# source: https://raw.githubusercontent.com/XQuant42/ft_pantheon/9cc88e804b9f553d3e9fe90539030b18cb8c0b2e/user_data/strategies/ep3mas2.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter from typing import Dict, List from functools import reduce from pandas import DataFrame from freqtrade.persistence import Trade from datetime import datetime, date, timedelta from technical.indicators import ichimoku, chaikin_money_flow from freqtrade.exchange import timeframe_to_prev_date # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_XQuant42_ft_pantheon__ep3mas2__20251123_060325(IStrategy): """ author@: ?? idea: this strategy is based on the link here: https://github.com/freqtrade/freqtrade-strategies/issues/95 """ # Minimal ROI designed for the strategy. # adjust based on market conditions. We would recommend to keep it low for quick turn arounds # This attribute will be overridden if the config file contains "minimal_roi" minimal_roi = { #"36000": 0.0, # non loosing after 25 days #"0": 0. "0": 100 } # Stoploss -disable stoploss = -0.99 #stoploss = -0.05 use_custom_stoploss = True # Trailing stoploss #trailing_stop = True #trailing_only_offset_is_reached = True #trailing_stop_positive = 0.015 #trailing_stop_positive_offset = 0.02 # Optimal timeframe for the strategy timeframe = '5m' # buy params epwindow = IntParameter(2, 100, default=7, space='buy') #buy_fast_ma_timeframe = IntParameter(2, 100, default=14, space='buy') #buy_slow_ma_timeframe = IntParameter(2, 100, default=28, space='buy') eptarg = DecimalParameter( 0, 4, decimals=4, default=2.25446, space='sell') epstop = DecimalParameter( 0, 4, decimals=4, default=0.29497, space='sell') epcat1 = CategoricalParameter(['open', 'high', 'low', 'close', 'volume', # 'ma_fast', 'ma_slow', {...} ], default='close', space='buy') epcat2 = CategoricalParameter(['open', 'high', 'low', 'close', 'volume', # 'ma_fast', 'ma_slow', {...} ], default='close', space='buy') epma1 = IntParameter(2, 100, default=25, space='buy') epma2 = IntParameter(2, 100, default=50, space='buy') epma3 = IntParameter(2, 100, default=100, space='buy') epma4 = IntParameter(2, 100, default=100, space='buy') epma5 = IntParameter(2, 100, default=100, space='buy') epma6 = IntParameter(2, 100, default=100, space='sell') # sell params #sell_mojo_ma_timeframe = IntParameter(2, 100, default=7, space='sell') #sell_fast_ma_timeframe = IntParameter(2, 100, default=14, space='sell') #sell_slow_ma_timeframe = IntParameter(2, 100, default=28, space='sell') #sell_div_max = DecimalParameter( # 0, 2, decimals=4, default=1.54593, space='sell') #sell_div_min = DecimalParameter( # 0, 2, decimals=4, default=2.81436, space='sell') def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Indicators #macd = ta.MACD(dataframe) #dataframe['ema25'] = ta.EMA(dataframe, timeperiod=25) dataframe['epm1'] = ta.EMA(dataframe, timeperiod=self.epma1.value) dataframe['epm2'] = ta.EMA(dataframe, timeperiod=self.epma2.value) dataframe['epm3'] = ta.EMA(dataframe, timeperiod=self.epma3.value) dataframe['epm4'] = ta.EMA(dataframe, timeperiod=self.epma4.value) dataframe['epm5'] = ta.EMA(dataframe, timeperiod=self.epma5.value) dataframe['epm6'] = ta.EMA(dataframe, timeperiod=self.epma6.value) #dataframe['macd'] = macd['macd'] #dataframe['macdsignal'] = macd['macdsignal'] #dataframe['macdhist'] = macd['macdhist'] #result = chaikin_money_flow(testdata_1m_btc, 14) #dataframe['cmf'] = chaikin_money_flow(dataframe) #dataframe['cci'] = ta.CCI(dataframe) #dataframe['eplow'] = dataframe['open'].rolling(2).min() #dataframe['eplow'] = dataframe['eplow'].fillna(1000000) #dataframe['target'] = dataframe['close'] + (dataframe["close"] - dataframe["eplow"]) * 1.5 #data_frame[self.value] = pd.rolling_min(data_frame[self.data], self.period) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: #Buy if last 5 candles show a strong downtrend (linear regression angle) and close is inferior to the 25 candle linear regression line - 1 * ATR (over 25 candles) dataframe.loc[ ( (dataframe['epm1'].rolling(self.epwindow.value).min() > dataframe['epm2'].rolling(self.epwindow.value).max()) & (dataframe['epm2'].rolling(self.epwindow.value).min() > dataframe['epm3'].rolling(self.epwindow.value).max()) & (dataframe[self.epcat1.value].rolling(self.epwindow.value).min() > dataframe['epm4'].rolling(self.epwindow.value).max()) & #(dataframe['close'].rolling(6).min() > dataframe['ema50'].rolling(6).max()) & #(dataframe['ema50'].rolling(6).min() > dataframe['ema100'].rolling(6).max()) & #(dataframe['macd'] - dataframe['macdsignal'] > 0.1) & qtpylib.crossed_above(dataframe[self.epcat2.value], dataframe['epm5']) & #qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']) & (dataframe['volume'] > 0) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Sell if RSI is greater than 31 and close is superior to the 25 candle linear regression line dataframe.loc[ ( (dataframe['close'] > 1000000) & #qtpylib.crossed_above(dataframe['close'], dataframe['ema25']) & #(dataframe['senkou_a'] > dataframe['senkou_b']) & (dataframe['volume'] > 0) ), 'sell'] = 1 return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Obtain pair dataframe. dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) # Look up trade candle. trade_candle = dataframe.loc[dataframe['date'] == trade_date] if not trade_candle.empty: #base_line = trade_candle['eplow'].iloc[0] #base_line = trade_candle['ema50'].iloc[0] #base_line = base_line + 0.5 #open_price = trade_candle['open'].iloc[0] #set_stoploss = (open_price / base_line) - 1.01 set_stoploss = trade.open_rate - (trade.open_rate) * self.epstop.value if current_rate - set_stoploss <= 0.001: #print ("current_rate:",current_rate) #print ("base_line:",base_line) #print ("sub:",current_rate - base_line) return -0.000001 #print ("current_rate:",current_rate) #print ("base_line:",base_line) return 1 def custom_sell(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Obtain pair dataframe. dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) # Look up trade candle. trade_candle = dataframe.loc[dataframe['date'] == trade_date] if not trade_candle.empty: #epstoploss = trade_candle['eplow'].iloc[0] epstoploss = trade_candle['epm6'].iloc[0] #open_price = trade_candle['open'].iloc[0] #eptarget = trade.open_rate + (trade.open_rate - epstoploss) * 1.5 eptarget = trade.open_rate + (trade.open_rate) * self.eptarg.value #print ("Target:",eptarget) #print ("epstoploss:",epstoploss) #print ("Price de abertura:",trade.open_rate) if current_rate >= eptarget: #Let prices stabilize before setting #print ("Atingiu a meta:",current_rate) #print ("Price de abertura:",trade.open_rate) #print ("epstoploss:",epstoploss) return 'sell_ep3mas' return 0