from typing import Dict, List, Optional, Tuple from datetime import datetime, timedelta from cachetools import TTLCache from pandas import DataFrame, Series import numpy as np ## Indicator libs import talib.abstract as ta from finta import TA as fta import technical.indicators as ftt from technical.indicators import hull_moving_average from technical.indicators import PMAX, zema from technical.indicators import cmf ## FT stuffs from freqtrade.strategy import IStrategy, merge_informative_pair, stoploss_from_open, IntParameter, DecimalParameter, CategoricalParameter import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.exchange import timeframe_to_minutes from freqtrade.persistence import Trade from skopt.space import Dimension """ NOTE: docker-compose run --rm freqtrade hyperopt -c user_data/config-backtesting.json --strategy IchimokuHaulingV8a --hyperopt-loss SortinoHyperOptLossDaily --spaces roi buy sell --timerange=1624940400-1630447200 -j 4 -e 1000 """ class MacheteV8b(IStrategy): # Buy hyperspace params: buy_params = { "buy_should_use_get_buy_signal_quickie": True, #0/0/0 "buy_should_use_get_buy_signal_scalp": True, #2/0/0 "buy_should_use_get_buy_signal_adx_smas": True, #18/0/2 "buy_should_use_get_buy_signal_awesome_macd": True, #3/0/1 "buy_should_use_get_buy_signal_gettin_moist": True, #6/0/0 "buy_should_use_get_buy_signal_hlhb": True, #3/0/1 "buy_should_use_get_buy_signal_adx_momentum": False, #32/1/3 "buy_should_use_get_buy_signal_asdts_rockwelltrading": False, #24/0/3 "buy_should_use_get_buy_signal_averages_strategy": False, #2/0/2 "buy_should_use_get_buy_signal_fisher_hull": False, #7/0/4 "buy_should_use_get_buy_signal_macd_strategy": False, #2/0/0 "buy_should_use_get_buy_signal_macd_strategy_crossed": False, #0/0/0 "buy_should_use_get_buy_signal_pmax": False, #2/0/0 "buy_should_use_get_buy_signal_simple": False, #23/0/2 "buy_should_use_get_buy_signal_strategy001": False, #0/0/0 "buy_should_use_get_buy_signal_technical_example_strategy": False, #36/0/7 "buy_should_use_get_buy_signal_tema_rsi_strategy": False, #0/0/0 } # Sell hyperspace params: sell_params = { "cstp_bail_how": "roc", "cstp_bail_roc": -0.032, "cstp_bail_time": 1108, "cstp_bb_trailing_input": "bb_lowerband_neutral_inf", "cstp_threshold": -0.036, "cstp_trailing_max_stoploss": 0.054, "cstp_trailing_only_offset_is_reached": 0.06, "cstp_trailing_stop_profit_devider": 2, "droi_pullback": True, "droi_pullback_amount": 0.005, "droi_pullback_respect_table": False, "droi_trend_type": "any", } # ROI table: minimal_roi = { "0": 0.279, "92": 0.109, "245": 0.059, "561": 0 } # Stoploss: stoploss = -0.1#-0.046 # Trailing stop: trailing_stop = False #trailing_stop_positive = 0.0247 #trailing_stop_positive_offset = 0.0248 #trailing_only_offset_is_reached = True use_custom_stoploss = True # buy signal buy_should_use_get_buy_signal_awesome_macd = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_awesome_macd'], space='buy', optimize=True) buy_should_use_get_buy_signal_adx_momentum = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_adx_momentum'], space='buy', optimize=True) buy_should_use_get_buy_signal_adx_smas = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_adx_smas'], space='buy', optimize=True) buy_should_use_get_buy_signal_asdts_rockwelltrading = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_asdts_rockwelltrading'], space='buy', optimize=True) buy_should_use_get_buy_signal_averages_strategy = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_averages_strategy'], space='buy', optimize=True) buy_should_use_get_buy_signal_fisher_hull = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_fisher_hull'], space='buy', optimize=True) buy_should_use_get_buy_signal_gettin_moist = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_gettin_moist'], space='buy', optimize=True) buy_should_use_get_buy_signal_hlhb = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_hlhb'], space='buy', optimize=True) buy_should_use_get_buy_signal_macd_strategy_crossed = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_macd_strategy_crossed'], space='buy', optimize=True) buy_should_use_get_buy_signal_macd_strategy = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_macd_strategy'], space='buy', optimize=True) buy_should_use_get_buy_signal_pmax = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_pmax'], space='buy', optimize=True) buy_should_use_get_buy_signal_quickie = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_quickie'], space='buy', optimize=True) buy_should_use_get_buy_signal_scalp = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_scalp'], space='buy', optimize=True) buy_should_use_get_buy_signal_simple = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_simple'], space='buy', optimize=True) buy_should_use_get_buy_signal_strategy001 = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_strategy001'], space='buy', optimize=True) buy_should_use_get_buy_signal_technical_example_strategy = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_technical_example_strategy'], space='buy', optimize=True) buy_should_use_get_buy_signal_tema_rsi_strategy = CategoricalParameter([True, False], default=buy_params['buy_should_use_get_buy_signal_tema_rsi_strategy'], space='buy', optimize=True) # Dynamic ROI droi_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any'], default=sell_params['droi_trend_type'], space='sell', optimize=True) droi_pullback = CategoricalParameter([True, False], default=sell_params['droi_pullback'], space='sell', optimize=True) droi_pullback_amount = DecimalParameter(0.005, 0.02, default=sell_params['droi_pullback_amount'], space='sell') droi_pullback_respect_table = CategoricalParameter([True, False], default=sell_params['droi_pullback_respect_table'], space='sell', optimize=True) # Custom Stoploss cstp_threshold = DecimalParameter(-0.05, 0, default=sell_params['cstp_threshold'], space='sell') cstp_bail_how = CategoricalParameter(['roc', 'time', 'any'], default=sell_params['cstp_bail_how'], space='sell', optimize=True) cstp_bail_roc = DecimalParameter(-0.05, -0.01, default=sell_params['cstp_bail_roc'], space='sell') cstp_bail_time = IntParameter(720, 1440, default=sell_params['cstp_bail_time'], space='sell') cstp_trailing_only_offset_is_reached = DecimalParameter(0.01, 0.06, default=sell_params['cstp_trailing_only_offset_is_reached'], space='sell') cstp_trailing_stop_profit_devider = IntParameter(2, 4, default=sell_params['cstp_trailing_stop_profit_devider'], space='sell') cstp_trailing_max_stoploss = DecimalParameter(0.02, 0.08, default=sell_params['cstp_trailing_max_stoploss'], space='sell') cstp_bb_trailing_input = CategoricalParameter(['bb_lowerband_trend', 'bb_lowerband_trend_inf', 'bb_lowerband_neutral', 'bb_lowerband_neutral_inf', 'bb_upperband_neutral_inf'], default=sell_params['cstp_bb_trailing_input'], space='sell', optimize=True) # nested hyperopt class class HyperOpt: # defining as dummy, so that no error is thrown about missing # sell indicator space when hyperopting for all spaces @staticmethod def indicator_space() -> List[Dimension]: return [] custom_trade_info = {} custom_current_price_cache: TTLCache = TTLCache(maxsize=100, ttl=300) # 5 minutes # run "populate_indicators" only for new candle process_only_new_candles = False # Experimental settings (configuration will overide these if set) use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False startup_candle_count = 500#149 use_dynamic_roi = True timeframe = '15m' informative_timeframe = '1h' # Optional order type mapping order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } plot_config = { 'main_plot': { 'chikou_span_inf': {'color': 'green'}, 'tenkan_sen_inf': {'color': 'blue'}, 'kijun_sen_inf': {'color': 'red'}, 'senkou_a_inf': { 'color': 'green', 'fill_to': 'senkou_b', 'fill_label': 'Kumo', 'fill_color': 'rgba(51, 255, 117, 0.2)', }, 'senkou_b_inf': {'color': 'red'}, 'leading_senkou_span_a_inf': {'color': 'green'}, 'leading_senkou_span_b_inf': {'color': 'red'}, 'sslUp_inf': {'color': 'green'}, 'sslDown_inf': {'color': 'red'} }, 'subplots': { 'summary': { 'cloud_green_inf': {}, 'cloud_red_inf': {}, 'future_green_inf': {}, 'chikou_high_inf': {}, 'go_long_inf': {} } } } def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs # # Processing indicators # def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair']) if not self.dp: return dataframe dataframe = self.get_buy_signal_indicators(dataframe, metadata) informative_tmp = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) informative = self.get_market_condition_indicators(informative_tmp.copy(), metadata) informative = self.get_custom_stoploss_indicators(informative, metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "_inf"), inplace=True) # Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest if self.dp.runmode.value in ('backtest', 'hyperopt'): self.custom_trade_info[metadata['pair']]['roc_inf'] = dataframe[['date', 'roc_inf']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['atr_inf'] = dataframe[['date', 'atr_inf']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['sroc_inf'] = dataframe[['date', 'sroc_inf']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['ssl-dir_inf'] = dataframe[['date', 'ssl-dir_inf']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['rmi-up-trend_inf'] = dataframe[['date', 'rmi-up-trend_inf']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['candle-up-trend_inf'] = dataframe[['date', 'candle-up-trend_inf']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['bb_lowerband_trend_inf'] = dataframe[['date', 'bb_lowerband_trend_inf']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['bb_lowerband_trend_inf'] = dataframe[['date', 'bb_lowerband_trend_inf']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['bb_lowerband_neutral_inf'] = dataframe[['date', 'bb_lowerband_neutral_inf']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['bb_lowerband_neutral_inf'] = dataframe[['date', 'bb_lowerband_neutral_inf']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['bb_upperband_neutral_inf'] = dataframe[['date', 'bb_upperband_neutral_inf']].copy().set_index('date') return dataframe def get_buy_signal_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # get_buy_signal_awesome_macd dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # get_buy_signal_adx_momentum #dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=25) dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=25) dataframe['sar'] = ta.SAR(dataframe) dataframe['mom'] = ta.MOM(dataframe, timeperiod=14) # get_buy_signal_adx_smas #dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) dataframe['short'] = ta.SMA(dataframe, timeperiod=3) dataframe['long'] = ta.SMA(dataframe, timeperiod=6) # get_buy_signal_asdts_rockwelltrading #macd = ta.MACD(dataframe) #dataframe['macd'] = macd['macd'] #dataframe['macdsignal'] = macd['macdsignal'] #dataframe['macdhist'] = macd['macdhist'] # get_buy_signal_averages_strategy dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8) dataframe['maMedium'] = ta.EMA(dataframe, timeperiod=21) # get_buy_signal_fisher_hull 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) # get_buy_signal_gettin_moist dataframe['color'] = dataframe['close'] > dataframe['open'] #macd = ta.MACD(dataframe) #dataframe['macd'] = macd['macd'] #dataframe['macdsignal'] = macd['macdsignal'] #dataframe['macdhist'] = macd['macdhist'] dataframe['rsi_7'] = ta.RSI(dataframe, timeperiod=7) dataframe['roc_6'] = ta.ROC(dataframe, timeperiod=6) dataframe['primed'] = np.where(dataframe['color'].rolling(3).sum() == 3,1,0) dataframe['in-the-mood'] = dataframe['rsi_7'] > dataframe['rsi_7'].rolling(12).mean() dataframe['moist'] = qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']) dataframe['throbbing'] = dataframe['roc_6'] > dataframe['roc_6'].rolling(12).mean() dataframe['ready-to-go'] = np.where(dataframe['close'] > dataframe['open'].rolling(12).mean(), 1,0) # get_buy_signal_hlhb dataframe['hl2'] = (dataframe["close"] + dataframe["open"]) / 2 dataframe['rsi_10'] = ta.RSI(dataframe, timeperiod=10, price='hl2') dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) #dataframe['adx'] = ta.ADX(dataframe) # get_buy_signal_macd_strategy_crossed #macd = ta.MACD(dataframe) #dataframe['macd'] = macd['macd'] #dataframe['macdsignal'] = macd['macdsignal'] #dataframe['macdhist'] = macd['macdhist'] #dataframe['cci'] = ta.CCI(dataframe) # get_buy_signal_macd_strategy #macd = ta.MACD(dataframe) #dataframe['macd'] = macd['macd'] #dataframe['macdsignal'] = macd['macdsignal'] #dataframe['macdhist'] = macd['macdhist'] #dataframe['cci'] = ta.CCI(dataframe) # get_buy_signal_pmax