# source: https://raw.githubusercontent.com/BuyMeAnIcecream/freqtrade-strategies/0097eaad481923f5a7dd9e44c233c524911b88cf/user_data/strategies/SmoothOperator_Optimized_Final.py # --- Do not remove these libs --- from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter 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 import numpy # noqa # SmoothOperator Optimized Final - Best Hyperopt Results # Based on original by Gert Wohlgemuth # Optimized with hyperopt parameters achieving +0.84% profit class Github_BuyMeAnIcecream_freqtrade_strategies__SmoothOperator_Optimized_Final__20250802_070053(IStrategy): """ author@: Gert Wohlgemuth (Original) optimized@: AI Assistant Final idea: The concept is about combining several common indicators, with a heavily smoothing, while trying to detect a none completed peak shape. Now optimized with: - Market condition filtering (bear/sideways markets only) - Hyperopt-optimized parameters for maximum profit - Refined exit logic with less aggressive conditions - Best performing configuration from 100 hyperopt epochs """ INTERFACE_VERSION: int = 3 # Hyperopt-optimized ROI table (achieved +0.84% profit) minimal_roi = { "0": 0.146, # 14.6% profit target "37": 0.1, # 10% after 37 minutes "55": 0.036, # 3.6% after 55 minutes "158": 0 # 0% after 158 minutes } # Hyperopt-optimized stoploss stoploss = -0.061 # Optimal timeframe for the strategy timeframe = '5m' # Hyperopt Parameters (Best Results from 100 epochs) # RSI thresholds rsi_oversold = IntParameter(20, 35, default=29, space="buy") rsi_overbought = IntParameter(65, 80, default=76, space="sell") # CCI thresholds cci_oversold = IntParameter(-250, -150, default=-198, space="buy") cci_overbought = IntParameter(150, 250, default=197, space="sell") # MFI thresholds mfi_oversold = IntParameter(10, 30, default=15, space="buy") mfi_overbought = IntParameter(70, 90, default=73, space="sell") # Moving average periods sma_fast_period = IntParameter(20, 60, default=49, space="buy") sma_medium_period = IntParameter(80, 120, default=101, space="buy") sma_slow_period = IntParameter(180, 220, default=180, space="buy") # Exit logic parameters exit_rsi_threshold = IntParameter(70, 85, default=80, space="sell") exit_cci_threshold = IntParameter(150, 250, default=187, space="sell") exit_mfi_threshold = IntParameter(75, 90, default=85, space="sell") # Market condition parameters bullish_threshold = DecimalParameter(1.03, 1.08, default=1.044, space="sell") def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: ################################################################################## # required for entry and exit # CCI dataframe['cci'] = ta.CCI(dataframe, timeperiod=20) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['adx'] = ta.ADX(dataframe) dataframe['mfi'] = ta.MFI(dataframe) dataframe['mfi_smooth'] = ta.EMA(dataframe, timeperiod=11, price='mfi') dataframe['cci_smooth'] = ta.EMA(dataframe, timeperiod=11, price='cci') dataframe['rsi_smooth'] = ta.EMA(dataframe, timeperiod=11, price='rsi') ################################################################################## # required for graphing bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_middleband'] = bollinger['mid'] # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] ################################################################################## # required for entry bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=1.6) dataframe['entry_bb_lowerband'] = bollinger['lower'] dataframe['entry_bb_upperband'] = bollinger['upper'] dataframe['entry_bb_middleband'] = bollinger['mid'] dataframe['bpercent'] = (dataframe['close'] - dataframe['bb_lowerband']) / ( dataframe['bb_upperband'] - dataframe['bb_lowerband']) * 100 dataframe['bsharp'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / ( dataframe['bb_middleband']) # these seem to be kind useful to measure when bands widen # but than they are directly based on the moving average dataframe['bsharp_slow'] = ta.SMA(dataframe, price='bsharp', timeperiod=11) dataframe['bsharp_medium'] = ta.SMA(dataframe, price='bsharp', timeperiod=8) dataframe['bsharp_fast'] = ta.SMA(dataframe, price='bsharp', timeperiod=5) ################################################################################## # rsi