# source: https://raw.githubusercontent.com/rmallarapu-bc/brahma/9287745fc036c2f4c00c586e598290885c2883b9/archive/McQueen1.py # --- Do not remove these libs --- 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'] # Williams %R 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_rmallarapu_bc_brahma__McQueen1__20240229_213751(IStrategy): """ Designed to use with McQueen1 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 designed for the strategy. minimal_roi = { "0": 0.1, } # Sell hyperspace params: sell_params = { "pHSL": -0.998, # 1.1% TTP "pPF_1": 0.011, "pPF_2": 0.065, "pSL_1": 0.011, "pSL_2": 0.062, } # Trailing stoploss trailing_stop = True trailing_stop_positive = 0.002 trailing_stop_positive_offset = 0.075 trailing_only_offset_is_reached = True # Max Deviation -0.349 stoploss = -0.998 # Custom stoploss use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 168 # Optimal timeframe for the strategy timeframe = '5m' # hard stoploss profit pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', load=True) # profit threshold 1, trigger point, SL_1 is used 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) # profit threshold 2, SL_2 is used 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: # hard stoploss profit 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 # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used. 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 # Only for hyperopt invalid return 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: # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) # Bollinger bands 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'] # Close delta dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() # Dip Protection 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) # SMA dataframe['sma_50'] = ta.SMA(dataframe['close'], timeperiod=50) dataframe['sma_200'] = ta.SMA(dataframe['close'], timeperiod=200) # CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) # ADX dataframe['adx'] = ta.ADX(dataframe) # %R dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_96'] = williams_r(dataframe, period=96) # MAMA / FAMA 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 (3, 2, 100) 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) # CTI informative['cti_40'] = pta.cti(informative["close"], length=40) # %R informative['r_96'] = williams_r(informative, period=96) informative['r_480'] = williams_r(informative, period=480) # 1h mama > fama for general trend check 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[ ( ( # Dip check (dataframe['close'] < dataframe['mama']) & (dataframe['r_14'] < -30) & (dataframe['cti'] < 3.0) & (dataframe['adx'] > 26) & (dataframe['mama_diff_1h'] > 0.003) & # Bull confirm (dataframe['mama'] > dataframe['fama']) & (dataframe['sma_50'] > dataframe['sma_200'] * 1.01) & (dataframe['mama_1h'] > dataframe['fama_1h'] * 1.01) & # Overpump check (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 McQueen1(Github_rmallarapu_bc_brahma__McQueen1__20240229_213751): # DCA options 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 # Auto compound calculation 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))) # Let unlimited stakes leave funds open for DCA orders 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 # DCA 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) # This calculates base order size stake_amount = stake_amount / self.max_dca_multiplier # This then calculates current safety order size 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