# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/MiniLambo2.py from skopt.space import Dimension, Integer import logging import logging from datetime import datetime, timezone from functools import reduce from typing import List import numpy as np import pandas_ta as pta import talib.abstract as ta import technical.indicators as ftt from pandas import DataFrame, Series from skopt.space import Dimension, Integer import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, merge_informative_pair) from freqtrade.strategy.interface import IStrategy logger = logging.getLogger(__name__) class Github_remiotore_freqtrade__MiniLambo2__20260111_210550(IStrategy): protection_params = { "protection_cooldown_period": 2, "protection_maxdrawdown_lookback_period_candles": 35, "protection_maxdrawdown_max_allowed_drawdown": 0.097, "protection_maxdrawdown_stop_duration_candles": 1, "protection_maxdrawdown_trade_limit": 6, "protection_stoplossguard_lookback_period_candles": 16, "protection_stoplossguard_stop_duration_candles": 29, "protection_stoplossguard_trade_limit": 3, } protection_cooldown_period = IntParameter(low=1, high=48, default=1, space="protection", optimize=True) protection_maxdrawdown_lookback_period_candles = IntParameter(low=1, high=48, default=1, space="protection", optimize=True) protection_maxdrawdown_trade_limit = IntParameter(low=1, high=8, default=4, space="protection", optimize=True) protection_maxdrawdown_stop_duration_candles = IntParameter(low=1, high=48, default=1, space="protection", optimize=True) protection_maxdrawdown_max_allowed_drawdown = DecimalParameter(low=0.01, high=0.20, default=0.1, space="protection", optimize=True) protection_stoplossguard_lookback_period_candles = IntParameter(low=1, high=48, default=1, space="protection", optimize=True) protection_stoplossguard_trade_limit = IntParameter(low=1, high=8, default=4, space="protection", optimize=True) protection_stoplossguard_stop_duration_candles = IntParameter(low=1, high=48, default=1, space="protection", optimize=True) @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": self.protection_cooldown_period.value }, { "method": "MaxDrawdown", "lookback_period_candles": self.protection_maxdrawdown_lookback_period_candles.value, "trade_limit": self.protection_maxdrawdown_trade_limit.value, "stop_duration_candles": self.protection_maxdrawdown_stop_duration_candles.value, "max_allowed_drawdown": self.protection_maxdrawdown_max_allowed_drawdown.value }, { "method": "StoplossGuard", "lookback_period_candles": self.protection_stoplossguard_lookback_period_candles.value, "trade_limit": self.protection_stoplossguard_trade_limit.value, "stop_duration_candles": self.protection_stoplossguard_stop_duration_candles.value, "only_per_pair": False } ] class HyperOpt: @staticmethod def generate_roi_table(params: dict): """ Generate the ROI table that will be used by Hyperopt This implementation generates the default legacy Freqtrade ROI tables. Change it if you need different number of steps in the generated ROI tables or other structure of the ROI tables. Please keep it aligned with parameters in the 'roi' optimization hyperspace defined by the roi_space method. """ roi_table = {} roi_table[0] = 0.05 roi_table[params['roi_t6']] = 0.04 roi_table[params['roi_t5']] = 0.03 roi_table[params['roi_t4']] = 0.02 roi_table[params['roi_t3']] = 0.01 roi_table[params['roi_t2']] = 0.0001 roi_table[params['roi_t1']] = -10 return roi_table @staticmethod def roi_space() -> List[Dimension]: """ Values to search for each ROI steps Override it if you need some different ranges for the parameters in the 'roi' optimization hyperspace. Please keep it aligned with the implementation of the generate_roi_table method. """ return [ Integer(240, 720, name='roi_t1'), Integer(120, 240, name='roi_t2'), Integer(90, 120, name='roi_t3'), Integer(60, 90, name='roi_t4'), Integer(30, 60, name='roi_t5'), Integer(1, 30, name='roi_t6'), ] minimal_roi = { "0": 0.05, "15": 0.04, "51": 0.03, "81": 0.02, "112": 0.01, "154": 0.0001, "400": -10 } stoploss = -0.10 trailing_stop = False trailing_stop_positive = 0.3207 trailing_stop_positive_offset = 0.3849 trailing_only_offset_is_reached = False timeframe = '1m' use_sell_signal = False sell_profit_only = False ignore_roi_if_buy_signal = False use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 200 plot_config = { "main_plot": { "ema_14": { "color": "#888b48", "type": "line" }, "buy_sell": { "sell_tag": {"color": "red"}, "buy_tag": {"color": "blue"}, }, }, "subplots": { "rsi4": { "rsi_4": { "color": "#888b48", "type": "line" } }, "rsi14": { "rsi_14": { "color": "#888b48", "type": "line" } }, "cti": { "cti": { "color": "#573892", "type": "line" } }, "ewo": { "EWO": { "color": "#573892", "type": "line" } }, "pct_change": { "pct_change": { "color": "#26782f", "type": "line" } } } } buy_params = { "lambo2_pct_change_high_period": 109, "lambo2_pct_change_high_ratio": -0.235, "lambo2_pct_change_low_period": 20, "lambo2_pct_change_low_ratio": -0.06, "lambo2_ema_14_factor": 0.981, "lambo2_rsi_14_limit": 39, "lambo2_rsi_21_limit": 39, "lambo2_rsi_4_limit": 44, } lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=False) lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=False) lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=False) lambo2_rsi_21_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_21_limit'], space='buy', optimize=False) lambo2_pct_change_low_period = IntParameter(1, 60, default=buy_params['lambo2_pct_change_low_period'], space='buy', optimize=True) lambo2_pct_change_low_ratio = DecimalParameter(low=-0.20, high=-0.01, decimals=3, default=buy_params['lambo2_pct_change_low_ratio'], space='buy', optimize=True) lambo2_pct_change_high_period = IntParameter(1, 180, default=buy_params['lambo2_pct_change_high_period'], space='buy', optimize=True) lambo2_pct_change_high_ratio = DecimalParameter(low=-0.30, high=-0.01, decimals=3, default=buy_params['lambo2_pct_change_high_ratio'], space='buy', optimize=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] informative_pairs += [("BTC/USDT", "1m")] informative_pairs += [("BTC/USDT", "1d")] return informative_pairs def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: sl_new = 1 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 = 0.015 elif (current_profit > 0.015): sl_new = 0.0075 return sl_new def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_21'] = ta.RSI(dataframe, timeperiod=21) dataframe['rsi_100'] = ta.RSI(dataframe, timeperiod=100) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['ewo'] = EWO(dataframe, 50, 200) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' lambo2 = ( (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) & (dataframe['close'].pct_change(periods=self.lambo2_pct_change_low_period.value) < float(self.lambo2_pct_change_low_ratio.value)) & (dataframe['close'].pct_change(periods=self.lambo2_pct_change_high_period.value) > float(self.lambo2_pct_change_high_ratio.value)) ) dataframe.loc[lambo2, 'buy_tag'] += 'lambo2 ' conditions.append(lambo2) dataframe.loc[reduce(lambda x, y: x | y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, current_time: datetime, **kwargs) -> bool: trade.sell_reason = f'{sell_reason} ({trade.buy_tag})' return True def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - (rolling_std * num_of_std) return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band) def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) def pct_change(a, b): return (b - a) / a def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif class MiniLambo_TBS(Github_remiotore_freqtrade__MiniLambo2__20260111_210550): process_only_new_candles = True custom_info_trail_buy = dict() trailing_buy_order_enabled = True trailing_expire_seconds = 1800 trailing_buy_uptrend_enabled = False trailing_expire_seconds_uptrend = 90 min_uptrend_trailing_profit = 0.02 debug_mode = True trailing_buy_max_stop = 0.02 # stop trailing buy if current_price > starting_price * (1+trailing_buy_max_stop) trailing_buy_max_buy = 0.000 # buy if price between uplimit (=min of serie (current_price * (1 + trailing_buy_offset())) and (start_price * 1+trailing_buy_max_buy)) init_trailing_dict = { 'trailing_buy_order_started': False, 'trailing_buy_order_uplimit': 0, 'start_trailing_price': 0, 'buy_tag': None, 'start_trailing_time': None, 'offset': 0, 'allow_trailing': False, } def trailing_buy(self, pair, reinit=False): if not pair in self.custom_info_trail_buy: self.custom_info_trail_buy[pair] = dict() if reinit or not 'trailing_buy' in self.custom_info_trail_buy[pair]: self.custom_info_trail_buy[pair]['trailing_buy'] = self.init_trailing_dict.copy() return self.custom_info_trail_buy[pair]['trailing_buy'] def trailing_buy_info(self, pair: str, current_price: float): current_time = datetime.now(timezone.utc) if not self.debug_mode: return trailing_buy = self.trailing_buy(pair) duration = 0 try: duration = (current_time - trailing_buy['start_trailing_time']) except TypeError: duration = 0 finally: logger.info( f"pair: {pair} : " f"start: {trailing_buy['start_trailing_price']:.4f}, " f"duration: {duration}, " f"current: {current_price:.4f}, " f"uplimit: {trailing_buy['trailing_buy_order_uplimit']:.4f}, " f"profit: {self.current_trailing_profit_ratio(pair, current_price) * 100:.2f}%, " f"offset: {trailing_buy['offset']}") def current_trailing_profit_ratio(self, pair: str, current_price: float) -> float: trailing_buy = self.trailing_buy(pair) if trailing_buy['trailing_buy_order_started']: return (trailing_buy['start_trailing_price'] - current_price) / trailing_buy['start_trailing_price'] else: return 0 def trailing_buy_offset(self, dataframe, pair: str, current_price: float): current_trailing_profit_ratio = self.current_trailing_profit_ratio(pair, current_price) last_candle = dataframe.iloc[-1] adapt = abs((last_candle['perc_norm'])) default_offset = 0.004 * (1 + adapt) # NOTE: default_offset 0.003 <--> 0.006 trailing_buy = self.trailing_buy(pair) if not trailing_buy['trailing_buy_order_started']: return default_offset last_candle = dataframe.iloc[-1] current_time = datetime.now(timezone.utc) trailing_duration = current_time - trailing_buy['start_trailing_time'] if trailing_duration.total_seconds() > self.trailing_expire_seconds: if (current_trailing_profit_ratio > 0) and (last_candle['buy'] == 1): return 'forcebuy' else: return None elif (self.trailing_buy_uptrend_enabled and ( trailing_duration.total_seconds() < self.trailing_expire_seconds_uptrend) and ( current_trailing_profit_ratio < (-1 * self.min_uptrend_trailing_profit))): return 'forcebuy' if current_trailing_profit_ratio < 0: return default_offset trailing_buy_offset = { 0.06: 0.02, 0.03: 0.01, 0: default_offset, } for key in trailing_buy_offset: if current_trailing_profit_ratio > key: return trailing_buy_offset[key] return default_offset def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_indicators(dataframe, metadata) self.trailing_buy(metadata['pair']) return dataframe def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: val = super().confirm_trade_entry(pair, order_type, amount, rate, time_in_force, **kwargs) if val: if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): val = False dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if (len(dataframe) >= 1): last_candle = dataframe.iloc[-1].squeeze() current_price = rate trailing_buy = self.trailing_buy(pair) trailing_buy_offset = self.trailing_buy_offset(dataframe, pair, current_price) if trailing_buy['allow_trailing']: if not trailing_buy['trailing_buy_order_started'] and (last_candle['buy'] == 1): trailing_buy['trailing_buy_order_started'] = True trailing_buy['trailing_buy_order_uplimit'] = last_candle['close'] trailing_buy['start_trailing_price'] = last_candle['close'] trailing_buy['buy_tag'] = last_candle['buy_tag'] trailing_buy['start_trailing_time'] = datetime.now(timezone.utc) trailing_buy['offset'] = 0 self.trailing_buy_info(pair, current_price) logger.info(f'start trailing buy for {pair} at {last_candle["close"]}') elif trailing_buy['trailing_buy_order_started']: if trailing_buy_offset == 'forcebuy': val = True ratio = "%.2f" % ((self.current_trailing_profit_ratio(pair, current_price)) * 100) self.trailing_buy_info(pair, current_price) logger.info( f"price OK for {pair} ({ratio} %, {current_price}), order may not be triggered if all slots are full") elif trailing_buy_offset is None: self.trailing_buy(pair, reinit=True) logger.info(f'STOP trailing buy for {pair} because "trailing buy offset" returned None') elif current_price < trailing_buy['trailing_buy_order_uplimit']: old_uplimit = trailing_buy["trailing_buy_order_uplimit"] self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit'] = min( current_price * (1 + trailing_buy_offset), self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit']) self.custom_info_trail_buy[pair]['trailing_buy']['offset'] = trailing_buy_offset self.trailing_buy_info(pair, current_price) logger.info( f'update trailing buy for {pair} at {old_uplimit} -> {self.custom_info_trail_buy[pair]["trailing_buy"]["trailing_buy_order_uplimit"]}') elif current_price < ( trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy)): val = True ratio = "%.2f" % ((self.current_trailing_profit_ratio(pair, current_price)) * 100) self.trailing_buy_info(pair, current_price) logger.info( f"current price ({current_price}) > uplimit ({trailing_buy['trailing_buy_order_uplimit']}) and lower than starting price price ({(trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy))}). OK for {pair} ({ratio} %), order may not be triggered if all slots are full") elif current_price > ( trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_stop)): self.trailing_buy(pair, reinit=True) self.trailing_buy_info(pair, current_price) logger.info( f'STOP trailing buy for {pair} because of the price is higher than starting price * {1 + self.trailing_buy_max_stop}') else: self.trailing_buy_info(pair, current_price) logger.info(f'price too high for {pair} !') else: logger.info(f"Wait for next buy signal for {pair}") if (val == True): self.trailing_buy_info(pair, rate) self.trailing_buy(pair, reinit=True) logger.info(f'STOP trailing buy for {pair} because I buy it') return val def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_buy_trend(dataframe, metadata) if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): last_candle = dataframe.iloc[-1].squeeze() trailing_buy = self.trailing_buy(metadata['pair']) if (last_candle['buy'] == 1): if not trailing_buy['trailing_buy_order_started']: open_trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True), ]).all() if not open_trades: logger.info(f"Set 'allow_trailing' to True for {metadata['pair']} to start trailing!!!") trailing_buy['allow_trailing'] = True initial_buy_tag = last_candle['buy_tag'] if 'buy_tag' in last_candle else 'buy signal' dataframe.loc[:, 'buy_tag'] = f"{initial_buy_tag} (start trail price {last_candle['close']})" else: if (trailing_buy['trailing_buy_order_started'] == True): logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger buy signal!!") dataframe.loc[:, 'buy'] = 1 dataframe.loc[:, 'buy_tag'] = trailing_buy['buy_tag'] return dataframe