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 harvester(IStrategy): # modified by sokoow@gmail.com minimal_roi = { "0": 100 } 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 position_adjustment_enable = True 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 buy_rmi = IntParameter(30, 50, default=35, optimize=True) buy_cci = IntParameter(-135, -90, default=-133, optimize=True) buy_srsi_fk = IntParameter(30, 50, default=25, optimize=True) buy_cci_length = IntParameter(25, 45, default=25, optimize=True) buy_rmi_length = IntParameter(8, 20, default=8, optimize=True) buy_bb_width = DecimalParameter(0.065, 0.135, default=0.095, optimize=True) buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, optimize=True) buy_roc_1h = IntParameter(-25, 200, default=10, optimize=True) buy_bb_width_1h = DecimalParameter(0.3, 2.0, default=0.3, optimize=True) is_optimize_clucha = False buy_clucha_bbdelta_close = DecimalParameter(0.01,0.05, default=0.02206, optimize=True) buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=1.02515, optimize=True) buy_clucha_close_bblower = DecimalParameter(0.001, 0.05, default=0.03669, optimize=True) buy_clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=0.04401, optimize=True) buy_clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=0.47782, optimize=True) buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize=True) buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize=True) buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize=True) 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=True) sell_bbmiddle_close = RealParameter(0.97, 1.1, default=1.07634, space='sell', optimize=True) sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , space='sell', optimize=True) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.08 , space='sell', optimize=True) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0 , space='sell', optimize=True) sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.5 ,space='sell', optimize=True) base_nb_candles_buy = IntParameter(8, 20, default=12, space='buy', optimize=True) base_nb_candles_sell = IntParameter(8, 50, default=22, space='sell', optimize=True) low_offset = DecimalParameter(0.985, 0.995, default=0.985, space='buy', optimize=True) high_offset = DecimalParameter(1.005, 1.015, default=1.014, space='sell', optimize=True) high_offset_2 = DecimalParameter(1.010, 1.020, default=1.01, space='sell', optimize=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='sell', decimals=3, optimize=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=True) sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='sell', decimals=3, optimize=True) sell_trail_profit_min_2 = DecimalParameter(0.04, 0.1, default=0.04, space='sell', decimals=3, optimize=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.11, space='sell', decimals=2, optimize=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='sell', decimals=3, optimize=True) pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', optimize=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell',optimize=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] informative_pairs += [("BTC/USDT", "5m"),] informative_pairs += [("SHIB/USDT", "5m"),] return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: info_tf = '5m' informative = self.dp.get_pair_dataframe('BTC/USDT', timeframe=info_tf) informative_btc = informative.copy().shift(1) informative = self.dp.get_pair_dataframe('SHIB/USDT', timeframe=info_tf) informative_shib = informative.copy().shift(1) dataframe['btc_close'] = informative_btc['close'] dataframe['shib_close'] = informative_shib['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) dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) 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, center=True).apply(lambda row: self.is_support(row), raw=True).shift(2) 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: btc_dump = ( (dataframe['btc_close'].rolling(24).max() >= (dataframe['btc_close'] * 1.03 )) ) shib_dump = ( (dataframe['shib_close'].rolling(24).max() >= (dataframe['shib_close'] * 1.03 )) ) rsi_check = ( (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) ) 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) & (btc_dump == 0) & (shib_dump == 0) ), ['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') dataframe.loc[ ((dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < 46) & (dataframe['rsi'] > 19) & (dataframe['close'] < dataframe['sma_15'] * 0.942) & (dataframe['cti'] < -0.86) ), ['enter_long', 'enter_tag']] = (1, 'NFINext32') dataframe.loc[ ((dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['close'] * 0.059) & (dataframe['ha_closedelta'] > dataframe['close'] * 0.023) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * 0.24) & (dataframe['close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['close'] < dataframe['close'].shift()) & (btc_dump == 0) ), ['enter_long', 'enter_tag']] = (1, 'sma_3') # dataframe.loc[ # ((dataframe['close'] < dataframe['vwap_lowerband']) & # (dataframe['tpct_change_1'] > 0.04) & # (dataframe['cti'] < -0.8) & # (dataframe['rsi'] < 35) & # (rsi_check) & # (btc_dump == 0) # ), # ['enter_long', 'enter_tag']] = (1, 'WVAP') return dataframe 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) # print(dir(dataframe)) last_candle = dataframe.iloc[-1].squeeze() candle_before_last = dataframe.iloc[-2].squeeze() candle_before_before_last = dataframe.iloc[-3].squeeze() trade_duration = (current_time - trade.open_date_utc).total_seconds() buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag if buy_tag == 'b1ewo' and current_profit >= 0.08: return 'b1sell' elif buy_tag == 'b1ewo' and current_profit >= 0.04 and trade_duration >= 3*24*3600: return 'tb1sell' elif current_profit >= 0.06 and buy_tag != 'b1ewo': return 'RR6' elif buy_tag != 'b1ewo' and last_candle['close'] < candle_before_last['close'] and candle_before_last['close'] > candle_before_before_last['close'] and current_profit >= 0.05: return 'pullback' # elif current_profit >= 0.05: # return 'RR5' # elif current_profit >= 0.04: # return 'RR4' # elif last_candle['rsi'] > 80 and current_profit >= 0.06: # return 'RSI' 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