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 logger = logging.getLogger(__name__) class 05_CombinedBinHAndClucV6H_OPT_02(IStrategy): minimal_roi = { "0": 0.0181 } max_open_trades = 5 stoploss = -0.99 use_custom_stoploss = True 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' startup_candle_count: int = 200 process_only_new_candles = False use_sell_signal = True sell_profit_only = True sell_profit_offset = 0.001 ignore_roi_if_buy_signal = True buy_params = { "buy_bin_bbdelta_close": 0.01, "buy_bin_closedelta_close": 0.007, "buy_bin_tail_bbdelta": 0.242, "buy_cluc_close_bblowerband": 1.0, "buy_cluc_volume": 28, "buy_long_rsi_diff": 44.444, "buy_minimum_conditions": 2, "buy_bin_enable": True, "buy_bin_guard": True, "buy_cluc_enable": True, "buy_cluc_guard": True, "buy_long_enable": True, } sell_params = { "cstp_bail_time": 1, "cstp_loss_threshold": 0.0, "sell_roi_override_rsi_threshold": 64 } cust_log_verbose = False 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 } buy_bin_bbdelta_close = DecimalParameter(0.0, 0.05, default=0.001, space='buy', optimize=cust_optimize['buy_bin_bbdelta_close'], load=True) buy_bin_closedelta_close = DecimalParameter(0.0, 0.03, default=0.007, decimals=4, space='buy', optimize=cust_optimize['buy_bin_closedelta_close'], load=True) buy_bin_tail_bbdelta = DecimalParameter(0.0, 1.0, default=0.242, 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) buy_cluc_close_bblowerband = DecimalParameter(0.0, 1.5, default=1, 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) buy_long_rsi_diff = DecimalParameter(40, 45, default=44.444, decimals=3, space='buy', optimize=cust_optimize['buy_long_rsi_diff'], load=True) 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) buy_minimum_conditions = IntParameter(1, 2, default=2, space='buy', optimize=cust_optimize['buy_minimum_conditions'], load=True) sell_roi_override_rsi_threshold = IntParameter(40, 70, default=64, space='sell', optimize=cust_optimize['sell_roi_override_rsi_threshold'], load=True) 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) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) 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: 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() 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() dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) 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 = [] dataframe.loc[:, 'buy_cond_bin'] = False dataframe.loc[:, 'buy_cond_cluc'] = False dataframe.loc[:, 'buy_cond_long'] = False dataframe.loc[:, 'conditions_count'] = 0 dataframe.loc[ ( ( ( (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) ) & 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 dataframe.loc[ ( ( ( (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (self.buy_cluc_guard.value == True) ) | (self.buy_cluc_guard.value == False) ) & (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 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 dataframe.loc[:, 'conditions_count'] = dataframe['buy_cond_bin'].astype(int) + dataframe['buy_cond_cluc'].astype(int) + dataframe['buy_cond_long'].astype(int) 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 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)}") if sell_reason == 'force_sell': return True 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 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']