# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Auto_EI_t4c0s.py 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__) 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_remiotore_freqtrade__Auto_EI_t4c0s__20260111_210550(IStrategy): """ ______ __ __ __ __ ______ __ __ __ __ __ ______ / \ / | _/ | / | / | / \ / \ / | / | / | / | / \ /$$$$$$ |$$ |____ / $$ | _______ $$ | /$$/ /$$$$$$ |$$ \ $$ | _$$ |_ $$ | $$ | _______ /$$$$$$ | _______ $$ | $$/ $$ \ $$$$ | / |$$ |/$$/ $$ ___$$ |$$$ \$$ | / $$ | $$ |__$$ | / |$$$ \$$ | / | $$ | $$$$$$$ | $$ | /$$$$$$$/ $$ $$< / $$< $$$$ $$ | $$$$$$/ $$ $$ |/$$$$$$$/ $$$$ $$ |/$$$$$$$/ $$ | __ $$ | $$ | $$ | $$ | $$$$$ \ _$$$$$ |$$ $$ $$ | $$ | __$$$$$$$$ |$$ | $$ $$ $$ |$$ \ $$ \__/ |$$ | $$ | _$$ |_ $$ \_____ $$ |$$ \ / \__$$ |$$ |$$$$ | $$ |/ | $$ |$$ \_____ $$ \$$$$ | $$$$$$ | $$ $$/ $$ | $$ |/ $$ |$$ |$$ | $$ |$$ $$/ $$ | $$$ |______$$ $$/ $$ |$$ |$$ $$$/ / $$/ $$$$$$/ $$/ $$/ $$$$$$/ $$$$$$$/ $$/ $$/ $$$$$$/ $$/ $$// |$$$$/ $$/ $$$$$$$/ $$$$$$/ $$$$$$$/ $$$$$$/ """ buy_params = { "atr_length": 23, "b01": 8.1, "b02": 3.2, "b03": 6.8, "b04": 9.3, "b05": 4.6, "b06": 1.8, "base_nb_candles_buy": 17, "buy_adx": 28, "buy_ema_cofi": 0.969, "buy_ewo_high": 5.687, "buy_fastd": 23, "buy_fastk": 30, "fib_dn": 2.5, "fib_up": 9.5, "increment": 1.0007, "lambo2_ema_14_factor": 0.967, "lambo2_rsi_14_limit": 27, "lambo2_rsi_4_limit": 32, "low_offset": 0.995, "mean_dn": 1.6, "mean_up": 3.3, "rsi_buy": 54, "window": 30, "x01": 4.2, "x02": 1.3, "x03": 1.2, "x04": 1.3, "x05": 4.2, "z01": 2.1, "zero_dn": 4.1, "zero_up": 6.6, } 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": 8.0, "s02": 3.5, "s03": 10.0, "s04": 6.3, "s05": 2.2, "s06": 6.8, "unclog1": 0.15, "unclog2": 0.16, "unclog3": 0.12, "unclog4": 0.18, "y01": 3.6, "y02": 3.4, "y03": 2.6, "zero_dns": 7.3, "zero_ups": 1.8, } 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 = -0.99 moon = DecimalParameter(0.001, 0.004, default=0.35, decimals=3, space="sell", optimize=True) unclog1 = DecimalParameter(0.1, 0.2, default=0.04, decimals=2, space="sell", optimize=True) unclog2 = DecimalParameter(0.08, 0.2, default=0.04, decimals=2, space="sell", optimize=True) unclog3 = DecimalParameter(0.10, 0.2, default=0.04, decimals=2, space="sell", optimize=True) unclog4 = DecimalParameter(0.14, 0.2, default=0.04, decimals=2, space="sell", optimize=True) day1 = IntParameter(1, 4, default=1, space="sell", optimize=True) day2 = IntParameter(2, 5, default=2, space="sell", optimize=True) day3 = IntParameter(3, 6, default=3, space="sell", optimize=True) day4 = IntParameter(4, 10, default=4, space="sell", optimize=True) 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_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 ) fast_ewo = 50 slow_ewo = 200 locked_stoploss = {} rsi_buy = IntParameter(30, 70, default=buy_params["rsi_buy"], space="buy", optimize=True) window = IntParameter(12, 70, default=48, space="buy", optimize=True) is_optimize_cofi = True buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97, optimize=is_optimize_cofi) buy_fastk = IntParameter(20, 30, default=20, optimize=is_optimize_cofi) buy_fastd = IntParameter(20, 30, default=20, optimize=is_optimize_cofi) buy_adx = IntParameter(20, 30, default=30, optimize=is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize=is_optimize_cofi) atr_length = IntParameter(10, 30, default=14, space="buy", optimize=True) increment = DecimalParameter( low=1.0005, high=1.001, default=1.0007, decimals=4, space="buy", optimize=True, load=True ) x01 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space="buy", optimize=True) x02 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space="buy", optimize=True) x03 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space="buy", optimize=True) x04 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space="buy", optimize=True) x05 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space="buy", optimize=True) y01 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space="sell", optimize=True) y02 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space="sell", optimize=True) y03 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space="sell", optimize=True) z01 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space="buy", optimize=True) b01 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) b02 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) b03 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) b04 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) b05 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) b06 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) s01 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) s02 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) s03 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) s04 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) s05 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) s06 = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) fib_dn = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) mean_dn = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) zero_dn = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) zero_up = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) mean_up = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) fib_up = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="buy", optimize=True) fib_dns = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) mean_dns = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) zero_dns = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) zero_ups = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) mean_ups = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) fib_ups = DecimalParameter(1.0, 10.0, default=2.5, decimals=1, space="sell", optimize=True) use_custom_stoploss = True process_only_new_candles = True last_entry_price = None 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 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.dp.send_msg( f"*** {pair} *** Profit {display_profit:.3f}% - {slt2:.3f}%/{sl2:.3f}% activated" ) logger.info( 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.dp.send_msg( f"*** {pair} *** Profit {display_profit:.3f}% - {slt1:.3f}%/{sl1:.3f}% activated" ) logger.info( 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.dp.send_msg( f"*** {pair} *** Profit {display_profit:.3f}% stoploss locked at {self.locked_stoploss[pair]:.4f}" ) logger.info( f"*** {pair} *** Profit {display_profit:.3f}% stoploss locked at {self.locked_stoploss[pair]:.4f}" ) return self.locked_stoploss[pair] if current_profit < -0.01: if pair in self.locked_stoploss: del self.locked_stoploss[pair] self.dp.send_msg(f"*** {pair} *** Stoploss reset.") logger.info(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 logger.info( f"{pair} Using Entry Price: {entry_price} | close: {dataframe['close'].iat[-1]} open: {dataframe['open'].iat[-1]} proposed_rate: {proposed_rate}" ) 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% logger.info( f"{pair} Incremented entry price: {entry_price} based on previous entry price : {self.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"] < 0.003): return False if exit_reason == "roi" and trade.calc_profit_ratio(rate) < 0.003: logger.info(f"{trade.pair} ROI is below 0") self.dp.send_msg(f"{trade.pair} ROI is below 0") return False if exit_reason == "partial_exit" and trade.calc_profit_ratio(rate) < 0: logger.info(f"{trade.pair} partial exit is below 0") self.dp.send_msg(f"{trade.pair} partial exit is below 0") return False if exit_reason == "trailing_stop_loss" and trade.calc_profit_ratio(rate) < 0: logger.info(f"{trade.pair} trailing stop price is below 0") self.dp.send_msg(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, ): if ( current_profit < -self.unclog4.value and (current_time - trade.open_date_utc).days >= self.day4.value ): return "unclog 4" if ( current_profit < -self.unclog3.value and (current_time - trade.open_date_utc).days >= self.day3.value ): return "unclog 3" if ( current_profit < -self.unclog2.value and (current_time - trade.open_date_utc).days >= self.day2.value ): return "unclog 2" if ( current_profit < -self.unclog1.value and (current_time - trade.open_date_utc).days >= self.day1.value ): return "unclog 1" use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False order_time_in_force = {"entry": "gtc", "exit": "gtc"} timeframe = "5m" position_adjustment_enable = False process_only_new_candles = True startup_candle_count = 200 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: for val in self.base_nb_candles_buy.range: dataframe[f"ma_buy_{val}"] = ta.EMA(dataframe, timeperiod=val) 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) 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 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 dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["rsi_fast"] = ta.RSI(dataframe, timeperiod=4) dataframe["rsi_slow"] = ta.RSI(dataframe, timeperiod=20) dataframe["ema_14"] = ta.EMA(dataframe, timeperiod=14) dataframe["rsi_4"] = ta.RSI(dataframe, timeperiod=4) dataframe["rsi_14"] = ta.RSI(dataframe, timeperiod=14) 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 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) rolling_window = dataframe["OHLC4"].rolling(self.window.value) rolling_max = rolling_window.max() rolling_min = rolling_window.min() ptp_value = rolling_window.apply(lambda x: np.ptp(x)) 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"] rolling_window_x = dataframe["OHLC4"].rolling(200) rolling_max_x = rolling_window_x.max() rolling_min_x = rolling_window_x.min() ptp_value_x = rolling_window_x.apply(lambda x: np.ptp(x)) 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"]) 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"] = 1 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, ) 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 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"] = 1 * self.z01.value dataframe.loc[(dataframe["general_weight"] < 0), "gen_buy"] = 0 dataframe.loc[(dataframe["general_weight"] < 0), "gen_sell"] = 1 * self.z01.value dataframe.loc[(dataframe["general_weight"] > 0), "gen_sell"] = 0 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"] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: ewo_fib_dn = ( (dataframe["EWO"] < dataframe["EWO_DN_FIB"]) & (dataframe["buy_decision"] > self.fib_dn.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["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["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["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["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["volume"] > 0) ) dataframe.loc[ewo_zero_up, "enter_long"] = 1 dataframe.loc[ewo_zero_up, "enter_tag"] = "ewo_zero_dn" return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: ewo_fib_dns = ( (dataframe["EWO"] < dataframe["EWO_DN_FIB"]) & (dataframe["sell_decision"] > self.fib_dns.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["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["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["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["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["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