# source: https://raw.githubusercontent.com/XQuant42/ft_pantheon/0dfb29b22d4982a2097bf5d7a7b7a2bbe9765178/user_data/strategies/CombinedBinHClucAndMADV5.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair ########################################################################################################### ## Github_XQuant42_ft_pantheon__CombinedBinHClucAndMADV5__20251016_083140 by ilya ## ## ## ## https://github.com/i1ya/freqtrade-strategies ## ## The stratagy most inspired by iterativ (authors of the CombinedBinHAndClucV6) ## ## ## ## ########################################################################################################### ## The main point of this strat is: ## ## - buy at dip ## ## - sell quick as fast as you can (release money for the next buy) ## ## - soft check if market if rising ## ## - hard check is market if fallen ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 2 and 4 open trades, with unlimited stake. ## ## With my pairlist which can be found in this repo. ## ## ## ## Ensure that you don't override any variables in your config.json. Especially ## ## the timeframe (must be 5m). ## ## ## ## sell_profit_only: ## ## True - risk more (gives you higher profit and higher Drawdown) ## ## False (default) - risk less (gives you less ~10-15% profit and much lower Drawdown) ## ## ## ########################################################################################################### ## DONATIONS 2 @iterativ (author of the original strategy) ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## ## ########################################################################################################### # SSL Channels def SSLChannels(dataframe, length = 7): df = dataframe.copy() df['ATR'] = ta.ATR(df, timeperiod=14) df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR'] df['smaLow'] = df['low'].rolling(length).mean() - df['ATR'] df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.nan)) df['hlv'] = df['hlv'].ffill() df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow']) df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh']) return df['sslDown'], df['sslUp'] class Github_XQuant42_ft_pantheon__CombinedBinHClucAndMADV5__20251016_083140(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.021, "40": 0.005, } stoploss = -0.99 # effectively disabled. timeframe = '5m' inf_1h = '1h' # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_buy_signal = False # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.025 # Custom stoploss use_custom_stoploss = True # Run "populate_indicators()" only for new candle. process_only_new_candles = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Manage losing trades and open room for better ones. if (current_profit < 0) & (current_time - timedelta(minutes=240) > trade.open_date_utc): return 0.01 return 0.99 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # EMA informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # SSL Channels ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) informative_1h['ssl_down'] = ssl_down_1h informative_1h['ssl_up'] = ssl_up_1h return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # strategy BinHV45 bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() # strategy ClucMay72018 bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() # EMA dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) # SMA dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ # When market at bull mode (guard) ( # strategy ClucMay72018 (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.99 * dataframe['bb_lowerband']) & # Guard is on, candle should dig not so hard (0,99) (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 21)) & (dataframe['volume'] > 0) ) | # When market at bear mode (without guard) ( # strategy ClucMay72018 (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.975 * dataframe['bb_lowerband']) & # Guard is off, candle should dig hard (0,975) (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20)) & (dataframe['volume'] < (dataframe['volume'].shift() * 4)) & # Don't buy if someone drop the market. (dataframe['rsi_1h'] < 15) & # Buy only at dip (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | # When market at bull mode (guard) ( # strategy MACD Low buy (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['volume'] < (dataframe['volume'].shift() * 4)) & # Don't buy if someone drop the market. (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | # When market at bear mode (without guard) ( # strategy MACD Low buy (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['volume'] < (dataframe['volume'].shift() * 4)) & # Don't buy if someone drop the market. (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( (dataframe['close'] < dataframe['sma_5']) & (dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['rsi'] < dataframe['rsi_1h'] - 43.276) & (dataframe['volume'] > 0) ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['close'] > dataframe['bb_middleband'] * 1.01) & # Don't be gready, sell fast (dataframe['volume'] > 0) # Make sure Volume is not 0 ) , 'sell' ] = 1 return dataframe