# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/dualwave.py from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame import talib.abstract as ta from technical import qtpylib, pivots_points import numpy as np import logging import pandas as pd import pandas_ta as pta import datetime from datetime import datetime, timedelta, timezone from typing import Optional import talib.abstract as ta from technical.util import resample_to_interval, resampled_merge from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, RealParameter, merge_informative_pair from freqtrade.strategy import stoploss_from_open import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade import technical.indicators as ftt logger = logging.getLogger('freqtrade') ### Change log ### ### Change log ### def PC(dataframe, in1, in2): df = dataframe.copy() pc = (in2 - in1) / in1 * 100 return pc class Github_DerSalvador_freqtrade_helm_chart__dualwave__20260115_122204(IStrategy): INTERFACE_VERSION = 3 ### Strategy parameters ### exit_profit_only = True ### No exiting at a loss use_custom_stoploss = True trailing_stop = False # True ignore_roi_if_entry_signal = True use_exit_signal = True stoploss = -0.25 # DCA Parameters position_adjustment_enable = True max_entry_position_adjustment = 0 max_dca_multiplier = 1 market_status = 0 minimal_roi = {'0': 0.215} ### Hyperoptable parameters ### # protections cooldown_lookback = IntParameter(24, 48, default=46, space='protection', optimize=True) stop_duration = IntParameter(12, 200, default=5, space='protection', optimize=True) use_stop_protection = BooleanParameter(default=True, space='protection', optimize=True) # SMA filterlength = IntParameter(low=15, high=35, default=25, space='exit', optimize=True) max_length = CategoricalParameter([24, 48, 72, 96, 144, 192, 240], default=48, space='entry', optimize=False) from15 = IntParameter(low=5, high=35, default=25, space='entry', optimize=True) from2 = IntParameter(low=2, high=10, default=5, space='entry', optimize=True) # Buy Parameters rsi_entry = IntParameter(55, 70, default=65, space='entry', optimize=True) rsi_entry_safe = IntParameter(40, 55, default=50, space='entry', optimize=True) rsi_ma_entrypc = IntParameter(-5, 5, default=0, space='entry', optimize=True) sma200_entry_pc = IntParameter(-5, 5, default=0, space='entry', optimize=True) willr_entry = IntParameter(-50, -20, default=-50, space='entry', optimize=True) auto_entry = IntParameter(3, 13, default=3, space='entry', optimize=True) auto_entry_down = IntParameter(3, 13, default=3, space='entry', optimize=True) auto_entry_bearzzz = IntParameter(3, 13, default=3, space='entry', optimize=True) auto_entry_bearzzz_down = IntParameter(3, 10, default=3, space='entry', optimize=True) auto_entry_2 = IntParameter(3, 10, default=3, space='entry', optimize=True) auto_entry_down_2 = IntParameter(3, 10, default=3, space='entry', optimize=True) auto_entry_bearzzz_2 = IntParameter(3, 10, default=3, space='entry', optimize=True) auto_entry_bearzzz_down_2 = IntParameter(3, 10, default=3, space='entry', optimize=True) fast_wave_entry = IntParameter(-20, 0, default=-20, space='entry', optimize=True) slow_wave_entry = IntParameter(-20, 0, default=-20, space='entry', optimize=True) fast_wave_entry_pc = IntParameter(-5, 5, default=0, space='entry', optimize=True) slow_wave_entry_pc = IntParameter(-5, 5, default=0, space='entry', optimize=True) entry_smoothing = IntParameter(low=3, high=35, default=15, space='entry', optimize=True) # Sell