import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta import pandas_ta as pta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame, Series from datetime import datetime from freqtrade.strategy import merge_informative_pair, DecimalParameter, IntParameter, stoploss_from_open from functools import reduce from technical.indicators import RMI, zema, VIDYA def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) def vwma(dataframe: DataFrame, length: int = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) return vwma def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series: slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma)) return slow_ma >= slow_ma.shift(1) # we just need true & false for ERI trend def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif def range_percent_change(dataframe: DataFrame, method, length: int) -> float: """ Rolling Percentage Change Maximum across interval. :param dataframe: DataFrame The original OHLC dataframe :param method: High to Low / Open to Close :param length: int The length to look back """ if method == 'HL': return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe[ 'low'].rolling(length).min() elif method == 'OC': return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe[ 'close'].rolling(length).min() else: raise ValueError(f"Method {method} not defined!") 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=f"{period} Williams %R", ) return wr * -100 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 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 vmap_b(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 top_percent_change(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'] class BBMod1(IStrategy): """ BBMod1 modified from BB_RPB_TSL ( https://github.com/jilv220/BB_RPB_TSL ) @author jilv220 Simple bollinger brand strategy inspired by this blog ( https://hacks-for-life.blogspot.com/2020/12/freqtrade -notes.html )RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff /blob/main/user_data/strategies/TheRealPullbackV2.py )The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya )I modified it to better suit my taste and added Hyperopt for this strategy. """ buy_params = { "buy_bb_width_1h": 0.954, "buy_roc_1h": 86, "buy_threshold": 0.003, "buy_bb_factor": 0.999, "buy_bb_delta": 0.025, "buy_bb_width": 0.095, "buy_cci": -116, "buy_cci_length": 25, "buy_rmi": 49, "buy_rmi_length": 17, "buy_srsi_fk": 32, "buy_closedelta": 12.148, "buy_ema_diff": 0.022, "buy_ema_high": 0.968, "buy_ema_low": 0.935, "buy_ewo": -5.001, "buy_rsi": 23, "buy_rsi_fast": 44, "buy_ema_high_2": 1.087, "buy_ema_low_2": 0.970, "buy_ewo_high_2": 4.179, "buy_rsi_ewo_2": 35, "buy_rsi_fast_ewo_2": 45, "buy_closedelta_local_dip": 12.044, "buy_ema_diff_local_dip": 0.024, "buy_ema_high_local_dip": 1.014, "buy_rsi_local_dip": 21, "buy_r_deadfish_bb_factor": 1.014, "buy_r_deadfish_bb_width": 0.299, "buy_r_deadfish_ema": 1.054, "buy_r_deadfish_volume_factor": 1.59, "buy_r_deadfish_cti": -0.115, "buy_r_deadfish_r14": -44.34, "buy_clucha_bbdelta_close": 0.01889, "buy_clucha_bbdelta_tail": 0.72235, "buy_clucha_close_bblower": 0.0127, "buy_clucha_closedelta_close": 0.00916, "buy_clucha_rocr_1h": 0.79492, "buy_adx": 13, "buy_cofi_r14": -85.016, "buy_cofi_cti": -0.892, "buy_ema_cofi": 1.147, "buy_ewo_high": 8.594, "buy_fastd": 28, "buy_fastk": 39, "buy_gumbo_ema": 1.121, "buy_gumbo_ewo_low": -9.442, "buy_gumbo_cti": -0.374, "buy_gumbo_r14": -51.971, "buy_sqzmom_ema": 0.981, "buy_sqzmom_ewo": -3.966, "buy_sqzmom_r14": -45.068, "buy_nfix_39_ema": 0.912, "buy_nfix_49_cti": -0.105, "buy_nfix_49_r14": -81.827, } sell_params = { "sell_cmf": -0.046, "sell_ema": 