# source: https://raw.githubusercontent.com/Stibra1/Multitimeframe_bot/06867e4ac8b83c414a8defa63f383d4771e00580/Multitimeframe.py from pandas import DataFrame from freqtrade.strategy import (IStrategy, IntParameter, informative) import talib.abstract as ta class github_Stibra1_Multitimeframe_bot__Multitimeframe__20230402_221334(IStrategy): timeframe = '5m' informative_timeframes = ['15m', '1h', '1d'] minimal_roi = {"0": 1} stoploss = -0.35 use_sell_signal = True sell_profit_only = False process_only_new_candles = True startup_candle_count = 100 INTERFACE_VERSION = 3 buy_rsi = IntParameter(15, 50, default=45, space='buy') buy_fastk = IntParameter(50, 99, default=81, space='buy') sell_rsi = IntParameter(50, 90, default=78, space='sell') sell_fastk = IntParameter(50, 100, default=62, space='sell') buy_rsi_15m = IntParameter(15, 50, default=49, space='buy') buy_fastk_15m = IntParameter(50, 99, default=69, space='buy') sell_rsi_15m = IntParameter(50, 90, default=56, space='sell') sell_fastk_15m = IntParameter(50, 100, default=69, space='sell') buy_rsi_1h = IntParameter(15, 50, default=46, space='buy') buy_fastk_1h = IntParameter(50, 99, default=72, space='buy') sell_rsi_1h = IntParameter(50, 90, default=69, space='sell') sell_fastk_1h = IntParameter(50, 100, default=95, space='sell') def informative_pairs(self): informative_pairs = [ ("AAVE/USDT", "15m"), ("AAVE/USDT", "1h"), ("AAVE/USDT", "1d"), ("ADA/USDT", "15m"), ("ADA/USDT", "1h"), ("ADA/USDT", "1d"), ("ALGO/USDT", "15m"), ("ALGO/USDT", "1h"), ("ALGO/USDT", "1d"), ("ANKR/USDT", "15m"), ("ANKR/USDT", "1h"), ("ANKR/USDT", "1d"), ("ATM/USDT", "15m"), ("ATM/USDT", "1h"), ("ATM/USDT", "1d"), ("ATOM/USDT", "15m"), ("ATOM/USDT", "1h"), ("ATOM/USDT", "1d"), ("AVA/USDT", "15m"), ("AVA/USDT", "1h"), ("AVA/USDT", "1d"), ("AVAX/USDT", "15m"), ("AVAX/USDT", "1h"), ("AVAX/USDT", "1d"), ("BTC/USDT", "15m"), ("BTC/USDT", "1h"), ("BTC/USDT", "1d"), ("DASH/USDT", "15m"), ("DASH/USDT", "1h"), ("DASH/USDT", "1d"), ("DOGE/USDT", "15m"), ("DOGE/USDT", "1h"), ("DOGE/USDT", "1d"), ("DOT/USDT", "15m"), ("DOT/USDT", "1h"), ("DOT/USDT", "1d"), ("EGLD/USDT", "15m"), ("EGLD/USDT", "1h"), ("EGLD/USDT", "1d"), ("ENJ/USDT", "15m"), ("ENJ/USDT", "1h"), ("ENJ/USDT", "1d"), ("EOS/USDT", "15m"), ("EOS/USDT", "1h"), ("EOS/USDT", "1d"), ("ETC/USDT", "15m"), ("ETC/USDT", "1h"), ("ETC/USDT", "1d"), ("ETH/USDT", "15m"), ("ETH/USDT", "1h"), ("ETH/USDT", "1d"), ("FIL/USDT", "15m"), ("FIL/USDT", "1h"), ("FIL/USDT", "1d"), ("FTM/USDT", "15m"), ("FTM/USDT", "1h"), ("FTM/USDT", "1d"), ("IOTA/USDT", "15m"), ("IOTA/USDT", "1h"), ("IOTA/USDT", "1d"), ("IOTX/USDT", "15m"), ("IOTX/USDT", "1h"), ("IOTX/USDT", "1d"), ("KAVA/USDT", "15m"), ("KAVA/USDT", "1h"), ("KAVA/USDT", "1d"), ("KSM/USDT", "15m"), ("KSM/USDT", "1h"), ("KSM/USDT", "1d"), ("LINK/USDT", "15m"), ("LINK/USDT", "1h"), ("LINK/USDT", "1d"), ("LPT/USDT", "15m"), ("LPT/USDT", "1h"), ("LPT/USDT", "1d"), ("LTC/USDT", "15m"), ("LTC/USDT", "1h"), ("LTC/USDT", "1d"), ("MANA/USDT", "15m"), ("MANA/USDT", "1h"), ("MANA/USDT", "1d"), ("MASK/USDT", "15m"), ("MASK/USDT", "1h"), ("MASK/USDT", "1d"), ("MATIC/USDT", "15m"), ("MATIC/USDT", "1h"), ("MATIC/USDT", "1d"), ("NEO/USDT", "15m"), ("NEO/USDT", "1h"), ("NEO/USDT", "1d"), ("QTUM/USDT", "15m"), ("QTUM/USDT", "1h"), ("QTUM/USDT", "1d"), ("SAND/USDT", "15m"), ("SAND/USDT", "1h"), ("SAND/USDT", "1d"), ("SOL/USDT", "15m"), ("SOL/USDT", "1h"), ("SOL/USDT", "1d"), ("STX/USDT", "15m"), ("STX/USDT", "1h"), ("STX/USDT", "1d"), ("SUSHI/USDT", "15m"), ("SUSHI/USDT", "1h"), ("SUSHI/USDT", "1d"), ("TRX/USDT", "15m"), ("TRX/USDT", "1h"), ("TRX/USDT", "1d"), ("UNI/USDT", "15m"), ("UNI/USDT", "1h"), ("UNI/USDT", "1d"), ("VET/USDT", "15m"), ("VET/USDT", "1h"), ("VET/USDT", "1d"), ("XLM/USDT", "15m"), ("XLM/USDT", "1h"), ("XLM/USDT", "1d"), ("XMR/USDT", "15m"), ("XMR/USDT", "1h"), ("XMR/USDT", "1d"), ("XRP/USDT", "15m"), ("XRP/USDT", "1h"), ("XRP/USDT", "1d"), ] return informative_pairs @informative('15m') @informative('1h') @informative('1d') def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['rsi'] < self.buy_rsi.value) & (dataframe['macd'] > dataframe['macdsignal'] ) & (dataframe['fastk'] > dataframe['fastd'] ) & (dataframe['fastk'] < self.buy_fastk.value) ), ['enter_long', 'enter_tag']] = (1, 'Buy_5m') dataframe.loc[ ( (dataframe['rsi_15m'] < self.buy_rsi_15m.value) & (dataframe['macd_15m'] > dataframe['macdsignal_15m'] ) & (dataframe['fastk_15m'] > dataframe['fastd_15m'] ) & (dataframe['fastk_15m'] < self.buy_fastk_15m.value) ), ['enter_long', 'enter_tag']] = (1, 'Buy_15m') dataframe.loc[ ( (dataframe['rsi_1h'] < self.buy_rsi_1h.value) & (dataframe['macd_1h'] > dataframe['macdsignal_1h'] ) & (dataframe['fastk_1h'] > dataframe['fastd_1h'] ) & (dataframe['fastk_1h'] < self.buy_fastk_1h.value) ), ['enter_long', 'enter_tag']] = (1, 'Buy_1h') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['rsi'] > self.sell_rsi.value) & (dataframe['macd'] < dataframe['macdsignal']) & (dataframe['fastk'] < dataframe['fastd']) & (dataframe['fastk'] > self.sell_fastk.value) & (dataframe['volume'] > 0) ), ['exit_long', 'exit_tag']] = (1, 'Sell_5m') dataframe.loc[ ( (dataframe['rsi_15m'] > self.sell_rsi_15m.value) & (dataframe['macd_15m'] < dataframe['macdsignal_15m']) & (dataframe['fastk_15m'] < dataframe['fastd_15m']) & (dataframe['fastk_15m'] > self.sell_fastk_15m.value) & (dataframe['volume_15m'] > 0) ), ['exit_long', 'exit_tag']] = (1, 'Sell_15m') dataframe.loc[ ( (dataframe['rsi_1h'] > self.sell_rsi_1h.value) & (dataframe['macd_1h'] < dataframe['macdsignal_1h']) & (dataframe['fastk_1h'] < dataframe['fastd_1h']) & (dataframe['fastk_1h'] > self.sell_fastk_1h.value) & (dataframe['volume_1h'] > 0) ), ['exit_long', 'exit_tag']] = (1, 'Sell_1h') return dataframe