Parameters rsi_exit = IntParameter(55, 70, default=50, space='exit', optimize=True) rsi_exit_safe = IntParameter(60, 80, default=70, space='exit', optimize=True) rsi_ma_exitpc = IntParameter(-5, 5, default=0, space='exit', optimize=True) sma200_exit_pc = IntParameter(-5, 5, default=0, space='exit', optimize=True) willr_exit = IntParameter(-50, -20, default=-20, space='exit', optimize=True) auto_exit_bull = IntParameter(3, 15, default=4, space='exit', optimize=True) auto_exit_bear = IntParameter(3, 15, default=4, space='exit', optimize=True) fast_wave_exit = IntParameter(-20, 10, default=0, space='exit', optimize=True) slow_wave_exit = IntParameter(-20, 10, default=0, space='exit', optimize=True) exit_smoothing = IntParameter(low=5, high=35, default=15, space='exit', optimize=True) ### Buy Weight Mulitpliers ### x01 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='entry', optimize=True) x02 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='entry', optimize=True) x03 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='entry', optimize=True) x04 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='entry', optimize=True) x05 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='entry', optimize=True) x06 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='entry', optimize=True) x07 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='entry', optimize=True) x08 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='entry', optimize=True) x09 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='entry', optimize=True) x10 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='entry', optimize=True) ### Sell Weight Mulitpliers ### y01 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='exit', optimize=True) y02 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='exit', optimize=True) y03 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='exit', optimize=True) y04 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='exit', optimize=True) y05 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='exit', optimize=True) y06 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='exit', optimize=True) y07 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='exit', optimize=True) y08 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='exit', optimize=True) y09 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='exit', optimize=True) y10 = DecimalParameter(1.0, 5.0, default=2.5, decimals=1, space='exit', optimize=True) #trailing stop loss optimiziation tsl_target5 = DecimalParameter(low=0.25, high=0.4, decimals=1, default=0.3, space='exit', optimize=True, load=True) ts5 = DecimalParameter(low=0.04, high=0.06, default=0.05, space='exit', optimize=True, load=True) tsl_target4 = DecimalParameter(low=0.15, high=0.25, default=0.2, space='exit', optimize=True, load=True) ts4 = DecimalParameter(low=0.03, high=0.05, default=0.045, space='exit', optimize=True, load=True) tsl_target3 = DecimalParameter(low=0.1, high=0.15, default=0.15, space='exit', optimize=True, load=True) ts3 = DecimalParameter(low=0.025, high=0.04, default=0.035, space='exit', optimize=True, load=True) tsl_target2 = DecimalParameter(low=0.08, high=0.1, default=0.1, space='exit', optimize=True, load=True) ts2 = DecimalParameter(low=0.015, high=0.03, default=0.02, space='exit', optimize=True, load=True) tsl_target1 = DecimalParameter(low=0.06, high=0.08, default=0.06, space='exit', optimize=True, load=True) ts1 = DecimalParameter(low=0.01, high=0.016, default=0.013, space='exit', optimize=True, load=True) tsl_target0 = DecimalParameter(low=0.04, high=0.06, default=0.03, space='exit', optimize=True, load=True) ts0 = DecimalParameter(low=0.008, high=0.015, default=0.01, space='exit', optimize=True, load=True) ## Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'ioc'} # Optimal timeframe for the strategy timeframe = '15m' informative_timeframe = '2h' process_only_new_candles = True startup_candle_count = 30 ### protections ### @property def protections(self): prot = [] prot.append({'method': 'CooldownPeriod', 'stop_duration_candles': self.cooldown_lookback.value}) if self.use_stop_protection.value: prot.append({'method': 'StoplossGuard', 'lookback_period_candles': 24 * 3, 'trade_limit': 2, 'stop_duration_candles': self.stop_duration.value, 'only_per_pair': False}) return prot def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def get_informative_indicators(self, metadata: dict): dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) return dataframe ### Dollar Cost Averaging ### # This is called when placing the initial order (opening trade) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: # We need to leave most of the funds for possible further DCA orders # This also applies to fixed stakes return proposed_stake / self.max_dca_multiplier def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: if current_profit > 0.1 and trade.nr_of_successful_exits == 0: # Take half of the profit at +10% return -(trade.stake_amount / 2) def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: for stop5 in self.tsl_target5.range: if current_profit > stop5: for stop5a in self.ts5.range: self.dp.send_msg(f'*** {pair} *** Profit: {current_profit} - lvl5 {stop5}/{stop5a} activated') return stop5a for stop4 in self.tsl_target4.range: if current_profit > stop4: for stop4a in self.ts4.range: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl4 {stop4}/{stop4a} activated') return stop4a for stop3 in self.tsl_target3.range: if current_profit > stop3: for stop3a in self.ts3.range: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl3 {stop3}/{stop3a} activated') return stop3a for stop2 in self.tsl_target2.range: if current_profit > stop2: for stop2a in self.ts2.range: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl2 {stop2}/{stop2a} activated') return stop2a for stop1 in self.tsl_target1.range: if current_profit > stop1: for stop1a in self.ts1.range: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl1 {stop1}/{stop1a} activated') return stop1a for stop0 in self.tsl_target0.range: if current_profit > stop0: for stop0a in self.ts0.range: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl0 {stop0}/{stop0a} activated') return stop0a return self.stoploss def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.dp: inf_tf = '2h' pair = metadata['pair'] informative = self.dp.get_pair_dataframe(pair=pair, timeframe=inf_tf) # RSI informative['rsi'] = ta.RSI(informative) informative['rsi_ma'] = ta.SMA(informative['rsi'], timeperiod=10) informative['rsi_ma_pcnt'] = PC(informative, informative['rsi_ma'], informative['rsi_ma'].shift(1)) # WaveTrend using OHLC4 or HA close - 3/21 ap = 0.25 * (informative['high'] + informative['low'] + informative['close'] + informative['open']) informative['esa'] = ta.EMA(ap, timeperiod=3) informative['d'] = ta.EMA(abs(ap - informative['esa']), timeperiod=3) informative['wave_ci'] = (ap - informative['esa']) / (0.015 * informative['d']) informative['wave_t1'] = ta.EMA(informative['wave_ci'], timeperiod=21) informative['wave_t2'] = ta.SMA(informative['wave_t1'], timeperiod=3) informative['t1_pc'] = PC(informative, informative['wave_t1'], informative['wave_t1'].shift(1)) # SMA informative['200_SMA'] = ta.SMA(informative['close'], timeperiod=200) informative['200_SMAPC'] = PC(informative, informative['200_SMA'], informative['200_SMA'].shift(1)) informative['from_200'] = ta.SMA(((informative['close'] + informative['open']) / 2 - informative['200_SMA']) / informative['close'] * 100, timeperiod=self.from2.value) ### BUYING WEIGHTS ### informative.loc[informative['rsi'] < self.rsi_entry.value, 'rsi_entry1'] = 1 informative.loc[informative['rsi'] > self.rsi_entry.value, 'rsi_entry1'] = -1 informative.loc[informative['rsi'] > informative['rsi_ma'], 'rsi_entry2'] = 1 informative.loc[informative['rsi'] < informative['rsi_ma'], 'rsi_entry2'] = -1 informative.loc[informative['rsi_ma_pcnt'] > self.rsi_ma_entrypc.value, 'rsi_entry3'] = 1 informative.loc[informative['rsi_ma_pcnt'] < self.rsi_ma_entrypc.value, 'rsi_entry3'] = -1 informative.loc[informative['rsi'] < self.rsi_entry_safe.value, 'rsi_entry4'] = 1 informative.loc[informative['rsi'] > self.rsi_entry_safe.value, 'rsi_entry4'] = 0 informative['rsi_weight'] = (informative['rsi_entry1'] + informative['rsi_entry2'] + informative['rsi_entry3'] + informative['rsi_entry4']) / 4 * self.x01.value informative.loc[(informative['close'] > informative['200_SMA']) & (informative['200_SMAPC'] > self.sma200_entry_pc.value), 'sma_entry1'] = 1 informative.loc[(informative['close'] < informative['200_SMA']) & (informative['200_SMAPC'] > self.sma200_entry_pc.value), 'sma_entry1'] = 1 informative.loc[(informative['close'] > informative['200_SMA']) & (informative['200_SMAPC'] < self.sma200_entry_pc.value), 'sma_entry1'] = -1 informative.loc[(informative['close'] < informative['200_SMA']) & (informative['200_SMAPC'] < self.sma200_entry_pc.value), 'sma_entry1'] = -1 informative.loc[informative['200_SMAPC'] > self.sma200_entry_pc.value, 'sma_entry2'] = 1 informative.loc[informative['200_SMAPC'] < self.sma200_entry_pc.value, 'sma_entry2'] = -1 informative['200SMA_weight'] = (informative['sma_entry1'] + informative['sma_entry2']) / 2 * self.x02.value informative['from_weight'] = 0 #-((informative['from_200']) * self.x03.value) informative.loc[informative['wave_t1'] < self.slow_wave_entry.value, 'wave_entry1'] = 1 informative.loc[informative['wave_t1'] > self.slow_wave_entry.value, 'wave_entry1'] = -1 informative.loc[informative['wave_t1'] > informative['wave_t1'].shift(1), 'wave_entry2'] = 1 informative.loc[informative['wave_t1'] < informative['wave_t1'].shift(1), 'wave_entry2'] = -1 informative.loc[informative['wave_t1'] > informative['wave_t2'], 'wave_entry3'] = 1 informative.loc[informative['wave_t1'] < informative['wave_t2'], 'wave_entry3'] = -1 informative['wave_weight'] = (informative['wave_entry1'] + informative['wave_entry2'] + informative['wave_entry3']) / 3 * self.x04.value informative['auto_entry'] = informative[['rsi_weight', 'wave_weight', '200SMA_weight', 'from_weight']].sum(axis=1) ### SELLING WEIGHTS ### informative.loc[informative['rsi'] > self.rsi_exit.value, 'rsi_exit1'] = 1 informative.loc[informative['rsi'] < self.rsi_exit.value, 'rsi_exit1'] = -1 informative.loc[informative['rsi'] < informative['rsi_ma'], 'rsi_exit2'] = 1 informative.loc[informative['rsi'] > informative['rsi_ma'], 'rsi_exit2'] = -1 informative.loc[informative['rsi_ma_pcnt'] < self.rsi_ma_exitpc.value, 'rsi_exit3'] = 1 informative.loc[informative['rsi_ma_pcnt'] > self.rsi_ma_exitpc.value, 'rsi_exit3'] = -1 informative.loc[informative['rsi'] > self.rsi_exit_safe.value, 'rsi_exit4'] = 1 informative.loc[informative['rsi'] < self.rsi_exit_safe.value, 'rsi_exit4'] = 0 informative['rsi_weight'] = (informative['rsi_exit1'] + informative['rsi_exit2'] + informative['rsi_exit3'] + informative['rsi_exit4']) / 4 * self.y01.value informative.loc[(informative['close'] > informative['200_SMA']) & (informative['200_SMAPC'] > self.sma200_exit_pc.value), 'sma_exit1'] = -1 informative.loc[(informative['close'] < informative['200_SMA']) & (informative['200_SMAPC'] > self.sma200_exit_pc.value), 'sma_exit1'] = -1 informative.loc[(informative['close'] > informative['200_SMA']) & (informative['200_SMAPC'] < self.sma200_exit_pc.value), 'sma_exit1'] = 1 informative.loc[(informative['close'] < informative['200_SMA']) & (informative['200_SMAPC'] < self.sma200_exit_pc.value), 'sma_exit1'] = 1 informative.loc[informative['200_SMAPC'] > self.sma200_exit_pc.value, 'sma_exit2'] = -1 informative.loc[informative['200_SMAPC'] < self.sma200_exit_pc.value, 'sma_exit2'] = 1 informative['200SMA_weight'] = (informative['sma_exit1'] + informative['sma_exit2']) / 2 * self.y02.value informative['from_weight_exit'] = 0 #((informative['from_200']) * self.y03.value) informative.loc[informative['wave_t1'] < self.slow_wave_exit.value, 'wave_exit1'] = -1 informative.loc[informative['wave_t1'] > self.slow_wave_exit.value, 'wave_exit1'] = 1 informative.loc[informative['wave_t1'] > informative['wave_t1'].shift(1), 'wave_exit2'] = -1 informative.loc[informative['wave_t1'] < informative['wave_t1'].shift(1), 'wave_exit2'] = 1 informative.loc[informative['wave_t1'] > informative['wave_t2'], 'wave_exit3'] = -1 informative.loc[informative['wave_t1'] < informative['wave_t2'], 'wave_exit3'] = 1 informative['wave_weight'] = (informative['wave_exit1'] + informative['wave_exit2'] + informative['wave_exit3']) / 3 * self.y04.value informative['auto_exit'] = informative[['rsi_weight', 'wave_weight', '200SMA_weight', 'from_weight_exit']].sum(axis=1) informative['auto_entry_decision'] = informative['auto_entry'] - informative['auto_exit'] informative['auto_exit_decision'] = informative['auto_exit'] - informative['auto_entry'] dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) ### 15m indicators ### # WaveTrend using OHLC4 or HA close - 3/21 ap = 0.25 * (dataframe['high'] + dataframe['low'] + dataframe['close'] + dataframe['open']) dataframe['esa'] = ta.EMA(ap, timeperiod=3) dataframe['d'] = ta.EMA(abs(ap - dataframe['esa']), timeperiod=3) dataframe['wave_ci'] = (ap - dataframe['esa']) / (0.015 * dataframe['d']) dataframe['wave_t1'] = ta.EMA(dataframe['wave_ci'], timeperiod=21) dataframe['wave_t2'] = ta.SMA(dataframe['wave_t1'], timeperiod=3) dataframe['t1_pc'] = PC(dataframe, dataframe['wave_t1'], dataframe['wave_t1'].shift(1)) # Filter ZEMA for length in self.filterlength.range: dataframe[f'ema_1{length}'] = ta.EMA(dataframe['close'], timeperiod=length) dataframe[f'ema_2{length}'] = ta.EMA(dataframe[f'ema_1{length}'], timeperiod=length) dataframe[f'ema_dif{length}'] = dataframe[f'ema_1{length}'] - dataframe[f'ema_2{length}'] dataframe[f'zema_{length}'] = dataframe[f'ema_1{length}'] + dataframe[f'ema_dif{length}'] # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_ma'] = ta.SMA(dataframe['rsi'], timeperiod=10) dataframe['rsi_ma_pcnt'] = PC(dataframe, dataframe['rsi_ma'], dataframe['rsi_ma'].shift(1)) # SMA