from typing import Optional from functools import reduce from typing import List import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import pandas as pd import pandas_ta as pta import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, DecimalParameter, stoploss_from_open, RealParameter, IntParameter, BooleanParameter from pandas import DataFrame, Series from datetime import datetime, timedelta, timezone 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) return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band) def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) class fahmibah_270(IStrategy): """ PASTE OUTPUT FROM HYPEROPT HERE Can be overridden for specific sub-strategies (stake currencies) at the bottom. """ buy_params = { "clucha_enabled": True, "bbdelta_close": 0.01889, "bbdelta_tail": 0.72235, "close_bblower": 0.0127, "closedelta_close": 0.00916, "rocr_1h": 0.79492, "fahmi1_enabled": True, "fahmi1_lower": 1.5, } sell_params = { "pHSL": -0.10, "pPF_1": 0.02, "pPF_2": 0.03, "pSL_1": 0.015, "pSL_2": 0.025, 'sell_fisher': 0.39075, 'sell_bbmiddle_close': 0.99754 } minimal_roi = { "0": 0.033, "10": 0.023, "40": 0.01, } stoploss = -0.10 # use custom stoploss trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.012 trailing_only_offset_is_reached = False """ END HYPEROPT """ timeframe = '5m' use_sell_signal = False sell_profit_only = False ignore_roi_if_buy_signal = False use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 200 order_types = { 'buy': 'limit', 'sell': 'limit', 'emergencysell': 'limit', 'forcebuy': "limit", 'forcesell': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } clucha_enabled = BooleanParameter(default=buy_params['clucha_enabled'], space='buy', optimize=False) rocr_1h = DecimalParameter(0.5, 1.0, default=0.54904, space='buy', decimals=5, optimize=False) bbdelta_close = DecimalParameter(0.0005, 0.02, default=0.01965, space='buy', decimals=5, optimize=False) closedelta_close = DecimalParameter(0.0005, 0.02, default=0.00556, space='buy', decimals=5, optimize=False) bbdelta_tail = DecimalParameter(0.7, 1.0, default=0.95089, space='buy', decimals=5, optimize=False) close_bblower = DecimalParameter(0.0005, 0.02, default=0.00799, space='buy', decimals=5, optimize=False) fahmi1_enabled = BooleanParameter(default=buy_params['fahmi1_enabled'], space='buy', optimize=False) fahmi1_lower = DecimalParameter(0.1, 1.5, default=0.8, space='buy', decimals=2, optimize=True) pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.03, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.02, 0.070, default=0.040, decimals=3, space='sell', load=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value if current_profit > PF_2: sl_profit = SL_2 + (current_profit - PF_2) elif current_profit > PF_1: sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['mid'] = mid bb_20_std2 = bollinger_bands(ha_typical_price(dataframe), window_size=20, num_of_std=2) dataframe['bb20_2_low'] = bb_20_std2['lower'] dataframe['bb20_2_mid'] = bb_20_std2['mid'] dataframe['bb20_2_upp'] = bb_20_std2['upper'] 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() dataframe['bb_lowerband'] = dataframe['lower'] dataframe['bb_middleband'] = dataframe['mid'] dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) dataframe['ema_200'] = ta.EMA(dataframe['ha_close'], timeperiod=200) inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) inf_heikinashi = qtpylib.heikinashi(informative) informative['ha_close'] = inf_heikinashi['close'] informative['ema_200'] = ta.EMA(informative['ha_close'], timeperiod=200) informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' fahmi1 = ( bool(self.fahmi1_enabled.value) & (dataframe['ha_close'] > dataframe['ema_200']) & (dataframe['ha_close'] < dataframe['bb20_2_low'] * self.fahmi1_lower.value) & (dataframe['ha_open'] - dataframe['ha_close'] < dataframe['bb20_2_upp'].shift(2) - dataframe['bb20_2_low'].shift(2)) ) dataframe.loc[fahmi1, 'buy_tag'] += 'fahmi1' conditions.append(fahmi1) fahmi2 = ( bool(self.fahmi1_enabled.value) & (dataframe['ha_close'] > dataframe['ema_200']) & (dataframe['ha_close'] < dataframe['bb20_2_low'] * self.fahmi1_lower.value) ) dataframe.loc[fahmi2, 'buy_tag'] += 'fahmi2' conditions.append(fahmi2) clucHA = ( bool(self.clucha_enabled.value) & (dataframe['rocr_1h'].gt(self.rocr_1h.value)) & (( (dataframe['lower'].shift().gt(0)) & (dataframe['bbdelta'].gt(dataframe['ha_close'] * self.bbdelta_close.value)) & (dataframe['closedelta'].gt(dataframe['ha_close'] * self.closedelta_close.value)) & (dataframe['tail'].lt(dataframe['bbdelta'] * self.bbdelta_tail.value)) & (dataframe['ha_close'].lt(dataframe['lower'].shift())) & (dataframe['ha_close'].le(dataframe['ha_close'].shift())) ) | ( (dataframe['ha_close'] < dataframe['ema_slow']) & (dataframe['ha_close'] < self.close_bblower.value * dataframe['bb_lowerband']) )) ) dataframe.loc[clucHA, 'buy_tag'] += 'clucHA_' conditions.append(clucHA) dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(), 'sell'] = 0 return dataframe