# source: https://raw.githubusercontent.com/RoboticAutomations/freqtradestrategies/52c4bd40414645f079a980cf4486dc7af4061efb/dirty%20LA/from-AIRA/heikin-ashi/PeekabooV2.py # directory_url: https://github.com/RoboticAutomations/freqtradestrategies/blob/main/dirty LA/from-AIRA/heikin-ashi/ # User: RoboticAutomations # Repository: freqtradestrategies # -------------------- # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from typing import Optional from pandas import DataFrame import warnings import pandas as pd # -------------------------------- import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter, BooleanParameter import technical.indicators as ftt import math import logging from scipy.signal import argrelextrema logger = logging.getLogger(__name__) class Github_RoboticAutomations_freqtradestrategies__PeekabooV2__20260915_233254(IStrategy): INTERFACE_VERSION = 2 @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 5 }, { "method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 20, "stop_duration_candles": 4, "max_allowed_drawdown": 0.2 }, { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "only_per_pair": False }, { "method": "LowProfitPairs", "lookback_period_candles": 6, "trade_limit": 2, "stop_duration_candles": 60, "required_profit": 0.02 }, { "method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "required_profit": 0.01 } ] # ROI table: minimal_roi = { "0": 0.23, "14": 0.079, "72": 0.01, "182": 0 } # Stoploss: stoploss = -0.15 # Trailing stop: use_custom_stoploss = True # Sell signal use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False ## Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } # Optimal timeframe for the strategy timeframe = '5m' startup_candle_count = 100 process_only_new_candles = True # DCA position_adjustment_enable = False # Custom Entry last_entry_price = None # Hyper-opt parameters base_nb_candles_buy = IntParameter(60, 80, default=72, space='buy', optimize=True) bull = IntParameter(60, 80, default=72, space='buy', optimize=True) bear = IntParameter(490, 510, default=500, space='buy', optimize=True) blah = IntParameter(200, 225, default=221, space='buy', optimize=True) up = DecimalParameter(low=1.020, high=1.025, default=1.021, decimals=3 ,space='buy', optimize=True, load=True) dn = DecimalParameter(low=0.983, high=0.987, default=0.984, decimals=3 ,space='buy', optimize=True, load=True) enable1 = BooleanParameter(default=True, space="buy", optimize=True) enable2 = BooleanParameter(default=True, space="buy", optimize=True) enable3 = BooleanParameter(default=True, space="buy", optimize=True) enable4 = BooleanParameter(default=True, space="buy", optimize=True) enable5 = BooleanParameter(default=True, space="buy", optimize=True) enable6 = BooleanParameter(default=True, space="buy", optimize=True) increment = DecimalParameter(low=1.0005, high=1.002, default=1.001, decimals=4 ,space='buy', optimize=True, load=True) # DCA initial_safety_order_trigger = DecimalParameter(low=-0.02, high=-0.010, default=-0.018, decimals=3 ,space='buy', optimize=True, load=True) max_safety_orders = IntParameter(1, 6, default=2, space='buy', optimize=True) safety_order_step_scale = DecimalParameter(low=1.05, high=1.5, default=1.25, decimals=2 ,space='buy', optimize=True, load=True) safety_order_volume_scale = DecimalParameter(low=1.1, high=2, default=1.4, decimals=1 ,space='buy', optimize=True, load=True) # Unclog Function days = IntParameter(2, 7, default=4, space='sell', optimize=True) loss = DecimalParameter(-0.07, -0.01, default=-0.04, space='sell', optimize=True) ### trailing stop loss optimiziation ### tsl_target3 = DecimalParameter(low=0.10, high=0.15, default=0.15, decimals=2, space='sell', optimize=True, load=True) ts3 = DecimalParameter(low=0.025, high=0.04, default=0.035, decimals=3, space='sell', optimize=True, load=True) tsl_target2 = DecimalParameter(low=0.06, high=0.10, default=0.1, decimals=3, space='sell', optimize=True, load=True) ts2 = DecimalParameter(low=0.015, high=0.03, default=0.02, decimals=3, space='sell', optimize=True, load=True) tsl_target1 = DecimalParameter(low=0.04, high=0.06, default=0.06, decimals=3, space='sell', optimize=True, load=True) ts1 = DecimalParameter(low=0.01, high=0.016, default=0.013, decimals=3, space='sell', optimize=True, load=True) tsl_target0 = DecimalParameter(low=0.02, high=0.04, default=0.03, decimals=3, space='sell', optimize=True, load=True) ts0 = DecimalParameter(low=0.008, high=0.015, default=0.013, decimals=3, space='sell', optimize=True, load=True) moon = IntParameter(80, 90, default=85, space='sell', optimize=True) ### Trailing Stop ### def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() if current_candle['rsi'] < self.moon.value: 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 else: for stop0 in self.tsl_target0.range: if (current_profit > stop0): self.dp.send_msg(f'*** {pair} *** Profit {current_profit} SWINGING FOR THE MOON!!!') return 0.99 return self.stoploss def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): if current_profit > self.initial_safety_order_trigger.value: logger.info(f"{trade.pair} - Current Profit: {current_profit} Trigger: {self.initial_safety_order_trigger.value}") return None dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) count_of_buys = 0 for order in trade.orders: if order.ft_is_open or order.ft_order_side != 'buy': continue if order.status == "closed": count_of_buys += 1 if 1 <= count_of_buys <= self.max_safety_orders.value: safety_order_trigger = abs(self.initial_safety_order_trigger.value) + (abs(self.initial_safety_order_trigger.value) * self.safety_order_step_scale.value * (math.pow(self.safety_order_step_scale.value,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale.value - 1)) if current_profit <= (-1 * abs(safety_order_trigger)): try: stake_amount = self.wallets.get_trade_stake_amount(trade.pair, None) stake_amount = stake_amount * math.pow(self.safety_order_volume_scale.value,(count_of_buys - 1)) amount = stake_amount / current_rate logger.info(f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}") return stake_amount except Exception as exception: logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}') return None else: stake_amount = self.wallets.get_trade_stake_amount(trade.pair, None) stake_amount = stake_amount * math.pow(self.safety_order_volume_scale.value,(count_of_buys - 1)) logger.info(f"{trade.pair} Next Safety Order #{count_of_buys} @ Trigger -{safety_order_trigger} Current Profit: {current_profit}") return None return None def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: return 4 def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): # Sell any positions at a loss if they are held for more than 7 days. if current_profit < self.loss.value and (current_time - trade.open_date_utc).days >= self.days.value: return 'unclog' def custom_entry_price(self, pair: str, trade: Optional['Trade'], current_time: datetime, proposed_rate: float, entry_tag: Optional[str], side: str, **kwargs) -> float: dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) entry_price = (dataframe['close'].iat[-1] + dataframe['open'].iat[-1] + proposed_rate) / 3 logger.info(f"{pair} Using Entry Price: {entry_price} | close: {dataframe['close'].iat[-1]} open: {dataframe['open'].iat[-1]} proposed_rate: {proposed_rate}") # Check if there is a stored last entry price and if it matches the proposed entry price if self.last_entry_price is not None and abs(entry_price - self.last_entry_price) < 0.0001: # Tolerance for floating-point comparison entry_price *= self.increment.value # Increment by 0.2% logger.info(f"{pair} Incremented entry price: {entry_price} based on previous entry price : {self.last_entry_price}.") # Update the last entry price self.last_entry_price = entry_price return entry_price def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: if