# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/turbov8.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import numpy as np import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade import technical.indicators as ftt from technical import qtpylib import freqtrade.vendor.qtpylib.indicators as qtpylib 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, stoploss_from_open # @Rallipanos # changes by IcHiAT # Buy hyperspace params: entry_params = {'base_nb_candles_entry': 12, 'ewo_high': 3.147, 'ewo_low': -17.145, 'low_offset': 0.987, 'rsi_entry': 57} # Sell hyperspace params: exit_params = {'base_nb_candles_exit': 22, 'high_offset': 1.008, 'high_offset_2': 1.016} 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_DerSalvador_freqtrade_helm_chart__turbov8__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 startup_candle_count = 400 run_from_bear = 0 # Stoploss: stoploss = -0.2 # DCA Parameters position_adjustment_enable = True max_entry_position_adjustment = 3 max_dca_multiplier = 1.5 ## Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'} # ROI table: minimal_roi = {'0': 0.99} # Optimal timeframe for the strategy timeframe = '5m' informative_timeframe = '1h' ### 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': 1, 'stop_duration_candles': self.stop_duration.value, 'only_per_pair': False}) return prot ### Hyperoptable parameters ### # entry optizimation max_epa = CategoricalParameter([0, 1, 2, 3], default=3, space='entry', optimize=True) # 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) # SMAOffset base_nb_candles_entry = IntParameter(5, 80, default=entry_params['base_nb_candles_entry'], space='entry', optimize=True) base_nb_candles_exit = IntParameter(5, 80, default=exit_params['base_nb_candles_exit'], space='exit', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=entry_params['low_offset'], space='entry', optimize=True) high_offset = DecimalParameter(0.95, 1.1, default=exit_params['high_offset'], space='exit', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=exit_params['high_offset_2'], space='exit', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=entry_params['ewo_low'], space='entry', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=entry_params['ewo_high'], space='entry', optimize=True) rsi_entry = IntParameter(30, 70, default=entry_params['rsi_entry'], space='entry', optimize=True) ### BTC fast_1h = IntParameter(3, 8, default=5, space='entry', optimize=True) slow_1h = IntParameter(25, 40, default=30, space='entry', optimize=True) # dca level optimization dca1 = DecimalParameter(low=0.01, high=0.03, decimals=2, default=0.02, space='entry', optimize=True, load=True) dca2 = DecimalParameter(low=0.03, high=0.05, decimals=2, default=0.04, space='entry', optimize=True, load=True) dca3 = DecimalParameter(low=0.05, high=0.07, decimals=2, default=0.06, space='entry', optimize=True, load=True) #trailing stop loss optimiziation tsl_target5 = DecimalParameter(low=0.3, 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=0.18, high=0.3, 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=0.12, high=0.18, 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=0.07, high=0.12, 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=0.04, high=0.07, 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=0.02, high=0.05, default=0.03, space='exit', optimize=True, load=True) ts0 = DecimalParameter(low=0.008, high=0.015, default=0.013, space='exit', optimize=True, load=True) def informative_pairs(self): pairs = self.dp.current_whitelist() pairs += ['BTC/USDT'] 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 +5% return -(trade.stake_amount / 2) if current_profit > -self.dca1.value and trade.nr_of_successful_entries == 1: return None if current_profit > -self.dca2.value and trade.nr_of_successful_entries == 2: return None if current_profit > -self.dca3.value and trade.nr_of_successful_entries == 3: return None # Obtain pair dataframe (just to show how to access it) dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) filled_entries = trade.select_filled_orders(trade.entry_side) count_of_entries = trade.nr_of_successful_entries # Grid Buy down every 2% with a small amount of capital try: # This returns first order stake size stake_amount = filled_entries[0].cost # This then calculates current safety order size if count_of_entries == 1: stake_amount = stake_amount * 0.166 elif count_of_entries == 2: stake_amount = stake_amount * 0.166 elif count_of_entries == 3: stake_amount = stake_amount * 0.166 else: stake_amount = stake_amount return stake_amount except Exception as exception: return None return None 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'*** FEEDBACK *** Pair {pair} profit {current_profit} - lvl5 {stop5a} stoploss 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'*** FEEDBACK *** Pair {pair} profit {current_profit} - lvl4 {stop4a} stoploss 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'*** FEEDBACK *** Pair {pair} profit {current_profit} - lvl3 {stop3a} stoploss 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'*** FEEDBACK *** Pair {pair} profit {current_profit} - lvl2 {stop2a} stoploss 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'*** FEEDBACK *** Pair {pair} profit {current_profit} - lvl1 {stop1a} stoploss 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'*** FEEDBACK *** Pair {pair} profit {current_profit} - lvl0 {stop0a} stoploss activated') return stop0a return self.stoploss def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate all ma_entry values for val in self.base_nb_candles_entry.range: dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all ma_exit values for val in self.base_nb_candles_exit.range: dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val) # BTC SMA 5/30 if self.dp: inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=f'BTC/USDT', timeframe=inf_tf) for fast in self.fast_1h.range: informative[f'sma_{fast}'] = ta.SMA(informative['close'], timeperiod=fast) for slow in self.slow_1h.range: informative[f'sma_{slow}'] = ta.SMA(informative['close'], timeperiod=slow) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) # SMA dataframe['200_SMA'] = ta.SMA(dataframe['close'], timeperiod=200) dataframe['30_SMA'] = ta.SMA(dataframe['close'], timeperiod=30) dataframe['5_SMA'] = ta.SMA(dataframe['close'], timeperiod=5) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) ### Changed this part ### if informative[f'sma_{fast}'].iloc[-1] > informative[f'sma_{slow}'].iloc[-1]: dataframe['Run_From_Bear'] = 0 if self.run_from_bear == 1: self.dp.send_msg(f'MARKET STATUS: Bear is gone! Time to wake up from hibernations...', always_send=True) self.run_from_bear = 0 elif (informative[f'sma_{fast}'].iloc[-1] < informative[f'sma_{slow}'].iloc[-1] and informative[f'sma_{fast}'].iloc[-1] < informative[f'sma_{fast}'].iloc[-2]).all(): dataframe['Run_From_Bear'] = 1 if self.run_from_bear == 0: self.dp.send_msg(f'MARKET STATUS: Bear sighted! Selling off and going into hibernation...', always_send=True) self.run_from_bear = 1 elif (informative[f'sma_{fast}'].iloc[-1] < informative[f'sma_{slow}'].iloc[-1] and informative[f'sma_{fast}'].iloc[-1] > informative[f'sma_{fast}'].iloc[-2]).all(): dataframe['Run_From_Bear'] = -1 self.dp.send_msg(f'MARKET STATUS: Bear getting sleepy! w3n m00n?...', always_send=True) else: dataframe['Run_From_Bear'] = -1 self.dp.send_msg(f'MARKET STATUS: Bear Lurking! Grab the Lube, This could hurt...', always_send=True) if (dataframe['30_SMA'].iloc[-1] > dataframe['200_SMA'].iloc[-1] and dataframe['30_SMA'].iloc[-1] > dataframe['30_SMA'].iloc[-2] and (dataframe['200_SMA'].iloc[-1] > dataframe['200_SMA'].iloc[-2])).all(): self.max_epa.value = 1 elif dataframe['30_SMA'].iloc[-1] > dataframe['200_SMA'].iloc[-1] and dataframe['30_SMA'].iloc[-1] > dataframe['30_SMA'].iloc[-2].all(): self.max_epa.value = 1 elif dataframe['30_SMA'].iloc[-1] < dataframe['200_SMA'].iloc[-1] and dataframe['30_SMA'].iloc[-1] > dataframe['30_SMA'].iloc[-2].all(): self.max_epa.value = 2 else: self.max_epa.value = 2 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['Run_From_Bear'] == 0) & (dataframe['volume'] > 0), ['enter_long', 'enter_tag']] = (1, 'EWO above high') dataframe.loc[(dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['Run_From_Bear'] == 0) & (dataframe['volume'] > 0), ['enter_long', 'enter_tag']] = (1, 'EWO below low') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[(dataframe['close'] > dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']), ['exit_long', 'exit_tag']] = (1, 'Close > Offset Hi') dataframe.loc[(dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']), ['exit_long', 'exit_tag']] = (1, 'Close > Offset Lo') dataframe.loc[(dataframe['Run_From_Bear'] == 1) & (dataframe['volume'] > 0), ['exit_long', 'exit_tag']] = (1, 'fucking bearzzz') return dataframe