# source: https://raw.githubusercontent.com/wynnforthework/quant-strategies-knowledge/523670ea407d3e361fdf8adcd2c74819d2275e83/freqtrade-strategies/turtle/new_turtle.py # directory_url: https://github.com/wynnforthework/quant-strategies-knowledge/blob/main/freqtrade-strategies/turtle/ # User: wynnforthework # Repository: quant-strategies-knowledge # -------------------- # --- Do not remove these libs --- from freqtrade.strategy import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_wynnforthework_quant_strategies_knowledge__new_turtle__20251007_112722(IStrategy): # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi" minimal_roi = { "0": 100 } # Optimal stoploss designed for the strategy # This attribute will be overridden if the config file contains "stoploss" stoploss = -0.3 # Optimal timeframe for the strategy timeframe = '1d' # trailing stoploss trailing_stop = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 # Experimental settings (configuration will overide these if set) use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False can_short = False # Optional order type mapping order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } Length = 28 Multiplier = 3.11 bardelay = 2 trailingmenu = "Re-entries"#options=["Normal","Re-entries","None"] trailinmode = "Custom" #options=["Auto","Custom"] usetrail = True if trailingmenu!="None" else False longTrailPerc = 6.58*0.01 shortTrailPerc = 5.76 * 0.01 def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [ #("ETH/USDT", "5m"), #("BTC/USDT", "5m"), #("BTC/USDT", "15m"), ] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ dataframe['_avgTR'] = ta.ATR(dataframe, 1) dataframe.loc[0, 'avgTR'] = dataframe.loc[0, '_avgTR'] for i in range(0, len(dataframe)): if(i>self.Length): _norm = 0.0 _sum = 0.0 for j in range(0,self.Length-1): weight = (j - i) * j _norm = _norm + weight _sum = _sum + dataframe.loc[i-self.Length+1+j,'_avgTR'] * weight dataframe.loc[i,'avgTR'] = _sum / _norm dataframe['highestC'] = dataframe['high'].rolling(self.Length).max() dataframe['lowestC'] = dataframe['low'].rolling(self.Length).min() dataframe['hiLimit'] = dataframe['highestC']-dataframe['avgTR']*self.Multiplier dataframe['loLimit'] = dataframe['lowestC']+dataframe['avgTR']*self.Multiplier dataframe.loc[0, 'ret'] = dataframe.loc[0, 'loLimit'] for i in range(0, len(dataframe)): if dataframe.loc[i,'close']>dataframe.loc[i,'hiLimit'] and \ dataframe.loc[i,'close']>dataframe.loc[i,'loLimit'] : dataframe.loc[i,'ret'] = dataframe.loc[i,'hiLimit'] else: if dataframe.loc[i,'close']0): if dataframe.loc[i,'close']>dataframe.loc[i,'ret']: dataframe.loc[i,'pos'] = 1 else: if dataframe.loc[i,'close']dataframe.loc[i-j,'close']): falling = False dataframe.loc[i, 'rising'] = rising dataframe.loc[i, 'falling'] = falling if dataframe.loc[i,'pos'] == 1 and \ (self.trailingmenu != "Normal" or (i>1 and dataframe.loc[i,'pos']!=dataframe.loc[i-1,'pos']) ) and \ dataframe.loc[i, 'rising'] and \ i>1+self.bardelay and dataframe.loc[i-1, 'trade']!=1 : dataframe.loc[i,'enterLong']=True if dataframe.loc[i,'pos'] == -1 and \ (self.trailingmenu != "Normal" or (i>1 and dataframe.loc[i,'pos']!=dataframe.loc[i-1,'pos']) ) and \ dataframe.loc[i, 'falling'] and \ i>1+self.bardelay and dataframe.loc[i-1, 'trade']!=-1 : dataframe.loc[i,'enterShort']=True if dataframe.loc[i,'enterLong'] : dataframe.loc[i,'trade'] =1 else: if dataframe.loc[i,'enterShort']: dataframe.loc[i,'trade'] =-1 else: if i<=1 : dataframe.loc[i,'trade'] = 0 else: dataframe.loc[i,'trade'] = dataframe.loc[i-1,'trade'] if(dataframe.loc[i,'trade']==1): stopValue = 0.0 if(self.trailinmode== "Auto"): stopValue = (dataframe.loc[i, 'high']+dataframe.loc[i, 'low'])/2 - 1 * dataframe.loc[i, 'avgTR'] else: stopValue = dataframe.loc[i, 'close'] * (1 - self.longTrailPerc) dataframe.loc[i, 'longStopPrice'] = stopValue if i>1: if stopValue>dataframe.loc[i-1, 'longStopPrice']: dataframe.loc[i, 'longStopPrice'] = stopValue else: dataframe.loc[i, 'longStopPrice'] = dataframe.loc[i-1, 'longStopPrice'] else: dataframe.loc[i, 'longStopPrice'] = 0 if(dataframe.loc[i,'trade']==-1): stopValue = 0.0 if(self.trailinmode== "Auto"): stopValue = (dataframe.loc[i, 'high']+dataframe.loc[i, 'low'])/2 + 1 * dataframe.loc[i, 'avgTR'] else: stopValue = dataframe.loc[i, 'close'] * (1 + self.shortTrailPerc) dataframe.loc[i, 'shortStopPrice'] = stopValue if i>1: if stopValuedataframe.loc[i, 'longStopPrice'] ): dataframe.loc[i, 'Long_exit'] = True dataframe.loc[i, 'Short_exit'] = False if(dataframe.loc[i, 'enterShort']!=True and dataframe.loc[i, 'trade']==-1 and self.usetrail and \ dataframe.loc[i, 'high']>dataframe.loc[i, 'shortStopPrice'] and \ dataframe.loc[i, 'open'] DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with entry column , 1: long, -1:short """ dataframe.loc[ ( dataframe['enterLong']==True ), 'enter_long'] = 1 dataframe.loc[ ( dataframe['enterShort']==True ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with exit column """ dataframe.loc[ ( (dataframe['Long_exit']==True) ), 'exit_long'] = 1 dataframe.loc[ ( (dataframe['Short_exit']==True) ), 'exit_short'] = 1 return dataframe