dataframe['200_SMA'] = ta.SMA(dataframe['close'], timeperiod=200) dataframe['200_SMAPC'] = PC(dataframe, dataframe['200_SMA'], dataframe['200_SMA'].shift(1)) # Plot 0 dataframe['zero'] = 0 # Williams R% dataframe['willr14'] = pta.willr(dataframe['high'], dataframe['low'], dataframe['close']) dataframe['willr14PC'] = PC(dataframe, dataframe['willr14'], dataframe['willr14'].shift(1)) for l in self.max_length.range: dataframe['min'] = dataframe['open'].rolling(l).min() dataframe['max'] = dataframe['close'].rolling(l).max() # distance from 200SMA max dataframe['from_200'] = ta.SMA(((dataframe['close'] + dataframe['open']) / 2 - dataframe['200_SMA']) / dataframe['close'] * 100, timeperiod=self.from15.value) ### Buying Weights ### dataframe.loc[dataframe['rsi'] < self.rsi_entry.value, 'rsi_entry1'] = 1 dataframe.loc[dataframe['rsi'] > self.rsi_entry.value, 'rsi_entry1'] = -1 dataframe.loc[dataframe['rsi'] > dataframe['rsi_ma'], 'rsi_entry2'] = 1 dataframe.loc[dataframe['rsi'] < dataframe['rsi_ma'], 'rsi_entry2'] = -1 dataframe.loc[dataframe['rsi_ma_pcnt'] > self.rsi_ma_entrypc.value, 'rsi_entry3'] = 1 dataframe.loc[dataframe['rsi_ma_pcnt'] < self.rsi_ma_entrypc.value, 'rsi_entry3'] = -1 dataframe.loc[dataframe['rsi'] < self.rsi_entry_safe.value, 'rsi_entry4'] = 1 dataframe.loc[dataframe['rsi'] > self.rsi_entry_safe.value, 'rsi_entry4'] = 0 dataframe['rsi_weight'] = (dataframe['rsi_entry1'] + dataframe['rsi_entry2'] + dataframe['rsi_entry3'] + dataframe['rsi_entry4']) / 4 * self.x05.value dataframe.loc[(dataframe['close'] > dataframe['200_SMA']) & (dataframe['200_SMAPC'] > self.sma200_entry_pc.value), 'sma_entry1'] = 1 dataframe.loc[(dataframe['close'] < dataframe['200_SMA']) & (dataframe['200_SMAPC'] > self.sma200_entry_pc.value), 'sma_entry1'] = 1 dataframe.loc[(dataframe['close'] > dataframe['200_SMA']) & (dataframe['200_SMAPC'] < self.sma200_entry_pc.value), 'sma_entry1'] = -1 dataframe.loc[(dataframe['close'] < dataframe['200_SMA']) & (dataframe['200_SMAPC'] < self.sma200_entry_pc.value), 'sma_entry1'] = -1 dataframe.loc[dataframe['200_SMAPC'] > self.sma200_entry_pc.value, 'sma_entry2'] = 1 dataframe.loc[dataframe['200_SMAPC'] < self.sma200_entry_pc.value, 'sma_entry2'] = -1 dataframe['200SMA_weight'] = (dataframe['sma_entry1'] + dataframe['sma_entry2']) / 2 * self.x06.value dataframe.loc[dataframe['willr14'] < self.willr_entry.value, 'willr_entry1'] = 1 dataframe.loc[dataframe['willr14'] > self.willr_entry.value, 'willr_entry1'] = -1 dataframe.loc[dataframe['willr14'] > -80, 'willr_entry2'] = 1 dataframe.loc[dataframe['willr14'] < -80, 'willr_entry2'] = -1 dataframe.loc[dataframe['willr14PC'] > 0, 'willr_entry3'] = 1 dataframe.loc[dataframe['willr14PC'] < 0, 'willr_entry3'] = -1 dataframe['willr_weight'] = (dataframe['willr_entry1'] + dataframe['willr_entry2'] + dataframe['willr_entry3']) / 3 * self.x07.value dataframe['from_weight'] = -(dataframe['from_200'] * self.x08.value) dataframe.loc[dataframe['wave_t1'] < self.fast_wave_entry.value, 'wave_entry1'] = 1 dataframe.loc[dataframe['wave_t1'] > self.fast_wave_entry.value, 'wave_entry1'] = -1 dataframe.loc[dataframe['wave_t1'] > dataframe['wave_t1'].shift(1), 'wave_entry2'] = 1 dataframe.loc[dataframe['wave_t1'] < dataframe['wave_t1'].shift(1), 