# source: https://raw.githubusercontent.com/minulislam/freqtrade-strategies/4c7137a75224633afd40f590d644c038a2391f9d/user_data/strategies/nfi/ClucHAnix_BB_RPB.py from datetime import datetime, timedelta, timezone 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 import technical.indicators as ftt from freqtrade.persistence import Trade, PairLocks from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, merge_informative_pair) from freqtrade.strategy.interface import IStrategy from pandas import DataFrame, Series from skopt.space import Dimension, Integer # from py3cw.request import Py3CW 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 github_minulislam_freqtrade_strategies__ClucHAnix_BB_RPB__20211227_174936(IStrategy): class HyperOpt: @staticmethod def generate_roi_table(params: dict): """ Generate the ROI table that will be used by Hyperopt This implementation generates the default legacy Freqtrade ROI tables. Change it if you need different number of steps in the generated ROI tables or other structure of the ROI tables. Please keep it aligned with parameters in the 'roi' optimization hyperspace defined by the roi_space method. """ roi_table = {} roi_table[0] = 0.05 roi_table[params['roi_t6']] = 0.04 roi_table[params['roi_t5']] = 0.03 roi_table[params['roi_t4']] = 0.02 roi_table[params['roi_t3']] = 0.01 roi_table[params['roi_t2']] = 0.0001 roi_table[params['roi_t1']] = -10 return roi_table @staticmethod def roi_space() -> List[Dimension]: """ Values to search for each ROI steps Override it if you need some different ranges for the parameters in the 'roi' optimization hyperspace. Please keep it aligned with the implementation of the generate_roi_table method. """ return [ Integer(240, 720, name='roi_t1'), Integer(120, 240, name='roi_t2'), Integer(90, 120, name='roi_t3'), Integer(60, 90, name='roi_t4'), Integer(30, 60, name='roi_t5'), Integer(1, 30, name='roi_t6'), ] # Buy hyperspace params: buy_params = { "buy_btc_safe_1d": -0.05, "antipump_threshold": 0.25, "clucha_bbdelta_close": 0.02206, "clucha_bbdelta_tail": 1.02515, "clucha_close_bblower": 0.03669, "clucha_closedelta_close": 0.04401, "clucha_enabled": True, "clucha_rocr_1h": 0.47782, "cofi_adx": 45, "cofi_ema": 1.329, "cofi_enabled": True, "cofi_ewo_high": 1.768, "cofi_fastd": 18, "cofi_fastk": 25, "ewo_1_enabled": False, "ewo_1_rsi_14": 55, "ewo_1_rsi_4": 12, "ewo_candles_buy": 29, "ewo_candles_sell": 15, "ewo_high": 2.119, "ewo_high_offset": 1.26902, "ewo_low": -16.218, "ewo_low_enabled": False, "ewo_low_offset": 0.99959, "ewo_low_rsi_4": 15, "lambo1_ema_14_factor": 0.981, "lambo1_enabled": True, "lambo1_rsi_14_limit": 52, "lambo1_rsi_4_limit": 37, "lambo2_ema_14_factor": 0.844, "lambo2_enabled": False, "lambo2_rsi_14_limit": 37, "lambo2_rsi_4_limit": 60, "local_trend_bb_factor": 1.03, "local_trend_closedelta": 25.831, "local_trend_ema_diff": 0.047, "local_trend_enabled": False, "nfi32_cti_limit": -0.27048, "nfi32_enabled": True, "nfi32_rsi_14": 75, "nfi32_rsi_4": 84, "nfi32_sma_factor": 0.7871, } # ROI table: minimal_roi = { "0": 0.05, "15": 0.04, "51": 0.03, "81": 0.02, "112": 0.01, "154": 0.0001, "200": -10 } # Stoploss: stoploss = -0.99 # use custom stoploss # Trailing stop: trailing_stop = False trailing_stop_positive = 0.3207 trailing_stop_positive_offset = 0.3849 trailing_only_offset_is_reached = False """ END HYPEROPT """ timeframe = '1m' # Make sure these match or are not overridden in config use_sell_signal = False sell_profit_only = False ignore_roi_if_buy_signal = False # Custom stoploss use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 200 order_types = { 'buy': 'market', 'sell': 'market', 'emergencysell': 'market', 'forcebuy': "market", 'forcesell': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } # ClucHA clucha_bbdelta_close = DecimalParameter(0.01,0.05, default=buy_params['clucha_bbdelta_close'], decimals=5, space='buy', optimize=True) clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=buy_params['clucha_bbdelta_tail'], decimals=5, space='buy', optimize=True) clucha_close_bblower = DecimalParameter(0.001, 0.05, default=buy_params['clucha_close_bblower'], decimals=5, space='buy', optimize=True) clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=buy_params['clucha_closedelta_close'], decimals=5, space='buy', optimize=True) clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=buy_params['clucha_rocr_1h'], decimals=5, space='buy', optimize=True) # lambo1 lambo1_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo1_ema_14_factor'], space='buy', optimize=True) lambo1_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo1_rsi_4_limit'], space='buy', optimize=True) lambo1_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo1_rsi_14_limit'], space='buy', optimize=True) # lambo2 lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True) lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True) lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True) # local_uptrend local_trend_ema_diff = DecimalParameter(0, 0.2, default=buy_params['local_trend_ema_diff'], space='buy', optimize=True) local_trend_bb_factor = DecimalParameter(0.8, 1.2, default=buy_params['local_trend_bb_factor'], space='buy', optimize=True) local_trend_closedelta = DecimalParameter(5.0, 30.0, default=buy_params['local_trend_closedelta'], space='buy', optimize=True) # ewo_1 and ewo_low ewo_candles_buy = IntParameter(2, 30, default=buy_params['ewo_candles_buy'], space='buy', optimize=True) ewo_candles_sell = IntParameter(2, 35, default=buy_params['ewo_candles_sell'], space='buy', optimize=True) ewo_low_offset = DecimalParameter(0.7, 1.2, default=buy_params['ewo_low_offset'], decimals=5, space='buy', optimize=True) ewo_high_offset = DecimalParameter(0.75, 1.5, default=buy_params['ewo_high_offset'], decimals=5, space='buy', optimize=True) ewo_high = DecimalParameter(2.0, 15.0, default=buy_params['ewo_high'], space='buy', optimize=True) ewo_1_rsi_14 = IntParameter(10, 100, default=buy_params['ewo_1_rsi_14'], space='buy', optimize=True) ewo_1_rsi_4 = IntParameter(1, 50, default=buy_params['ewo_1_rsi_4'], space='buy', optimize=True) ewo_low_rsi_4 = IntParameter(1, 50, default=buy_params['ewo_low_rsi_4'], space='buy', optimize=True) ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) # cofi cofi_ema = DecimalParameter(0.6, 1.4, default=buy_params['cofi_ema'] , space='buy', optimize=True) cofi_fastk = IntParameter(1, 100, default=buy_params['cofi_fastk'], space='buy', optimize=True) cofi_fastd = IntParameter(1, 100, default=buy_params['cofi_fastd'], space='buy', optimize=True) cofi_adx = IntParameter(1, 100, default=buy_params['cofi_adx'], space='buy', optimize=True) cofi_ewo_high = DecimalParameter(1.0, 15.0, default=buy_params['cofi_ewo_high'], space='buy', optimize=True) # nfi32 nfi32_rsi_4 = IntParameter(1, 100, default=buy_params['nfi32_rsi_4'], space='buy', optimize=True) nfi32_rsi_14 = IntParameter(1, 100, default=buy_params['nfi32_rsi_4'], space='buy', optimize=True) nfi32_sma_factor = DecimalParameter(0.7, 1.2, default=buy_params['nfi32_sma_factor'], decimals=5, space='buy', optimize=True) nfi32_cti_limit = DecimalParameter(-1.2, 0, default=buy_params['nfi32_cti_limit'], decimals=5, space='buy', optimize=True) buy_btc_safe_1d = DecimalParameter(-0.5, -0.015, default=buy_params['buy_btc_safe_1d'], optimize=True) antipump_threshold = DecimalParameter(0, 0.4, default=buy_params['antipump_threshold'], space='buy', optimize=True) ewo_1_enabled = BooleanParameter(default=buy_params['ewo_1_enabled'], space='buy', optimize=True) ewo_low_enabled = BooleanParameter(default=buy_params['ewo_low_enabled'], space='buy', optimize=True) cofi_enabled = BooleanParameter(default=buy_params['cofi_enabled'], space='buy', optimize=True) lambo1_enabled = BooleanParameter(default=buy_params['lambo1_enabled'], space='buy', optimize=True) lambo2_enabled = BooleanParameter(default=buy_params['lambo2_enabled'], space='buy', optimize=True) local_trend_enabled = BooleanParameter(default=buy_params['local_trend_enabled'], space='buy', optimize=True) nfi32_enabled = BooleanParameter(default=buy_params['nfi32_enabled'], space='buy', optimize=True) clucha_enabled = BooleanParameter(default=buy_params['clucha_enabled'], space='buy', optimize=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] informative_pairs += [("BTC/USDT", "1m")] informative_pairs += [("BTC/USDT", "1d")] return informative_pairs ############################################################################ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: sl_new = 1 if (current_profit > 0.2): sl_new = 0.05 elif (current_profit > 0.1): sl_new = 0.03 elif (current_profit > 0.06): sl_new = 0.02 elif (current_profit > 0.03): sl_new = 0.015 elif (current_profit > 0.015): sl_new = 0.0075 return sl_new ############################################################################ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Heikin Ashi Candles heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20) # CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) # Cofi stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['adx'] = ta.ADX(dataframe) # Set Up Bollinger Bands mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['mid'] = mid bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() # # ClucHA dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['bb_lowerband'] = dataframe['lower'] dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) # Elliot dataframe['EWO'] = EWO(dataframe, 50, 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['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) ### BTC protection dataframe['btc_1m']= self.dp.get_pair_dataframe('BTC/USDT', timeframe='1m')['close'] btc_1d = self.dp.get_pair_dataframe('BTC/USDT', timeframe='1d')[['date', 'close']].rename(columns={"close": "btc"}).shift(1) dataframe = merge_informative_pair(dataframe, btc_1d, '1m', '1d', ffill=True) # Pump strength dataframe['zema_30'] = ftt.zema(dataframe, period=30) dataframe['zema_200'] = ftt.zema(dataframe, period=200) dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30'] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_buy.value)) dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_sell.value)) is_btc_safe = ( (pct_change(dataframe['btc_1d'], dataframe['btc_1m']).fillna(0) > self.buy_btc_safe_1d.value) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) is_pump_safe = ( (dataframe['pump_strength'] < self.antipump_threshold.value) ) lambo1 = ( bool(self.lambo1_enabled.value) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo1_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo1_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo1_rsi_14_limit.value)) ) dataframe.loc[lambo1, 'buy_tag'] += 'lambo1_' conditions.append(lambo1) lambo2 = ( bool(self.lambo2_enabled.value) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2, 'buy_tag'] += 'lambo2_' conditions.append(lambo2) local_uptrend = ( bool(self.local_trend_enabled.value) & (dataframe['ema_26'] > dataframe['ema_14']) & (dataframe['ema_26'] - dataframe['ema_14'] > dataframe['open'] * self.local_trend_ema_diff.value) & (dataframe['ema_26'].shift() - dataframe['ema_14'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband2'] * self.local_trend_bb_factor.value) & (dataframe['closedelta'] > dataframe['close'] * self.local_trend_closedelta.value / 1000 ) ) dataframe.loc[local_uptrend, 'buy_tag'] += 'local_uptrend_' conditions.append(local_uptrend) nfi_32 = ( bool(self.nfi32_enabled.value) & (dataframe['rsi_20'] < dataframe['rsi_20'].shift(1)) & (dataframe['rsi_4'] < self.nfi32_rsi_4.value) & (dataframe['rsi_14'] > self.nfi32_rsi_14.value) & (dataframe['close'] < dataframe['sma_15'] * self.nfi32_sma_factor.value) & (dataframe['cti'] < self.nfi32_cti_limit.value) ) dataframe.loc[nfi_32, 'buy_tag'] += 'nfi_32_' conditions.append(nfi_32) ewo_1 = ( bool(self.ewo_1_enabled.value) & (dataframe['rsi_4'] < self.ewo_1_rsi_4.