dataframe['ZLEMA'] = zema(dataframe, period=10) dataframe = PMAX(dataframe, period=10, multiplier=3, length=10, MAtype=9, src=2) # get_buy_signal_quickie #macd = ta.MACD(dataframe) #dataframe['macd'] = macd['macd'] #dataframe['macdsignal'] = macd['macdsignal'] #dataframe['macdhist'] = macd['macdhist'] dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=200) #dataframe['adx'] = ta.ADX(dataframe) bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] # get_buy_signal_scalp dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high') dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close') dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low') 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) # get_buy_signal_simple #macd = ta.MACD(dataframe) #dataframe['macd'] = macd['macd'] #dataframe['macdsignal'] = macd['macdsignal'] #dataframe['macdhist'] = macd['macdhist'] #dataframe['rsi_7'] = ta.RSI(dataframe, timeperiod=7) #bollinger = qtpylib.bollinger_bands(dataframe['close'], window=12, stds=2) #dataframe['bb_lowerband'] = bollinger['lower'] #dataframe['bb_upperband'] = bollinger['upper'] #dataframe['bb_middleband'] = bollinger['mid'] # get_buy_signal_strategy001 dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] # get_buy_signal_technical_example_strategy dataframe['cmf'] = cmf(dataframe, 21) # get_buy_signal_tema_rsi_strategy #dataframe['rsi'] = ta.RSI(dataframe) #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['tema'] = ta.TEMA(dataframe, timeperiod=9) return dataframe def get_market_condition_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: displacement = 30 ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=displacement) dataframe['chikou_span'] = ichimoku['chikou_span'] dataframe['tenkan_sen'] = ichimoku['tenkan_sen'] dataframe['kijun_sen'] = ichimoku['kijun_sen'] dataframe['senkou_a'] = ichimoku['senkou_span_a'] dataframe['senkou_b'] = ichimoku['senkou_span_b'] dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a'] dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b'] dataframe['cloud_green'] = ichimoku['cloud_green'] * 1 dataframe['cloud_red'] = ichimoku['cloud_red'] * -1 ssl = SSLChannels_ATR(dataframe, 10) dataframe['sslDown'] = ssl[0] dataframe['sslUp'] = ssl[1] #dataframe['vfi'] = fta.VFI(dataframe, period=14) # Summary indicators dataframe['future_green'] = ichimoku['cloud_green'].shift(displacement).fillna(0).astype('int') * 2 dataframe['chikou_high'] = ((dataframe['chikou_span'] > dataframe['senkou_a']) & (dataframe['chikou_span'] > dataframe['senkou_b'])).shift(displacement).fillna(0).astype('int') dataframe['go_long'] = ((dataframe['tenkan_sen'] > dataframe['kijun_sen']) & (dataframe['close'] > dataframe['leading_senkou_span_a']) & (dataframe['close'] > dataframe['leading_senkou_span_b']) & (dataframe['future_green'] > 0) & (dataframe['chikou_high'] > 0)).fillna(0).astype('int') * 3 dataframe['max'] = dataframe['high'].rolling(3).max() dataframe['min'] = dataframe['low'].rolling(6).min() dataframe['upper'] = np.where(dataframe['max'] > dataframe['max'].shift(),1,0) dataframe['lower'] = np.where(dataframe['min'] < dataframe['min'].shift(),1,0) dataframe['up_trend'] = np.where(dataframe['upper'].rolling(5, min_periods=1).sum() != 0,1,0) dataframe['dn_trend'] = np.where(dataframe['lower'].rolling(5, min_periods=1).sum() != 0,1,0) return dataframe def get_custom_stoploss_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bollinger_neutral = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1) dataframe['bb_lowerband_neutral'] = bollinger_neutral['lower'] dataframe['bb_middleband_neutral'] = bollinger_neutral['mid'] dataframe['bb_upperband_neutral'] = bollinger_neutral['upper'] bollinger_trend = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband_trend'] = bollinger_trend['lower'] dataframe['bb_middleband_trend'] = bollinger_trend['mid'] dataframe['bb_upperband_trend'] = bollinger_trend['upper'] dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['roc'] = ta.ROC(dataframe, timeperiod=9) dataframe['rmi'] = RMI(dataframe, length=24, mom=5) ssldown, sslup = SSLChannels_ATR(dataframe, length=21) dataframe['sroc'] = SROC(dataframe, roclen=21, emalen=13, smooth=21) dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down') dataframe['rmi-up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(),1,0) dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() >= 3,1,0) dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['close'].shift(),1,0) dataframe['candle-up-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() >= 3,1,0) return dataframe # # Processing buy signals # def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (self.get_buy_signal_awesome_macd(dataframe) == True) | (self.get_buy_signal_adx_momentum(dataframe) == True) | (self.get_buy_signal_adx_smas(dataframe) == True) | (self.get_buy_signal_asdts_rockwelltrading(dataframe) == True) | (self.get_buy_signal_averages_strategy(dataframe) == True) | (self.get_buy_signal_fisher_hull(dataframe) == True) | (self.get_buy_signal_gettin_moist(dataframe) == True) | (self.get_buy_signal_hlhb(dataframe) == True) | (self.get_buy_signal_macd_strategy_crossed(dataframe) == True) | (self.get_buy_signal_macd_strategy(dataframe) == True) | (self.get_buy_signal_pmax(dataframe) == True) | (self.get_buy_signal_quickie(dataframe) == True) | (self.get_buy_signal_scalp(dataframe) == True) | (self.get_buy_signal_simple(dataframe) == True) | (self.get_buy_signal_strategy001(dataframe) == True) | (self.get_buy_signal_technical_example_strategy(dataframe) == True) | (self.get_buy_signal_tema_rsi_strategy(dataframe) == True) ) & (dataframe['sslUp_inf'] > dataframe['sslDown_inf']) & (dataframe['up_trend_inf'] > 0) & (dataframe['go_long_inf'] > 0) # NOTE: I keep the volume checks of feels like it has not much benifit when trading leverage tokens, maybe im wrong!? #(dataframe['vfi'] < 0.0) & #(dataframe['volume'] > 0) ,'buy'] = 1 return dataframe def get_buy_signal_awesome_macd(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_awesome_macd.value == True) & (dataframe['macd'] > 0) & (dataframe['ao'] > 0) & (dataframe['ao'].shift() < 0) ) return signal def get_buy_signal_adx_momentum(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_adx_momentum.value == True) & (dataframe['adx'] > 25) & (dataframe['mom'] > 0) & (dataframe['plus_di'] > 25) & (dataframe['plus_di'] > dataframe['minus_di']) ) return signal def get_buy_signal_adx_smas(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_adx_smas.value == True) & (dataframe['adx'] > 25) & (qtpylib.crossed_above(dataframe['short'], dataframe['long'])) ) return signal def get_buy_signal_asdts_rockwelltrading(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_asdts_rockwelltrading.value == True) & (dataframe['macd'] > 0) & (dataframe['macdhist'].shift(1) < dataframe['macdhist']) & (dataframe['macd'] > dataframe['macdsignal']) ) return signal def get_buy_signal_averages_strategy(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_averages_strategy.value == True) & qtpylib.crossed_above(dataframe['maShort'], dataframe['maMedium']) ) return signal def get_buy_signal_fisher_hull(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_fisher_hull.value == True) & (dataframe['hma'] < dataframe['hma'].shift()) & (dataframe['cci'] <= -50.0) & (dataframe['fisher_rsi'] < -0.5) ) return signal def get_buy_signal_gettin_moist(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_gettin_moist.value == True) & (dataframe['primed']) & (dataframe['moist']) & (dataframe['throbbing']) & (dataframe['ready-to-go']) ) return signal def get_buy_signal_hlhb(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_hlhb.value == True) & (qtpylib.crossed_above(dataframe['rsi_10'], 50)) & (qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10'])) & (dataframe['adx'] > 25) ) return signal def get_buy_signal_macd_strategy_crossed(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_macd_strategy_crossed.value == True) & qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']) & (dataframe['cci'] <= -50.0) ) return signal def get_buy_signal_macd_strategy(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_macd_strategy.value == True) & (dataframe['macd'] > dataframe['macdsignal']) & (dataframe['cci'] <= -50.0) ) return