# source: https://raw.githubusercontent.com/nmenescardi/some_freqtrade_stuff/dc3fa62d1eb0391a0f80eae2d3cd25b5d0d03ab9/strategies/05_CombinedBinHAndClucV6H_OPT.py # --- Do not remove these libs -------------------------------------------------------------------- import freqtrade.vendor.qtpylib.indicators as qtpylib import talib.abstract as ta import logging import pandas as pd import numpy as np from pandas import DataFrame, Series, DatetimeIndex, merge from freqtrade.exchange import timeframe_to_minutes from freqtrade.strategy import IntParameter, DecimalParameter, CategoricalParameter, merge_informative_pair from freqtrade.strategy.interface import IStrategy from freqtrade.persistence import Trade from datetime import datetime, timedelta from functools import reduce ########################################################################################################### ## CombinedBinHAndClucV6 by iterativ ## ## hyperoptable additions and refactoring by nightshift2k ## ## ## ## source & discussion -> freqtrade discord: ## ## https://discord.gg/ZdsMQ3tE ## ## ## ## Freqtrade https://github.com/freqtrade/freqtrade ## ## The authors of the original CombinedBinHAndCluc https://github.com/freqtrade/freqtrade-strategies ## ## V6 by iterativ. ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 20 to 40 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m) & sell_profit_only (must be true). ## ## ## ########################################################################################################### ## DONATIONS ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## ## ########################################################################################################### # ------------------------------------------------------------------------------------------------- # --- logger for parameter merging output, only remove if you remove it further down too! --------- logger = logging.getLogger(__name__) # ------------------------------------------------------------------------------------------------- class Github_nmenescardi_some_freqtrade_stuff__05_CombinedBinHAndClucV6H_OPT__20210702_093024(IStrategy): minimal_roi = { "0": 0.0181 } max_open_trades = 5 # Using custom stoploss stoploss = -0.99 use_custom_stoploss = True # Trailing stoploss trailing_stop = True trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.025 timeframe = '5m' informative_timeframe = '1h' # derived from EMA 200 startup_candle_count: int = 200 # Run "populate_indicators()" only for new candle. process_only_new_candles = False use_sell_signal = True sell_profit_only = True sell_profit_offset = 0.001 ignore_roi_if_buy_signal = True # hyperspace default buy params buy_params = { "buy_cluc_close_bblowerband": 0.445, "buy_cluc_volume": 28, "buy_long_rsi_diff": 41.426, "buy_bin_bbdelta_close": 0.007, "buy_bin_closedelta_close": 0.0108, "buy_bin_enable": True, "buy_bin_guard": True, "buy_bin_tail_bbdelta": 0.622, "buy_cluc_enable": True, "buy_cluc_guard": True, "buy_long_enable": True, "buy_minimum_conditions": 1, } # hyperspace default sell params sell_params = { "cstp_bail_time": 1, "cstp_loss_threshold": 0.0, "sell_roi_override_rsi_threshold": 58, } # if you want to see which buy conditions were met # or if there is an trade exit override due to high RSI set to True # logger will output the buy and trade exit conditions cust_log_verbose = False ########################################################################################################### ## IMPORTANT NOTICE REGARDING HYPEROPT: ## ## Use the optimize dict below to enable optimization (set to True) of one or more ## ## hyperspace parameters. ## ## ## ## Please be aware: Enabling ALL at once will very likely produce very low results! ## ## So please enable max. 1-2 parameters at once, you'll need to play around ## ## what makes more or less sense. At the end it always depends on a lot more ## ## parameters (stake, max trades, hyperopt-loss, etc.) ## ########################################################################################################### # use this switches to enable the optimization of hyperspace params cust_optimize = { 'buy_bin_bbdelta_close': True, 'buy_bin_closedelta_close': True, 'buy_bin_tail_bbdelta': True, 'buy_bin_guard': False, 'buy_cluc_close_bblowerband': True, 'buy_cluc_volume': True, 'buy_cluc_guard': False, 'buy_long_rsi_diff': True, 'buy_bin_enable': False, 'buy_cluc_enable': False, 'buy_long_enable': False, 'buy_minimum_conditions': True, 'sell_roi_override_rsi_threshold': True, 'cstp_bail_time': False, 'cstp_loss_threshold': False } # bin buy parameters buy_bin_bbdelta_close = DecimalParameter(0.0, 0.05, default=0.007, space='buy', optimize=cust_optimize['buy_bin_bbdelta_close'], load=True) buy_bin_closedelta_close = DecimalParameter(0.0, 0.03, default=0.0108, decimals=4, space='buy', optimize=cust_optimize['buy_bin_closedelta_close'], load=True) buy_bin_tail_bbdelta = DecimalParameter(0.0, 1.0, default=0.622, decimals=3, space='buy', optimize=cust_optimize['buy_bin_tail_bbdelta'], load=True) buy_bin_guard = CategoricalParameter([True, False], default=True, space='buy', optimize=cust_optimize['buy_bin_guard'], load=True) # cluc buy parameters buy_cluc_close_bblowerband = DecimalParameter(0.0, 1.5, default=0.445, decimals=3, space='buy', optimize=cust_optimize['buy_cluc_close_bblowerband'], load=True) buy_cluc_volume = IntParameter(10, 40, default=28, space='buy', optimize=cust_optimize['buy_cluc_volume'], load=True) buy_cluc_guard = CategoricalParameter([True, False], default=True, space='buy', optimize=cust_optimize['buy_cluc_guard'], load=True) # log buy parameters buy_long_rsi_diff = DecimalParameter(40, 45, default=41.426, decimals=3, space='buy', optimize=cust_optimize['buy_long_rsi_diff'], load=True) # enable bin, cluc or long buy_bin_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=cust_optimize['buy_bin_enable'], load=True) buy_cluc_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=cust_optimize['buy_cluc_enable'], load=True) buy_long_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=cust_optimize['buy_long_enable'], load=True) # minimum