# --- Do not remove these libs --- 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) # Volume Weighted Moving Average def vwma(dataframe: DataFrame, length: int = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" # Calculate Result pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) return vwma # Modified Elder Ray Index 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!") # Williams %R 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 # Chaikin Money Flow 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 TA_Collector_v1(IStrategy): # (1) sell rework ########################################################################## # Hyperopt result area # buy space 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 space 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, } # Optimal timeframe for the strategy timeframe = '1m' inf_5m = '5m' inf_1h = '1h' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Disabled stoploss = -0.99 # Custom stoploss use_custom_stoploss = True use_sell_signal = True ############################################################################ ## Buy params 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) ## Slippage params 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 params 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." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # Bollinger bands 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'] # CMF informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20) # RSI & MRSI & MSI OSCILLATOR informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14) informative_1h['mrsi_7'] = ta.SMA(informative_1h['rsi_14'], timeperiod=7) informative_1h['rsi_osc'] = (informative_1h['rsi_14'] - informative_1h['mrsi_7']) / informative_1h['mrsi_7'] * 100 # Heikin Ashi 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'] # VWAP informative_1h['vwap'] = qtpylib.rolling_vwap(informative_1h) # VWAP OSCILLATOR informative_1h['vwap_osc'] = (informative_1h['close'] - informative_1h['vwap']) / informative_1h['vwap'] * 100 # OBV informative_1h['obv'] = ta.OBV(informative_1h['close'], informative_1h['volume']) # OBV HA informative_1h['ha_obv'] = ta.OBV(informative_1h['ha_close'], informative_1h['volume']) # OBV MA informative_1h['mobv_3'] = ta.SMA(informative_1h['ha_obv'], timeperiod=3) # OBV OSCILLATOR informative_1h['obv_osc'] = (informative_1h['ha_obv'] - informative_1h['mobv_3']) / informative_1h['mobv_3'] * 100 # Volume Oscilator informative_1h['vma_20'] = ta.SMA(informative_1h['volume'], timeperiod=20) informative_1h['vol_osc'] = (informative_1h['volume'] - informative_1h['vma_20']) / informative_1h['vma_20'] * 100 # MACD macd = ta.MACD(informative_1h, 20, 5) informative_1h['macd'] = macd['macd'] informative_1h['macd_signal'] = macd['macdsignal'] informative_1h['macd_histogram'] = macd['macdhist'] informative_1h['macd_osc'] = informative_1h['macd_histogram'] / informative_1h['close'] * 100 # SMA informative_1h['sma_6'] = ta.SMA(informative_1h, timeperiod=6) informative_1h['sma_9'] = ta.SMA(informative_1h, timeperiod=9) informative_1h['sma_30'] = ta.SMA(informative_1h, timeperiod=30) informative_1h['sma_60'] = ta.SMA(informative_1h, timeperiod=60) informative_1h['sma_100'] = ta.SMA(informative_1h, timeperiod=100) # SMA OSCILLATOR informative_1h['sma_6_osc'] = (informative_1h['close'] - informative_1h['sma_6']) / informative_1h['sma_6'] * 100 informative_1h['sma_9_osc'] = (informative_1h['close'] - informative_1h['sma_9']) / informative_1h['sma_9'] * 100 informative_1h['sma_30_osc'] = (informative_1h['close'] - informative_1h['sma_30']) / informative_1h['sma_30'] * 100 informative_1h['sma_60_osc'] = (informative_1h['close'] - informative_1h['sma_60']) / informative_1h['sma_60'] * 100 informative_1h['sma_100_osc'] = (informative_1h['close'] - informative_1h['sma_100']) / informative_1h['sma_100'] * 100 # Stochastic Slow stoch = ta.STOCH(informative_1h) informative_1h['stoch_slowd'] = stoch['slowd'] informative_1h['stoch_slowk'] = stoch['slowk'] informative_1h['stoch_slow_osc'] = (informative_1h['stoch_slowk'] - informative_1h['stoch_slowd']) / informative_1h['stoch_slowd'] * 100 # Stochastic Fast stoch_fast = ta.STOCHF(informative_1h) informative_1h["stoch_fastd"] = stoch_fast["fastd"] informative_1h["stoch_fastk"] = stoch_fast["fastk"] return informative_1h def informative_5m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_5m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_5m) # Bollinger bands 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'] # CMF informative_5m['cmf'] = chaikin_money_flow(informative_5m, 20) # RSI & MRSI & RSI OSCILLATOR informative_5m['rsi_14'] = ta.RSI(informative_5m, timeperiod=14) informative_5m['mrsi_7'] = ta.SMA(informative_5m['rsi_14'], timeperiod=7) informative_5m['rsi_osc'] = (informative_5m['rsi_14'] - informative_5m['mrsi_7']) / informative_5m['mrsi_7'] * 100 # Heikin Ashi 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'] # VWAP informative_5m['vwap'] = qtpylib.rolling_vwap(informative_5m) # VWAP OSCILLATOR informative_5m['vwap_osc'] = (informative_5m['close'] - informative_5m['vwap']) / informative_5m['vwap'] * 100 # OBV informative_5m['obv'] = ta.OBV(informative_5m['close'], informative_5m['volume']) # OBV HA informative_5m['ha_obv'] = ta.OBV(informative_5m['ha_close'], informative_5m['volume']) # OBV MA informative_5m['mobv_3'] = ta.SMA(informative_5m['ha_obv'], timeperiod=3) # OBV OSCILLATOR informative_5m['obv_osc'] = (informative_5m['ha_obv'] - informative_5m['mobv_3']) / informative_5m['mobv_3'] * 100 # Volume Oscilator informative_5m['vma_20'] = ta.SMA(informative_5m['volume'], timeperiod=20) informative_5m['vol_osc'] = (informative_5m['volume'] - informative_5m['vma_20']) / informative_5m['vma_20'] * 100 # MACD macd = ta.MACD(informative_5m, 20, 5) informative_5m['macd'] = macd['macd'] informative_5m['macd_signal'] = macd['macdsignal'] informative_5m['macd_histogram'] = macd['macdhist'] informative_5m['macd_osc'] = informative_5m['macd_histogram'] / informative_5m['close'] * 100 # SMA informative_5m['sma_6'] = ta.SMA(informative_5m, timeperiod=6) informative_5m['sma_9'] = ta.SMA(informative_5m, timeperiod=9) informative_5m['sma_30'] = ta.SMA(informative_5m, timeperiod=30) informative_5m['sma_60'] = ta.SMA(informative_5m, timeperiod=60) informative_5m['sma_100'] = ta.SMA(informative_5m, timeperiod=100) # SMA OSCILLATOR informative_5m['sma_6_osc'] = (informative_5m['close'] - informative_5m['sma_6']) / informative_5m['sma_6'] * 100 informative_5m['sma_9_osc'] = (informative_5m['close'] - informative_5m['sma_9']) / informative_5m['sma_9'] * 100 informative_5m['sma_30_osc'] = (informative_5m['close'] - informative_5m['sma_30']) / informative_5m['sma_30'] * 100 informative_5m['sma_60_osc'] = (informative_5m['close'] - informative_5m['sma_60']) / informative_5m['sma_60'] * 100 informative_5m['sma_100_osc'] = (informative_5m['close'] - informative_5m['sma_100']) / informative_5m['sma_100'] * 100 # Stochastic Slow stoch = ta.STOCH(informative_5m) informative_5m['stoch_slowd'] = stoch['slowd'] informative_5m['stoch_slowk'] = stoch['slowk'] informative_5m['stoch_slow_osc'] = (informative_5m['stoch_slowk'] - informative_5m['stoch_slowd']) / informative_5m['stoch_slowd'] * 100 # Stochastic Fast stoch_fast = ta.STOCHF(informative_5m) informative_5m["stoch_fastd"] = stoch_fast["fastd"] informative_5m["stoch_fastk"] = stoch_fast["fastk"] return informative_5m ############################################################################ ### Custom functions def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: return 1 # sl_new = 1 # if (current_profit > 0.2): # sl_new = 0.05 # elif (current_profit > 0.1): # sl_new = 0.03 # elif (current_profit > 0.06): # sl_new = 0.02 # elif (current_profit > 0.03): # sl_new = 0.015 # return sl_new # From NFIX def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): return None # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # last_candle = dataframe.iloc[-1] # previous_candle_1 = dataframe.iloc[-2] # previous_candle_2 = dataframe.iloc[-3] # max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) # max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate) # buy_tag = 'empty' # if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: # buy_tag = trade.buy_tag # buy_tags = buy_tag.split() # # sell trail # if 0.012 > current_profit >= 0.0: # if (max_profit > (current_profit + 0.045)) and (last_candle['rsi'] < 46.0): # return f"sell_profit_t_0_1( {buy_tag})" # elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 32.0): # return f"sell_profit_t_0_2( {buy_tag})" # elif (max_profit > (current_profit + 0.05)) and (last_candle['rsi'] < 48.0): # return f"sell_profit_t_0_3( {buy_tag})" # elif 0.02 > current_profit >= 0.012: # if (max_profit > (current_profit + 0.01)) and (last_candle['rsi'] < 39.0): # return f"sell_profit_t_1_1( {buy_tag})" # elif (max_profit > (current_profit + 0.035)) and (last_candle['rsi'] < 45.0) and (last_candle['cmf'] < -0.0) and (last_candle['cmf_1h'] < -0.0): # return f"sell_profit_t_1_2( {buy_tag})" # elif (max_profit > (current_profit + 0.02)) and (last_candle['rsi'] < 40.0) and (last_candle['cmf'] < -0.0) and (last_candle['cti_1h'] > 0.8): # return f"sell_profit_t_1_4( {buy_tag})" # elif (max_profit > (current_profit + 0.04)) and (last_candle['rsi'] < 49.0) and (last_candle['cmf_1h'] < -0.0): # return f"sell_profit_t_1_5( {buy_tag})" # elif (max_profit > (current_profit + 0.06)) and (last_candle['rsi'] < 43.0) and (last_candle['cmf'] < -0.0): # return f"sell_profit_t_1_7( {buy_tag})" # elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 40.0) and (last_candle['cmf'] < -0.1) and (last_candle['rsi_1h'] < 50.0): # return f"sell_profit_t_1_9( {buy_tag})" # elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 46.0) and (last_candle['cmf'] < -0.0) and (last_candle['r_480_1h'] > -20.0): # return f"sell_profit_t_1_10( {buy_tag})" # elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 42.0): # return f"sell_profit_t_1_11( {buy_tag})" # elif (max_profit > (current_profit + 0.01)) and (last_candle['rsi'] < 44.0) and (last_candle['cmf'] < -0.25): # return f"sell_profit_t_1_12( {buy_tag})" # # sell cti_r # if 0.012 > current_profit >= 0.0 : # if (last_candle['cti'] > self.sell_cti_r_cti.value) and (last_candle['r_14'] > self.sell_cti_r_r.value): # return f"sell_profit_t_cti_r_0_1( {buy_tag})" # # main sell # if current_profit > 0.02: # if (last_candle['momdiv_sell_1h'] == True): # return f"signal_profit_q_momdiv_1h( {buy_tag})" # if (last_candle['momdiv_sell'] == True): # return f"signal_profit_q_momdiv( {buy_tag})" # if (last_candle['momdiv_coh'] == True): # return f"signal_profit_q_momdiv_coh( {buy_tag})" # # sell bear # if last_candle['close'] < last_candle['ema_200']: # if 0.02 > current_profit >= 0.01: # if (last_candle['rsi'] < 34.0) and (last_candle['cmf'] < 0.0): # return f"sell_profit_u_bear_1_1( {buy_tag})" # elif (last_candle['rsi'] < 44.0) and (last_candle['cmf'] < -0.4): # return f"sell_profit_u_bear_1_2( {buy_tag})" # # sell quick # if (0.06 > current_profit > 0.02) and (last_candle['rsi'] > 80.0): # return f"signal_profit_q_1( {buy_tag})" # if (0.06 > current_profit > 0.02) and (last_candle['cti'] > 0.95): # return f"signal_profit_q_2( {buy_tag})" # if (0.06 > current_profit > 0.02) and (last_candle['pm'] <= last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.1): # return f"signal_profit_q_pmax_bull( {buy_tag})" # if (0.06 > current_profit > 0.02) and (last_candle['pm'] > last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.016): # return f"signal_profit_q_pmax_bear( {buy_tag})" # # sell scalp # if (current_profit > 0 and buy_tag in [ 'nfix_39 ']): # if ( # (current_profit > 0) # and (last_candle['fisher'] > 0.39075) # and (last_candle['ha_high'] <= previous_candle_1['ha_high']) # and (previous_candle_1['ha_high'] <= previous_candle_2['ha_high']) # and (last_candle['ha_close'] <= previous_candle_1['ha_close']) # and (last_candle['ema_4'] > last_candle['ha_close']) # and (last_candle['ha_close'] * 0.99754 > last_candle['bb_middleband2']) # ): # return f"sell_scalp( {buy_tag})" # if ( # (current_profit < -0.05) # and (last_candle['close'] < last_candle['ema_200'] * 0.988) # and (last_candle['cmf'] < -0.046) # and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < 0.022) # and last_candle['rsi'] > previous_candle_1['rsi'] # and (last_candle['rsi'] > (last_candle['rsi_1h'] + 10.0)) # ): # return f"sell_stoploss_u_e_1( {buy_tag})" # # stoploss - deadfish # if ( (current_profit < self.sell_deadfish_profit.value) # and (last_candle['close'] < last_candle['ema_200']) # and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) # and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) # and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) # ): # return f"sell_stoploss_deadfish( {buy_tag})" # # stoploss - bleeding # #if ( (current_profit < -0.05) # #and (last_candle['close'] < last_candle['ema_200']) # #and (last_candle['cti_mean_24'] < self.sell_bleeding_cti.value) # #and (last_candle['r_14_mean_24'] < self.sell_bleeding_r14.value) # #and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_bleeding_volume_factor.value) # #): # #return f"sell_stoploss_bleeding( {buy_tag})" # return None ## Confirm Entry def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # max_slip = self.max_slip.value # if(len(dataframe) < 1): # return False # dataframe = dataframe.iloc[-1].squeeze() # if ((rate > dataframe['close'])) : # slippage = ( (rate / dataframe['close']) - 1 ) * 100 # if slippage < max_slip: # return True # else: # return False return False ############################################################################ def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Basic OHLCV dataframe['open_1m'] = dataframe['open'] dataframe['high_1m'] = dataframe['high'] dataframe['low_1m'] = dataframe['low'] dataframe['close_1m'] = dataframe['close'] dataframe['volume_1m'] = dataframe['volume'] dataframe['date_1m'] = dataframe['date'] # Bollinger bands bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2_1m'] = bollinger2['lower'] dataframe['bb_middleband2_1m'] = bollinger2['mid'] dataframe['bb_upperband2_1m'] = bollinger2['upper'] bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3_1m'] = bollinger3['lower'] dataframe['bb_middleband3_1m'] = bollinger3['mid'] dataframe['bb_upperband3_1m'] = bollinger3['upper'] # CMF dataframe['cmf_1m'] = chaikin_money_flow(dataframe, 20) # RSI & MA RSI & MSI OSCILLATOR dataframe['rsi_14_1m'] = ta.RSI(dataframe, timeperiod=14) dataframe['mrsi_7_1m'] = ta.SMA(dataframe['rsi_14_1m'], timeperiod=7) dataframe['rsi_osc_1m'] = (dataframe['rsi_14_1m'] - dataframe['mrsi_7_1m']) / dataframe['mrsi_7_1m'] * 100 # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open_1m'] = heikinashi['open'] dataframe['ha_close_1m'] = heikinashi['close'] dataframe['ha_high_1m'] = heikinashi['high'] dataframe['ha_low_1m'] = heikinashi['low'] # VWAP dataframe['vwap_1m'] = qtpylib.rolling_vwap(dataframe) # VWAP OSCILLATOR dataframe['vwap_osc_1m'] = (dataframe['close'] - dataframe['vwap_1m']) / dataframe['vwap_1m'] * 100 # OBV dataframe['obv_1m'] = ta.OBV(dataframe['close'], dataframe['volume']) # OBV HA dataframe['ha_obv_1m'] = ta.OBV(dataframe['ha_close_1m'], dataframe['volume']) # OBV MA dataframe['mobv_3_1m'] = ta.SMA(dataframe['ha_obv_1m'], timeperiod=3) # OBV OSCILLATOR dataframe['obv_osc_1m'] = (dataframe['ha_obv_1m'] - dataframe['mobv_3_1m']) / dataframe['mobv_3_1m'] * 100 # Volume Oscilator dataframe['vma_20_1m'] = ta.SMA(dataframe['volume'], timeperiod=20) dataframe['vol_osc_1m'] = (dataframe['volume'] - dataframe['vma_20_1m']) / dataframe['vma_20_1m'] * 100 # MACD macd = ta.MACD(dataframe, 20, 