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] * self.ewo_low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi_14'] < self.ewo_1_rsi_14.value) & (dataframe['close'] < (dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] * self.ewo_high_offset.value)) ) dataframe.loc[ewo_1, 'buy_tag'] += 'ewo1_' conditions.append(ewo_1) ewo_low = ( bool(self.ewo_low_enabled.value) & (dataframe['rsi_4'] < self.ewo_low_rsi_4.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] * self.ewo_low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['close'] < (dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] * self.ewo_high_offset.value)) ) dataframe.loc[ewo_low, 'buy_tag'] += 'ewo_low_' conditions.append(ewo_low) cofi = ( bool(self.cofi_enabled.value) & (dataframe['open'] < dataframe['ema_8'] * self.cofi_ema.value) & (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) & (dataframe['fastk'] < self.cofi_fastk.value) & (dataframe['fastd'] < self.cofi_fastd.value) & (dataframe['adx'] > self.cofi_adx.value) & (dataframe['EWO'] > self.cofi_ewo_high.value) ) dataframe.loc[cofi, 'buy_tag'] += 'cofi_' conditions.append(cofi) clucHA = ( bool(self.clucha_enabled.value) & (dataframe['rocr_1h'].gt(self.clucha_rocr_1h.value)) & (( (dataframe['lower'].shift().gt(0)) & (dataframe['bbdelta'].gt(dataframe['ha_close'] * self.clucha_bbdelta_close.value)) & (dataframe['ha_closedelta'].gt(dataframe['ha_close'] * self.clucha_closedelta_close.value)) & (dataframe['tail'].lt(dataframe['bbdelta'] * self.clucha_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.clucha_close_bblower.value * dataframe['bb_lowerband']) )) ) dataframe.loc[clucHA, 'buy_tag'] += 'clucHA_' conditions.append(clucHA) dataframe.loc[ # is_btc_safe & # broken? # is_pump_safe & reduce(lambda x, y: x | y, conditions), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # dataframe.loc[ # (dataframe['fisher'] > self.sell_fisher.value) & # (dataframe['ha_high'].le(dataframe['ha_high'].shift(1))) & # (dataframe['ha_high'].shift(1).le(dataframe['ha_high'].shift(2))) & # (dataframe['ha_close'].le(dataframe['ha_close'].shift(1))) & # (dataframe['ema_fast'] > dataframe['ha_close']) & # ((dataframe['ha_close'] * self.sell_bbmiddle_close.value) > dataframe['bb_middleband']) & # (dataframe['volume'] > 0) # , # 'sell' # ] = 1 return dataframe def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, current_time: datetime, **kwargs) -> bool: trade.sell_reason = sell_reason + "_" + trade.buy_tag return True # def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, # time_in_force: str, current_time: datetime, **kwargs) -> bool: # """ # Called right before placing a buy order. # Timing for this function is critical, so avoid doing heavy computations or # network requests in this method. # For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ # When not implemented by a strategy, returns True (always confirming). # :param pair: Pair that's about to be bought. # :param order_type: Order type (as configured in order_types). usually limit or market. # :param amount: Amount in target (quote) currency that's going to be traded. # :param rate: Rate that's going to be used when using limit orders # :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled). # :param current_time: datetime object, containing the current datetime # :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. # :return bool: When True is returned, then the buy-order is placed on the exchange. # False aborts the process # """ # coin, currency = pair.split('/') # p3cw = Py3CW( # key='.....', # secret='......', # ) # p3cw.request( # entity='bots', # action='start_new_deal', # action_id='123123', # payload={ # "bot_id": 123123, # "pair": f"{currency}_{coin}", # }, # ) # PairLocks.lock_pair( # pair=pair, # until=datetime.now(timezone.utc) + timedelta(minutes=5), # reason="Send 3c buy order" # ) # return False def pct_change(a, b): return (b - a) / a def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif