import freqtrade.vendor.qtpylib.indicators as qtpylib from typing import Dict, List, Optional import numpy as np import talib.abstract as ta from freqtrade.strategy import IStrategy, informative from freqtrade.strategy import (merge_informative_pair, DecimalParameter, IntParameter, BooleanParameter, timeframe_to_minutes) from pandas import DataFrame, Series from functools import reduce from freqtrade.persistence import Trade from datetime import datetime, timedelta, timezone from freqtrade.exchange import timeframe_to_prev_date from technical.indicators import zema import math import pandas_ta as pta ######################################################################################################################################################## # Indicator? ######################################################################################################################################################## def get_ticker_indicator(self): return int(self.timeframe[:-1]) ######################################################################################################################################################## # ######################################################################################################################################################## # Elliot Wave Oscillator def EWO(dataframe, sma1_length=5, sma2_length=35): df = dataframe.copy() sma1 = ta.SMA(df, timeperiod=sma1_length) sma2 = ta.SMA(df, timeperiod=sma2_length) smadif = (sma1 - sma2) / df['close'] * 100 return smadif ######################################################################################################################################################## # ######################################################################################################################################################## # Volume Weighted Moving Average def vwma(dataframe, length = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" # Calculate Result pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) vwma = vwma.fillna(0, inplace=True) return vwma ######################################################################################################################################################## # VIDYA ######################################################################################################################################################## def vidya(dataframe, length=10, alpha=0.2): """Indicator: Variable Index Dynamic Average (VIDYA)""" # Calculate True Range (TR) and Volatility tr = ta.TRANGE(dataframe['high'], dataframe['low'], dataframe['close']) volatility = ta.SMA(tr, timeperiod=length) # Calculate VIDYA diff = dataframe['close'] - ta.EMA(dataframe['close'], timeperiod=length) vidya = ta.EMA(diff, timeperiod=length, alpha=alpha) # Apply smoothing to VIDYA based on the volatility vidya = vidya * (1 + alpha * (volatility / ta.SMA(dataframe['close'], timeperiod=length))) vidya = vidya.fillna(0) # Handle any NaN values return vidya ######################################################################################################################################################## # ######################################################################################################################################################## # PMAX def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = f'MA_{MAtype}_{length}' atr = f'ATR_{period}' pm = f'pm_{period}_{multiplier}_{length}_{MAtype}' pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}' # MAtype==1 --> EMA # MAtype==2 --> DEMA # MAtype==3 --> T3 # MAtype==4 --> SMA # MAtype==5 --> VIDYA # MAtype==6 --> TEMA # MAtype==7 --> WMA # MAtype==8 --> VWMA # MAtype==9 --> zema if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 elif src == 3: masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: mavalue = vidya(df, length=length) elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(df, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) df[atr] = ta.ATR(df, timeperiod=period) df['basic_ub'] = mavalue + ((multiplier/10) * df[atr]) df['basic_lb'] = mavalue - ((multiplier/10) * df[atr]) basic_ub = df['basic_ub'].values final_ub = np.full(len(df), 0.00) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.00) for i in range(period, len(df)): final_ub[i] = basic_ub[i] if ( basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1] final_lb[i] = basic_lb[i] if ( basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1] df['final_ub'] = final_ub df['final_lb'] = final_lb pm_arr = np.full(len(df), 0.00) for i in range(period, len(df)): pm_arr[i] = ( final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i]) else final_lb[i] if ( pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i]) else final_lb[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i]) else final_ub[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]) else 0.00) pm = Series(pm_arr) # Mark the trend direction up/down pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN) return pm, pmx ######################################################################################################################################################## # ######################################################################################################################################################## # smoothed Heiken Ashi def HA(dataframe, smoothing=None): df = dataframe.copy() df['HA_Close']=(df['open'] + df['high'] + df['low'] + df['close'])/4 df.reset_index(inplace=True) ha_open = [ (df['open'][0] + df['close'][0]) / 2 ] [ ha_open.append((ha_open[i] + df['HA_Close'].values[i]) / 2) for i in range(0, len(df)-1) ] df['HA_Open'] = ha_open df.set_index('index', inplace=True) df['HA_High']=df[['HA_Open','HA_Close','high']].max(axis=1) df['HA_Low']=df[['HA_Open','HA_Close','low']].min(axis=1) if smoothing is not None: sml = abs(int(smoothing)) if sml > 0: df['Smooth_HA_O']=ta.EMA(df['HA_Open'], sml) df['Smooth_HA_C']=ta.EMA(df['HA_Close'], sml) df['Smooth_HA_H']=ta.EMA(df['HA_High'], sml) df['Smooth_HA_L']=ta.EMA(df['HA_Low'], sml) return df ######################################################################################################################################################## # ######################################################################################################################################################## def pump_warning(dataframe, perc=15): df = dataframe.copy() df["change"] = df["high"] - df["low"] df["test1"] = (df["close"] > df["open"]) df["test2"] = ((df["change"]/df["low"]) > (perc/100)) df["result"] = (df["test1"] & df["test2"]).astype('int') return df['result'] ######################################################################################################################################################## # ######################################################################################################################################################## def tv_wma(dataframe, length = 9, field="close") -> DataFrame: """ Source: Tradingview "Moving Average Weighted" Pinescript Author: Unknown Args : dataframe : Pandas Dataframe length : WMA length field : Field to use for the calculation Returns : dataframe : Pandas DataFrame with new columns 'tv_wma' """ norm = 0 sum = 0 for i in range(1, length - 1): weight = (length - i) * length norm = norm + weight sum = sum + dataframe[field].shift(i) * weight dataframe["tv_wma"] = (sum / norm) if norm > 0 else 0 return dataframe["tv_wma"] ######################################################################################################################################################## # ######################################################################################################################################################## def tv_hma(dataframe, length = 9, field="close") -> DataFrame: """ Source: Tradingview "Hull Moving Average" Pinescript Author: Unknown Args : dataframe : Pandas Dataframe length : HMA length field : Field to use for the calculation Returns : dataframe : Pandas DataFrame with new columns 'tv_hma' """ dataframe["h"] = 2 * tv_wma(dataframe, math.floor(length / 2), field) - tv_wma(dataframe, length, field) dataframe["tv_hma"] = tv_wma(dataframe, math.floor(math.sqrt(length)), "h") # dataframe.drop("h", inplace=True, axis=1) return dataframe["tv_hma"] ########################################################################################################### ## MultiMA_TSL, modded by stash86, based on SMAOffsetProtectOptV1 (modded by Perkmeister) ## ## Based on @Lamborghini Store's SMAOffsetProtect strat, heavily based on @tirail's original SMAOffset## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ## Thanks to ## ## - Perkmeister, for their snippets for the sell signals and decaying EMA sell ## ## - ChangeToTower, for the PMax idea ## ## - JimmyNixx, for their snippet to limit close value from the peak (that I modify into 5m tf check) ## ## - froggleston, for the Heikinashi check snippet from Cryptofrog ## ## - Uzirox, for their pump detection code ## ## ## ## ## ########################################################################################################### ######################################################################################################################################################## class MultiMA_TSL3(IStrategy): ######################################################################################################################################################## # Huperopt Protection ######################################################################################################################################################## DATESTAMP = 0 SELLMA = 1 SELL_TRIGGER=2 IN_TRADE = 3 TRADE_OPEN_DATE = 4 SELLMA_VALID = 5 # Protection hyperspace params: protection_params = { "low_profit_lookback": 48, "low_profit_min_req": 0.04, "low_profit_stop_duration": 14, "cooldown_lookback": 2, # value loaded from strategy "stoploss_lookback": 72, # value loaded from strategy "stoploss_stop_duration": 20, # value loaded from strategy } cooldown_lookback = IntParameter(2, 48, default=2, space="protection", optimize=False) low_profit_optimize = False low_profit_lookback = IntParameter(2, 60, default=20, space="protection", optimize=low_profit_optimize) low_profit_stop_duration = IntParameter(12, 200, default=20, space="protection", optimize=low_profit_optimize) low_profit_min_req = DecimalParameter(-0.05, 0.05, default=-0.05, space="protection", decimals=2, optimize=low_profit_optimize) @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) prot.append({ "method": "LowProfitPairs", "lookback_period_candles": self.low_profit_lookback.value, "trade_limit": 1, "stop_duration": int(self.low_profit_stop_duration.value), "required_profit": self.low_profit_min_req.value }) return prot ######################################################################################################################################################## # Hyperotp Bys and sell ######################################################################################################################################################## buy_params = { "base_nb_candles_buy_trima": 15, "base_nb_candles_buy_trima2": 38, "low_offset_trima": 0.959, "low_offset_trima2": 0.949, "base_nb_candles_buy_ema": 9, "base_nb_candles_buy_ema2": 75, "low_offset_ema": 1.067, "low_offset_ema2": 0.973, "base_nb_candles_buy_zema": 25, "base_nb_candles_buy_zema2": 53, "low_offset_zema": 0.958, "low_offset_zema2": 0.961, "base_nb_candles_buy_hma": 70, "base_nb_candles_buy_hma2": 12, "low_offset_hma": 0.948, "low_offset_hma2": 