5) dataframe['macd_1m'] = macd['macd'] dataframe['macd_signal_1m'] = macd['macdsignal'] dataframe['macd_histogram_1m'] = macd['macdhist'] dataframe['macd_osc_1m'] = dataframe['macd_histogram_1m'] / dataframe['close_1m'] * 100 # SMA dataframe['sma_6_1m'] = ta.SMA(dataframe, timeperiod=6) dataframe['sma_9_1m'] = ta.SMA(dataframe, timeperiod=9) dataframe['sma_30_1m'] = ta.SMA(dataframe, timeperiod=30) dataframe['sma_60_1m'] = ta.SMA(dataframe, timeperiod=30) dataframe['sma_100_1m'] = ta.SMA(dataframe, timeperiod=100) # SMA OSCILLATOR dataframe['sma_6_osc_1m'] = (dataframe['close'] - dataframe['sma_6_1m']) / dataframe['sma_6_1m'] * 100 dataframe['sma_9_osc_1m'] = (dataframe['close'] - dataframe['sma_9_1m']) / dataframe['sma_9_1m'] * 100 dataframe['sma_30_osc_1m'] = (dataframe['close'] - dataframe['sma_30_1m']) / dataframe['sma_30_1m'] * 100 dataframe['sma_60_osc_1m'] = (dataframe['close'] - dataframe['sma_60_1m']) / dataframe['sma_60_1m'] * 100 dataframe['sma_100_osc_1m'] = (dataframe['close'] - dataframe['sma_100_1m']) / dataframe['sma_100_1m'] * 100 # Stochastic Slow stoch = ta.STOCH(dataframe) dataframe['stoch_slowd_1m'] = stoch['slowd'] dataframe['stoch_slowk_1m'] = stoch['slowk'] dataframe['stoch_slow_osc_1m'] = (dataframe['stoch_slowk_1m'] - dataframe['stoch_slowd_1m']) / dataframe['stoch_slowd_1m'] * 100 # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe["stoch_fastd_1m"] = stoch_fast["fastd"] dataframe["stoch_fastk_1m"] = stoch_fast["fastk"] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ # # Hammer: values [0, 100] # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # # Inverted Hammer: values [0, 100] # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # # Dragonfly Doji: values [0, 100] # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # # Piercing Line: values [0, 100] # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # # Morningstar: values [0, 100] # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # # Three White Soldiers: values [0, 100] # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ # # Hanging Man: values [0, 100] # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # # Shooting Star: values [0, 100] # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # # Gravestone Doji: values [0, 100] # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # # Dark Cloud Cover: values [0, 100] # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # # Evening Doji Star: values [0, 100] # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # # Evening Star: values [0, 100] # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ # # Three Line Strike: values [0, -100, 100] # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # # Spinning Top: values [0, -100, 100] # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # # Engulfing: values [0, -100, 100] # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # # Harami: values [0, -100, 100] # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # # Three Outside Up/Down: values [0, -100, 100] # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # # Three Inside Up/Down: values [0, -100, 100] # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] return dataframe ############################################################################ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) # The indicators for the 5m informative timeframe informative_5m = self.informative_5m_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_5m, self.timeframe, self.inf_5m, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) # send data to the destination server 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) # requests.post('http://10.25.96.7:8999/data-gateway/submit-data', json=postData) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # disable all buy conditions # if 1==1: dataframe.loc[:, 'buy'] = 0 return 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_sqzOff = ( # (dataframe['bb_lowerband2'] < dataframe['kc_lowerband_28_1']) & # (dataframe['bb_upperband2'] > dataframe['kc_upperband_28_1']) # ) # is_break = ( # (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) # ) # 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_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_ewo_2 = ( # (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & # (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & # (dataframe['rsi_fast'] < self.buy_rsi_fast_ewo_2.value) & # (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low_2.value) & # (dataframe['EWO'] > self.buy_ewo_high_2.value) & # (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high_2.value) & # (dataframe['rsi'] < self.buy_rsi_ewo_2.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'] > 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['ha_close'] < dataframe['ha_close'].shift()) # ) # ) # 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_gumbo = ( # Modified from gumbo1, creadit goes to original author @raph92 # (dataframe['EWO'] < self.buy_gumbo_ewo_low.value) & # (dataframe['bb_middleband2_1h'] >= dataframe['T3_1h']) & # (dataframe['T3'] <= dataframe['ema_8'] * self.buy_gumbo_ema.value) & # (dataframe['cti'] < self.buy_gumbo_cti.value) & # (dataframe['r_14'] < self.buy_gumbo_r14.value) # ) # is_sqzmom = ( # Modified from squeezeMomentum, credit goes to original author @LazyBear of TradingView # (is_sqzOff) & # (dataframe['linreg_val_20'].shift(2) > dataframe['linreg_val_20'].shift(1)) & # (dataframe['linreg_val_20'].shift(1) < dataframe['linreg_val_20']) & # (dataframe['linreg_val_20'] < 0) & # (dataframe['close'] < dataframe['ema_13'] * self.buy_sqzmom_ema.value) & # (dataframe['EWO'] < self.buy_sqzmom_ewo.value) & # (dataframe['r_14'] < self.buy_sqzmom_r14.value) # ) # # NFI quick mode, credit goes to @iterativ # is_nfi_13 = ( # (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & # (dataframe['close'] < dataframe['sma_30'] * 0.99) & # (dataframe['cti'] < -0.92) & # (dataframe['EWO'] < -5.585) & # (dataframe['cti_1h'] < -0.88) & # (dataframe['crsi_1h'] > 10.0) # ) # is_nfi_32 = ( # NFIX 26 # (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & # (dataframe['rsi_fast'] < 46) & # (dataframe['rsi'] > 25.0) & # (dataframe['close'] < dataframe['sma_15'] * 0.93) & # (dataframe['cti'] < -0.9) # ) # 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_additional_check = ( # (dataframe['roc_1h'] < self.buy_roc_1h.value) & # (dataframe['bb_width_1h'] < self.buy_bb_width_1h.value) # ) # ## Additional Check # is_BB_checked = is_dip & is_break # ## Condition Append # 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_ewo_2) # ~2.86 / 91.5% / 33.31% D # dataframe.loc[is_ewo_2, 'buy_tag'] += 'ewo2 ' # 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_cofi) # ~0.4 / 94.4% / 9.59% D # dataframe.loc[is_cofi, 'buy_tag'] += 'cofi ' # conditions.append(is_gumbo) # ~2.63 / 90.6% / 41.49% D # dataframe.loc[is_gumbo, 'buy_tag'] += 'gumbo ' # conditions.append(is_sqzmom) # ~3.14 / 92.4% / 64.14% D # dataframe.loc[is_sqzmom, 'buy_tag'] += 'sqzmom ' # conditions.append(is_nfi_13) # ~0.4 / 100% D # dataframe.loc[is_nfi_13, 'buy_tag'] += 'nfi_13 ' # 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 ' # if conditions: # dataframe.loc[ # is_additional_check # & # reduce(lambda x, y: x | y, conditions) # , 'buy' ] = 1 # return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # disable sell dataframe.loc[:, 'sell'] = 0 return dataframe # PMAX 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}' # MAtype==1 --> EMA # MAtype==2 --> DEMA # MAtype==3 --> T3 # MAtype==4 --> SMA # MAtype==5 --> VIDYA # MAtype==6 --> TEMA # MAtype==7 --> WMA # MAtype==8 --> VWMA # MAtype==9 --> zema 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) # Mark the trend direction up/down pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN) return pm, pmx # Mom DIV 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']