# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/TA_Collector_v1.py # directory_url: https://github.com/remiotore/ccxt-freqtrade/blob/main/strategies/ # User: remiotore # Repository: ccxt-freqtrade # -------------------- 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, DatetimeIndex, merge from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open from freqtrade.exchange import timeframe_to_prev_date from functools import reduce from technical.indicators import RMI, zema, ichimoku import json import requests 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 SROC(dataframe, roclen=21, emalen=13, smooth=21): df = dataframe.copy() roc = ta.ROC(df, timeperiod=roclen) ema = ta.EMA(df, timeperiod=emalen) sroc = ta.ROC(ema, timeperiod=smooth) return sroc 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') class Github_remiotore_ccxt_freqtrade__TA_Collector_v1__20260111_210550(IStrategy): buy_params = { "max_slip": 0.983, "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": 17.922, "buy_ema_diff": 0.026, "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": 0.049, "buy_clucha_bbdelta_tail": 1.146, "buy_clucha_close_bblower": 0.018, "buy_clucha_closedelta_close": 0.017, "buy_clucha_rocr_1h": 0.526, "buy_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, } minimal_roi = { "0": 0.205, "81": 0.038, "292": 0.005, } timeframe = '1m' inf_5m = '5m' inf_1h = '1h' process_only_new_candles = True 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) 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) is_optimize_slip = False max_slip = DecimalParameter(0.33, 1.00, default=0.33, decimals=3, optimize=is_optimize_slip , space='buy', load=True) 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) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_1h) for pair in pairs] informative_pairs.extend([(pair, self.inf_5m) 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) 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'] bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=3) informative_1h['bb_lowerband3'] = bollinger3['lower'] informative_1h['bb_middleband3'] = bollinger3['mid'] informative_1h['bb_upperband3'] = bollinger3['upper'] informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20) informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14) informative_1h['rsi_4'] = ta.RSI(informative_1h, timeperiod=4) informative_1h['rsi_20'] = ta.RSI(informative_1h, timeperiod=20) inf_heikinashi = qtpylib.heikinashi(informative_1h) informative_1h['ha_open'] = inf_heikinashi['open'] informative_1h['ha_high'] = inf_heikinashi['high'] informative_1h['ha_low'] = inf_heikinashi['low'] informative_1h['ha_close'] = inf_heikinashi['close'] informative_1h['vwap'] = qtpylib.rolling_vwap(informative_1h) informative_1h['obv'] = ta.OBV(informative_1h['close'], informative_1h['volume']) macd = ta.MACD(informative_1h, 20, 5) informative_1h['macd'] = macd['macd'] informative_1h['macd_signal'] = macd['macdsignal'] informative_1h['macd_histogram'] = macd['macdhist'] return informative_1h def informative_5m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." informative_5m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_5m) bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative_5m), window=20, stds=2) informative_5m['bb_lowerband2'] = bollinger2['lower'] informative_5m['bb_middleband2'] = bollinger2['mid'] informative_5m['bb_upperband2'] = bollinger2['upper'] bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(informative_5m), window=20, stds=3) informative_5m['bb_lowerband3'] = bollinger3['lower'] informative_5m['bb_middleband3'] = bollinger3['mid'] informative_5m['bb_upperband3'] = bollinger3['upper'] informative_5m['cmf'] = chaikin_money_flow(informative_5m, 20) informative_5m['rsi_14'] = ta.RSI(informative_5m, timeperiod=14) informative_5m['rsi_4'] = ta.RSI(informative_5m, timeperiod=4) informative_5m['rsi_20'] = ta.RSI(informative_5m, timeperiod=20) inf_heikinashi = qtpylib.heikinashi(informative_5m) informative_5m['ha_open'] = inf_heikinashi['open'] informative_5m['ha_high'] = inf_heikinashi['high'] informative_5m['ha_low'] = inf_heikinashi['low'] informative_5m['ha_close'] = inf_heikinashi['close'] informative_5m['vwap'] = qtpylib.rolling_vwap(informative_5m) informative_5m['obv'] = ta.OBV(informative_5m['close'], informative_5m['volume']) macd = ta.MACD(informative_5m, 20, 5) informative_5m['macd'] = macd['macd'] informative_5m['macd_signal'] = macd['macdsignal'] informative_5m['macd_histogram'] = macd['macdhist'] return informative_5m def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: return 1 def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): return None def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: return False def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> 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['cmf'] = chaikin_money_flow(dataframe, 20) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20) 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['vwap'] = qtpylib.rolling_vwap(dataframe) dataframe['obv'] = ta.OBV(dataframe['close'], dataframe['volume']) macd = ta.MACD(dataframe, 20, 5) dataframe['macd'] = macd['macd'] dataframe['macd_signal'] = macd['macdsignal'] dataframe['macd_histogram'] = macd['macdhist'] 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) informative_5m = self.informative_5m_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_5m, self.timeframe, self.inf_5m, ffill=True) dataframe = self.normal_tf_indicators(dataframe, metadata) postData = { "pair": metadata['pair'], "strategy": self.config['strategy'], "data": dataframe.iloc[-1:].to_json(orient = "records") } requests.post('http://192.168.1.7:9000/data-gateway/submit-data', json=postData) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'buy'] = 0 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'sell'] = 0 return dataframe def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = f'MA_{MAtype}_{length}' atr = f'ATR_{period}' pm = f'pm_{period}_{multiplier}_{length}_{MAtype}' pmx = f'pmX_{period}_{multiplier}_{length}_{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 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, matype=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']