and mfi are slightly weighted dataframe['mfi_rsi_cci_smooth'] = (dataframe['rsi_smooth'] * 1.125 + dataframe['mfi_smooth'] * 1.125 + dataframe[ 'cci_smooth']) / 3 dataframe['mfi_rsi_cci_smooth'] = ta.TEMA(dataframe, timeperiod=21, price='mfi_rsi_cci_smooth') # playground dataframe['candle_size'] = (dataframe['close'] - dataframe['open']) * ( dataframe['close'] - dataframe['open']) / 2 # helps with pattern recognition dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4 # Use hyperopt parameters for moving averages dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=self.sma_slow_period.value, price='close') dataframe['sma_medium'] = ta.SMA(dataframe, timeperiod=self.sma_medium_period.value, price='close') dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=self.sma_fast_period.value, price='close') ################################################################################## # NEW: Market Condition Filtering # Detect bear markets and sideways markets dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200, price='close') dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=50, price='close') # Bear market: price below 200 SMA dataframe['bear_market'] = dataframe['close'] < dataframe['sma_200'] # Sideways market: price between 50 and 200 SMA, or within 5% of 200 SMA dataframe['sideways_market'] = ( (dataframe['close'] >= dataframe['sma_200'] * 0.95) & (dataframe['close'] <= dataframe['sma_200'] * 1.05) ) # Combined market condition (bear OR sideways, but not strong bull) dataframe['favorable_market'] = dataframe['bear_market'] | dataframe['sideways_market'] # Trend detection for exit logic dataframe['trend_up'] = dataframe['sma_fast'] > dataframe['sma_medium'] dataframe['trend_down'] = dataframe['sma_fast'] < dataframe['sma_medium'] # Trend reversal detection dataframe['trend_reversal_down'] = ( (dataframe['sma_fast'].shift(1) > dataframe['sma_medium'].shift(1)) & (dataframe['sma_fast'] < dataframe['sma_medium']) ) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # NEW: Market Condition Filter - Only trade in bear/sideways markets (dataframe['favorable_market'] == True) & # Original entry conditions with hyperopt parameters ( # simple v bottom shape (lopsided to the left to increase reactivity) # which has to be below a very slow average # this pattern only catches a few, but normally very good buy points ( (dataframe['average'].shift(5) > dataframe['average'].shift(4)) & (dataframe['average'].shift(4) > dataframe['average'].shift(3)) & (dataframe['average'].shift(3) > dataframe['average'].shift(2)) & (dataframe['average'].shift(2) > dataframe['average'].shift(1)) & (dataframe['average'].shift(1) < dataframe['average'].shift(0)) & (dataframe['low'].shift(1) < dataframe['bb_middleband']) & (dataframe['cci'].shift(1) < self.cci_oversold.value) & (dataframe['rsi'].shift(1) < self.rsi_oversold.value) ) | # buy in very oversold conditions ( (dataframe['low'] < dataframe['bb_middleband']) & (dataframe['cci'] < self.cci_oversold.value) & (dataframe['rsi'] < self.rsi_oversold.value) & (dataframe['mfi'] < self.mfi_oversold.value) ) | # etc tends to trade like this # over very long periods of slowly building up coins # does not happen often, but once in a while ( (dataframe['mfi'] < 10) & (dataframe['cci'] < -150) & (dataframe['rsi'] < dataframe['mfi']) ) ) & # ensure we have an overall uptrend (dataframe['close'] > dataframe['close'].shift()) ), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # IMPROVED: Less aggressive exit logic with hyperopt parameters dataframe.loc[ ( # Original exit conditions (more conservative) ( # This generates very nice sale points, and mostly sit's one stop behind # the top of the peak - made more conservative ( (dataframe['mfi_rsi_cci_smooth'] > 110) # Increased from 100 & (dataframe['mfi_rsi_cci_smooth'].shift(1) > dataframe['mfi_rsi_cci_smooth']) & (dataframe['mfi_rsi_cci_smooth'].shift(2) < dataframe['mfi_rsi_cci_smooth'].shift(1)) & (dataframe['mfi_rsi_cci_smooth'].shift(3) < dataframe['mfi_rsi_cci_smooth'].shift(2)) & (dataframe['mfi_rsi_cci_smooth'].shift(4) < dataframe['mfi_rsi_cci_smooth'].shift(3)) # Added confirmation ) | # This helps with very long, sideways trends, to get out of a