# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/aqumaaa.py import freqtrade.vendor.qtpylib.indicators as qtpylib 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, stoploss_from_open, 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 pandas as pd import time logger = logging.getLogger(__name__) def ewo(dataframe, sma1_length=5, sma2_length=35): sma1 = ta.EMA(dataframe, timeperiod=sma1_length) sma2 = ta.EMA(dataframe, timeperiod=sma2_length) smadif = (sma1 - sma2) / dataframe['close'] * 100 return smadif def top_percent_change_dca(dataframe: DataFrame, length: int) -> float: if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] def EWO(dataframe, ema_length=5, ema2_length=3): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif def williams_r(dataframe: DataFrame, period: int = 14) -> Series: highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name="{0} Williams %R".format(period), ) return WR * -100 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 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 chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) 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') 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_remiotore_freqtrade__aqumaaa__20260111_210550(IStrategy): can_short = True buy_params = { "bbdelta_close": 0.00082, "bbdelta_tail": 0.85788, "close_bblower": 0.00128, "closedelta_close": 0.00987, "low_offset": 0.991, "rocr1_1h": 0.9346, "rocr_1h": 0.65666, "base_nb_candles_buy": 12, "buy_bb_delta": 0.025, "buy_bb_factor": 0.995, "buy_bb_width": 0.095, "buy_bb_width_1h": 1.074, "buy_cci": -116, "buy_cci_length": 25, "buy_closedelta": 15.0, "buy_clucha_bbdelta_close": 0.049, "buy_clucha_bbdelta_tail": 1.146, "buy_clucha_close_bblower": 0.018, "buy_clucha_closedelta_close": 0.017, "buy_clucha_rocr_1h": 0.526, "buy_ema_diff": 0.025, "buy_rmi": 49, "buy_rmi_length": 17, "buy_roc_1h": 10, "buy_srsi_fk": 32, } sell_params = { "high_offset": 1.012, "high_offset_2": 1.016, "sell_deadfish_bb_factor": 1.089, "sell_deadfish_bb_width": 0.11, "sell_deadfish_profit": -0.107, "sell_deadfish_volume_factor": 1.761, "base_nb_candles_sell": 22, "pHSL": -0.397, "pPF_1": 0.012, "pPF_2": 0.07, "pSL_1": 0.015, "pSL_2": 0.068, "sell_bbmiddle_close": 1.09092, "sell_fisher": 0.46406, "sell_trail_down_1": 0.03, "sell_trail_down_2": 0.015, "sell_trail_profit_max_1": 0.4, "sell_trail_profit_max_2": 0.11, "sell_trail_profit_min_1": 0.1, "sell_trail_profit_min_2": 0.04, } short_params = { "short_bb_delta": 0.025, "short_bb_factor": 1.005, "short_bb_width": 0.095, "short_bb_width_1h": 1.074, "short_cci": 116, "short_cci_length": 25, "short_closedelta": 15.0, "short_ema_diff": 0.025, "short_rmi": 51, "short_rmi_length": 17, "short_roc_1h": -10, "short_srsi_fk": 68, "short_low_offset": 1.009, "short_high_offset": 0.991, "short_clucha_bbdelta_close": 0.049, "short_clucha_bbdelta_tail": 1.146, "short_clucha_close_bbupper": 0.018, "short_clucha_closedelta_close": 0.017, "short_clucha_rocr_1h": 0.526, "short_44_ma_offset": 1.018, "short_44_ewo": 18.143, "short_44_cti": 0.8, "short_44_r_1h": 75.0, "short_37_ma_offset": 1.02, "short_37_ewo": -9.8, "short_37_rsi": 44.0, "short_37_cti": 0.7, "short_ema_open_mult_7": 0.030, "short_cti_7": 0.89, } minimal_roi = { "0": 0.276, "32": 0.105, "88": 0.037, "208": 0 } position_adjustment_enable = True stoploss = -0.99 trailing_stop = False trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.10 trailing_only_offset_is_reached = True timeframe = '5m' use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False use_custom_stoploss = True process_only_new_candles = True startup_candle_count = 168 order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False, } 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) fast_ewo = 50 slow_ewo = 200 buy_44_ma_offset = 0.982 buy_44_ewo = -18.143 buy_44_cti = -0.8 buy_44_r_1h = -75.0 buy_37_ma_offset = 0.98 buy_37_ewo = 9.8 buy_37_rsi = 56.0 buy_37_cti = -0.7 buy_ema_open_mult_7 = 0.030 buy_cti_7 = -0.89 is_optimize_dip = False buy_rmi = IntParameter(30, 50, default=35, optimize=is_optimize_dip) buy_cci = IntParameter(-135, -90, default=-133, optimize=is_optimize_dip) buy_srsi_fk = IntParameter(30, 50, default=25, optimize=is_optimize_dip) buy_cci_length = IntParameter(25, 45, default=25, optimize=is_optimize_dip) buy_rmi_length = IntParameter(8, 20, default=8, optimize=is_optimize_dip) is_optimize_break = False buy_bb_width = DecimalParameter(0.065, 0.135, default=0.095, optimize=is_optimize_break) buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, optimize=is_optimize_break) is_optimize_check = False buy_roc_1h = IntParameter(-25, 200, default=10, optimize=is_optimize_check) buy_bb_width_1h = DecimalParameter(0.3, 2.0, default=0.3, optimize=is_optimize_check) is_optimize_clucha = False buy_clucha_bbdelta_close = DecimalParameter(0.01, 0.05, default=0.02206, optimize=is_optimize_clucha) buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=1.02515, optimize=is_optimize_clucha) buy_clucha_close_bblower = DecimalParameter(0.001, 0.05, default=0.03669, optimize=is_optimize_clucha) buy_clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=0.04401, optimize=is_optimize_clucha) buy_clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=0.47782, optimize=is_optimize_clucha) is_optimize_local_uptrend = False buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize=is_optimize_local_uptrend) buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize=False) buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize=is_optimize_local_uptrend) rocr_1h = RealParameter(0.5, 1.0, default=0.54904, space='buy', optimize=True) rocr1_1h = RealParameter(0.5, 1.0, default=0.72, space='buy', optimize=True) bbdelta_close = RealParameter(0.0005, 0.02, default=0.01965, space='buy', optimize=True) closedelta_close = RealParameter(0.0005, 0.02, default=0.00556, space='buy', optimize=True) bbdelta_tail = RealParameter(0.7, 1.0, default=0.95089, space='buy', optimize=True) close_bblower = RealParameter(0.0005, 0.02, default=0.00799, space='buy', 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 = True 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_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True) 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) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs 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 = lena = len(filled_buys) filled_sells = trade.select_filled_orders('sell') count_of_sells = len(filled_sells) if last_candle is not None: if trade.is_short == False: 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' elif (current_profit > 0) & (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 'sell signal1' elif (current_profit > 0) & (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 'sell signal1 * 1.01' elif (current_profit > 0) & (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)) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']): return 'sell signal2' elif (current_profit < self.sell_deadfish_profit.value) & (last_candle['close'] < last_candle['ema_200']) & (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) & (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) & (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) & (last_candle['cmf'] < 0.0): return 'sell_stoploss_deadfish' else: if (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (((trade.open_rate - trade.min_rate) / 100) > (current_profit + self.sell_trail_down_1.value)): return 'short_trail_target_1' elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (((trade.open_rate - trade.min_rate) / 100) > (current_profit + self.sell_trail_down_2.value)): return 'short_trail_target_2' elif (current_profit > 3) & (last_candle['rsi'] < 15): return 'short_RSI-15 target' elif (current_profit > 0) & (count_of_sells < 4) & (last_candle['close'] < last_candle['hma_50']) & (last_candle['close'] < (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.short_params['short_high_offset'])) & (last_candle['rsi'] < 50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] < last_candle['rsi_slow']): return 'short_sell_signal1' elif (current_profit > 0) & (count_of_sells >= 4) & (last_candle['close'] < last_candle['hma_50'] * 0.99) & (last_candle['close'] < (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.short_params['short_high_offset'])) & (last_candle['rsi'] < 50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] < last_candle['rsi_slow']): return 'short_sell_signal1 * 0.99' elif (current_profit > 0) & (last_candle['close'] < last_candle['hma_50']) & (last_candle['close'] < (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.short_params['short_low_offset'])) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] < last_candle['rsi_slow']): return 'short_sell_signal2' elif (current_profit < self.sell_deadfish_profit.value) & (last_candle['close'] > last_candle['ema_200']) & (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) & (last_candle['close'] < last_candle['bb_middleband2'] * (1 / self.sell_deadfish_bb_factor.value)) & (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) & (last_candle['cmf'] > 0.0): return 'short_sell_stoploss_deadfish' return None def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value if trade.is_short: if current_profit > PF_2 + 0.02: # Tighter stoploss for shorts in high profit sl_profit = SL_2 + (current_profit - (PF_2 + 0.02)) * 0.5 elif current_profit > PF_1: sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL else: if current_profit > PF_2: sl_profit = SL_2 + (current_profit - PF_2) elif current_profit > PF_1: sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: logger.info(f"Using provided dataframe for {metadata['pair']}, last_candle_date={dataframe['date'].iloc[-1]}") inf_tf = '5m' informative = self.dp.get_pair_dataframe('BTC/USDT:USDT', timeframe=inf_tf) if informative.empty: logger.warning(f"No data available for BTC/USDT:USDT on {inf_tf} timeframe, using provided dataframe") informative = dataframe.copy() informative_btc = informative.copy().shift(1) dataframe['btc_close'] = informative_btc['close'] dataframe['btc_ema_fast'] = ta.EMA(informative_btc, timeperiod=20) dataframe['btc_ema_slow'] = ta.EMA(informative_btc, timeperiod=25) dataframe['down'] = (dataframe['btc_ema_fast'] < dataframe['btc_ema_slow']).astype('int') 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) 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']) bollinger2_40 