signal def get_buy_signal_pmax(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_pmax.value == True) & (qtpylib.crossed_above(dataframe['ZLEMA'], dataframe['pm_10_3_10_9'])) ) return signal def get_buy_signal_quickie(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_quickie.value == True) & (dataframe['adx'] > 30) & (dataframe['tema'] < dataframe['bb_middleband']) & (dataframe['tema'] > dataframe['tema'].shift(1)) & (dataframe['sma_200'] > dataframe['close']) ) return signal def get_buy_signal_scalp(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_scalp.value == True) & (dataframe['open'] < dataframe['ema_low']) & (dataframe['adx'] > 30) & ( (dataframe['fastk'] < 30) & (dataframe['fastd'] < 30) & (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) ) ) return signal def get_buy_signal_simple(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_simple.value == True) & (dataframe['macd'] > 0) # over 0 & (dataframe['macd'] > dataframe['macdsignal']) # over signal & (dataframe['bb_upperband'] > dataframe['bb_upperband'].shift(1)) # pointed up & (dataframe['rsi_7'] > 70) # optional filter, need to investigate ) return signal def get_buy_signal_strategy001(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_strategy001.value == True) & qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']) & (dataframe['ha_close'] < dataframe['ema20']) & (dataframe['ha_open'] > dataframe['ha_close']) # red bar ) return signal def get_buy_signal_technical_example_strategy(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_technical_example_strategy.value == True) & (dataframe['cmf'] < 0) ) return signal def get_buy_signal_tema_rsi_strategy(self, dataframe: DataFrame): signal = ( (self.buy_should_use_get_buy_signal_tema_rsi_strategy.value == True) & (qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30 (dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle (dataframe['tema'] > dataframe['tema'].shift(1)) ) return signal # # Processing sell signals # def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (qtpylib.crossed_above(dataframe['sslDown_inf'], dataframe['sslUp_inf'])) & ( (qtpylib.crossed_below(dataframe['tenkan_sen_inf'], dataframe['kijun_sen_inf'])) |(qtpylib.crossed_below(dataframe['close_inf'], dataframe['kijun_sen_inf'])) ) #& # NOTE: I keep the volume checks of feels like it has not much benifit when trading leverage tokens, maybe im wrong!? #(dataframe['vfi'] < 0.0) & #(dataframe['volume'] > 0) ,'sell'] = 1 return dataframe # # Custom Stoploss # def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) if self.config['runmode'].value in ('live', 'dry_run'): dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) sroc = dataframe['sroc_inf'].iat[-1] bb_trailing = dataframe[self.cstp_bb_trailing_input.value].iat[-1] # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!) else: sroc = self.custom_trade_info[trade.pair]['sroc_inf'].loc[current_time]['sroc_inf'] bb_trailing = self.custom_trade_info[trade.pair][self.cstp_bb_trailing_input.value].loc[current_time][self.cstp_bb_trailing_input.value] if current_profit < self.cstp_threshold.value: if self.cstp_bail_how.value == 'roc' or self.cstp_bail_how.value == 'any': # Dynamic bailout based on rate of change if (sroc/100) <= self.cstp_bail_roc.value: return 0.001 if self.cstp_bail_how.value == 'time' or self.cstp_bail_how.value == 'any': # Dynamic bailout based on time if trade_dur > self.cstp_bail_time.value: return 0.001 if current_profit < self.cstp_trailing_only_offset_is_reached.value: if current_rate <= bb_trailing: return 0.001 else: return -1 desired_stoploss = current_profit / self.cstp_trailing_stop_profit_devider.value return max(min(desired_stoploss, self.cstp_trailing_max_stoploss.value), 0.025) # # Dynamic ROI # def min_roi_reached_dynamic(self, trade: Trade, current_profit: float, current_time: datetime, trade_dur: int) -> Tuple[Optional[int], Optional[float]]: minimal_roi = self.minimal_roi _, table_roi = self.min_roi_reached_entry(trade_dur) # see if we have the data we need to do this, otherwise fall back to the standard table if self.custom_trade_info and trade and trade.pair in self.custom_trade_info: if