0.988, "sell_ema_close_delta": 0.022, "sell_deadfish_profit": -0.063, "sell_deadfish_bb_factor": 0.954, "sell_deadfish_bb_width": 0.043, "sell_deadfish_volume_factor": 2.37, "sell_cti_r_cti": 0.844, "sell_cti_r_r": -19.99, "base_nb_candles_sell": 24, "high_offset": 0.991, "high_offset_2": 0.997, "pHSL": -0.99, "pPF_1": 0.019, "pPF_2": 0.05, "pSL_1": 0.017, "pSL_2": 0.045, } minimal_roi = { "0": 100 } timeframe = '5m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 120 order_types = { 'buy': 'limit', 'sell': 'limit', 'emergencysell': 'limit', 'forcebuy': "limit", 'forcesell': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } stoploss = -0.99 use_custom_stoploss = True use_sell_signal = True 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_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) is_optimize_local_dip = False buy_ema_diff_local_dip = DecimalParameter(0.022, 0.027, default=0.025, optimize=is_optimize_local_dip) buy_ema_high_local_dip = DecimalParameter(0.90, 1.2, default=0.942, optimize=is_optimize_local_dip) buy_closedelta_local_dip = DecimalParameter(12.0, 18.0, default=15.0, optimize=is_optimize_local_dip) buy_rsi_local_dip = IntParameter(15, 45, default=28, optimize=is_optimize_local_dip) buy_crsi_local_dip = IntParameter(10, 18, default=10, optimize=False) is_optimize_ewo = False buy_rsi_fast = IntParameter(35, 50, default=45, optimize=is_optimize_ewo) buy_rsi = IntParameter(15, 35, default=35, optimize=is_optimize_ewo) buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize=is_optimize_ewo) buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942, optimize=is_optimize_ewo) buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084, optimize=is_optimize_ewo) is_optimize_ewo_2 = False buy_rsi_fast_ewo_2 = IntParameter(15, 50, default=45, optimize=is_optimize_ewo_2) buy_rsi_ewo_2 = IntParameter(15, 50, default=35, optimize=is_optimize_ewo_2) buy_ema_low_2 = DecimalParameter(0.90, 1.2, default=0.970, optimize=is_optimize_ewo_2) buy_ema_high_2 = DecimalParameter(0.90, 1.2, default=1.087, optimize=is_optimize_ewo_2) buy_ewo_high_2 = DecimalParameter(2, 12, default=4.179, optimize=is_optimize_ewo_2) is_optimize_r_deadfish = False buy_r_deadfish_ema = DecimalParameter(0.90, 1.2, default=1.087, optimize=is_optimize_r_deadfish) buy_r_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, optimize=is_optimize_r_deadfish) buy_r_deadfish_bb_factor = DecimalParameter(0.90, 1.2, default=1.0, optimize=is_optimize_r_deadfish) buy_r_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, optimize=is_optimize_r_deadfish) is_optimize_r_deadfish_protection = False buy_r_deadfish_cti = DecimalParameter(-0.6, -0.0, default=-0.5, optimize=is_optimize_r_deadfish_protection) buy_r_deadfish_r14 = DecimalParameter(-60, -44, default=-60, optimize=is_optimize_r_deadfish_protection) 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_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) buy_clucha_close_bblower = DecimalParameter(0.0005, 0.02, default=0.00799, optimize=is_optimize_clucha) is_optimize_cofi = False buy_ema_cofi = DecimalParameter(0.94, 1.2, default=0.97, optimize=is_optimize_cofi) buy_fastk = IntParameter(0, 40, default=20, optimize=is_optimize_cofi) buy_fastd = IntParameter(0, 40, default=20, optimize=is_optimize_cofi) buy_adx = IntParameter(0, 30, default=30, optimize=is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize=is_optimize_cofi) is_optimize_cofi_protection = False buy_cofi_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_cofi_protection) buy_cofi_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_cofi_protection) is_optimize_gumbo = False