import freqtrade.vendor.qtpylib.indicators as qtpylib from datetime import datetime, timedelta import numpy as np from functools import reduce import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, DecimalParameter, RealParameter,IntParameter,informative from pandas import DataFrame, Series from datetime import datetime import math import logging from freqtrade.persistence import Trade import pandas_ta as pta from technical.indicators import RMI import technical.indicators as ftt import threading import requests from freqtrade.vendor.qtpylib import indicators logger = logging.getLogger(__name__) log = logging.getLogger(__name__) def top_percent_change_dca(dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] def VWAPB(dataframe, window_size=20, num_of_std=1): df = dataframe.copy() df['vwap'] = qtpylib.rolling_vwap(df,window=window_size) rolling_std = df['vwap'].rolling(window=window_size).std() df['vwap_low'] = df['vwap'] - (rolling_std * num_of_std) df['vwap_high'] = df['vwap'] + (rolling_std * num_of_std) return df['vwap_low'], df['vwap'], df['vwap_high'] def calc_zvwap(dataframe, pds, source1): volume = dataframe['volume'].rolling(pds).mean() close = dataframe['close'].rolling(pds).mean() mean = indicators.sma(volume * source1, pds) / indicators.sma(volume, pds) vwapsd = indicators.sma(pow(source1 - mean, 2), pds).apply(lambda x: pow(x, 0.5)) zvwap = (close - mean) / vwapsd return zvwap def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe['volume'] cmf = (mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum()) if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf') class x_zvwap06(IStrategy): """ PASTE OUTPUT FROM HYPEROPT HERE Can be overridden for specific sub-strategies (stake currencies) at the bottom. """ buy_params = { "tcp_percent_4_value": 0.04, "cti": -0.087, "buy_rsi": 35 } sell_params = { "pHSL": -0.397, "pPF_1": 0.012, "pPF_2": 0.07, "pSL_1": 0.015, "pSL_2": 0.068, "sell_bbmiddle_close": 1.0909210168690215, "sell_fisher": 0.46405736994786184, "base_nb_candles_sell": 22, "high_offset": 1.014, "high_offset_2": 1.01, "sell_u_e_2_cmf": -0.0, "sell_u_e_2_ema_close_delta": 0.016, "sell_u_e_2_rsi": 10, "sell_deadfish_bb_factor": 1.002, "sell_deadfish_bb_width": 0.031, "sell_deadfish_profit": -0.109, "sell_deadfish_volume_factor": 1.512 } minimal_roi = { "0": 100 } @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 5 } ] stoploss = -0.39 # use custom stoploss trailing_stop = True trailing_stop_positive = 0.006 #povodne 0.001 trailing_stop_positive_offset = 0.08 #povodne 0.012 trailing_only_offset_is_reached = True """ END HYPEROPT """ timeframe = '5m' use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False custom_info = {} use_custom_stoploss = False process_only_new_candles = True startup_candle_count = 168 order_types = { 'entry': 'market', 'exit': 'market', 'emergencyexit': 'market', 'forceentry': "market", 'forceexit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } tcp_percent_4_value = DecimalParameter(0.01, 0.19, default=0.04 , space='buy', optimize = False) cti = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = False) buy_rsi = IntParameter(15, 30, default=35, optimize = False) dump= DecimalParameter(-1, 0, default=-0.15, space='buy', optimize=True) dump_buy= DecimalParameter(0, 0.5, default=0.15, space='buy', optimize=True) range_filter_length = IntParameter(10, 200, default=200, space='buy', optimize= False) sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=True) delay_time = IntParameter(90, 1440, default=300, space='sell', optimize=True) fask_trailing = DecimalParameter(0.001, 0.02, default=0.001, space='sell', optimize=True) sell_fisher = RealParameter(0.1, 0.5, default=0.38414, space='sell', optimize=False) sell_bbmiddle_close = RealParameter(0.97, 1.1, default=1.07634, space='sell', optimize=False) is_optimize_deadfish = False sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , space='sell', optimize = is_optimize_deadfish) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.08 , space='sell', optimize = is_optimize_deadfish) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0 , space='sell', optimize = is_optimize_deadfish) sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.5 ,space='sell', optimize = is_optimize_deadfish) base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_2 = DecimalParameter(0.04, 0.1, default=0.04, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.11, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='sell', decimals=3, optimize=False, load=True) pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', optimize=False, load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=False, load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=False, load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell',optimize=False, load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=False,load=True) antipump_threshold = DecimalParameter(0, 0.4, default=0.15, space='buy', optimize=False) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs @informative('15m') def populate_indicators_15m(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) dataframe['smi'], dataframe['smi_ma'], dataframe['smi_trend'] = smi_trend(dataframe, 9, 3, 'EMA', 10) return dataframe def colored(self,param, color): color_codes = { 'red': '\033[91m', 'green': '\033[92m', 'yellow': '\033[93m', } return color_codes[color] + param + '\033[0m' def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: filled_entries = trade.select_filled_orders(trade.entry_side) min_profit_rate = trade.max_rate if trade.is_short else trade.min_rate max_profit_rate = trade.min_rate if trade.is_short else trade.max_rate min_profit = (trade.calc_profit_ratio(min_profit_rate) * 100) max_profit = (trade.calc_profit_ratio(max_profit_rate) * 100) profit = (trade.calc_profit_ratio(rate) * 100) filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) ll = self.custom_info[f'{pair}']['Level'] if profit < 0: if ll == '0' or ll == 0 : log.info( f'EXIT Pair: {pair:13} | Level: {count_of_buys} | Exit Reason: {exit_reason} | Min_profit: {min_profit:.2f}% | Max_profit: {max_profit:.2f}% | profit: {profit:.2f}% | {current_time}') else: self.log( f'EXIT Pair: {pair:13} | Level: {count_of_buys} | Exit Reason: {exit_reason} | Min_profit: {min_profit:.2f}% | Max_profit: {max_profit:.2f}% | profit: {profit:.2f}% | {current_time}',color='red') else: self.log( f'EXIT Pair: {pair:13} | Level: {count_of_buys} | Exit Reason: {exit_reason} | Min_profit: {min_profit:.2f}% | Max_profit: {max_profit:.2f}% | profit: {profit:.2f}% | {current_time}',color='green') self.custom_info[f'{pair}']['Level'] = 0 if self.config['telegram']['enabled'] == True: min_profit_rate = trade.max_rate if trade.is_short else trade.min_rate max_profit_rate = trade.min_rate if trade.is_short else trade.max_rate min_profit = (trade.calc_profit_ratio(min_profit_rate) * 100) max_profit = (trade.calc_profit_ratio(max_profit_rate) * 100) start_time = datetime.now() self.dp.send_msg( f"↕️ {pair} Min profit: {min_profit:.2f}% Max profit: {max_profit:.2f}%\n" f"💰 {pair} Open fee: {trade.fee_open * 100:.4f}% Close fee: {trade.fee_close * 100:.4f}%" ) logger.info(f"{pair} took {datetime.now() - start_time} to send telegram message") return True def