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=40, stds=2) dataframe['bb_lowerband2_40'] = bollinger2_40['lower'] dataframe['bb_middleband2_40'] = bollinger2_40['mid'] dataframe['bb_upperband2_40'] = bollinger2_40['upper'] 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_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'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] dataframe['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs() dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['mid'] = mid dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['bb_lowerband'] = dataframe['lower'] dataframe['bb_middleband'] = dataframe['mid'] bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3'] = bollinger3['lower'] dataframe['bb_middleband3'] = bollinger3['mid'] dataframe['bb_upperband3'] = bollinger3['upper'] dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2']) dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3) dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1) dataframe['vwap_low'] = vwap_low dataframe['vwap'] = vwap dataframe['vwap_high'] = vwap_high dataframe['vwap_width'] = ((dataframe['vwap_high'] - dataframe['vwap_low']) / dataframe['vwap']) * 100 dataframe['ema_vwap_diff_50'] = ((dataframe['ema_50'] - dataframe['vwap_low']) / 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['ewo'] = ewo(dataframe, 50, 200) for val in self.buy_rmi_length.range: dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4) for val in self.buy_cci_length.range: dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['ema_5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema_10'] = ta.EMA(dataframe, timeperiod=10) dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75) 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) inf_heikinashi = qtpylib.heikinashi(informative) informative['ha_close'] = inf_heikinashi['close'] informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168) informative['rsi_14'] = ta.RSI(informative, timeperiod=14) informative['cmf'] = chaikin_money_flow(informative, 20) sup_series = informative['low'].rolling(window=5).apply(lambda row: self.is_support(row), raw=True) informative['sup_level'] = Series(np.where(sup_series, np.where(informative['close'] < informative['open'], informative['close'], informative['open']), float('NaN'))).ffill() informative['roc'] = ta.ROC(informative, timeperiod=9) informative['r_480'] = williams_r(informative, period=480) bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2) informative['bb_lowerband2'] = bollinger2['lower'] informative['bb_middleband2'] = bollinger2['mid'] informative['bb_upperband2'] = bollinger2['upper'] informative['bb_width'] = ((informative['bb_upperband2'] - informative['bb_lowerband2']) / informative['bb_middleband2']) informative['r_84'] = williams_r(informative, period=84) informative['cti_40'] = pta.cti(informative["close"], length=40) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) last_candle = dataframe.iloc[-1] logger.info(f"Indicators for {metadata['pair']}: rmi={last_candle[f'rmi_length_{self.buy_rmi_length.value}']}, cci={last_candle[f'cci_length_{self.buy_cci_length.value}']}, srsi_fk={last_candle['srsi_fk']}, bb_delta={last_candle['bb_delta']}, bb_width={last_candle['bb_width']}, last_candle_date={last_candle['date']}") return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: desired_leverage = 1.0 max_leverage = self.dp.get_pair_leverage(metadata['pair'], 1.0) if hasattr(self.dp, 'get_pair_leverage') else 1.0 leverage = min(desired_leverage, max_leverage) logger.info(f"Setting leverage for {metadata['pair']}: {leverage}, Max leverage: {max_leverage}") # Long entry signals - DIP signal dip_conditions = [ (dataframe[f'rmi_length_{self.buy_rmi_length.value}'] < self.buy_rmi.value), (dataframe[f'cci_length_{self.buy_cci_length.value}'] <= self.buy_cci.value), (dataframe['srsi_fk'] < self.buy_srsi_fk.value), (dataframe['bb_delta'] > self.buy_bb_delta.value), (dataframe['bb_width'] > self.buy_bb_width.value), (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000), (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value), (dataframe['roc_1h'] < self.buy_roc_1h.value), (dataframe['bb_width_1h'] < self.buy_bb_width_1h.value), ] if dip_conditions[0].any(): logger.info(f"DIP signal conditions for {metadata['pair']}: {list(zip(['rmi', 'cci', 'srsi_fk', 'bb_delta', 'bb_width', 'closedelta', 'close_bblower', 'roc_1h', 'bb_width_1h'], [cond.iloc[-1] for cond in dip_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, dip_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'DIP signal', leverage) # Long entry signals - Break signal break_conditions = [ (dataframe['bb_delta'] > self.buy_bb_delta.value), (dataframe['bb_width'] > self.buy_bb_width.value), (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000), (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value), (dataframe['roc_1h'] < self.buy_roc_1h.value), (dataframe['bb_width_1h'] < self.buy_bb_width_1h.value), ] if break_conditions[0].any(): logger.info(f"Break signal conditions for {metadata['pair']}: {list(zip(['bb_delta', 'bb_width', 'closedelta', 'close_bblower', 'roc_1h', 'bb_width_1h'], [cond.iloc[-1] for cond in