buy_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_gumbo) buy_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, optimize=is_optimize_gumbo) is_optimize_gumbo_protection = False buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_gumbo_protection) buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_gumbo_protection) is_optimize_sqzmom_protection = False buy_sqzmom_ema = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_sqzmom_protection) buy_sqzmom_ewo = DecimalParameter(-12, 12, default=0, optimize=is_optimize_sqzmom_protection) buy_sqzmom_r14 = DecimalParameter(-100, -22, default=-50, optimize=is_optimize_sqzmom_protection) is_optimize_nfix_39 = True buy_nfix_39_ema = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_nfix_39) is_optimize_nfix_49_protection = False buy_nfix_49_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_nfix_49_protection) buy_nfix_49_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_nfix_49_protection) is_optimize_btc_safe = False buy_btc_safe = IntParameter(-300, 50, default=-200, optimize=is_optimize_btc_safe) buy_btc_safe_1d = DecimalParameter(-0.075, -0.025, default=-0.05, optimize=is_optimize_btc_safe) buy_threshold = DecimalParameter(0.003, 0.012, default=0.008, optimize=is_optimize_btc_safe) 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) sell_btc_safe = IntParameter(-400, -300, default=-365, optimize=False) is_optimize_sell_stoploss = False sell_cmf = DecimalParameter(-0.4, 0.0, default=0.0, optimize=is_optimize_sell_stoploss) sell_ema_close_delta = DecimalParameter(0.022, 0.027, default=0.024, optimize=is_optimize_sell_stoploss) sell_ema = DecimalParameter(0.97, 0.99, default=0.987, optimize=is_optimize_sell_stoploss) is_optimize_deadfish = False sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, optimize=is_optimize_deadfish) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05, optimize=is_optimize_deadfish) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0, optimize=is_optimize_deadfish) sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, optimize=is_optimize_deadfish) is_optimize_bleeding = False sell_bleeding_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_bleeding) sell_bleeding_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_bleeding) sell_bleeding_volume_factor = DecimalParameter(1, 2.5, default=1.0, optimize=is_optimize_bleeding) is_optimize_cti_r = False sell_cti_r_cti = DecimalParameter(0.55, 1, default=0.5, optimize=is_optimize_cti_r) sell_cti_r_r = DecimalParameter(-15, 0, default=-20, optimize=is_optimize_cti_r) base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True) pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) informative_1h['ema_8'] = ta.EMA(informative_1h, timeperiod=8) informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) informative_1h['cti'] = pta.cti(informative_1h["close"], length=20) informative_1h['cti_40'] = pta.cti(informative_1h["close"], length=40) crsi_closechange = informative_1h['close'] / informative_1h['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) informative_1h['crsi'] = (ta.RSI(informative_1h['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative_1h['close'], 100)) / 3 informative_1h['r_96'] = williams_r(informative_1h, period=96) informative_1h['r_480'] = williams_r(informative_1h, period=480) bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) informative_1h['bb_lowerband2'] = bollinger2['lower'] informative_1h['bb_middleband2'] = bollinger2['mid'] informative_1h['bb_upperband2'] = bollinger2['upper'] informative_1h['bb_width'] = ((informative_1h['bb_upperband2'] - informative_1h['bb_lowerband2']) / informative_1h['bb_middleband2']) informative_1h['roc'] = ta.ROC(dataframe, timeperiod=9) mom = momdiv(informative_1h) informative_1h['momdiv_buy'] = mom['momdiv_buy'] informative_1h['momdiv_sell'] = mom['momdiv_sell'] informative_1h['momdiv_coh'] = mom['momdiv_coh'] informative_1h['momdiv_col'] = mom['momdiv_col'] informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20) inf_heikinashi = qtpylib.heikinashi(informative_1h) informative_1h['ha_close'] = inf_heikinashi['close'] informative_1h['rocr'] = ta.ROCR(informative_1h['ha_close'], timeperiod=168) informative_1h['T3'] = t3(informative_1h) informative_1h['EWO'] = EWO(informative_1h, 50, 200) informative_1h['hl_pct_change_5'] = range_percent_change(informative_1h, 'HL', 5) informative_1h['low_5'] = informative_1h['low'].shift().rolling(5).min() informative_1h['safe_dump_50'] = ( (informative_1h['hl_pct_change_5'] < 0.66) | (informative_1h['close'] < informative_1h['low_5']) | ( informative_1h['close'] > informative_1h['open'])) return informative_1h def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: stoploss = 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 buy_tag = '' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag buy_tags = buy_tag.split() if len(buy_tags) == 1: for i in ["vwap", "clucHA", "nfi_38", "clucHA2"]: if i in buy_tags: pf_1 = 0.01 sl_1 = 0.008 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 = stoploss if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) def normal_tf_indicators(self, dataframe: DataFrame) -> DataFrame: 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'] 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_width'] = ( (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2']) for val in self.buy_cci_length.range: dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val) dataframe['cci'] = ta.CCI(dataframe, 26) dataframe['cci_long'] = ta.CCI(dataframe, 170) for val in self.buy_rmi_length.range: dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4) for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20) dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21) dataframe['sma_28'] = ta.SMA(dataframe, timeperiod=28) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) crsi_closechange = dataframe['close'] / dataframe['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC( dataframe['close'], 100)) / 3 dataframe['ema_4'] = ta.EMA(dataframe, timeperiod=4) dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) 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) dataframe['EWO'] = EWO(dataframe, 50, 200) dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_32'] = williams_r(dataframe, period=32) dataframe['r_64'] = williams_r(dataframe, period=64) dataframe['r_96'] = williams_r(dataframe, period=96) dataframe['r_480'] = williams_r(dataframe, period=480) dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) dataframe['mfi'] = ta.MFI(dataframe) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] bollinger2_40 = qtpylib.bollinger_bands(ha_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['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs() dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['adx'] = ta.ADX(dataframe) dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, ma_type=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) mom = momdiv(dataframe) dataframe['momdiv_buy'] = mom['momdiv_buy'] dataframe['momdiv_sell'] = mom['momdiv_sell'] dataframe['momdiv_coh'] = mom['momdiv_coh'] dataframe['momdiv_col'] = mom['momdiv_col'] dataframe['T3'] = t3(dataframe) dataframe['trange'] = ta.TRANGE(dataframe) dataframe['range_ma_28'] = ta.SMA(dataframe['trange'], 