log(self, param, color): log.info(self.colored(param, color)) def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) buy_tag = '' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: buy_tag = trade.enter_tag if (last_candle is not None): if (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_1.value)): return 'trail_target_1' elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_2.value)): return 'trail_target_2' elif (current_profit > 3) & (last_candle['rsi'] > 85): return 'RSI-85 target' if (current_profit > 0.05) & (count_of_buys < 4) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (last_candle['rsi']>50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']): return f"sell signal1( {buy_tag})" if (current_profit > 0.05) & (count_of_buys >= 4) & (last_candle['close'] > last_candle['hma_50'] * 1.01) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (last_candle['rsi']>50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']): return f"sell signal1 * 1.01( {buy_tag})" if (current_profit > 0.05) & (count_of_buys < 4) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)): return f"sell signal2( {buy_tag})" if (current_profit > 0.05) & (count_of_buys >= 4) & (last_candle['close'] > last_candle['hma_50'] * 1.01) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']) : return f"sell signal2 * 1.01( {buy_tag})" if (current_profit > 0.05) & (last_candle['rsi'] < 42.0) & (last_candle['cmf'] < -0.4): return f"profit exit1( {buy_tag})" elif (current_profit > 0.05) & (last_candle['rsi'] < 43.0) & (last_candle['cmf'] < -0.0) & (last_candle['cmf_15m'] < -0.0) & (last_candle['cmf_1h'] < -0.0) : return f"profit exit2( {buy_tag})" elif (current_profit > 0.05) & (last_candle['rsi'] < 41.0) & (last_candle['cmf'] < -0.2) & (last_candle['cmf_1h'] < -0.0): return f"profit exit3( {buy_tag})" elif (current_profit > 0.05) & (last_candle['rsi'] < 44.0) & (last_candle['cmf'] < -0.1) & (last_candle['cmf_15m'] < -0.1) & (last_candle['cmf_1h'] < -0.1) : return f"profit exit4( {buy_tag})" elif (current_profit > 0.05) & (last_candle['rsi'] < 40.0) & (last_candle['cmf'] < -0.2) & (last_candle['cmf_15m'] < -0.2): return f"pprofit exit5( {buy_tag})" elif (current_profit > 0.05) & (last_candle['rsi'] < 43.0) & (last_candle['cmf'] < -0.4) & (last_candle['cmf_15m'] < -0.0) & (last_candle['cmf_1h'] < -0.0) : return f"profit exit6( {buy_tag})" elif (current_profit > 0.02) & (last_candle['rsi'] < 30.0) & (last_candle['cmf'] < -0.4): return f"profit exit bear1( {buy_tag})" elif (current_profit > 0.03) & (last_candle['rsi'] < 35.0) & (last_candle['cmf'] < -0.4): return f"profit exit bear2( {buy_tag})" elif (current_profit > 0.01) & (last_candle['rsi'] < 25.0) & (last_candle['cmf'] < -0.6): return f"profit exit bear3( {buy_tag})" if ( (current_profit < self.sell_deadfish_profit.value) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (last_candle['cmf'] < 0.0) ): return f"sell_stoploss_deadfishD( {buy_tag})" def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if current_time - timedelta(minutes=int(self.delay_time.value)) > trade.open_date_utc: if current_profit >= -0.01: return -0.003 if current_time - timedelta(minutes=int(self.delay_time.value) * 2) > trade.open_date_utc: if current_profit >= -0.02: return -0.006 dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() if current_profit > 0: if current_candle["fastk"] > self.sell_fastx.value: return self.fask_trailing.value return self.stoploss def is_support(self, row_data) -> bool: conditions = [] for row in range(len(row_data)-1): if row < len(row_data)/2: conditions.append(row_data[row] > row_data[row+1]) else: conditions.append(row_data[row] < row_data[row+1]) return reduce(lambda x, y: x & y, conditions) @informative('5m', 'BTC/{stake}') def populate_indicators_btc_5m(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['btc_close'] = dataframe['close'] dataframe['btc_rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['btc_ema_fast'] = ta.EMA(dataframe, timeperiod=20) dataframe['btc_ema_slow'] = ta.EMA(dataframe, timeperiod=25) dataframe['down'] = (dataframe['btc_ema_fast'] < dataframe['btc_ema_slow']).astype('int') return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['smi'], dataframe['smi_ma'], dataframe['smi_trend'] = smi_trend(dataframe, 9, 3, 'EMA', 10) dataframe[f'range{self.range_filter_length.value}'] = range_filter(dataframe=dataframe,length=self.range_filter_length.value) if not metadata["pair"] in self.custom_info: self.custom_info[metadata["pair"]] = {} self.custom_info[metadata["pair"]]['Level'] = {} dataframe ['test'] = ( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.125 )) & ( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.225 )) ) ) for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] 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'] 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['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['zvwap'] = calc_zvwap(dataframe, pds=14, source1=dataframe['close']) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_fast1'] = dataframe['rsi_fast'].shift(1) dataframe['rsi_fast2'] = dataframe['rsi_fast'].shift(2) dataframe['rsi_fast_avg'] = (dataframe['rsi_fast'] + dataframe['rsi_fast1'] + dataframe['rsi_fast2']) / 3 dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1) vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1) dataframe['vwap_low'] = vwap_low dataframe['vwap_upperband'] = vwap_high dataframe['vwap_middleband'] = vwap dataframe['vwap_lowerband'] = vwap_low dataframe['vwap_width'] = ( (dataframe['vwap_upperband'] - dataframe['vwap_lowerband']) / dataframe['vwap_middleband'] ) * 100 dataframe['ema_vwap_diff_50'] = ( ( dataframe['ema_50'] - dataframe['vwap_lowerband'] ) / dataframe['ema_50'] ) dataframe['tpct_change_0'] = top_percent_change_dca(dataframe,0) dataframe['tpct_change_1'] = top_percent_change_dca(dataframe,1) dataframe['tcp_percent_4'] = top_percent_change_dca(dataframe , 4) dataframe['tpct_change_0'] = top_percent_change_dca(dataframe, 0) dataframe['tpct_change_1'] = top_percent_change_dca(dataframe, 1) dataframe['tpct_change_2'] = top_percent_change_dca(dataframe, 2) dataframe['tpct_change_4'] = top_percent_change_dca(dataframe, 4) dataframe['tpct_change_12'] = top_percent_change_dca(dataframe, 12) dataframe['tpct_change_144'] = top_percent_change_dca(dataframe, 144) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['ema_5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema_10'] = ta.EMA(dataframe, timeperiod=10) dataframe['zvwap']= calc_zvwap(dataframe, pds=14, source1=dataframe['close']) dataframe['zvwap_dip'] = (dataframe['zvwap'] <= -0.5) dataframe['zvwap_cross'] = (dataframe['zvwap'] > 0) & (dataframe['zvwap'].shift(1) < 0) dataframe['zvwap_buy'] = dataframe['zvwap_dip'] & dataframe['zvwap_cross'] 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'] rsi = ta.RSI(dataframe) dataframe["rsi"] = rsi rsi = 0.1 * (rsi - 50) dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) informative['rsi_14'] = ta.RSI(dataframe, timeperiod=14) informative['cmf'] = chaikin_money_flow(dataframe, 20) informative['roc'] = ta.ROC(informative, timeperiod=9) informative['live_data_ok'] = (dataframe['volume'].rolling(window=48, min_periods=48).min() > 