# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/McQueen1.py from freqtrade.strategy.interface import IStrategy import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas_ta as pta import talib.abstract as ta import numpy as np from pandas import DataFrame, Series, DatetimeIndex, merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open import math import logging logger = logging.getLogger(__name__) def top_percent_change(dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return WR * -100 class Github_remiotore_freqtrade__McQueen1__20260111_210550(IStrategy): """ Designed to use with Github_remiotore_freqtrade__McQueen1__20260111_210550 DCA settings TTP: 1.1%(0.2%), BO: 38.0 USDT, SO: 38.0 USDT, OS: 1.2, SS: 1.13, MAD: 2, SOS: 1.6, MSTC: 11 2021/12 Crash ========================================================== BUY TAG STATS =========================================================== | TAG | Buys | Avg Profit % | Cum Profit % | Tot Profit USDT | Tot Profit % | Avg Duration | Win Draw Loss Win% | |-------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------| | TOTAL | 412 | 1.14 | 469.43 | 1157.492 | 0.45 | 5:04:00 | 412 0 0 100 | 2021/05 Crash ========================================================== BUY TAG STATS =========================================================== | TAG | Buys | Avg Profit % | Cum Profit % | Tot Profit USDT | Tot Profit % | Avg Duration | Win Draw Loss Win% | |-------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------| | TOTAL | 197 | 1.25 | 245.79 | 631.840 | 0.25 | 4:22:00 | 197 0 0 100 | 2021/09 - 2021/11 Bull ========================================================== BUY TAG STATS =========================================================== | TAG | Buys | Avg Profit % | Cum Profit % | Tot Profit USDT | Tot Profit % | Avg Duration | Win Draw Loss Win% | |-------+--------+----------------+----------------+-------------------+----------------+----------------+-------------------------| | TOTAL | 327 | 1.30 | 424.98 | 961.187 | 0.37 | 3:26:00 | 326 0 1 99.7 | """ minimal_roi = { "0": 0.1, } sell_params = { "pHSL": -0.998, "pPF_1": 0.011, "pPF_2": 0.065, "pSL_1": 0.011, "pSL_2": 0.062, } trailing_stop = True trailing_stop_positive = 0.002 trailing_stop_positive_offset = 0.075 trailing_only_offset_is_reached = True stoploss = -0.998 use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 168 timeframe = '5m' pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True) def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value if current_profit > PF_2: sl_profit = SL_2 + (current_profit - PF_2) elif current_profit > PF_1: sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=17, stds=1) dataframe['bb_lowerband'] = bollinger1['lower'] dataframe['bb_middleband'] = bollinger1['mid'] dataframe['bb_upperband'] = bollinger1['upper'] dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tpct_change_0'] = top_percent_change(dataframe, 0) dataframe['tpct_change_1'] = top_percent_change(dataframe, 1) dataframe['tpct_change_2'] = top_percent_change(dataframe, 2) dataframe['tpct_change_4'] = top_percent_change(dataframe, 4) dataframe['tpct_change_5'] = top_percent_change(dataframe, 5) dataframe['tpct_change_9'] = top_percent_change(dataframe, 9) dataframe['sma_50'] = ta.SMA(dataframe['close'], timeperiod=50) dataframe['sma_200'] = ta.SMA(dataframe['close'], timeperiod=200) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['adx'] = ta.ADX(dataframe) dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_96'] = williams_r(dataframe, period=96) dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2 dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe['hl2'], 0.5, 0.05) dataframe['mama_diff'] = ( ( dataframe['mama'] - dataframe['fama'] ) / dataframe['hl2'] ) crsi_closechange = dataframe['close'] / dataframe['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3 inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) informative['cti_40'] = pta.cti(informative["close"], length=40) informative['r_96'] = williams_r(informative, period=96) informative['r_480'] = williams_r(informative, period=480) informative['hl2'] = (informative['high'] + informative['low']) / 2 informative['mama'], informative['fama'] = ta.MAMA(informative['hl2'], 0.5, 0.05) informative['mama_diff'] = ( ( informative['mama'] - informative['fama'] ) / informative['hl2'] ) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: is_crash_1 = ( (dataframe['tpct_change_1'] < 0.08) & (dataframe['tpct_change_2'] < 0.08) & (dataframe['tpct_change_4'] < 0.10) ) dataframe.loc[ ( ( (dataframe['close'] < dataframe['mama']) & (dataframe['r_14'] < -30) & (dataframe['cti'] < 3.0) & (dataframe['adx'] > 26) & (dataframe['mama_diff_1h'] > 0.003) & (dataframe['mama'] > dataframe['fama']) & (dataframe['sma_50'] > dataframe['sma_200'] * 1.01) & (dataframe['mama_1h'] > dataframe['fama_1h'] * 1.01) & (dataframe['rsi_84'] < 55) & (dataframe['rsi_112'] < 55) & (dataframe['cti_40_1h'] < 0.73) & (dataframe['r_96_1h'] < -6) & (dataframe['mama_diff_1h'] < 0.027) & (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.03 )) ) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['close'] > dataframe['bb_upperband'] * 0.999) & (dataframe['rsi'] > 76) ), 'sell'] = 0 return dataframe class Github_remiotore_freqtrade__McQueen1__20260111_210550(Github_remiotore_freqtrade__McQueen1__20260111_210550): position_adjustment_enable = True initial_safety_order_trigger = -0.016 max_safety_orders = 5 safety_order_step_scale = 1.13 #SS safety_order_volume_scale = 1.2 #OS max_dca_multiplier = (1 + max_safety_orders) if (max_safety_orders > 0): if (safety_order_volume_scale > 1): max_dca_multiplier = (2 + (safety_order_volume_scale * (math.pow(safety_order_volume_scale, (max_safety_orders - 1)) - 1) / (safety_order_volume_scale - 1))) elif (safety_order_volume_scale < 1): max_dca_multiplier = (2 + (safety_order_volume_scale * (1 - math.pow(safety_order_volume_scale, (max_safety_orders - 1))) / (1 - safety_order_volume_scale))) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, **kwargs) -> float: if self.config['stake_amount'] == 'unlimited': return proposed_stake / self.max_dca_multiplier return proposed_stake 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: return None count_of_buys = trade.nr_of_successful_buys if 1 <= count_of_buys <= self.max_safety_orders: safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_buys) if (self.safety_order_step_scale > 1): safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1)) elif (self.safety_order_step_scale < 1): safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (1 - math.pow(self.safety_order_step_scale,(count_of_buys - 1))) / (1 - self.safety_order_step_scale)) if current_profit <= (-1 * abs(safety_order_trigger)): try: stake_amount = self.wallets.get_trade_stake_amount(trade.pair, None) stake_amount = stake_amount / self.max_dca_multiplier stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, (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 return None