break_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, break_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'Break signal', leverage) # Long entry signals - cluc_HA clucha_conditions = [ (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h.value), (dataframe['bb_lowerband2_40'].shift() > 0), (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close.value), (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close.value), (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_clucha_bbdelta_tail.value), (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()), (dataframe['close'] > (dataframe['sup_level_1h'] * 0.88)), (dataframe['ha_close'] < dataframe['ha_close'].shift()), ] if clucha_conditions[0].any(): logger.info(f"cluc_HA signal conditions for {metadata['pair']}: {list(zip(['rocr_1h', 'bb_lowerband2_40', 'bb_delta_cluc', 'ha_closedelta', 'tail', 'ha_close_bblower', 'close_sup_level', 'ha_close_shift'], [cond.iloc[-1] for cond in clucha_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, clucha_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'cluc_HA', leverage) # Long entry signals - NFIX39 nfix39_conditions = [ (dataframe['ema_200'] > (dataframe['ema_200'].shift(12) * 1.01)), (dataframe['ema_200'] > (dataframe['ema_200'].shift(48) * 1.07)), (dataframe['bb_lowerband2_40'].shift().gt(0)), (dataframe['bb_delta_cluc'].gt(dataframe['close'] * 0.056)), (dataframe['closedelta'].gt(dataframe['close'] * 0.01)), (dataframe['tail'].lt(dataframe['bb_delta_cluc'] * 0.5)), (dataframe['close'].lt(dataframe['bb_lowerband2_40'].shift())), (dataframe['close'].le(dataframe['close'].shift())), (dataframe['close'] > dataframe['ema_50'] * 0.912), ] if nfix39_conditions[0].any(): logger.info(f"NFIX39 signal conditions for {metadata['pair']}: {list(zip(['ema_200_12', 'ema_200_48', 'bb_lowerband2_40', 'bb_delta_cluc', 'closedelta', 'tail', 'close_bblower', 'close_shift', 'close_ema50'], [cond.iloc[-1] for cond in nfix39_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, nfix39_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'NFIX39', leverage) # Long entry signals - NFIX29 nfix29_conditions = [ (dataframe['close'] > (dataframe['sup_level_1h'] * 0.72)), (dataframe['close'] < (dataframe['ema_16'] * 0.982)), (dataframe['EWO'] < -10.0), (dataframe['cti'] < -0.9), ] if nfix29_conditions[0].any(): logger.info(f"NFIX29 signal conditions for {metadata['pair']}: {list(zip(['close_sup_level', 'close_ema16', 'EWO', 'cti'], [cond.iloc[-1] for cond in nfix29_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, nfix29_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'NFIX29', leverage) # Long entry signals - local_uptrend local_uptrend_conditions = [ (dataframe['ema_26'] > dataframe['ema_12']), (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff.value), (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100), (dataframe['close'] < dataframe['bb_lowerband2'] * self.buy_bb_factor.value), (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000), ] if local_uptrend_conditions[0].any(): logger.info(f"local_uptrend signal conditions for {metadata['pair']}: {list(zip(['ema_26_12', 'ema_diff', 'ema_shift', 'close_bblower', 'closedelta'], [cond.iloc[-1] for cond in local_uptrend_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, local_uptrend_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'local_uptrend', leverage) # Long entry signals - vwap vwap_conditions = [ (dataframe['close'] < dataframe['vwap_low']), (dataframe['tcp_percent_4'] > 0.053), (dataframe['cti'] < -0.8), (dataframe['rsi'] < 35), (dataframe['rsi_84'] < 60), (dataframe['rsi_112'] < 60), (dataframe['volume'] > 0), ] if vwap_conditions[0].any(): logger.info(f"vwap signal conditions for {metadata['pair']}: {list(zip(['close_vwap_low', 'tcp_percent_4', 'cti', 'rsi', 'rsi_84', 'rsi_112', 'volume'], [cond.iloc[-1] for cond in vwap_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, vwap_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'vwap', leverage) # Long entry signals - insta_signal insta_conditions = [ (dataframe['bb_width_1h'] > 0.131), (dataframe['r_14'] < -51), (dataframe['r_84_1h'] < -70), (dataframe['cti'] < -0.845), (dataframe['cti_40_1h'] < -0.735), ((dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.1))), (dataframe['btc_close'].rolling(24).max() >= (dataframe['btc_close'] * 1.03)), ] if insta_conditions[0].any(): logger.info(f"insta_signal conditions for {metadata['pair']}: {list(zip(['bb_width_1h', 'r_14', 'r_84_1h', 'cti', 'cti_40_1h', 'close_rolling', 'btc_close_rolling'], [cond.iloc[-1] for cond in insta_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, insta_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'insta_signal', leverage) # Long entry signals - NFINext44 nfin44_conditions = [ (dataframe['close'] < (dataframe['ema_16'] * self.buy_44_ma_offset)), (dataframe['ewo'] < self.buy_44_ewo), (dataframe['cti'] < self.buy_44_cti), (dataframe['r_480_1h'] < self.buy_44_r_1h), (dataframe['volume'] > 0), ] if nfin44_conditions[0].any(): logger.info(f"NFINext44 signal conditions for {metadata['pair']}: {list(zip(['close_ema16', 'ewo', 'cti', 'r_480_1h', 'volume'], [cond.iloc[-1] for cond in