0) informative['tcp_percent_72'] = self.top_percent_change_dca(dataframe,72) informative['cti_40'] = pta.cti(informative["close"], length=40) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['hma_5'] = qtpylib.hull_moving_average(dataframe['close'], window=30) dataframe['hma_4'] = qtpylib.hull_moving_average(dataframe['close'], window=15) dataframe['long_term_price_drop'] = np.where( ( ( dataframe['close'].rolling(4).max() * 0.96 > dataframe['close'].rolling(6).mean() #0 buys 20220101 ) | ( dataframe['close'].rolling(6).max() * 0.96 > dataframe['close'].rolling(6).mean() #0 buys 20220101 ) | ( dataframe['close'].rolling(6).max() * 0.95 > dataframe['close'].rolling(6).mean() #0 buys 20220101 ) | ( dataframe['close'].rolling(8).max() * 0.95 > dataframe['close'].rolling(6).mean() #0 buys 20220101 ) | ( dataframe['close'].rolling(8).max() * 0.94 > dataframe['close'].rolling(6).mean() ) | ( dataframe['close'].rolling(12).max() * 0.92 > dataframe['close'].rolling(6).mean() ) | ( dataframe['close'].rolling(24).max() * 0.90 > dataframe['close'].rolling(6).mean() ) ), 1, 0) dataframe['nfixdip'] = np.where( (dataframe['tpct_change_0'] < 0.032) | (dataframe['tpct_change_2'] < 0.06) | (dataframe['tpct_change_12'] < 0.24) | (dataframe['tpct_change_144'] < 0.6), 1, 0 ) dataframe['deadfish'] = np.where( (dataframe['close'] < dataframe['ema_200']) & (dataframe['bb_width'] < self.sell_deadfish_bb_width.value) & (dataframe['close'] > dataframe['bb_middleband2'] * self.sell_deadfish_bb_factor.value) & (dataframe['cmf'] < 0.0) & (dataframe['ha_open'] > dataframe['ha_close']), 1, 0) dataframe["deadfish2"] = dataframe["deadfish"].shift(1) dataframe["deadfish3"] = dataframe["deadfish"].shift(2) dataframe["deadfish4"] = dataframe["deadfish"].shift(3) dataframe['pump_protection_strict'] = np.where( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.125 )) & ( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.225 )) ), 1, 0 ) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' """ Generates a buy signal based on the BUY Rule conditions. """ fast_ema = ta.EMA(dataframe['close'], window=13) slow_ema = ta.EMA(dataframe['close'], window=55) zvwap = calc_zvwap(dataframe, pds=14, source1=dataframe['close']) dip_condition = zvwap <= -0.5 cross_above_zero_condition = (zvwap > 0) & (zvwap.shift() < 0) ema_condition = fast_ema > slow_ema dip_nfix = ( (dataframe['tpct_change_0'] < 0.032) & (dataframe['tpct_change_2'] < 0.06) & (dataframe['tpct_change_12'] < 0.24) & (dataframe['tpct_change_144'] < 0.6) ) dataframe.loc[ ( (dataframe['close'] < zvwap) & (dataframe['tcp_percent_4'] > self.tcp_percent_4_value.value ) & # 0.053) (dataframe['cti'] < self.cti.value) & # zobrate z bbrtr -0.087 (dataframe['rsi'] < self.buy_rsi.value) & (dataframe['rsi_84'] < 60) & #rsi check (dataframe['rsi_112'] < 60) & #rsi check (dataframe['deadfish'] == 0 ) & (dataframe['deadfish2'] == 0 ) & (dataframe['deadfish3'] == 0 ) & (dataframe['deadfish4'] == 0 ) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag']] = (1, 'zvwap') dataframe.loc[ ( dip_condition & cross_above_zero_condition & ema_condition & (dataframe['close'] < zvwap) ), ['enter_long', 'enter_tag']] = (1, 'zvwap2') dataframe.loc[ ( (dataframe['close'] < dataframe['zvwap']) & (dataframe['tcp_percent_4'] > 0.065) & (dataframe['cti'] < -0.8) & (dataframe['rsi'] < 35) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['live_data_ok_1h'])& (dataframe['tcp_percent_72_1h'] < 0.45) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag']] = (1, 'zvwap_pricedown') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe['volume'] > 0), 'sell' ] = 0 return dataframe initial_safety_order_trigger = -0.018 max_safety_orders = 6#8 safety_order_step_scale = 1.2 safety_order_volume_scale = 1.4 def