0.941, "buy_condition_trima_enable": True, "buy_condition_zema_enable": True, "buy_condition_hma_enable": True, "ewo_high": 2.615, "ewo_high2": 2.188, "ewo_low": -19.632, "ewo_low2": -19.955, "rsi_buy": 60, "rsi_buy2": 45, } sell_params = { "base_nb_candles_ema_sell": 5, "high_offset_sell_ema": 0.994, } # ROI table: minimal_roi = { "0": 100 } stoploss = -0.25 # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.018 use_custom_stoploss = True ######################################################################################################################################################## # ######################################################################################################################################################## # Optimal timeframe for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 400 # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # storage dict for custom info custom_info = { } ######################################################################################################################################################## # Parameters ######################################################################################################################################################## optimize_sell_ema = False base_nb_candles_ema_sell = IntParameter(5, 80, default=20, space='sell', optimize=False) high_offset_sell_ema = DecimalParameter(0.99, 1.1, default=1.012, space='sell', optimize=False) base_nb_candles_ema_sell2 = IntParameter(5, 80, default=20, space='sell', optimize=False) # Multi Offset optimize_buy_ema = False base_nb_candles_buy_ema = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_ema) low_offset_ema = DecimalParameter(0.9, 1.1, default=0.958, space='buy', optimize=optimize_buy_ema) base_nb_candles_buy_ema2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_ema) low_offset_ema2 = DecimalParameter(0.9, 1.1, default=0.958, space='buy', optimize=optimize_buy_ema) optimize_buy_trima = False base_nb_candles_buy_trima = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_trima) low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_trima) base_nb_candles_buy_trima2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_trima) low_offset_trima2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_trima) optimize_buy_zema = False base_nb_candles_buy_zema = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_zema) low_offset_zema = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_zema) base_nb_candles_buy_zema2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_zema) low_offset_zema2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_zema) optimize_buy_hma = False base_nb_candles_buy_hma = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_hma) low_offset_hma = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_hma) base_nb_candles_buy_hma2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_hma) low_offset_hma2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_hma) buy_condition_enable_optimize = False buy_condition_trima_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) buy_condition_zema_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) buy_condition_hma_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) # Protection ewo_check_optimize = False ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, space='buy', optimize=ewo_check_optimize) ewo_high = DecimalParameter(2.0, 12.0, default=6.0, space='buy', optimize=ewo_check_optimize) ewo_low2 = DecimalParameter(-20.0, -8.0, default=-20.0, space='buy', optimize=ewo_check_optimize) ewo_high2 = DecimalParameter(2.0, 12.0, default=6.0, space='buy', optimize=ewo_check_optimize) rsi_buy_optimize = False rsi_buy = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize) rsi_buy2 = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize) buy_rsi_fast = IntParameter(0, 50, default=35, space='buy', optimize=False) fast_ewo = IntParameter(10, 50, default=50, space='buy', optimize=False) slow_ewo = IntParameter(100, 200, default=200, space='buy', optimize=False) ######################################################################################################################################################## # Indicators ######################################################################################################################################################## def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # EWO dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) heikinashi["volume"] = dataframe["volume"] # Profit Maximizer - PMAX dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) dataframe = HA(dataframe, 4) if self.config['runmode'].value in ('live', 'dry_run'): # Exchange downtime protection dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) else: dataframe['live_data_ok'] = True # Check if the entry already exists if not metadata["pair"] in self.custom_info: # Create empty entry for this pair {datestamp, sellma, sell_trigger, in_trade, trade_open_date, sellma_valid} self.custom_info[metadata["pair"]] = ['', 0, 0, 0, '', 0] if (self.dp.runmode.value in ('live', 'dry_run')): dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) *self.low_offset_ema2.value dataframe['ema_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) dataframe['sellma'] = dataframe['ema_sell'] if(self.custom_info[metadata['pair']][self.IN_TRADE] == 1): # in trade trade_open_candle = dataframe.loc[dataframe['date'] == self.custom_info[metadata['pair']][self.TRADE_OPEN_DATE]] if(len(trade_open_candle) > 0): trade_open_index = trade_open_candle.index[0] row = trade_open_index last_row = dataframe.tail(1).index.item() # print("last_row = " + str(last_row)) # smoothing coefficients emaLength = 32 alpha = 2 /(1 + emaLength) sell_ema = dataframe['sellma'].iloc[row] row += 1 while (row <= last_row): # update sell_ema and store in dataframe sell_ema = (alpha * dataframe['close'].iloc[row]) + ((1 - alpha) * sell_ema) # Resetting decaying ema? if(dataframe['close'].iloc[row] < dataframe['ema_offset_buy'].iloc[row]): sell_ema = dataframe['ema_sell'].iloc[row] dataframe['sellma'].iloc[row] = sell_ema row += 1 self.custom_info[metadata['pair']][self.SELLMA_VALID] = 1 dataframe['sellma_offset'] = dataframe['sellma'] * self.high_offset_sell_ema.value return dataframe ######################################################################################################################################################## # ENTRY TREND ######################################################################################################################################################## def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if not (self.dp.runmode.value in ('live', 'dry_run')): dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) *self.low_offset_ema2.value dataframe['ema_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) dataframe.loc[:, 'enter_tag'] = '' dataframe.loc[:, 'buy_copy'] = 0 dataframe.loc[:, 'enter_long'] = 0 if (self.buy_condition_trima_enable.value): dataframe['trima_offset_buy'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima.value)) *self.low_offset_trima.value dataframe['trima_offset_buy2'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima2.value)) *self.low_offset_trima2.value buy_offset_trima = ( ( (dataframe['close'] < dataframe['trima_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['trima_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_trima, 'enter_tag'] += 'trima ' conditions.append(buy_offset_trima) if (self.buy_condition_zema_enable.value): dataframe['zema_offset_buy'] = zema(dataframe, int(self.base_nb_candles_buy_zema.value)) *self.low_offset_zema.value dataframe['zema_offset_buy2'] = zema(dataframe, int(self.base_nb_candles_buy_zema2.value)) *self.low_offset_zema2.value buy_offset_zema = ( ( (dataframe['close'] < dataframe['zema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['zema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_zema, 'enter_tag'] += 'zema ' conditions.append(buy_offset_zema) if (self.buy_condition_hma_enable.value): dataframe['hma_offset_buy'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value dataframe['hma_offset_buy2'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_buy_hma2.value)) *self.low_offset_hma2.value buy_offset_hma = ( ( ( (dataframe['close'] < dataframe['hma_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < 35) ) | ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['rsi'] < 30) ) ) & (dataframe['rsi_fast'] < 30) ) dataframe.loc[buy_offset_hma, 'enter_tag'] += 'hma ' conditions.append(buy_offset_hma) add_check = ( (dataframe['live_data_ok']) & (dataframe['close'] < dataframe['Smooth_HA_L']) & (dataframe['close'] < (dataframe['ema_sell'] * self.high_offset_sell_ema.value)) & (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.10 )) & (dataframe['Smooth_HA_O'].shift(1) < dataframe['Smooth_HA_H'].shift(1)) & (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & ( ( (dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & ( (dataframe['ewo'] < self.ewo_low.value) | ( (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) ) ) ) | ( (dataframe['close'] < dataframe['ema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & ( (dataframe['ewo'] < self.ewo_low2.value) | ( (dataframe['ewo'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_buy2.value) ) ) ) ) & (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ (add_check & reduce(lambda x, y: x | y, conditions)), ['buy_copy','enter_long'] ]=(1,1) return dataframe ######################################################################################################################################################## # Confirm Entry ######################################################################################################################################################## def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if(len(dataframe) < 1): return False last_candle = dataframe.iloc[-1].squeeze() if ((rate > last_candle['close'])) : return False self.custom_info[pair][self.DATESTAMP] = last_candle['date'] self.custom_info[pair][self.SELLMA] = last_candle['ema_sell'] self.custom_info[pair][self.IN_TRADE] = 1 return True ######################################################################################################################################################## # CUSTOM EXIT ######################################################################################################################################################## def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if(len(dataframe) < 1): return False last_candle = dataframe.iloc[-1] if(self.custom_info[pair][self.DATESTAMP] != last_candle['date']): # new candle, update EMA and check sell if not(self.dp.runmode.value in ('live', 'dry_run')): # backtest or hyperopt sell_ema = self.custom_info[pair][self.SELLMA] if(sell_ema == 0): sell_ema = last_candle['ema_sell'] # new candle, update EMA # smoothing coefficients emaLength = 32 alpha = 2 /(1 + emaLength) # update sell_ema sell_ema = (alpha * last_candle['close']) + ((1 - alpha) * sell_ema) # Resetting decaying ema? if(last_candle['close'] < last_candle['ema_offset_buy']): sell_ema = last_candle['ema_sell'] self.custom_info[pair][self.SELLMA] = sell_ema self.custom_info[pair][self.DATESTAMP] = last_candle['date'] if((last_candle['close'] > (sell_ema * self.high_offset_sell_ema.value)) & (last_candle['buy_copy'] == 0)): return 'Decaying EMA BT' else: # live or dry if (self.custom_info[pair][self.IN_TRADE] == 1): if(self.custom_info[pair][self.SELLMA_VALID] == 1): # in a trade, populate_indicators() will have calculated the new sellma_offset if((last_candle['close'] > last_candle['sellma_offset']) & (last_candle['buy_copy'] == 