# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/newstrategy_modify_support.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 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: """ 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 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: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ 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: """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') 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__newstrategy_modify_support__20260111_210550(IStrategy): """ PASTE OUTPUT FROM HYPEROPT HERE Can be overridden for specific sub-strategies (stake currencies) at the bottom. MaxDrawDownRelativeHyperOptLoss 31/900: 801 trades. 800/0/1 Wins/Draws/Losses. Avg profit 4.04%. Median profit 3.70%. Total profit 57.02887841 USDT ( 5.70%). Avg duration 3:05:00 min. Objective: -14799833083.36105 """ 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, # value loaded from strategy "buy_bb_delta": 0.025, # value loaded from strategy "buy_bb_factor": 0.995, # value loaded from strategy "buy_bb_width": 0.095, # value loaded from strategy "buy_bb_width_1h": 1.074, # value loaded from strategy "buy_cci": -116, # value loaded from strategy "buy_cci_length": 25, # value loaded from strategy "buy_closedelta": 15.0, # value loaded from strategy "buy_clucha_bbdelta_close": 0.049, # value loaded from strategy "buy_clucha_bbdelta_tail": 1.146, # value loaded from strategy "buy_clucha_close_bblower": 0.018, # value loaded from strategy "buy_clucha_closedelta_close": 0.017, # value loaded from strategy "buy_clucha_rocr_1h": 0.526, # value loaded from strategy "buy_ema_diff": 0.025, # value loaded from strategy "buy_rmi": 49, # value loaded from strategy "buy_rmi_length": 17, # value loaded from strategy "buy_roc_1h": 10, # value loaded from strategy "buy_srsi_fk": 32, # value loaded from strategy } 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, # value loaded from strategy "pHSL": -0.397, # value loaded from strategy "pPF_1": 0.012, # value loaded from strategy "pPF_2": 0.07, # value loaded from strategy "pSL_1": 0.015, # value loaded from strategy "pSL_2": 0.068, # value loaded from strategy "sell_bbmiddle_close": 1.09092, # value loaded from strategy "sell_fisher": 0.46406, # value loaded from strategy "sell_trail_down_1": 0.03, # value loaded from strategy "sell_trail_down_2": 0.015, # value loaded from strategy "sell_trail_profit_max_1": 0.4, # value loaded from strategy "sell_trail_profit_max_2": 0.11, # value loaded from strategy "sell_trail_profit_min_1": 0.1, # value loaded from strategy "sell_trail_profit_min_2": 0.04, # value loaded from strategy } minimal_roi = { "0": 0.276, "32": 0.105, "88": 0.037, "208": 0 } position_adjustment_enable = True stoploss = -0.99 # use custom stoploss trailing_stop = False trailing_stop_positive = 0.02 #povodne 0.001 trailing_stop_positive_offset = 0.10 #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 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 } 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_sell(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) 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) & (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' if (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' if (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' return 'sell stoploss1' 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['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) and (last_candle['cmf'] < 0.0) ): return f"sell_stoploss_deadfish" 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 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: inf_tf = '5m' informative = self.dp.get_pair_dataframe('BTC/USDT', timeframe=inf_tf) 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) 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['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['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs() 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(dataframe, timeperiod=14) informative['cmf'] = chaikin_money_flow(dataframe, 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) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ((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 ) & # from BinH (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) ), ['enter_long', 'enter_tag']] = (1, 'DIP signal') dataframe.loc[ ((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 ) & # from BinH (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) ), ['enter_long', 'enter_tag']] = (1, 'Break signal') dataframe.loc[ ((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()) ), ['enter_long', 'enter_tag']] = (1, 'cluc_HA') dataframe.loc[ ((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) ), ['enter_long', 'enter_tag']] = (1, 'NFIX39') dataframe.loc[ ((dataframe['close'] > (dataframe['sup_level_1h'] * 0.72)) & (dataframe['close'] < (dataframe['ema_16'] * 0.982)) & (dataframe['EWO'] < -10.0) & (dataframe['cti'] < -0.9) ), ['enter_long', 'enter_tag']] = (1, 'NFIX29') dataframe.loc[ ((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 ) ), ['enter_long', 'enter_tag']] = (1, 'local_uptrend') dataframe.loc[ ( (dataframe['close'] < dataframe['vwap_low']) & (dataframe['tcp_percent_4'] > 0.053) & # 0.053) (dataframe['cti'] < -0.8) & # -0.8) (dataframe['rsi'] < 35) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag']] = (1, 'vwap') dataframe.loc[ ((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 )) ), ['enter_long', 'enter_tag']] = (1, 'insta_signal') dataframe.loc[ ((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) ), ['enter_long', 'enter_tag']] = (1, 'NFINext44') dataframe.loc[ ((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) ), ['enter_long', 'enter_tag']] = (1, 'NFINext37') dataframe.loc[ ((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) ), ['enter_long', 'enter_tag']] = (1, 'NFINext7') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (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), 'sell' ] = 0 return dataframe initial_safety_order_trigger = -0.018 max_safety_orders = 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 == 1 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']) : 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): 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) : 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']): return None elif count_of_buys == 5 and (last_candle['cmf_1h'] < 0.00) 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['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} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30") return None elif count_of_buys == 6 and (last_candle['cmf_1h'] < 0.00) 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['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} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30") return None elif count_of_buys == 7 and (last_candle['cmf_1h'] < 0.00) 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['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} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30") return None elif count_of_buys == 8 and (last_candle['cmf_1h'] < 0.00) 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['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} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30") 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 logger.info(f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}") return stake_amount except Exception as exception: logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {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 == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: mavalue = VIDYA(df, length=length) elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(df, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=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