'wave_entry2'] = -1 dataframe.loc[dataframe['wave_t1'] > dataframe['wave_t2'], 'wave_entry3'] = 1 dataframe.loc[dataframe['wave_t1'] < dataframe['wave_t2'], 'wave_entry3'] = -1 dataframe.loc[(dataframe['wave_t1'] > dataframe['wave_t1_2h']) & (dataframe['wave_t1_2h'] < self.slow_wave_entry.value), 'wave_entry4'] = 1 dataframe.loc[(dataframe['wave_t1'] < dataframe['wave_t1_2h']) & (dataframe['wave_t1_2h'] < self.slow_wave_entry.value), 'wave_entry4'] = 2 dataframe['wave_weight'] = (dataframe['wave_entry1'] + dataframe['wave_entry2'] + dataframe['wave_entry3'] + dataframe['wave_entry4']) / 4 * self.x09.value dataframe['auto_entry'] = dataframe[['rsi_weight', 'willr_weight', '200SMA_weight', 'from_weight', 'wave_weight']].sum(axis=1) ### SELLING ### dataframe.loc[dataframe['rsi'] > self.rsi_exit.value, 'rsi_exit1'] = 1 dataframe.loc[dataframe['rsi'] < self.rsi_exit.value, 'rsi_exit1'] = -1 dataframe.loc[dataframe['rsi'] > dataframe['rsi_ma'], 'rsi_exit2'] = -1 dataframe.loc[dataframe['rsi'] < dataframe['rsi_ma'], 'rsi_exit2'] = 1 dataframe.loc[dataframe['rsi_ma_pcnt'] > self.rsi_ma_exitpc.value, 'rsi_exit3'] = -1 dataframe.loc[dataframe['rsi_ma_pcnt'] < self.rsi_ma_exitpc.value, 'rsi_exit3'] = 1 dataframe.loc[dataframe['rsi'] < self.rsi_exit_safe.value, 'rsi_exit4'] = 0 dataframe.loc[dataframe['rsi'] > self.rsi_exit_safe.value, 'rsi_exit4'] = 1 dataframe['rsi_weight_exit'] = (dataframe['rsi_exit1'] + dataframe['rsi_exit2'] + dataframe['rsi_exit3'] + dataframe['rsi_exit4']) / 4 * self.y05.value dataframe.loc[(dataframe['close'] > dataframe['200_SMA']) & (dataframe['200_SMAPC'] > self.sma200_exit_pc.value), 'sma_exit1'] = 1 dataframe.loc[(dataframe['close'] < dataframe['200_SMA']) & (dataframe['200_SMAPC'] > self.sma200_exit_pc.value), 'sma_exit1'] = 2 dataframe.loc[(dataframe['close'] > dataframe['200_SMA']) & (dataframe['200_SMAPC'] < self.sma200_exit_pc.value), 'sma_exit1'] = -2 dataframe.loc[(dataframe['close'] < dataframe['200_SMA']) & (dataframe['200_SMAPC'] < self.sma200_exit_pc.value), 'sma_exit1'] = -1 dataframe.loc[dataframe['200_SMAPC'] > self.sma200_exit_pc.value, 'sma_exit2'] = 1 dataframe.loc[dataframe['200_SMAPC'] < self.sma200_exit_pc.value, 'sma_exit2'] = -1 dataframe['200SMA_weight_exit'] = (dataframe['sma_exit1'] + dataframe['sma_exit2']) / 2 * self.y06.value dataframe.loc[dataframe['willr14'] < self.willr_exit.value, 'willr_exit1'] = -1 dataframe.loc[dataframe['willr14'] > self.willr_exit.value, 'willr_exit1'] = 1 dataframe.loc[dataframe['willr14'] > -10, 'willr_exit2'] = 1 dataframe.loc[dataframe['willr14'] < -10, 'willr_exit2'] = -1 dataframe.loc[dataframe['willr14PC'] > 0, 'willr_exit3'] = -1 dataframe.loc[dataframe['willr14PC'] < 0, 'willr_exit3'] = 1 dataframe['willr_weight_exit'] = (dataframe['willr_exit1'] + dataframe['willr_exit2'] + dataframe['willr_exit3']) / 3 * self.y07.value dataframe['from_weight_exit'] = dataframe['from_200'] * self.y08.value dataframe.loc[dataframe['wave_t1'] < self.slow_wave_exit.value, 'wave_exit1'] = -1 dataframe.loc[dataframe['wave_t1'] > self.slow_wave_exit.value, 'wave_exit1'] = 1 dataframe.loc[dataframe['wave_t1'] > dataframe['wave_t1'].shift(1), 'wave_exit2'] = -1 dataframe.loc[dataframe['wave_t1'] < dataframe['wave_t1'].shift(1), 'wave_exit2'] = 1 dataframe.loc[dataframe['wave_t1'] > dataframe['wave_t2'], 'wave_exit3'] = -1 dataframe.loc[dataframe['wave_t1'] < dataframe['wave_t2'], 'wave_exit3'] = 1 