28) dataframe['kc_upperband_28_1'] = dataframe['sma_28'] + dataframe['range_ma_28'] dataframe['kc_lowerband_28_1'] = dataframe['sma_28'] - dataframe['range_ma_28'] dataframe['range_ma_20'] = ta.SMA(dataframe['trange'], 20) dataframe['kc_upperband_20_2'] = dataframe['sma_20'] + dataframe['range_ma_20'] * 2 dataframe['kc_lowerband_20_2'] = dataframe['sma_20'] - dataframe['range_ma_20'] * 2 dataframe['kc_bb_delta'] = (dataframe['kc_lowerband_20_2'] - dataframe['bb_lowerband2']) / dataframe[ 'bb_lowerband2'] * 100 dataframe['hh_20'] = ta.MAX(dataframe['high'], 20) dataframe['ll_20'] = ta.MIN(dataframe['low'], 20) dataframe['avg_hh_ll_20'] = (dataframe['hh_20'] + dataframe['ll_20']) / 2 dataframe['avg_close_20'] = ta.SMA(dataframe['close'], 20) dataframe['avg_val_20'] = (dataframe['avg_hh_ll_20'] + dataframe['avg_close_20']) / 2 dataframe['linreg_val_20'] = ta.LINEARREG(dataframe['close'] - dataframe['avg_val_20'], 20, 0) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) dataframe['moderi_96'] = moderi(dataframe, 96) vwap_low, vwap, vwap_high = vmap_b(dataframe, 20, 1) dataframe['vwap_low'] = vwap_low dataframe['tcp_percent_4'] = top_percent_change(dataframe, 4) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) dataframe = self.normal_tf_indicators(dataframe) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' is_dip = ( (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) ) is_break = ( (dataframe['bb_delta'] > self.buy_bb_delta.value) & (dataframe['bb_width'] > self.buy_bb_width.value) & (dataframe['closedelta'] > dataframe['close'] * 17.922 / 1000) & # use origin bb_rpb_tsl value (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value) ) is_local_uptrend = ( # from NFI next gen, credit goes to @iterativ (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) ) is_local_uptrend2 = ( # use origin bb_rpb_tsl value (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * 0.026) & (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'] * 17.922 / 1000) ) is_local_dip = ( (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff_local_dip.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ema_high_local_dip.value) & (dataframe['rsi'] < self.buy_rsi_local_dip.value) & (dataframe['crsi'] > self.buy_crsi_local_dip.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta_local_dip.value / 1000) ) is_ewo = ( # from SMA offset (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) & (dataframe['EWO'] > self.buy_ewo.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) & (dataframe['rsi'] < self.buy_rsi.value) ) is_r_deadfish = ( # reverse deadfish (dataframe['ema_100'] < dataframe['ema_200'] * self.buy_r_deadfish_ema.value) & (dataframe['bb_width'] > self.buy_r_deadfish_bb_width.value) & (dataframe['close'] < dataframe['bb_middleband2'] * self.buy_r_deadfish_bb_factor.value) & (dataframe['volume_mean_12'] > dataframe['volume_mean_24'] * self.buy_r_deadfish_volume_factor.value) & (dataframe['cti'] < self.buy_r_deadfish_cti.value) & (dataframe['r_14'] < self.buy_r_deadfish_r14.value) ) is_clucha = ( (dataframe['rocr_1h'].gt(self.buy_clucha_rocr_1h.value)) & (dataframe['bb_lowerband2_40'].shift().gt(0)) & (dataframe['bb_delta_cluc'].gt(dataframe['ha_close'] * self.buy_clucha_bbdelta_close.value)) & (dataframe['ha_closedelta'].gt(dataframe['ha_close'] * self.buy_clucha_closedelta_close.value)) & (dataframe['tail'].lt(dataframe['bb_delta_cluc'] * self.buy_clucha_bbdelta_tail.value)) & (dataframe['ha_close'].lt(dataframe['bb_lowerband2_40'].shift())) & (dataframe['ha_close'].le(dataframe['ha_close'].shift())) ) is_clucha2 = ( (dataframe['rocr_1h'].gt(self.buy_clucha_rocr_1h.value)) & (dataframe['ha_close'] < dataframe['ema_slow']) & (dataframe['ha_close'] < self.buy_clucha_close_bblower.value * dataframe['bb_lowerband2_40']) ) is_cofi = ( # Modified from cofi, credit goes to original author "slack user CofiBit" (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) & (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) & (dataframe['fastk'] < self.buy_fastk.value) & (dataframe['fastd'] < self.buy_fastd.value) & (dataframe['adx'] > self.buy_adx.value) & (dataframe['EWO'] > self.buy_ewo_high.value) & (dataframe['cti'] < self.buy_cofi_cti.value) & (dataframe['r_14'] < self.buy_cofi_r14.value) ) is_nfi_32 = ( # NFIX 26 (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) ) is_nfi_33 = ( (dataframe['close'] < (dataframe['ema_13'] * 0.978)) & (dataframe['EWO'] > 8) & (dataframe['cti'] < -0.88) & (dataframe['rsi'] < 32) & (dataframe['r_14'] < -98.0) & (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.5)) ) is_nfi_38 = ( (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['close'] < dataframe['sma_75'] * 0.98) & (dataframe['EWO'] < -4.4) & (dataframe['cti'] < -0.95) & (dataframe['r_14'] < -97) & (dataframe['crsi_1h'] > 0.5) ) is_nfix_5 = ( (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['close'] < dataframe['sma_75'] * 0.932) & (dataframe['EWO'] > 3.6) & (dataframe['cti'] < -0.9) & (dataframe['r_14'] < -97.0) ) is_nfix_39 = ( (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (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_13'] * self.buy_nfix_39_ema.value) ) is_nfix_49 = ( (dataframe['ema_26'].shift(3) > dataframe['ema_12'].shift(3)) & (dataframe['ema_26'].shift(3) - dataframe['ema_12'].shift(3) > dataframe['open'].shift(3) * 0.032) & (dataframe['ema_26'].shift(9) - dataframe['ema_12'].shift(9) > dataframe['open'].shift(3) / 100) & (dataframe['close'].shift(3) < dataframe['ema_20'].shift(3) * 0.916) & (dataframe['rsi'].shift(3) < 32.5) & (dataframe['crsi'].shift(3) > 18.0) & (dataframe['cti'] < self.buy_nfix_49_cti.value) & (dataframe['r_14'] < self.buy_nfix_49_r14.value) ) is_nfi7_33 = ( (dataframe['moderi_96']) & (dataframe['cti'] < -0.88) & (dataframe['close'] < (dataframe['ema_13'] * 0.988)) & (dataframe['EWO'] > 6.4) & (dataframe['rsi'] < 32.0) & (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.0)) ) is_nfi7_37 = ( (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['close'] < dataframe['sma_75'] * 0.98) & (dataframe['EWO'] > 9.8) & (dataframe['rsi'] < 56.0) & (dataframe['cti'] < -0.7) & (dataframe['safe_dump_50_1h']) ) is_vwap = ( (dataframe['close'] < dataframe['vwap_low']) & (dataframe['tcp_percent_4'] > 0.04) & (dataframe['cti'] < -0.8) & (dataframe['rsi'] < 35) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['volume'] > 0) ) is_bb_checked = is_dip & is_break conditions.append(is_bb_checked) # ~2.32 / 91.1% / 46.27% D dataframe.loc[is_bb_checked, 'buy_tag'] += 'bb ' conditions.append(is_local_uptrend) # ~3.28 / 92.4% / 69.72% dataframe.loc[is_local_uptrend, 'buy_tag'] += 'local_uptrend ' conditions.append(is_local_dip) # ~0.76 / 91.1% / 15.54% dataframe.loc[is_local_dip, 'buy_tag'] += 'local_dip ' conditions.append(is_ewo) # ~0.92 / 92.0% / 43.74% D dataframe.loc[is_ewo, 'buy_tag'] += 'ewo ' conditions.append(is_r_deadfish) # ~0.99 / 86.9% / 21.93% D dataframe.loc[is_r_deadfish, 'buy_tag'] += 'r_deadfish ' conditions.append(is_clucha) # ~7.2 / 92.5% / 97.98% D dataframe.loc[is_clucha, 'buy_tag'] += 'clucHA ' conditions.append(is_clucha2) dataframe.loc[is_clucha2, 'buy_tag'] += 'clucHA2 ' conditions.append(is_cofi) # ~0.4 / 94.4% / 9.59% D dataframe.loc[is_cofi, 'buy_tag'] += 'cofi ' conditions.append(is_nfi_32) # ~0.78 / 92.0 % / 37.41% D dataframe.loc[is_nfi_32, 'buy_tag'] += 'nfi_32 ' conditions.append(is_nfi_33) # ~0.11 / 100% D dataframe.loc[is_nfi_33, 'buy_tag'] += 'nfi_33 ' conditions.append(is_nfi_38) # ~1.13 / 88.5% / 31.34% D dataframe.loc[is_nfi_38, 'buy_tag'] += 'nfi_38 ' conditions.append(is_nfix_5) # ~0.25 / 97.7% / 6.53% D dataframe.loc[is_nfix_5, 'buy_tag'] += 'nfix_5 ' conditions.append(is_nfix_39) # ~5.33 / 91.8% / 58.57% D dataframe.loc[is_nfix_39, 'buy_tag'] += 'nfix_39 ' conditions.append(is_nfix_49) # ~0.33 / 100% / 0% D dataframe.loc[is_nfix_49, 'buy_tag'] += 'nfix_49 ' conditions.append(is_nfi7_33) # ~0.71 / 91.3% / 28.94% D dataframe.loc[is_nfi7_33, 'buy_tag'] += 'nfi7_33 ' conditions.append(is_nfi7_37) # ~0.46 / 92.6% / 17.05% D dataframe.loc[is_nfi7_37, 'buy_tag'] += 'nfi7_37 ' conditions.append(is_vwap) dataframe.loc[is_vwap, 'buy_tag'] += 'vwap ' conditions.append(is_local_uptrend2) dataframe.loc[is_local_uptrend2, 'buy_tag'] += 'local_uptrend2 ' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [( (dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > ( dataframe[ f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) ) | ( (dataframe['sma_9'] > ( dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1) * 0.005)) & (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > ( dataframe[ f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) )] if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ] = 1 return dataframe def pmax(df, period, multiplier, length, ma_type, src): period = int(period) multiplier = int(multiplier) length = int(length) ma_type = int(ma_type) src = int(src) mavalue = f'MA_{ma_type}_{length}' atr = f'ATR_{period}' 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 ma_type == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif ma_type == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif ma_type == 3: mavalue = ta.T3(masrc, timeperiod=length) elif ma_type == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif ma_type == 5: mavalue = VIDYA(df, length=length) elif ma_type == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif ma_type == 7: mavalue = ta.WMA(df, timeperiod=length) elif ma_type == 8: mavalue = vwma(df, length) elif ma_type == 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 def momdiv(dataframe: DataFrame, mom_length: int = 10, bb_length: int = 20, bb_dev: float = 2.0, lookback: int = 30) -> DataFrame: mom: Series = ta.MOM(dataframe, timeperiod=mom_length) upperband, middleband, lowerband = ta.BBANDS(mom, timeperiod=bb_length, nbdevup=bb_dev, nbdevdn=bb_dev, ma_type=0) buy = qtpylib.crossed_below(mom, lowerband) sell = qtpylib.crossed_above(mom, upperband) hh = dataframe['high'].rolling(lookback).max() ll = dataframe['low'].rolling(lookback).min() coh = dataframe['high'] >= hh col = dataframe['low'] <= ll df = DataFrame({ "momdiv_mom": mom, "momdiv_upperb": upperband, "momdiv_lowerb": lowerband, "momdiv_buy": buy, "momdiv_sell": sell, "momdiv_coh": coh, "momdiv_col": col, }, index=dataframe['close'].index) return df def t3(dataframe, length=5): """ T3 Average by HPotter on Tradingview https://www.tradingview.com/script/qzoC9H1I-T3-Average/ """ df = dataframe.copy() df['xe1'] = ta.EMA(df['close'], timeperiod=length) df['xe2'] = ta.EMA(df['xe1'], timeperiod=length) df['xe3'] = ta.EMA(df['xe2'], timeperiod=length) df['xe4'] = ta.EMA(df['xe3'], timeperiod=length) df['xe5'] = ta.EMA(df['xe4'], timeperiod=length) df['xe6'] = ta.EMA(df['xe5'], timeperiod=length) b = 0.7 c1 = -b * b * b c2 = 3 * b * b + 3 * b * b * b c3 = -6 * b * b - 3 * b - 3 * b * b * b c4 = 1 + 3 * b + b * b * b + 3 * b * b df['T3Average'] = c1 * df['xe6'] + c2 * df['xe5'] + c3 * df['xe4'] + c4 * df['xe3'] return df['T3Average']