nfin44_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, nfin44_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'NFINext44', leverage) # Long entry signals - NFINext37 nfin37_conditions = [ (dataframe['pm'] > dataframe['pmax_thresh']), (dataframe['close'] < dataframe['sma_75'] * self.buy_37_ma_offset), (dataframe['ewo'] > self.buy_37_ewo), (dataframe['rsi'] < self.buy_37_rsi), (dataframe['cti'] < self.buy_37_cti), ] if nfin37_conditions[0].any(): logger.info(f"NFINext37 signal conditions for {metadata['pair']}: {list(zip(['pm_pmax', 'close_sma75', 'ewo', 'rsi', 'cti'], [cond.iloc[-1] for cond in nfin37_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, nfin37_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'NFINext37', leverage) # Long entry signals - NFINext7 nfin7_conditions = [ (dataframe['ema_26'] > dataframe['ema_12']), ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_7)), ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)), (dataframe['cti'] < self.buy_cti_7), ] if nfin7_conditions[0].any(): logger.info(f"NFINext7 signal conditions for {metadata['pair']}: {list(zip(['ema_26_12', 'ema_diff', 'ema_shift', 'cti'], [cond.iloc[-1] for cond in nfin7_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, nfin7_conditions), ['enter_long', 'enter_tag', 'leverage']] = (1, 'NFINext7', leverage) # Short entry signals - short_DIP_signal short_dip_conditions = [ (dataframe[f'rmi_length_{self.buy_rmi_length.value}'] > self.short_params['short_rmi']), (dataframe[f'cci_length_{self.buy_cci_length.value}'] >= self.short_params['short_cci']), (dataframe['srsi_fk'] > self.short_params['short_srsi_fk']), (dataframe['bb_delta'] > self.short_params['short_bb_delta']), (dataframe['bb_width'] > self.short_params['short_bb_width']), (dataframe['closedelta'] > dataframe['close'] * self.short_params['short_closedelta'] / 1000), (dataframe['close'] > dataframe['bb_upperband3'] * self.short_params['short_bb_factor']), (dataframe['roc_1h'] > self.short_params['short_roc_1h']), (dataframe['bb_width_1h'] < self.short_params['short_bb_width_1h']), ] if short_dip_conditions[0].any(): logger.info(f"short_DIP_signal conditions for {metadata['pair']}: {list(zip(['rmi', 'cci', 'srsi_fk', 'bb_delta', 'bb_width', 'closedelta', 'close_bbupper', 'roc_1h', 'bb_width_1h'], [cond.iloc[-1] for cond in short_dip_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_dip_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_DIP_signal', leverage) # Short entry signals - short_Break_signal short_break_conditions = [ (dataframe['bb_delta'] > self.short_params['short_bb_delta']), (dataframe['bb_width'] > self.short_params['short_bb_width']), (dataframe['closedelta'] > dataframe['close'] * self.short_params['short_closedelta'] / 1000), (dataframe['close'] > dataframe['bb_upperband3'] * self.short_params['short_bb_factor']), (dataframe['roc_1h'] > self.short_params['short_roc_1h']), (dataframe['bb_width_1h'] < self.short_params['short_bb_width_1h']), ] if short_break_conditions[0].any(): logger.info(f"short_Break_signal conditions for {metadata['pair']}: {list(zip(['bb_delta', 'bb_width', 'closedelta', 'close_bbupper', 'roc_1h', 'bb_width_1h'], [cond.iloc[-1] for cond in short_break_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_break_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_Break_signal', leverage) # Short entry signals - short_local_downtrend short_downtrend_conditions = [ (dataframe['ema_26'] < dataframe['ema_12']), (dataframe['ema_12'] - dataframe['ema_26'] > dataframe['open'] * self.short_params['short_ema_diff']), (dataframe['ema_12'].shift() - dataframe['ema_26'].shift() > dataframe['open'] / 100), (dataframe['close'] > dataframe['bb_upperband2'] * self.short_params['short_bb_factor']), (dataframe['closedelta'] > dataframe['close'] * self.short_params['short_closedelta'] / 1000), ] if short_downtrend_conditions[0].any(): logger.info(f"short_local_downtrend conditions for {metadata['pair']}: {list(zip(['ema_26_12', 'ema_diff', 'ema_shift', 'close_bbupper', 'closedelta'], [cond.iloc[-1] for cond in short_downtrend_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_downtrend_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_local_downtrend', leverage) # Short entry signals - short_vwap short_vwap_conditions = [ (dataframe['close'] > dataframe['vwap_high']), (dataframe['tcp_percent_4'] < -0.053), (dataframe['cti'] > 0.8), (dataframe['rsi'] > 65), (dataframe['rsi_84'] > 60), (dataframe['rsi_112'] > 60), (dataframe['volume'] > 0), ] if short_vwap_conditions[0].any(): logger.info(f"short_vwap conditions for {metadata['pair']}: {list(zip(['close_vwap_high', 'tcp_percent_4', 'cti', 'rsi', 'rsi_84', 'rsi_112', 'volume'], [cond.iloc[-1] for cond in short_vwap_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_vwap_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_vwap', leverage) # Short entry signals - short_cluc_HA short_clucha_conditions = [ (dataframe['rocr_1h'] < self.short_params['short_clucha_rocr_1h']), (dataframe['bb_upperband2_40'].shift() > 0), (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.short_params['short_clucha_bbdelta_close']), (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.short_params['short_clucha_closedelta_close']), (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.short_params['short_clucha_bbdelta_tail']), (dataframe['ha_close'] > dataframe['bb_upperband2_40'].shift()), (dataframe['close'] < (dataframe['sup_level_1h'] * 