dataframe.loc[(dataframe['wave_t1'] > dataframe['wave_t1_2h']) & (dataframe['wave_t1_2h'] > self.slow_wave_exit.value), 'wave_exit4'] = 1 dataframe.loc[(dataframe['wave_t1'] < dataframe['wave_t1_2h']) & (dataframe['wave_t1_2h'] > self.slow_wave_exit.value), 'wave_exit4'] = 2 dataframe['wave_weight'] = (dataframe['wave_exit1'] + dataframe['wave_exit2'] + dataframe['wave_exit3'] + dataframe['wave_exit4']) / 4 * self.y09.value dataframe['auto_exit'] = dataframe[['rsi_weight_exit', 'willr_weight_exit', '200SMA_weight_exit', 'from_weight_exit']].sum(axis=1) # dataframe['auto_entry_decision'] = ta.SMA((dataframe['auto_entry'] - dataframe['auto_exit']), timeperiod=self.entry_smoothing.value) # dataframe['auto_exit_decision'] = ta.SMA((dataframe['auto_exit'] - dataframe['auto_entry']), timeperiod=self.exit_smoothing.value) dataframe['auto_entry_decision'] = dataframe['auto_entry'] - dataframe['auto_exit'] dataframe['auto_exit_decision'] = dataframe['auto_exit'] - dataframe['auto_entry'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(dataframe['auto_entry_decision'] >= self.auto_entry.value) & (dataframe['auto_entry_decision_2h'] >= self.auto_entry_2.value) & (dataframe['auto_entry_decision'] >= dataframe['auto_entry_decision'].shift(1)) & (dataframe['200_SMA_2h'] > dataframe['200_SMA_2h'].shift(8)) & (dataframe['200_SMA'] > dataframe['200_SMA'].shift(1)) & (dataframe['volume'] > 0), ['enter_long', 'enter_tag']] = (1, 'auto entry bullzzz up') dataframe.loc[(dataframe['auto_entry_decision'] >= self.auto_entry.value + self.auto_entry_down.value) & (dataframe['auto_entry_decision_2h'] >= self.auto_entry_2.value + self.auto_entry_down_2.value) & (dataframe['auto_entry_decision'] >= dataframe['auto_entry_decision'].shift(1)) & (dataframe['200_SMA_2h'] > dataframe['200_SMA_2h'].shift(8)) & (dataframe['200_SMA'] < dataframe['200_SMA'].shift(1)) & (dataframe['volume'] > 0), ['enter_long', 'enter_tag']] = (1, 'auto entry bullzzz down') dataframe.loc[(dataframe['auto_entry_decision'] >= self.auto_entry.value + self.auto_entry_bearzzz.value) & (dataframe['auto_entry_decision_2h'] >= self.auto_entry_2.value + self.auto_entry_bearzzz_2.value) & (dataframe['auto_entry_decision'] >= dataframe['auto_entry_decision'].shift(1)) & (dataframe['200_SMA_2h'] < dataframe['200_SMA_2h'].shift(8)) & (dataframe['200_SMA'] > dataframe['200_SMA'].shift(1)) & (dataframe['volume'] > 0), ['enter_long', 'enter_tag']] = (1, 'auto entry bearzzz up') dataframe.loc[(dataframe['auto_entry_decision'] >= self.auto_entry.value + self.auto_entry_bearzzz.value + self.auto_entry_bearzzz_down.value) & (dataframe['auto_entry_decision_2h'] >= self.auto_entry_2.value + self.auto_entry_bearzzz_down_2.value) & (dataframe['auto_entry_decision'] >= dataframe['auto_entry_decision'].shift(1)) & (dataframe['200_SMA_2h'] < dataframe['200_SMA_2h'].shift(8)) & (dataframe['200_SMA'] < dataframe['200_SMA'].shift(1)) & (dataframe['volume'] > 0), ['enter_long', 'enter_tag']] = (1, 'auto entry bearzzz down') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(dataframe['auto_exit_decision'] >= self.auto_exit_bull.value + self.auto_exit_bear.value) & (dataframe['200_SMA_2h'] > dataframe['200_SMA_2h'].shift(8)) & (dataframe['volume'] > 0), ['exit_long', 'exit_tag']] = (1, 'auto_exit_bull') dataframe.loc[(dataframe['auto_exit_decision'] >= self.auto_exit_bear.value) & (dataframe['200_SMA_2h'] < dataframe['200_SMA_2h'].shift(8)) & (dataframe['volume'] > 0), ['exit_long', 'exit_tag']] = (1, 'auto_exit_bear') return dataframe