top_percent_change_dca(self, dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): if current_profit > self.initial_safety_order_trigger: return None dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) if count_of_buys > 0: last_order = filled_buys[-1] if last_order.order_filled_date is not None: tz = last_order.order_filled_date.tzinfo current_time_with_tz = current_time.replace(tzinfo=tz) time_difference = current_time_with_tz - last_order.order_filled_date if time_difference.total_seconds() < 300: logger.info(f"Skipping SO buy for {trade.pair} because 300 sec have not passed since the last filled buy order.") return None if last_candle['pump_strength'] < -0.15 : logger.info(f"DCA for {trade.pair} waiting for pump_strength ({last_candle['pump_strength']}) to rise above -0.15") return None if count_of_buys > 1 and last_candle['rsi_fast_avg'] < 20 : logger.info(f"DCA for {trade.pair} waiting for rsi_fast ({last_candle['rsi_fast_avg']}) to rise above 20") return if count_of_buys > 2 and last_candle['smi_trend_15m'] < -1 : logger.info(f"DCA for {trade.pair} waiting for smi_trend 1+ actual trend ({last_candle['smi_trend_15m']})") return None if 1 <= count_of_buys <= self.max_safety_orders: safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_buys) if (self.safety_order_step_scale > 1): safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1)) elif (self.safety_order_step_scale < 1): safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (1 - math.pow(self.safety_order_step_scale,(count_of_buys - 1))) / (1 - self.safety_order_step_scale)) if current_profit <= (-1 * abs(safety_order_trigger)): try: stake_amount = filled_buys[0].cost stake_amount = stake_amount * math.pow(self.safety_order_volume_scale,(count_of_buys - 1)) amount = stake_amount / current_rate return stake_amount except Exception as exception: return None return None def range_filter(dataframe, length = 10, min_rate_of_change=0.01): df = dataframe[['date','open','high','low','close','volume']].copy() highest_high = df['high'].rolling(length).max() lowest_low = df['low'].rolling(length).min() df['range'] = ((highest_high - lowest_low) / lowest_low) return df['range'] def smi_trend(df: DataFrame, k_length=9, d_length=3, smoothing_type='EMA', smoothing=10): """ Stochastic Momentum Index (SMI) Trend Indicator SMI > 0 and SMI > MA: (2) Bull SMI < 0 and SMI > MA: (1) Possible Bullish Reversal SMI > 0 and SMI < MA: (-1) Possible Bearish Reversal SMI < 0 and SMI < MA: (-2) Bear Returns: pandas.Series: New feature generated """ ll = df['low'].rolling(window=k_length).min() hh = df['high'].rolling(window=k_length).max() diff = hh - ll rdiff = df['close'] - (hh + ll) / 2 avgrel = rdiff.ewm(span=d_length).mean().ewm(span=d_length).mean() avgdiff = diff.ewm(span=d_length).mean().ewm(span=d_length).mean() smi = np.where(avgdiff != 0, (avgrel / (avgdiff / 2) * 100), 0) if smoothing_type == 'SMA': smi_ma = ta.SMA(smi, timeperiod=smoothing) elif smoothing_type == 'EMA': smi_ma = ta.EMA(smi, timeperiod=smoothing) elif smoothing_type == 'WMA': smi_ma = ta.WMA(smi, timeperiod=smoothing) elif smoothing_type == 'DEMA': smi_ma = ta.DEMA(smi, timeperiod=smoothing) elif smoothing_type == 'TEMA': smi_ma = ta.TEMA(smi, timeperiod=smoothing) else: raise ValueError("Choose an MA Type: 'SMA', 'EMA', 'WMA', 'DEMA', 'TEMA'") conditions = [ (np.greater(smi, 0) & np.greater(smi, smi_ma)), # (2) Bull (np.less(smi, 0) & np.greater(smi, smi_ma)), # (1) Possible Bullish Reversal (np.greater(smi, 0) & np.less(smi, smi_ma)), # (-1) Possible Bearish Reversal (np.less(smi, 0) & np.less(smi, smi_ma)) # (-2) Bear ] smi_trend = np.select(conditions, [2, 1, -1, -2]) return smi, smi_ma, smi_trend