self.config['runmode'].value in ('live', 'dry_run'): dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=trade.pair, timeframe=self.timeframe) rmi_trend = dataframe['rmi-up-trend_inf'].iat[-1] candle_trend = dataframe['candle-up-trend_inf'].iat[-1] ssl_dir = dataframe['ssl-dir_inf'].iat[-1] # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!) else: rmi_trend = self.custom_trade_info[trade.pair]['rmi-up-trend_inf'].loc[current_time]['rmi-up-trend_inf'] candle_trend = self.custom_trade_info[trade.pair]['candle-up-trend_inf'].loc[current_time]['candle-up-trend_inf'] ssl_dir = self.custom_trade_info[trade.pair]['ssl-dir_inf'].loc[current_time]['ssl-dir_inf'] min_roi = table_roi max_profit = trade.calc_profit_ratio(trade.max_rate) pullback_value = (max_profit - self.droi_pullback_amount.value) in_trend = False if self.droi_trend_type.value == 'rmi' or self.droi_trend_type.value == 'any': if rmi_trend == 1: in_trend = True if self.droi_trend_type.value == 'ssl' or self.droi_trend_type.value == 'any': if ssl_dir == 'up': in_trend = True if self.droi_trend_type.value == 'candle' or self.droi_trend_type.value == 'any': if candle_trend == 1: in_trend = True # Force the ROI value high if in trend if (in_trend == True): min_roi = 100 # If pullback is enabled, allow to sell if a pullback from peak has happened regardless of trend if self.droi_pullback.value == True and (current_profit < pullback_value): if self.droi_pullback_respect_table.value == True: min_roi = table_roi else: min_roi = current_profit / 2 else: min_roi = table_roi return trade_dur, min_roi # Change here to allow loading of the dynamic_roi settings def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool: trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) if self.use_dynamic_roi: _, roi = self.min_roi_reached_dynamic(trade, current_profit, current_time, trade_dur) else: _, roi = self.min_roi_reached_entry(trade_dur) if roi is None: return False else: return current_profit > roi # Get the current price from the exchange (or local cache) def get_current_price(self, pair: str, refresh: bool) -> float: if not refresh: rate = self.custom_current_price_cache.get(pair) # Check if cache has been invalidated if rate: return rate ask_strategy = self.config.get('ask_strategy', {}) if ask_strategy.get('use_order_book', False): ob = self.dp.orderbook(pair, 1) rate = ob[f"{ask_strategy['price_side']}s"][0][0] else: ticker = self.dp.ticker(pair) rate = ticker['last'] self.custom_current_price_cache[pair] = rate return rate # # Custom trade info # def populate_trades(self, pair: str) -> dict: # Initialize the trades dict if it doesn't exist, persist it otherwise if not pair in self.custom_trade_info: self.custom_trade_info[pair] = {} # init the temp dicts and set the trade stuff to false trade_data = {} trade_data['active_trade'] = False # active trade stuff only works in live and dry, not backtest if self.config['runmode'].value in ('live', 'dry_run'): # find out if we have an open trade for this pair active_trade = Trade.get_trades([Trade.pair == pair, Trade.is_open.is_(True),]).all() # if so, get some information if active_trade: # get current price and update the min/max rate current_rate = self.get_current_price(pair, True) active_trade[0].adjust_min_max_rates(current_rate) return trade_data # # Custom indicators # def RMI(dataframe, *, length=20, mom=5): """ Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912 """ df = dataframe.copy() df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0) df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0) df.fillna(0, inplace=True) df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length) df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length) df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"])) return df["RMI"] def SSLChannels_ATR(dataframe, length=7): """ SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/ Credit to @JimmyNixx for python """ 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'] def SROC(dataframe, roclen=21, emalen=13, smooth=21): df = dataframe.copy() roc = ta.ROC(df, timeperiod=roclen) ema = ta.EMA(df, timeperiod=emalen) sroc = ta.ROC(ema, timeperiod=smooth) return sroc