exit_reason == 'trailing_stop_loss' and trade.calc_profit_ratio(rate) < 0: logger.info(f"{trade.pair} trailing stop price is below 0") self.dp.send_msg(f'{trade.pair} trailing stop price is below 0') return False return True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) pair = metadata['pair'] heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] dataframe['ha_closedelta'] = (heikinashi['close'] - heikinashi['close'].shift()) dataframe["&s-extrema"] = 0 dataframe['sma'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] dataframe['sma_up'] = dataframe['sma'] * self.up.value dataframe['sma_dn'] = dataframe['sma'] * self.dn.value #Bull Mode if dataframe['sma'].iloc[-1] > dataframe['sma'].iloc[-3]: order = self.bull.value logger.debug(f"{pair} BULL MODE!!!") dataframe.loc[:, 'MODE'] = 1 #Bear Mode elif (dataframe['sma'].iloc[-1] < dataframe['sma'].iloc[-3]) and (dataframe['close'].iloc[-1] > dataframe['sma'].iloc[-1]): order = self.bear.value logger.debug(f"{pair} BEAR MODE!!!") dataframe.loc[:, 'MODE'] = -1 #Blah Mode else: order = self.blah.value logger.debug(f"{pair} SIDEWAYS MODE!!!") dataframe.loc[:, 'MODE'] = 0 min_peaks = argrelextrema( dataframe["ha_open"].values, np.less, order=order ) max_peaks = argrelextrema( dataframe["ha_close"].values, np.greater, order=order ) for mp in min_peaks[0]: dataframe.at[mp, "&s-extrema"] = -1 for mp in max_peaks[0]: dataframe.at[mp, "&s-extrema"] = 1 dataframe["minima"] = np.where(dataframe["&s-extrema"] == -1, 1, 0) dataframe["maxima"] = np.where(dataframe["&s-extrema"] == 1, 1, 0) dataframe['maxima_check'] = dataframe['maxima'].rolling(3).apply(lambda x: int((x != 1).all()), raw=True).fillna(0) dataframe['minima_check'] = dataframe['minima'].rolling(3).apply(lambda x: int((x != 1).all()), raw=True).fillna(0) maxima_indices = dataframe[dataframe['maxima'] ==1].index minima_indices = dataframe[dataframe['minima'] ==1].index distances = [] for max_index in maxima_indices: for min_index in minima_indices: distances.append(abs(max_index - min_index)) dataframe['mean_distance'] = np.mean(distances) if distances else 0 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: condition1 = ( (dataframe['minima_check'] == 0) & (dataframe['MODE'] == 1) & (self.enable1.value == True) & (dataframe['close'] < dataframe['sma_up']) ) dataframe.loc[condition1, 'enter_long'] = 1 dataframe.loc[condition1, 'enter_tag'] = 'minima_check BULL UP' condition2 = ( (dataframe['minima_check'] == 0) & (dataframe['MODE'] == 1) & (self.enable2.value == True) & (dataframe['close'] < dataframe['sma']) ) dataframe.loc[condition2, 'enter_long'] = 1 dataframe.loc[condition2, 'enter_tag'] = 'minima_check BULL MID' condition3 = ( (dataframe['minima_check'] == 0) & (dataframe['MODE'] == 1) & (self.enable3.value == True) & (dataframe['close'] < dataframe['sma_dn']) ) dataframe.loc[condition3, 'enter_long'] = 1 dataframe.loc[condition3, 'enter_tag'] = 'minima_check BULL DOWN' condition4 = ( (dataframe['minima_check'] == 0) & (dataframe['MODE'] == 0) & (self.enable4.value == True) & (dataframe['close'] < dataframe['sma_up']) ) dataframe.loc[condition4, 'enter_long'] = 1 dataframe.loc[condition4, 'enter_tag'] = 'minima_check SIDEWAYS UP' condition5 = ( (dataframe['minima_check'] == 0) & (dataframe['MODE'] == 0) & (self.enable5.value == True) & (dataframe['close'] < dataframe['sma']) ) dataframe.loc[condition5, 'enter_long'] = 1 dataframe.loc[condition5, 'enter_tag'] = 'minima_check SIDEWAYS MID' condition6 = ( (dataframe['minima_check'] == 0) & (dataframe['MODE'] == 0) & (self.enable6.value == True) & (dataframe['close'] < dataframe['sma_dn']) ) dataframe.loc[condition6, 'enter_long'] = 1 dataframe.loc[condition6, 'enter_tag'] = 'minima_check SIDEWAYS DOWN' return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[dataframe['maxima'] == 1, 'exit_tag'] = 'maxima' dataframe.loc[dataframe['maxima_check'] == 0, 'exit_tag'] = 'maxima_check' return dataframe