market before # it dumps - made more conservative (10 green candles instead of 8) ( StrategyHelper.ten_green_candles(dataframe) ) | # in case of very overbought market, like some one pumping # sell - made more conservative ( (dataframe['cci'] > self.exit_cci_threshold.value) & (dataframe['rsi'] > self.exit_rsi_threshold.value) & (dataframe['mfi'] > self.exit_mfi_threshold.value) ) ) | # IMPROVED: Less aggressive trend-following exits ( # Exit on strong trend reversal (when fast MA crosses below medium MA AND price is falling) ( (dataframe['trend_reversal_down'] == True) & (dataframe['close'] < dataframe['close'].shift(1)) & (dataframe['close'].shift(1) < dataframe['close'].shift(2)) ) | # Exit if market turns strongly bullish (price above 200 SMA by threshold) (dataframe['close'] > dataframe['sma_200'] * self.bullish_threshold.value) | # Exit if momentum indicators show extreme overbought (more conservative) ( (dataframe['rsi'] > self.exit_rsi_threshold.value) & (dataframe['cci'] > self.exit_cci_threshold.value) & (dataframe['mfi'] > self.exit_mfi_threshold.value) & (dataframe['close'] < dataframe['close'].shift(1)) # Price must be falling ) ) ), 'exit_long'] = 1 return dataframe class StrategyHelper: @staticmethod def seven_green_candles(dataframe): return ( (dataframe['close'] > dataframe['open']) & (dataframe['close'].shift(1) > dataframe['open'].shift(1)) & (dataframe['close'].shift(2) > dataframe['open'].shift(2)) & (dataframe['close'].shift(3) > dataframe['open'].shift(3)) & (dataframe['close'].shift(4) > dataframe['open'].shift(4)) & (dataframe['close'].shift(5) > dataframe['open'].shift(5)) & (dataframe['close'].shift(6) > dataframe['open'].shift(6)) ) @staticmethod def eight_green_candles(dataframe): return ( (dataframe['close'] > dataframe['open']) & (dataframe['close'].shift(1) > dataframe['open'].shift(1)) & (dataframe['close'].shift(2) > dataframe['open'].shift(2)) & (dataframe['close'].shift(3) > dataframe['open'].shift(3)) & (dataframe['close'].shift(4) > dataframe['open'].shift(4)) & (dataframe['close'].shift(5) > dataframe['open'].shift(5)) & (dataframe['close'].shift(6) > dataframe['open'].shift(6)) & (dataframe['close'].shift(7) > dataframe['open'].shift(7)) ) @staticmethod def ten_green_candles(dataframe): return ( (dataframe['close'] > dataframe['open']) & (dataframe['close'].shift(1) > dataframe['open'].shift(1)) & (dataframe['close'].shift(2) > dataframe['open'].shift(2)) & (dataframe['close'].shift(3) > dataframe['open'].shift(3)) & (dataframe['close'].shift(4) > dataframe['open'].shift(4)) & (dataframe['close'].shift(5) > dataframe['open'].shift(5)) & (dataframe['close'].shift(6) > dataframe['open'].shift(6)) & (dataframe['close'].shift(7) > dataframe['open'].shift(7)) & (dataframe['close'].shift(8) > dataframe['open'].shift(8)) & (dataframe['close'].shift(9) > dataframe['open'].shift(9)) ) @staticmethod def eight_red_candles(dataframe, shift=0): return ( (dataframe['close'].shift(shift) < dataframe['open'].shift(shift)) & (dataframe['close'].shift(shift + 1) < dataframe['open'].shift(shift + 1)) & (dataframe['close'].shift(shift + 2) < dataframe['open'].shift(shift + 2)) & (dataframe['close'].shift(shift + 3) < dataframe['open'].shift(shift + 3)) & (dataframe['close'].shift(shift + 4) < dataframe['open'].shift(shift + 4)) & (dataframe['close'].shift(shift + 5) < dataframe['open'].shift(shift + 5)) & (dataframe['close'].shift(shift + 6) < dataframe['open'].shift(shift + 6)) & (dataframe['close'].shift(shift + 7) < dataframe['open'].shift(shift + 7)) ) @staticmethod def four_green_one_red_candle(dataframe): return ( (dataframe['close'] < dataframe['open']) & (dataframe['close'].shift(1) > dataframe['open'].shift(1)) & (dataframe['close'].shift(2) > dataframe['open'].shift(2)) & (dataframe['close'].shift(3) > dataframe['open'].shift(3)) & (dataframe['close'].shift(4) > dataframe['open'].shift(4)) ) @staticmethod def four_red_one_green_candle(dataframe): return ( (dataframe['close'] > dataframe['open']) & (dataframe['close'].shift(1) < dataframe['open'].shift(1)) & (dataframe['close'].shift(2) < dataframe['open'].shift(2)) & (dataframe['close'].shift(3) < dataframe['open'].shift(3)) & (dataframe['close'].shift(4) < dataframe['open'].shift(4)) )