib_dns' ewo_mean_dns = ( (dataframe['EWO'] > dataframe['EWO_DN_FIB']) & (dataframe['EWO'] < dataframe['EWO_MEAN_DN']) & (dataframe['sell_decision'] > self.mean_dns.value) & (dataframe['gen_sell'] > self.s02.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_mean_dns, 'exit_long'] = 1 dataframe.loc[ewo_mean_dns, 'exit_tag'] = 'ewo_mean_dns' ewo_zero_dns = ( (dataframe['EWO'] > dataframe['EWO_MEAN_DN']) & (dataframe['EWO'] < 0) & (dataframe['sell_decision'] > self.zero_dns.value) & (dataframe['gen_sell'] > self.s03.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_zero_dns, 'exit_long'] = 1 dataframe.loc[ewo_zero_dns, 'exit_tag'] = 'ewo_zero_dns' ewo_fib_ups = ( (dataframe['EWO'] > dataframe['EWO_UP_FIB']) & (dataframe['sell_decision'] > self.fib_ups.value) & (dataframe['gen_sell'] > self.s04.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_fib_ups, 'exit_long'] = 1 dataframe.loc[ewo_fib_ups, 'exit_tag'] = 'ewo_fib_ups' ewo_mean_ups = ( (dataframe['EWO'] < dataframe['EWO_UP_FIB']) & (dataframe['EWO'] > dataframe['EWO_MEAN_UP']) & (dataframe['sell_decision'] > self.mean_ups.value) & (dataframe['gen_sell'] > self.s05.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_mean_ups, 'exit_long'] = 1 dataframe.loc[ewo_mean_ups, 'exit_tag'] = 'ewo_mean_ups' ewo_zero_ups = ( (dataframe['EWO'] < dataframe['EWO_MEAN_UP']) & (dataframe['EWO'] > 0) & (dataframe['sell_decision'] > self.zero_ups.value) & (dataframe['gen_sell'] > self.s06.value) & (dataframe['volume'] > 0) ) dataframe.loc[ewo_zero_ups, 'exit_long'] = 1 dataframe.loc[ewo_zero_ups, 'exit_tag'] = 'ewo_zero_ups' return dataframe def pct_change(a, b): return (b - a) / a