import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from pandas import DataFrame, DatetimeIndex, merge, Series from technical.indicators import hull_moving_average """ def hull_moving_average(dataframe, period, field='close') -> ndarray: from pyti.hull_moving_average import hull_moving_average as hma return hma(dataframe[field], period) """ class FisherHull(IStrategy): buy_params = { } sell_params = { } minimal_roi = { '0': 1000 } stoploss = -0.27654 trailing_stop = True trailing_stop_positive = 0.32606 trailing_stop_positive_offset = 0.33314 trailing_only_offset_is_reached = True """ END HYPEROPT """ timeframe = '1m' use_sell_signal = False sell_profit_only = False ignore_roi_if_buy_signal = True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['hma'] = hull_moving_average(dataframe, 14, 'close') dataframe['cci'] = ta.CCI(dataframe, timeperiod=14) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['hma'] < dataframe['hma'].shift()) & (dataframe['cci'] <= -50.0) & (dataframe['fisher_rsi'] < -0.5) & (dataframe['volume'] > 0) ) , 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['hma'] > dataframe['hma'].shift()) & (dataframe['cci'] >= 100.0) & (dataframe['fisher_rsi'] > 0.5) & (dataframe['volume'] > 0) ), 'sell' ] = 1 return dataframe