0)): return 'Decaying EMA' trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) self.custom_info[pair][self.TRADE_OPEN_DATE] = trade_date self.custom_info[pair][self.IN_TRADE] = 1 return False ######################################################################################################################################################## # EXIT TREND ######################################################################################################################################################## def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'exit_tag'] = '' if(self.custom_info[metadata['pair']][self.SELLMA_VALID] == 1) and (self.dp.runmode.value in ('live', 'dry_run')): sell_cond_2 = ( (dataframe['close'] > dataframe['sellma_offset']) & (dataframe['volume'] > 0) ) conditions.append(sell_cond_2) dataframe.loc[sell_cond_2, 'exit_tag'] += 'Decaying EMA ' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'exit' ]=1 return dataframe ######################################################################################################################################################## # Confirm Exit ######################################################################################################################################################## def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: self.custom_info[pair][self.SELL_TRIGGER] = 0 self.custom_info[pair][self.IN_TRADE] = 0 self.custom_info[pair][self.SELLMA_VALID] = 0 return True ######################################################################################################################################################## # CUSTOM Stoploss ######################################################################################################################################################## def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: sl_new = 1 if(self.custom_info[pair][self.SELL_TRIGGER] == 1): if not self.config['runmode'].value in ('backtest', 'hyperopt'): sl_new = 0.001 if (current_profit > 0.2): sl_new = 0.05 elif (current_profit > 0.1): sl_new = 0.03 elif (current_profit > 0.06): sl_new = 0.02 elif (current_profit > 0.03): sl_new = 0.01 return sl_new ######################################################################################################################################################## class MultiMA_TSL3a(MultiMA_TSL3): ######################################################################################################################################################## informative_timeframe = '1h' timeframe_15m = '15m' min_rsi_sell = 50 min_rsi_sell_15m = 70 max_change_pump = 35 ######################################################################################################################################################## # ######################################################################################################################################################## def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] informative_pairs.extend([(pair, self.timeframe_15m) for pair in pairs]) return informative_pairs ######################################################################################################################################################## def get_informative_15m_indicators(self, metadata: dict): dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.timeframe_15m) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe ######################################################################################################################################################## # ######################################################################################################################################################## def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_indicators(dataframe, metadata) informative_15m = self.get_informative_15m_indicators(metadata) dataframe = merge_informative_pair(dataframe, informative_15m, self.timeframe, self.timeframe_15m, ffill=True) drop_columns = [(s + "_" + self.timeframe_15m) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) # pump detector dataframe['pump'] = pump_warning(dataframe, perc=int(self.max_change_pump)) return dataframe ######################################################################################################################################################## # ######################################################################################################################################################## 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) if(len(dataframe) < 1): return False last_candle = dataframe.iloc[-1] if(self.custom_info[pair][self.DATESTAMP] != last_candle['date']): # new candle, update EMA and check sell if not(self.dp.runmode.value in ('live', 'dry_run')): # backtest or hyperopt sell_ema = self.custom_info[pair][self.SELLMA] if(sell_ema == 0): sell_ema = last_candle['ema_sell'] # new candle, update EMA # smoothing coefficients emaLength = 32 alpha = 2 /(1 + emaLength) # update sell_ema sell_ema = (alpha * last_candle['close']) + ((1 - alpha) * sell_ema) # Resetting decaying ema? if(last_candle['close'] < last_candle['ema_offset_buy']): sell_ema = last_candle['ema_sell'] self.custom_info[pair][self.SELLMA] = sell_ema self.custom_info[pair][self.DATESTAMP] = last_candle['date'] if((last_candle['close'] > (sell_ema * self.high_offset_sell_ema.value)) & (last_candle['buy_copy'] == 0)): return 'Decaying EMA BT ' trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) self.custom_info[pair][self.TRADE_OPEN_DATE] = trade_date self.custom_info[pair][self.IN_TRADE] = 1 return False ######################################################################################################################################################## # ######################################################################################################################################################## def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) *self.low_offset_ema2.value dataframe['ema_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) dataframe.loc[:, 'enter_tag'] = '' dataframe.loc[:, 'buy_copy'] = 0 dataframe.loc[:, 'enter_long'] = 0 if (self.buy_condition_trima_enable.value): dataframe['trima_offset_buy'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima.value)) *self.low_offset_trima.value dataframe['trima_offset_buy2'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima2.value)) *self.low_offset_trima2.value buy_offset_trima = ( ( (dataframe['close'] < dataframe['trima_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['trima_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_trima, 'enter_tag'] += 'trima ' conditions.append(buy_offset_trima) if (self.buy_condition_zema_enable.value): dataframe['zema_offset_buy'] = zema(dataframe, int(self.base_nb_candles_buy_zema.value)) *self.low_offset_zema.value dataframe['zema_offset_buy2'] = zema(dataframe, int(self.base_nb_candles_buy_zema2.value)) *self.low_offset_zema2.value buy_offset_zema = ( ( (dataframe['close'] < dataframe['zema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['zema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_zema, 'enter_tag'] += 'zema ' conditions.append(buy_offset_zema) if (self.buy_condition_hma_enable.value): dataframe['hma_offset_buy'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value dataframe['hma_offset_buy2'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_buy_hma2.value)) *self.low_offset_hma2.value buy_offset_hma = ( ( ( (dataframe['close'] < dataframe['hma_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < 35) ) | ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['rsi'] < 30) ) ) & (dataframe['rsi_fast'] < 30) ) dataframe.loc[buy_offset_hma, 'enter_tag'] += 'hma ' conditions.append(buy_offset_hma) add_check = ( (dataframe['live_data_ok']) & (dataframe['close'] < dataframe['Smooth_HA_L']) & (dataframe['close'] < (dataframe['ema_sell'] * self.high_offset_sell_ema.value)) & (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.10 )) & (dataframe['Smooth_HA_O'].shift(1) < dataframe['Smooth_HA_H'].shift(1)) & (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['pump'].rolling(20).max() < 1) & ( ( (dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & ( (dataframe['ewo'] < self.ewo_low.value) | ( (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) ) ) ) | ( (dataframe['close'] < dataframe['ema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & ( (dataframe['ewo'] < self.ewo_low2.value) | ( (dataframe['ewo'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_buy2.value) ) ) ) ) & (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ (add_check & reduce(lambda x, y: x | y, conditions)), ['buy_copy','enter_long'] ]=(1,1) return dataframe ######################################################################################################################################################## # ######################################################################################################################################################## def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'exit_tag'] = '' sell_cond_1 = ( (dataframe['rsi_fast_15m'] > self.min_rsi_sell_15m) & (dataframe['rsi'] > self.min_rsi_sell) & (dataframe['volume'] > 0) ) conditions.append(sell_cond_1) dataframe.loc[sell_cond_1, 'exit_tag'] += 'RSI 15m Overbought ' if(self.custom_info[metadata['pair']][self.SELLMA_VALID] == 1) and (self.dp.runmode.value in ('live', 'dry_run')): sell_cond_2 = ( (dataframe['close'] > dataframe['sellma_offset']) & (dataframe['volume'] > 0) ) conditions.append(sell_cond_2) dataframe.loc[sell_cond_2, 'exit_tag'] += 'Decaying EMA ' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'exit_long' ]=1 return dataframe ######################################################################################################################################################## class MultiMA_TSL3b(IStrategy): ######################################################################################################################################################## buy_params = { "base_nb_candles_buy_trima": 17, "base_nb_candles_buy_trima2": 37, "low_offset_trima": 0.941, "low_offset_trima2": 0.914, "base_nb_candles_buy_ema": 80, "base_nb_candles_buy_ema2": 79, "low_offset_ema": 1.074, "low_offset_ema2": 0.942, "base_nb_candles_buy_zema": 62, "base_nb_candles_buy_zema2": 73, "low_offset_zema": 0.961, "low_offset_zema2": 0.98, "base_nb_candles_buy_hma": 80, "base_nb_candles_buy_hma2": 75, "low_offset_hma": 0.96, "low_offset_hma2": 0.965, "base_nb_candles_buy_vwma": 26, "base_nb_candles_buy_vwma2": 16, "low_offset_vwma": 0.949, "low_offset_vwma2": 0.951, "ewo_high": 5.8, "ewo_high2": 6.0, "ewo_low": -10.8, "ewo_low2": -15.3, "rsi_buy": 60, "rsi_buy2": 45, } sell_params = { "base_nb_candles_ema_sell": 6, "high_offset_sell_ema": 0.991, } # ROI table: minimal_roi = { "0": 100 } ######################################################################################################################################################## # ######################################################################################################################################################## stoploss = -0.25 # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.018 use_custom_stoploss = False # Optimal timeframe for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 age_filter = 30 ######################################################################################################################################################## # ######################################################################################################################################################## optimize_sell_ema = False base_nb_candles_ema_sell = IntParameter(5, 80, default=20, space='sell', optimize=False) high_offset_sell_ema = DecimalParameter(0.99, 1.1, default=1.012, space='sell', optimize=False) base_nb_candles_ema_sell2 = IntParameter(5, 80, default=20, space='sell', optimize=False) # Multi Offset optimize_buy_ema = False base_nb_candles_buy_ema = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_ema'], space='buy', optimize=optimize_buy_ema) low_offset_ema = DecimalParameter(0.9, 1.1, default=buy_params['low_offset_ema'], space='buy', optimize=optimize_buy_ema) base_nb_candles_buy_ema2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_ema2'], space='buy', optimize=optimize_buy_ema) low_offset_ema2 = DecimalParameter(0.9, 1.1, default=buy_params['low_offset_ema2'], space='buy', optimize=optimize_buy_ema) optimize_buy_trima = False base_nb_candles_buy_trima = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_trima'], space='buy', optimize=optimize_buy_trima) low_offset_trima = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_trima'], space='buy', optimize=optimize_buy_trima) base_nb_candles_buy_trima2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_trima2'], space='buy', optimize=optimize_buy_trima) low_offset_trima2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_trima2'], space='buy', optimize=optimize_buy_trima) optimize_buy_zema = False base_nb_candles_buy_zema = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_zema'], space='buy', optimize=optimize_buy_zema) low_offset_zema = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_zema'], space='buy', optimize=optimize_buy_zema) base_nb_candles_buy_zema2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_zema2'], space='buy', optimize=optimize_buy_zema) low_offset_zema2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_zema2'], space='buy', optimize=optimize_buy_zema) optimize_buy_hma = False base_nb_candles_buy_hma = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_hma'], space='buy', optimize=optimize_buy_hma) low_offset_hma = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_hma'], space='buy', optimize=optimize_buy_hma) base_nb_candles_buy_hma2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_hma2'], space='buy', optimize=optimize_buy_hma) low_offset_hma2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_hma2'], space='buy', optimize=optimize_buy_hma) optimize_buy_vwma = False base_nb_candles_buy_vwma = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_vwma'], space='buy', optimize=optimize_buy_vwma) low_offset_vwma = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_vwma'], space='buy', optimize=optimize_buy_vwma) base_nb_candles_buy_vwma2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_vwma2'], space='buy', optimize=optimize_buy_vwma) low_offset_vwma2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_vwma2'], space='buy', optimize=optimize_buy_vwma) buy_condition_enable_optimize = False buy_condition_trima_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) buy_condition_zema_enable = BooleanParameter(default=False, space='buy', optimize=buy_condition_enable_optimize) buy_condition_hma_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) buy_condition_vwma_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) # Protection ewo_check_optimize = False ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, decimals = 1, space='buy', optimize=ewo_check_optimize) ewo_high = DecimalParameter(2.0, 12.0, default=6.0, decimals = 1, space='buy', optimize=ewo_check_optimize) ewo_low2 = DecimalParameter(-20.0, -8.0, default=-20.0, decimals = 1, space='buy', optimize=ewo_check_optimize) ewo_high2 = DecimalParameter(2.0, 12.0, default=6.0, decimals = 1, space='buy', optimize=ewo_check_optimize) rsi_buy_optimize = False rsi_buy = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize) rsi_buy2 = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize) buy_rsi_fast = IntParameter(0, 50, default=35, space='buy', optimize=False) fast_ewo = IntParameter(10, 50, default=50, space='buy', optimize=False) slow_ewo = IntParameter(100, 200, default=200, space='buy', optimize=False) min_rsi_sell = IntParameter(30, 100, default=50, space='sell', optimize=False) ######################################################################################################################################################## # ######################################################################################################################################################## # Protection hyperspace params: protection_params = { "low_profit_lookback": 48, "low_profit_min_req": 0.04, "low_profit_stop_duration": 14, "cooldown_lookback": 2, # value loaded from strategy "stoploss_lookback": 72, # value loaded from strategy "stoploss_stop_duration": 20, # value loaded from strategy } cooldown_lookback = IntParameter(2, 48, default=2, space="protection", optimize=False) low_profit_optimize = False low_profit_lookback = IntParameter(2, 60, default=20, space="protection", optimize=low_profit_optimize) low_profit_stop_duration = IntParameter(12, 200, default=20, space="protection", optimize=low_profit_optimize) low_profit_min_req = DecimalParameter(-0.05, 0.05, default=-0.05, space="protection", decimals=2, optimize=low_profit_optimize) @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) prot.append({ "method": "LowProfitPairs", "lookback_period_candles": self.low_profit_lookback.value, "trade_limit": 1, "stop_duration": int(self.low_profit_stop_duration.value), "required_profit": self.low_profit_min_req.value }) return prot ######################################################################################################################################################## # ######################################################################################################################################################## @informative('1d') def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['age_filter_ok'] = (dataframe['volume'].rolling(window=self.age_filter, min_periods=self.age_filter).min() > 0) return dataframe ######################################################################################################################################################## # ######################################################################################################################################################## def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # EWO dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) heikinashi["volume"] = dataframe["volume"] # Profit Maximizer - PMAX dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) dataframe = HA(dataframe, 4) if self.config['runmode'].value in ('live', 'dry_run'): # Exchange downtime protection dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) else: dataframe['live_data_ok'] = True if (self.dp.runmode.value in ('live', 'dry_run')): dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) *self.low_offset_ema2.value dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) * self.high_offset_sell_ema.value return dataframe ######################################################################################################################################################## # ######################################################################################################################################################## def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if not (self.dp.runmode.value in ('live', 'dry_run')): dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) *self.low_offset_ema2.value dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) * self.high_offset_sell_ema.value dataframe.loc[:, 'enter_tag'] = '' dataframe.loc[:, 'buy_copy'] = 0 dataframe.loc[:, 'enter_long'] = 0 if (self.buy_condition_trima_enable.value): dataframe['trima_offset_buy'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima.value)) *self.low_offset_trima.value dataframe['trima_offset_buy2'] = ta.TRIMA(dataframe, int(self.base_nb_candles_buy_trima2.value)) *self.low_offset_trima2.value buy_offset_trima = ( ( (dataframe['close'] < dataframe['trima_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['trima_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_trima, 'enter_tag'] += 'trima ' conditions.append(buy_offset_trima) if (self.buy_condition_zema_enable.value): dataframe['zema_offset_buy'] = zema(dataframe, int(self.base_nb_candles_buy_zema.value)) *self.low_offset_zema.value dataframe['zema_offset_buy2'] = zema(dataframe, int(self.base_nb_candles_buy_zema2.value)) *self.low_offset_zema2.value buy_offset_zema = ( ( (dataframe['close'] < dataframe['zema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['zema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_zema, 'enter_tag'] += 'zema ' conditions.append(buy_offset_zema) if (self.buy_condition_hma_enable.value): dataframe['hma_offset_buy'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value dataframe['hma_offset_buy2'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma2.value)) *self.low_offset_hma2.value buy_offset_hma = ( ( ( (dataframe['close'] < dataframe['hma_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < 35) ) | ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['rsi'] < 30) ) ) & (dataframe['rsi_fast'] < 30) ) dataframe.loc[buy_offset_hma, 'enter_tag'] += 'hma ' conditions.append(buy_offset_hma) if (self.buy_condition_vwma_enable.value): dataframe['vwma_offset_buy'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma.value)) *self.low_offset_vwma.value dataframe['vwma_offset_buy2'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma2.value)) *self.low_offset_vwma2.value buy_offset_vwma = ( ( ( (dataframe['close'] < dataframe['vwma_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['vwma_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) ) dataframe.loc[buy_offset_vwma, 'enter_tag'] += 'vwma ' conditions.append(buy_offset_vwma) add_check = ( (dataframe['live_data_ok']) & (dataframe['age_filter_ok_1d']) & (dataframe['close'] < dataframe['Smooth_HA_L']) & (dataframe['close'] < dataframe['ema_offset_sell']) & (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.10 )) & (dataframe['Smooth_HA_O'].shift(1) < dataframe['Smooth_HA_H'].shift(1)) & (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & ( ( (dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & ( (dataframe['ewo'] < self.ewo_low.value) | ( (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) ) ) ) | ( (dataframe['close'] < dataframe['ema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & ( (dataframe['ewo'] < self.ewo_low2.value) | ( (dataframe['ewo'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_buy2.value) ) ) ) ) & (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ (add_check & reduce(lambda x, y: x | y, conditions)), 'enter_long' ]=1 return dataframe ######################################################################################################################################################## # Exit Trend ######################################################################################################################################################## def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_tag'] = '' conditions = [] sell_cond_1 = ( (dataframe['close'] > dataframe['ema_offset_sell']) & (dataframe['volume'] > 0) & (dataframe['rsi'] > self.min_rsi_sell.value) ) conditions.append(sell_cond_1) dataframe.loc[sell_cond_1, 'exit_tag'] += 'EMA ' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'exit_long' ]=1 return dataframe