conditions to match in buy buy_minimum_conditions = IntParameter(1, 2, default=1, space='buy', optimize=cust_optimize['buy_minimum_conditions'], load=True) # if RSI above threshold, override ROI sell_roi_override_rsi_threshold = IntParameter(40, 70, default=58, space='sell', optimize=cust_optimize['sell_roi_override_rsi_threshold'], load=True) # custom stoploss, if trade open > x hours and loss > threshold then sell cstp_bail_time = IntParameter(1, 36, default=5, space='sell', optimize=cust_optimize['cstp_bail_time']) cstp_loss_threshold = DecimalParameter(-0.25, 0, default=0, decimals=2, space='sell', optimize=cust_optimize['cstp_loss_threshold']) """ Informative Pairs """ def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs """ Informative Timeframe Indicators """ def get_informative_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) # EMA dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # SSL Channels ssldown, sslup = SSLChannels_ATR(dataframe, 20) dataframe['ssl-up'] = sslup dataframe['ssl-down'] = ssldown dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down') return dataframe """ Main Timeframe Indicators """ def get_main_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # strategy BinHV45 bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() # strategy ClucMay72018 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['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() # EMA dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # SMA dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # drop NAN in hyperopt to fix "'<' not supported between instances of 'str' and 'int' error if self.config['runmode'].value == 'hyperopt': dataframe = dataframe.dropna() return dataframe """ Populate Informative and Main Timeframe Indicators """ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.config['runmode'].value in ('backtest', 'hyperopt'): assert (timeframe_to_minutes(self.timeframe) <= 5), "Backtest this strategy in a timeframe of 5m or less." assert self.dp, "DataProvider is required for multiple timeframes." informative = self.get_informative_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) dataframe = self.get_main_indicators(dataframe, metadata) return dataframe """ Buy Signal """ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # reset additional dataframe rows dataframe.loc[:, 'buy_cond_bin'] = False dataframe.loc[:, 'buy_cond_cluc'] = False dataframe.loc[:, 'buy_cond_long'] = False dataframe.loc[:, 'conditions_count'] = 0 # strategy BinHV45 + guards by @iterativ dataframe.loc[ ( # @iterativ adition (guard) ( ( (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (self.buy_bin_guard.value == True) ) | (self.buy_bin_guard.value == False) ) & # strategy BinHV45 dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bin_bbdelta_close.value) & dataframe['closedelta'].gt(dataframe['close'] * self.buy_bin_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bin_tail_bbdelta.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (self.buy_bin_enable.value == True) ), 'buy_cond_bin' ] = 1 # strategy ClucMay72018 + guards by @iterativ dataframe.loc[ ( # @iterativ addition (guard) ( ( (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (self.buy_cluc_guard.value == True) ) | (self.buy_cluc_guard.value == False) ) & # strategy ClucMay72018 (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < self.buy_cluc_close_bblowerband.value * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * self.buy_cluc_volume.value)) & (self.buy_cluc_enable.value == True) ), 'buy_cond_cluc' ] = 1 # long strategy by @iterativ dataframe.loc[ ( (dataframe['close'] < dataframe['sma_5']) & (dataframe['ssl-dir_1h'] == 'up') & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_long_rsi_diff.value) & (self.buy_long_enable.value == True) ), 'buy_cond_long' ] = 1 # count the amount of conditions met dataframe.loc[:, 'conditions_count'] = dataframe['buy_cond_bin'].astype(int) + dataframe['buy_cond_cluc'].astype(int) + dataframe['buy_cond_long'].astype(int) # append the minimum amount of conditions to be met conditions.append(dataframe['conditions_count'] >= self.buy_minimum_conditions.value) conditions.append(dataframe['volume'].gt(0)) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy' ] = 1 # verbose logging enable only for verbose information or troubleshooting if self.cust_log_verbose == True: for index, row in dataframe.iterrows(): if row['buy'] == 1: buy_cond_details = f"count={int(row['conditions_count'])}/bin={int(row['buy_cond_bin'])}/cluc={int(row['buy_cond_cluc'])}/long={int(row['buy_cond_long'])}" logger.info(f"{metadata['pair']} - candle: {row['date']} - buy condition - details: {buy_cond_details}") return dataframe """ Sell Signal """ def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell' ] = 1 return dataframe """ Custom Stop Loss """ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if (current_profit < self.cstp_loss_threshold.value) & (current_time - timedelta(hours=int(self.cstp_bail_time.value)) > trade.open_date_utc): return 0.01 return self.stoploss """ Trade Exit Confirmation """ def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if self.cust_log_verbose == True: logger.info(f"{pair} - candle: {last_candle['date']} - exit trade {sell_reason} with profit {trade.calc_profit_ratio(rate)}") # failsafe for user triggered forced sells > always have highest prio! if sell_reason == 'force_sell': return True # Prevent ROI trigger, if there is more potential, in order to maximize profit if last_candle is not None and ((sell_reason == 'roi') ): rsi = 0 if 'rsi' in last_candle.index: rsi = last_candle['rsi'] if ( (rsi >= self.sell_roi_override_rsi_threshold.value) ): if self.cust_log_verbose == True: logger.info(f"{pair} - candle: {last_candle['date']} - not exiting trade with current profit {trade.calc_profit_ratio(rate)}, rsi = {rsi} which is > than {self.sell_roi_override_rsi_threshold.value}") return False else: return True return True # --- custom indicators --------------------------------------------------------------------------- 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']