# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Discord_brideofcluckie5.py # --- Do not remove these libs --- import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np # -------------------------------- import talib.abstract as ta from freqtrade.strategy import IStrategy, merge_informative_pair from pandas import DataFrame, Series from technical.indicators import RMI def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - (rolling_std * num_of_std) upper_band = rolling_mean + (rolling_std * num_of_std) return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band), np.nan_to_num(upper_band) 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_remiotore_freqtrade__Discord_brideofcluckie5__20260111_210550(IStrategy): # Buy hyperspace params: buy_params = { 'bbdelta-close': 0.0234, 'bbdelta-tail': 1.19184, 'close-bblower': 0.02269, 'closedelta-close': 0.00235, 'volume': 78 } # Sell hyperspace params: sell_params = { 'sell-bbmiddle-close': 0.74102 } # ROI table: minimal_roi = { "0": 0.01, "45": 0.0025, "1440": -1 } timeframe = '5m' stoploss = -0.99 trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.02 trailing_only_offset_is_reached = True use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True informative_timeframe = '1h' informative_timeframe2 = '15m' def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] informative_pairs =+ [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def get_1H_informative_indicators(self, dataframe: DataFrame, metadata: dict): ssl_down, ssl_up = SSLChannels(dataframe, 25) dataframe['ssl_down'] = ssl_down dataframe['ssl_up'] = ssl_up dataframe['ssl_high'] = (ssl_up > ssl_down).astype('int') * 3 dataframe['mfi'] = ta.MFI(dataframe, timeperiod=25) stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] dataframe['RMI'] = RMI(dataframe, length=8, mom=4) # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=3) dataframe['go_long'] = ( (dataframe['ssl_high'] > 0) & (dataframe['mfi'].shift().rolling(3).mean() > dataframe['mfi']) & (dataframe['srsi_fk'].shift().rolling(3).mean() > dataframe['srsi_fk']) & (dataframe['RMI'].shift().rolling(3).mean() < dataframe['RMI']) ).astype('int') * 4 dataframe['go_long_avg'] = dataframe['go_long'].shift().rolling(24).mean() return dataframe def get_15M_informative_indicators(self, dataframe: DataFrame, metadata: dict): ssl_down_ftf, ssl_up_ftf = SSLChannels(dataframe, 7) dataframe['ssl_down_ftf'] = ssl_down_ftf dataframe['ssl_up_ftf'] = ssl_up_ftf dataframe['ssl_high_ftf'] = (ssl_up_ftf > ssl_down_ftf) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not self.dp: return dataframe informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) informative2 = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe2) informative = self.get_1H_informative_indicators(informative.copy(), metadata) informative2 = self.get_15M_informative_indicators(informative2.copy(), metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) dataframe = merge_informative_pair(dataframe, informative2, self.timeframe, self.informative_timeframe2, ffill=True) skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] skip_columns2 = [(s + "_" + self.informative_timeframe2) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if (not s in skip_columns) else s, inplace=True) dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe2), "") if (not s in skip_columns2) else s, inplace=True) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # strategy BinHV45 mid, lower, upper = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['mid'] = mid dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() # strategy ClucMay72018 dataframe['bb_lowerband'] = dataframe['lower'] dataframe['bb_middleband'] = dataframe['mid'] dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.buy_params dataframe.loc[ (dataframe['go_long'] > 0) & (dataframe['go_long_avg'] < 1) & #hyperopt (dataframe['ssl_high_ftf'] == 1) & (dataframe['ssl_high_ftf'].shift().rolling(3).mean() > 0 ) & (( # strategy BinHV45 (dataframe['lower'].shift().gt(0)) & (dataframe['bbdelta'].gt(dataframe['ha_close'] * params['bbdelta-close'])) & (dataframe['closedelta'].gt(dataframe['ha_close'] * params['closedelta-close'])) & (dataframe['tail'].lt(dataframe['bbdelta'] * params['bbdelta-tail'])) & (dataframe['ha_close'].lt(dataframe['lower'].shift())) & dataframe['ha_close'].le(dataframe['ha_close'].shift()) ) | ( # strategy ClucMay72018 (dataframe['ha_close'] < dataframe['ema_slow']) & (dataframe['ha_close'] < (params['close-bblower'] * dataframe['bb_lowerband'])) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * params['volume'])) )), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: params = self.sell_params dataframe.loc[ #(dataframe['go_long'] == 0) & ((dataframe['ha_close'] * params['sell-bbmiddle-close']) > dataframe['bb_middleband']) & (dataframe['volume'] > 0) , 'sell' ] = 1 return dataframe