1.12)), (dataframe['ha_close'] > dataframe['ha_close'].shift()), ] if short_clucha_conditions[0].any(): logger.info(f"short_cluc_HA conditions for {metadata['pair']}: {list(zip(['rocr_1h', 'bb_upperband2_40', 'bb_delta_cluc', 'ha_closedelta', 'tail', 'ha_close_bbupper', 'close_sup_level', 'ha_close_shift'], [cond.iloc[-1] for cond in short_clucha_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_clucha_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_cluc_HA', leverage) # Short entry signals - short_NFIX39 short_nfix39_conditions = [ (dataframe['ema_200'] < (dataframe['ema_200'].shift(12) * 0.99)), (dataframe['ema_200'] < (dataframe['ema_200'].shift(48) * 0.93)), (dataframe['bb_upperband2_40'].shift().gt(0)), (dataframe['bb_delta_cluc'].gt(dataframe['close'] * 0.056)), (dataframe['closedelta'].gt(dataframe['close'] * 0.01)), (dataframe['tail'].lt(dataframe['bb_delta_cluc'] * 0.5)), (dataframe['close'].gt(dataframe['bb_upperband2_40'].shift())), (dataframe['close'].ge(dataframe['close'].shift())), (dataframe['close'] < dataframe['ema_50'] * 1.088), ] if short_nfix39_conditions[0].any(): logger.info(f"short_NFIX39 conditions for {metadata['pair']}: {list(zip(['ema_200_12', 'ema_200_48', 'bb_upperband2_40', 'bb_delta_cluc', 'closedelta', 'tail', 'close_bbupper', 'close_shift', 'close_ema50'], [cond.iloc[-1] for cond in short_nfix39_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_nfix39_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_NFIX39', leverage) # Short entry signals - short_NFIX29 short_nfix29_conditions = [ (dataframe['close'] < (dataframe['sup_level_1h'] * 1.28)), (dataframe['close'] > (dataframe['ema_16'] * 1.018)), (dataframe['EWO'] > 10.0), (dataframe['cti'] > 0.9), ] if short_nfix29_conditions[0].any(): logger.info(f"short_NFIX29 conditions for {metadata['pair']}: {list(zip(['close_sup_level', 'close_ema16', 'EWO', 'cti'], [cond.iloc[-1] for cond in short_nfix29_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_nfix29_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_NFIX29', leverage) # Short entry signals - short_insta_signal short_insta_conditions = [ (dataframe['bb_width_1h'] > 0.131), (dataframe['r_14'] > 51), (dataframe['r_84_1h'] > 70), (dataframe['cti'] > 0.845), (dataframe['cti_40_1h'] > 0.735), ((dataframe['close'].rolling(48).min() <= (dataframe['close'] * 0.9))), (dataframe['btc_close'].rolling(24).min() <= (dataframe['btc_close'] * 0.97)), ] if short_insta_conditions[0].any(): logger.info(f"short_insta_signal conditions for {metadata['pair']}: {list(zip(['bb_width_1h', 'r_14', 'r_84_1h', 'cti', 'cti_40_1h', 'close_rolling', 'btc_close_rolling'], [cond.iloc[-1] for cond in short_insta_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_insta_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_insta_signal', leverage) # Short entry signals - short_NFINext44 short_nfin44_conditions = [ (dataframe['close'] > (dataframe['ema_16'] * self.short_params['short_44_ma_offset'])), (dataframe['ewo'] > self.short_params['short_44_ewo']), (dataframe['cti'] > self.short_params['short_44_cti']), (dataframe['r_480_1h'] > self.short_params['short_44_r_1h']), (dataframe['volume'] > 0), ] if short_nfin44_conditions[0].any(): logger.info(f"short_NFINext44 conditions for {metadata['pair']}: {list(zip(['close_ema16', 'ewo', 'cti', 'r_480_1h', 'volume'], [cond.iloc[-1] for cond in short_nfin44_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_nfin44_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_NFINext44', leverage) # Short entry signals - short_NFINext37 short_nfin37_conditions = [ (dataframe['pm'] < dataframe['pmax_thresh']), (dataframe['close'] > dataframe['sma_75'] * self.short_params['short_37_ma_offset']), (dataframe['ewo'] < self.short_params['short_37_ewo']), (dataframe['rsi'] > self.short_params['short_37_rsi']), (dataframe['cti'] > self.short_params['short_37_cti']), ] if short_nfin37_conditions[0].any(): logger.info(f"short_NFINext37 conditions for {metadata['pair']}: {list(zip(['pm_pmax', 'close_sma75', 'ewo', 'rsi', 'cti'], [cond.iloc[-1] for cond in short_nfin37_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_nfin37_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_NFINext37', leverage) # Short entry signals - short_NFINext7 short_nfin7_conditions = [ (dataframe['ema_26'] < dataframe['ema_12']), ((dataframe['ema_12'] - dataframe['ema_26']) > (dataframe['open'] * self.short_params['short_ema_open_mult_7'])), ((dataframe['ema_12'].shift() - dataframe['ema_26'].shift()) > (dataframe['open'] / 100)), (dataframe['cti'] > self.short_params['short_cti_7']), ] if short_nfin7_conditions[0].any(): logger.info(f"short_NFINext7 conditions for {metadata['pair']}: {list(zip(['ema_26_12', 'ema_diff', 'ema_shift', 'cti'], [cond.iloc[-1] for cond in short_nfin7_conditions]))}") dataframe.loc[reduce(lambda x, y: x & y, short_nfin7_conditions), ['enter_short', 'enter_tag', 'leverage']] = (1, 'short_NFINext7', leverage) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Long exit