# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/strategy_v2.py from freqtrade.strategy.interface import IStrategy from technical.indicator_helpers import fishers_inverse import freqtrade.vendor.qtpylib.indicators as qtpylib from pandas import DataFrame, DatetimeIndex, merge import numpy # noqa import talib.abstract as ta class Github_remiotore_freqtrade__strategy_v2__20260111_210550(IStrategy): minimal_roi = { } stoploss = -0.15 ticker_interval = '5m' def get_ticker_indicator(self): return int(self.ticker_interval[:-1]) def populate_indicators(self, dataframe: DataFrame) -> DataFrame: from technical.util import resample_to_interval from technical.util import resampled_merge dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200) dataframe_short = resample_to_interval(dataframe, self.get_ticker_indicator() * 3) dataframe_long = resample_to_interval(dataframe, self.get_ticker_indicator() * 7) dataframe_short['rsi'] = ta.RSI(dataframe_short, timeperiod=14) dataframe_long['rsi'] = ta.RSI(dataframe_long, timeperiod=14) dataframe = resampled_merge(dataframe, dataframe_short) dataframe = resampled_merge(dataframe, dataframe_long) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe.fillna(method='ffill', inplace=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: dataframe.loc[ ( (dataframe['ema50'] >= dataframe['ema200']) & (dataframe['rsi'] < (dataframe['rsi_y'] - 20)) & (dataframe['rsi'] != 0) & (dataframe['rsi_x'] != 0) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame: dataframe.loc[ ( (dataframe['rsi'] > dataframe['rsi_x']) & (dataframe['rsi'] > dataframe['rsi_y']) | (dataframe['rsi'] > 90) & (dataframe['rsi_x'] > 90) ), 'sell'] = 1 return dataframe class Github_remiotore_freqtrade__strategy_v2__20260111_210550(IStrategy): minimal_roi = { "1440": 0.2 } stoploss = -0.15 ticker_interval = '15m' def get_ticker_indicator(self): return int(self.ticker_interval[:-1]) def populate_indicators(self, dataframe: DataFrame) -> DataFrame: from technical.util import resample_to_interval from technical.util import resampled_merge dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200) stoch = ta.STOCH(dataframe, fastk_period= 5, slowk_period= 2, slowk_matype=0, slowd_period= 2, slowd_matype=0) dataframe['slowd15'] = stoch['slowd'] dataframe['slowk15'] = stoch['slowk'] stoch = ta.STOCH(dataframe, fastk_period= 10, slowk_period= 3, slowk_matype=0, slowd_period= 3, slowd_matype=0) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=24) dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=24) dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband'] 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['sma3'] = ta.SMA(dataframe, timeperiod=3) dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) dataframe['sma20'] = ta.SMA(dataframe, timeperiod=20) dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) dataframe['sma220'] = ta.SMA(dataframe, timeperiod=220) dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200) dataframe['willr'] = ta.WILLR(dataframe, timeperiod=28) dataframe_short = resample_to_interval(dataframe, self.get_ticker_indicator() * 3) dataframe_long = resample_to_interval(dataframe, self.get_ticker_indicator() * 7) dataframe_short['rsi'] = ta.RSI(dataframe_short, timeperiod=14) dataframe_long['rsi'] = ta.RSI(dataframe_long, timeperiod=14) dataframe['cci'] = ta.CCI(dataframe, timeperiod=20) dataframe['mfi'] = ta.MFI(dataframe) dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) dataframe = resampled_merge(dataframe, dataframe_short) dataframe = resampled_merge(dataframe, dataframe_long) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe.fillna(method='ffill', inplace=True) dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi']) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) dataframe['resample_rsi_2'] = dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*3)] dataframe['resample_rsi_8'] = dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*7)] dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4 return dataframe def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: dataframe.loc[ ( (dataframe['ema50'] >= dataframe['ema200']) & (dataframe['rsi'] < (dataframe['resample_{}_rsi'.format(self.get_ticker_indicator() * 7)] - 20)) & (dataframe['rsi'] != 0) & (dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*3)] != 0) ), 'buy'] = 1 print(dataframe['rsi']) return dataframe def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame: dataframe.loc[ ( ( ( (dataframe['rsi'] > dataframe['resample_rsi_2']) & (dataframe['rsi'] > dataframe['resample_rsi_8']) | (dataframe['rsi'] > 90) & (dataframe['resample_rsi_2'] > 90) ) ) ), 'sell'] = 1 print(dataframe['rsi']) return dataframe class Github_remiotore_freqtrade__strategy_v2__20260111_210550(IStrategy): minimal_roi = { "60": 0.01, "30": 0.03, "20": 0.04, "0": 0.05 } stoploss = -0.03 ticker_interval = '5m' def populate_indicators(self, dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ stoch_5 = ta.STOCH(dataframe, fastk_period=9, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) stoch_15 = ta.STOCH(dataframe, fastk_period=27, slowk_period=9, slowk_matype=0, slowd_period=9, slowd_matype=0) dataframe['slowd_5'] = stoch_5['slowd'] dataframe['slowk_5'] = stoch_5['slowk'] dataframe['slowd_15'] = stoch_15['slowd'] dataframe['slowk_15'] = stoch_15['slowk'] dataframe['stochJ_5'] = (3 * dataframe['slowk_5']) - (2 * dataframe['slowd_5']) dataframe['stochJ_15'] = (3 * dataframe['slowk_15']) - (2 * dataframe['slowd_15']) stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['rsi'] = ta.RSI(dataframe) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) return dataframe def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['rsi'] < 45) & (dataframe['slowk_15'] > dataframe['slowd_15']) & (dataframe['slowk_15'] < 50) & (dataframe['slowk_5'] < 25) & (dataframe['stochJ_5'] > dataframe['slowd_5']) & (dataframe['fastk'] > dataframe['fastd']) & (dataframe['fastk'] >= 0) & (dataframe['fastk'] < 50) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['stochJ_15'] >= 100) & (dataframe['fisher_rsi'] >= 0.8) & (dataframe['fastk'] >= 100) ), 'sell'] = 1 return dataframe