# source: https://raw.githubusercontent.com/XQuant42/ft_pantheon/b7127cbcf1e809f94ae075d73604b6dad7ad8cb3/user_data/strategies/Dracula.py from freqtrade.strategy import DecimalParameter, IntParameter from freqtrade.strategy.interface import IStrategy from pandas import DataFrame # -------------------------------- from pandas import DataFrame, Series from freqtrade.persistence import Trade from datetime import datetime import talib.abstract as taa import talib.abstract as ta from functools import reduce import numpy as np ########################################################################################################### ## Github_XQuant42_ft_pantheon__Dracula__20251018_032502 by 6h057 ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 1 open trade, with unlimited stake. ## ## A pairlist with 80 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (should be 5m). ## ## ## ########################################################################################################### ########################################################################################################### ## DONATIONS ## ## ## ## ## ########################################################################################################### def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = taa.EMA(df, timeperiod=ema_length) ema2 = taa.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif def chaikin_money_flow(dataframe, n=20, fillna=False): """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ df = dataframe.copy() mfv = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= df['volume'] cmf = (mfv.rolling(n, min_periods=0).sum() / df['volume'].rolling(n, min_periods=0).sum()) if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf') class SupResFinder(): def isSupport(self, df, i): support = df['bb_bbl_i'][i] == 1 and ( df['bb_bbl_i'][i+1] == 0 or df['close'][i+1] > df['open'][i+1]) and df['close'][i] < df['open'][i] return support def isResistance(self, df, i): resistance = df['bb_bbh_i'][i] == 1 and ( df['bb_bbh_i'][i+1] == 0 or df['close'][i+1] < df['open'][i+1]) and df['close'][i] > df['open'][i] return resistance def getSupport(self, df): levels = [df['close'][0]] for i in range(1, df.shape[0]-2): if self.isSupport(df, i): o = df['open'][i] c = df['close'][i] l = c if c < o else o levels.append(l) else: levels.append(levels[-1]) levels.append(levels[-1]) levels.append(levels[-1]) return levels def getResistance(self, df): levels = [df['open'][0]] for i in range(1, df.shape[0]-2): if self.isResistance(df, i): o = df['open'][i] c = df['close'][i] l = c if c > o else o levels.append(l) else: levels.append(levels[-1]) levels.append(levels[-1]) levels.append(levels[-1]) return levels class Github_XQuant42_ft_pantheon__Dracula__20251018_032502(IStrategy): # Buy hyperspace params: buy_params = { "buy_bbt": 0.035, "ewo_high": 5.638, "ewo_low": -19.993, "low_offset": 0.978, "rsi_buy": 61, } # Sell hyperspace params: sell_params = { "high_offset": 1.006 } # ROI table: minimal_roi = { "0": 10 } info_timeframe = "5m" # Stoploss: stoploss = -0.2 min_lost = -0.005 buy_bbt = DecimalParameter( 0, 100, decimals=4, default=0.023, space='buy') # Buy hypers timeframe = '1m' # Protection fast_ewo = 50 slow_ewo = 200 # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 custom_info = {} supResFinder = SupResFinder() def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['bb_bbh'] = ta.volatility.bollinger_hband(close=dataframe["close"], window=20) dataframe['bb_bbl'] = ta.volatility.bollinger_lband(close=dataframe["close"], window=20) dataframe['bb_bbh_i'] = dataframe['high'] >= dataframe['bb_bbh'] dataframe['bb_bbl_i'] = ta.volatility.bollinger_lband_indicator( close=dataframe["low"], window=20) dataframe['bb_bbt'] = (dataframe['bb_bbh'] - dataframe['bb_bbl']) / dataframe['bb_bbh'] dataframe['ema'] = taa.EMA(dataframe, timeperiod=150) dataframe['resistance'] = self.supResFinder.getResistance(dataframe) dataframe['support'] = self.supResFinder.getSupport(dataframe) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) # RSI dataframe['rsi'] = taa.RSI(dataframe, timeperiod=14) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: prev = dataframe.shift(1) prev1 = dataframe.shift(2) lost_protect = (dataframe['ema'] > (dataframe['close'] * 1.07)).rolling(10).sum() == 0 item_buy_logic = [] item_buy_logic.append(dataframe['volume'] > 0) item_buy_logic.append(dataframe['cmf'] > 0) item_buy_logic.append(prev['bb_bbl_i'] == 1) item_buy_logic.append(prev['close'] >= prev1['support']) item_buy_logic.append(prev['ema'] < prev['close']) item_buy_logic.append((dataframe['open'] < dataframe['close'])) item_buy_logic.append(prev['open'] > prev['close']) item_buy_logic.append((dataframe['bb_bbt'] > self.buy_bbt.value)) item_buy_logic.append(lost_protect) dataframe.loc[ reduce(lambda x, y: x & y, item_buy_logic), ['buy', 'buy_tag']] = (1, f'buy_1') item_buy_logic = [] item_buy_logic.append(dataframe['volume'] > 0) item_buy_logic.append(dataframe['cmf'] > 0) item_buy_logic.append(dataframe['bb_bbl_i'] == 1) item_buy_logic.append(dataframe['open'] >= prev1['support']) item_buy_logic.append(prev['ema'] < prev['close']) item_buy_logic.append((dataframe['open'] < dataframe['close'])) item_buy_logic.append((dataframe['bb_bbt'] > self.buy_bbt.value)) item_buy_logic.append(lost_protect) dataframe.loc[ reduce(lambda x, y: x & y, item_buy_logic), ['buy', 'buy_tag']] = (1, f'buy_2') return dataframe def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() prev_candle = dataframe.iloc[-2].squeeze() prev1_candle = dataframe.iloc[-3].squeeze() if prev_candle['bb_bbh_i'] == 1 \ and last_candle['close'] < last_candle['open'] \ and prev_candle['close'] > prev_candle['open'] \ and prev_candle['close'] < prev1_candle['resistance'] \ and last_candle['volume'] > 0: return 'sell_signal_1_'+trade.buy_tag elif last_candle['bb_bbh_i'] == 1 \ and last_candle['close'] < last_candle['open'] \ and last_candle['open'] < prev1_candle['resistance'] \ and last_candle['volume'] > 0: return 'sell_signal_2_'+trade.buy_tag elif last_candle['close'] < last_candle['open'] \ and last_candle['ema'] > (last_candle['close'] * 1.07) \ and last_candle['volume'] > 0: return 'stop_loss_'+trade.buy_tag elif (last_candle['close'] < last_candle['open']) and (last_candle['close'] <= (last_candle['bb_bbl'] * 1.002)) and current_profit >= 0: return 'take_profit_'+trade.buy_tag elif 'sma' in trade.buy_tag and current_profit >= 0.01: return 'sma' elif 'sma' in trade.buy_tag and (last_candle['close'] > (last_candle['ema_49'] * self.high_offset.value)): return 'stop_loss_sma' return None def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'sell'] = 0 return dataframe