signals long_exit_conditions = [ (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), (dataframe['rsi_14_1h'] < 30), # Added safety condition ] dataframe.loc[reduce(lambda x, y: x & y, long_exit_conditions), 'exit_long'] = 1 # Short exit signals short_exit_conditions = [ (dataframe['fisher'] < -self.sell_fisher.value), (dataframe['ha_low'].ge(dataframe['ha_low'].shift(1))), (dataframe['ha_low'].shift(1).ge(dataframe['ha_low'].shift(2))), (dataframe['ha_close'].ge(dataframe['ha_close'].shift(1))), (dataframe['ema_fast'] < dataframe['ha_close']), ((dataframe['ha_close'] * (2 - self.sell_bbmiddle_close.value)) < dataframe['bb_middleband']), (dataframe['volume'] > 0), (dataframe['rsi_14_1h'] > 70), # Added safety condition ] dataframe.loc[reduce(lambda x, y: x & y, short_exit_conditions), 'exit_short'] = 1 return dataframe initial_safety_order_trigger = -0.05 max_safety_orders = 8 safety_order_step_scale = 1.2 safety_order_volume_scale = 1.4 def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): desired_leverage = 1.0 max_leverage = self.dp.get_pair_leverage(trade.pair, 1.0) if hasattr(self.dp, 'get_pair_leverage') else 1.0 trade.leverage = min(desired_leverage, max_leverage) logger.info(f"DCA for {trade.pair}: Setting leverage to {trade.leverage}, Max leverage: {max_leverage}") logger.info(f"DCA for {trade.pair}: trade_id={trade.id}, is_open={trade.is_open}, is_short={trade.is_short}, orders={len(trade.orders)}, current_profit={current_profit}") logger.info(f"DCA for {trade.pair}: trade_orders={[order.__dict__ for order in trade.orders]}") if current_profit > self.initial_safety_order_trigger: logger.debug(f"DCA for {trade.pair}: Profit {current_profit} > {self.initial_safety_order_trigger}, skipping DCA") return None max_retries = 3 for attempt in range(max_retries): try: ohlcv = self.dp.get_pair_dataframe(pair=trade.pair, timeframe=self.timeframe) dataframe = ohlcv.copy() dataframe = self.populate_indicators(dataframe, {'pair': trade.pair}) last_candle = dataframe.iloc[-1].squeeze() logger.info(f"D ASSOCIATE for {trade.pair}: Successfully fetched latest OHLCV on attempt {attempt + 1}, last_candle_date={last_candle['date']}") break except Exception as e: logger.warning(f"DCA for {trade.pair}: Attempt {attempt + 1}/{max_retries} failed to fetch OHLCV: {str(e)}") if attempt < max_retries - 1: time.sleep(1) if dataframe is None or last_candle is None: logger.error(f"DCA for {trade.pair}: Failed to fetch latest OHLCV, using cached dataframe") dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() logger.info(f"DCA for {trade.pair}: Using cached dataframe, last_candle_date={last_candle['date']}") if trade.is_short: filled_sells = trade.select_filled_orders('sell') count_of_sells = len(filled_sells) logger.info(f"DCA for {trade.pair}: filled_sells={filled_sells}, count_of_sells={count_of_sells}, initial_cost={filled_sells[0].cost if filled_sells else 0}, volume_scale={self.safety_order_volume_scale}, leverage={trade.leverage}") if count_of_sells == 1 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] > last_candle['open']): logger.info(f"DCA for {trade.pair}: Skipped due to tpct_change_0={last_candle['tpct_change_0']} > 0.018 and close > open") return None elif count_of_sells == 2 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] > last_candle['open']) and (last_candle['ema_vwap_diff_50'] < 0.215): logger.info(f"DCA for {trade.pair}: Skipped due to tpct_change_0={last_candle['tpct_change_0']} > 0.018, close > open, ema_vwap_diff_50={last_candle['ema_vwap_diff_50']} < 0.215") return None elif count_of_sells == 3 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] > last_candle['open']) and (last_candle['ema_vwap_diff_50'] < 0.215): logger.info(f"DCA for {trade.pair}: Skipped due to tpct_change_0={last_candle['tpct_change_0']} > 0.018, close > open, ema_vwap_diff_50={last_candle['ema_vwap_diff_50']} < 0.215") return None elif count_of_sells == 4 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] > last_candle['open']) and (last_candle['ema_vwap_diff_50'] < 0.215) and (last_candle['ema_5'] >= last_candle['ema_10']): logger.info(f"DCA for {trade.pair}: Skipped due to tpct_change_0={last_candle['tpct_change_0']} > 0.018, close > open, ema_vwap_diff_50={last_candle['ema_vwap_diff_50']} < 0.215, ema_5 >= ema_10") return None elif count_of_sells >= 5 and (last_candle['cmf_1h'] > 0.0) and (last_candle['close'] > last_candle['open']) and (last_candle['rsi_14_1h'] > 30) and (last_candle['tpct_change_0'] > 0.018) and (last_candle['ema_vwap_diff_50'] < 0.215) and (last_candle['ema_5'] >= last_candle['ema_10']): logger.info(f"DCA for {trade.pair}: Waiting for cmf_1h ({last_candle['cmf_1h']}) to fall below 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']}) to fall below 30") return None if count_of_sells == 0 and trade.is_open: logger.warning(f"DCA for {trade.pair}: No filled sell orders, estimating initial sell from trade data") stake_amount = trade.amount * trade.open_rate count_of_sells = 1 else: stake_amount = filled_sells[0].cost if filled_sells else min_stake if count_of_sells == 0 and trade.is_open: logger.warning(f"{trade.pair}의 DCA: 체결된 매도 주문 없음, 거래 데이터로 초기 매도 추정") stake_amount = trade.amount * trade.open_rate count_of_sells = 1 else: stake_amount = filled_sells[0].cost if filled_sells else min_stake if count_of_sells <= self.max_safety_orders: safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_sells) 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_sells - 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_sells - 1))) / (1 - self.safety_order_step_scale)) logger.info(f"{trade.pair}의 DCA: safety_order_trigger={safety_order_trigger}, current_profit={current_profit}, tpct_change_0={last_candle['tpct_change_0']}, rsi_14_1h={last_candle['rsi_14_1h']}") if current_profit <= (-1 * abs(safety_order_trigger)): try: stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, count_of_sells - 1) amount = stake_amount / current_rate logger.info(f"{trade.pair}의 안전 주문 매도 #{count_of_sells} 시작, 스테이크 금액={stake_amount}, 수량={amount}") return stake_amount except Exception as exception: logger.error(f"{trade.pair}의 스테이크 금액 계산 중 오류 발생: {str(exception)}") return None else: filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) logger.info(f"{trade.pair}의 DCA: filled_buys={filled_buys}, count_of_buys={count_of_buys}, initial_cost={filled_buys[0].cost if filled_buys else 0}, volume_scale={self.safety_order_volume_scale}, leverage={trade.leverage}") if count_of_buys == 1 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']): logger.info(f"{trade.pair}의 DCA: tpct_change_0={last_candle['tpct_change_0']} > 0.018 및 close < open으로 인해 스킵") return None elif count_of_buys == 2 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']) and (last_candle['ema_vwap_diff_50'] < 0.215): logger.info(f"{trade.pair}의 DCA: tpct_change_0={last_candle['tpct_change_0']} > 0.018, close < open, ema_vwap_diff_50={last_candle['ema_vwap_diff_50']} < 0.215로 인해 스킵") return None elif count_of_buys == 3 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']) and (last_candle['ema_vwap_diff_50'] < 0.215): logger.info(f"{trade.pair}의 DCA: tpct_change_0={last_candle['tpct_change_0']} > 0.018, close < open, ema_vwap_diff_50={last_candle['ema_vwap_diff_50']} < 0.215로 인해 스킵") return None elif count_of_buys == 4 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']) and (last_candle['ema_vwap_diff_50'] < 0.215) and (last_candle['ema_5'] >= last_candle['ema_10']): logger.info(f"{trade.pair}의 DCA: tpct_change_0={last_candle['tpct_change_0']} > 0.018, close < open, ema_vwap_diff_50={last_candle['ema_vwap_diff_50']} < 0.215, ema_5 >= ema_10으로 인해 스킵") return None elif count_of_buys >= 5 and (last_candle['cmf_1h'] < 0.0) and (last_candle['close'] < last_candle['open']) and (last_candle['rsi_14_1h'] < 30) and (last_candle['tpct_change_0'] > 0.018) and (last_candle['ema_vwap_diff_50'] < 0.215) and (last_candle['ema_5'] >= last_candle['ema_10']): logger.info(f"{trade.pair}의 DCA: cmf_1h ({last_candle['cmf_1h']})가 0 초과로 상승하기를 대기. rsi_1h ({last_candle['rsi_14_1h']})가 30 초과로 상승하기를 대기") return None if count_of_buys == 0 and trade.is_open: logger.warning(f"{trade.pair}의 DCA: 체결된 매수 주문 없음, 거래 데이터로 초기 매수 추정") stake_amount = trade.amount * trade.open_rate count_of_buys = 1 else: stake_amount = filled_buys[0].cost if filled_buys else min_stake if 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)) logger.info(f"{trade.pair}의 DCA: safety_order_trigger={safety_order_trigger}, current_profit={current_profit}, tpct_change_0={last_candle['tpct_change_0']}, rsi_14_1h={last_candle['rsi_14_1h']}") if current_profit <= (-1 * abs(safety_order_trigger)): try: stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, count_of_buys - 1) amount = stake_amount / current_rate logger.info(f"{trade.pair}의 안전 주문 매수 #{count_of_buys} 시작, 스테이크 금액={stake_amount}, 수량={amount}") return stake_amount except Exception as exception: logger.error(f"{trade.pair}의 스테이크 금액 계산 중 오류 발생: {str(exception)}") return None return None def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = 'MA_' + str(MAtype) + '_' + str(length) atr = 'ATR_' + str(period) pm = 'pm_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype) pmx = 'pmX_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype) if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 elif src == 3: masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) else: mavalue = ta.EMA(masrc, timeperiod=length) df[atr] = ta.ATR(df, timeperiod=period) df['basic_ub'] = mavalue + ((multiplier/10) * df[atr]) df['basic_lb'] = mavalue - ((multiplier/10) * df[atr]) basic_ub = df['basic_ub'].values final_ub = np.full(len(df), 0.00) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.00) for i in range(period, len(df)): final_ub[i] = basic_ub[i] if ( basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1] final_lb[i] = basic_lb[i] if ( basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1] df['final_ub'] = final_ub df['final_lb'] = final_lb pm_arr = np.full(len(df), 0.00) for i in range(period, len(df)): pm_arr[i] = ( final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i]) else final_lb[i] if ( pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i]) else final_lb[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i]) else final_ub[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]) else 0.00) pm = Series(pm_arr) pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN) return pm, pmx