# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/turbov8_2.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 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) buy_params = { "base_nb_candles_buy": 12, "ewo_high": 3.147, "ewo_low": -17.145, "low_offset": 0.987, "rsi_buy": 57, } sell_params = { "base_nb_candles_sell": 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_remiotore_freqtrade__turbov8_2__20260111_210550(IStrategy): INTERFACE_VERSION = 2 exit_profit_only = True ### No selling 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 = -0.20 position_adjustment_enable = True max_entry_position_adjustment = 3 max_dca_multiplier = 1.5 order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } minimal_roi = { "0": 0.99, } timeframe = '5m' informative_timeframe = '1h' @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 max_epa = CategoricalParameter([0, 1, 2, 3], default=3, space="buy", optimize=True) 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) base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True) fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) fast_1h = IntParameter(3, 8, default=5, space='buy', optimize=True) slow_1h = IntParameter(25, 40, default=30, space='buy', optimize=True) dca1 = DecimalParameter(low=0.01, high=0.03, decimals=2, default=0.02, space='buy', optimize=True, load=True) dca2 = DecimalParameter(low=0.03, high=0.05, decimals=2, default=0.04, space='buy', optimize=True, load=True) dca3 = DecimalParameter(low=0.05, high=0.07, decimals=2, default=0.06, space='buy', optimize=True, load=True) tsl_target5 = DecimalParameter(low=0.3, high=0.4, decimals=1, default=0.3, space='sell', optimize=True, load=True) ts5 = DecimalParameter(low=0.04, high=0.06, default=0.05, space='sell', optimize=True, load=True) tsl_target4 = DecimalParameter(low=0.18, high=0.3, default=0.2, space='sell', optimize=True, load=True) ts4 = DecimalParameter(low=0.03, high=0.05, default=0.045, space='sell', optimize=True, load=True) tsl_target3 = DecimalParameter(low=0.12, high=0.18, default=0.15, space='sell', optimize=True, load=True) ts3 = DecimalParameter(low=0.025, high=0.04, default=0.035, space='sell', optimize=True, load=True) tsl_target2 = DecimalParameter(low=0.07, high=0.12, default=0.1, space='sell', optimize=True, load=True) ts2 = DecimalParameter(low=0.015, high=0.03, default=0.02, space='sell', optimize=True, load=True) tsl_target1 = DecimalParameter(low=0.04, high=0.07, default=0.06, space='sell', optimize=True, load=True) ts1 = DecimalParameter(low=0.01, high=0.016, default=0.013, space='sell', optimize=True, load=True) tsl_target0 = DecimalParameter(low=0.02, high=0.05, default=0.03, space='sell', optimize=True, load=True) ts0 = DecimalParameter(low=0.008, high=0.015, default=0.013, space='sell', 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 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: 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 > 0.10 and trade.nr_of_successful_exits == 0: 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 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 try: stake_amount = filled_entries[0].cost 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: for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) 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) 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) dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) 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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['rsi_fast'] <35) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.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_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.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_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[ ( (dataframe['close'] > dataframe['hma_50']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.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[f'ma_sell_{self.base_nb_candles_sell.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