# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/SmoothOperator.py # --- Do not remove these libs --- from freqtrade.strategy.interface 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 import numpy # noqa # DO NOT USE, just playing with smooting and graphs! class Github_DerSalvador_freqtrade_helm_chart__SmoothOperator__20260115_122204(IStrategy): INTERFACE_VERSION = 3 '\n\n author@: Gert Wohlgemuth\n\n idea:\n\n The concept is about combining several common indicators, with a heavily smoothing, while trying to detect\n a none completed peak shape.\n ' # Minimal ROI designed for the strategy. # we only exit after 100%, unless our exit points are found before minimal_roi = {'0': 0.1} # Optimal stoploss designed for the strategy # This attribute will be overridden if the config file contains "stoploss" # should be converted to a trailing stop loss stoploss = -0.05 # Optimal timeframe for the strategy timeframe = '5m' 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') # playgound 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 dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=200, price='close') dataframe['sma_medium'] = ta.SMA(dataframe, timeperiod=100, price='close') dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=50, price='close') return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # protection against pump and dump # (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) # # & (dataframe['macd'] < dataframe['macdsignal']) # & (dataframe['macd'] > 0) # # spike below entry band for 3 consecutive ticks # & (dataframe['low'] < dataframe['entry_bb_lowerband']) # & (dataframe['low'].shift(1) < dataframe['bb_lowerband'].shift(1)) # & (dataframe['low'].shift(2) < dataframe['bb_lowerband'].shift(2)) # # pattern recognition # & ( # (dataframe['close'] > dataframe['open']) # | (dataframe['CDLHAMMER'] == 100) # | (dataframe['CDLINVERTEDHAMMER'] == 100) # | (dataframe['CDLDRAGONFLYDOJI'] == 100) # ) # bottom curve detection # & (dataframe['mfi_rsi_cci_smooth'] < 0) # # | # 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 entry points # entry in very oversold conditions # etc tends to trade like this # over very long periods of slowly building up coins # does not happen often, but once in a while # ensure we have an overall uptrend dataframe.loc[((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) < -100) & (dataframe['rsi'].shift(1) < 30) | (dataframe['low'] < dataframe['bb_middleband']) & (dataframe['cci'] < -200) & (dataframe['rsi'] < 30) & (dataframe['mfi'] < 30) | (dataframe['mfi'] < 10) & (dataframe['cci'] < -150) & (dataframe['rsi'] < dataframe['mfi'])) & (dataframe['close'] > dataframe['close'].shift()), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # different strategy used for exit points, due to be able to duplicate it to 100% # This generates very nice sale points, and mostly sit's one stop behind # the top of the peak # This helps with very long, sideways trends, to get out of a market before # it dumps # in case of very overbought market, like some one pumping # exit dataframe.loc[(dataframe['mfi_rsi_cci_smooth'] > 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)) | StrategyHelper.eight_green_candles(dataframe) | (dataframe['cci'] > 200) & (dataframe['rsi'] > 70), 'exit_long'] = 1 return dataframe class StrategyHelper: """ simple helper class to predefine a couple of patterns for our strategy """ @staticmethod def seven_green_candles(dataframe): """ evaluates if we are having 7 green candles in a row :param self: :param dataframe: :return: """ return (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift(1) < dataframe['close'].shift(1)) & (dataframe['open'].shift(2) < dataframe['close'].shift(2)) & (dataframe['open'].shift(3) < dataframe['close'].shift(3)) & (dataframe['open'].shift(4) < dataframe['close'].shift(4)) & (dataframe['open'].shift(5) < dataframe['close'].shift(5)) & (dataframe['open'].shift(6) < dataframe['close'].shift(6)) & (dataframe['open'].shift(7) < dataframe['close'].shift(7)) @staticmethod def eight_green_candles(dataframe): """ evaluates if we are having 8 green candles in a row :param self: :param dataframe: :return: """ return (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift(1) < dataframe['close'].shift(1)) & (dataframe['open'].shift(2) < dataframe['close'].shift(2)) & (dataframe['open'].shift(3) < dataframe['close'].shift(3)) & (dataframe['open'].shift(4) < dataframe['close'].shift(4)) & (dataframe['open'].shift(5) < dataframe['close'].shift(5)) & (dataframe['open'].shift(6) < dataframe['close'].shift(6)) & (dataframe['open'].shift(7) < dataframe['close'].shift(7)) & (dataframe['open'].shift(8) < dataframe['close'].shift(8)) @staticmethod def eight_red_candles(dataframe, shift=0): """ evaluates if we are having 8 red candles in a row :param self: :param dataframe: :param shift: shift the pattern by n :return: """ return (dataframe['open'].shift(shift) > dataframe['close'].shift(shift)) & (dataframe['open'].shift(1 + shift) > dataframe['close'].shift(1 + shift)) & (dataframe['open'].shift(2 + shift) > dataframe['close'].shift(2 + shift)) & (dataframe['open'].shift(3 + shift) > dataframe['close'].shift(3 + shift)) & (dataframe['open'].shift(4 + shift) > dataframe['close'].shift(4 + shift)) & (dataframe['open'].shift(5 + shift) > dataframe['close'].shift(5 + shift)) & (dataframe['open'].shift(6 + shift) > dataframe['close'].shift(6 + shift)) & (dataframe['open'].shift(7 + shift) > dataframe['close'].shift(7 + shift)) & (dataframe['open'].shift(8 + shift) > dataframe['close'].shift(8 + shift)) @staticmethod def four_green_one_red_candle(dataframe): """ evaluates if we are having a red candle and 4 previous green :param self: :param dataframe: :return: """ return (dataframe['open'] > dataframe['close']) & (dataframe['open'].shift(1) < dataframe['close'].shift(1)) & (dataframe['open'].shift(2) < dataframe['close'].shift(2)) & (dataframe['open'].shift(3) < dataframe['close'].shift(3)) & (dataframe['open'].shift(4) < dataframe['close'].shift(4)) @staticmethod def four_red_one_green_candle(dataframe): """ evaluates if we are having a green candle and 4 previous red :param self: :param dataframe: :return: """ return (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift(1) > dataframe['close'].shift(1)) & (dataframe['open'].shift(2) > dataframe['close'].shift(2)) & (dataframe['open'].shift(3) > dataframe['close'].shift(3)) & (dataframe['open'].shift(4) > dataframe['close'].shift(4))