# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/price_down_latest_v25.py from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame, Series import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter, informative import technical.indicators as ftt import pandas_ta as pta import math import logging logger = logging.getLogger(__name__) buy_params = { "base_nb_candles_buy": 12, "rsi_buy": 58, "ewo_high": 3.001, "ewo_low": -10.289, "low_offset": 0.987, "lambo2_ema_14_factor": 0.979, "lambo2_enabled": True, "lambo2_rsi_14_limit": 39, "lambo2_rsi_4_limit": 44, "downtrend_limit": 0.91, "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, } sell_params = { "base_nb_candles_sell": 22, "high_offset": 1.014, "high_offset_2": 1.01, "sell_deadfish_profit": -0.063, "sell_deadfish_bb_factor": 0.954, "sell_deadfish_bb_width": 0.043, "sell_deadfish_volume_factor": 2.37 } def EWO(dataframe, ema_length=5, ema2_length=3): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) 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 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 zlema(dataframe, timeperiod): lag = int(math.floor((timeperiod - 1) / 2) ) if isinstance(dataframe, Series): ema_data = dataframe + (dataframe - dataframe.shift(lag)) else: ema_data = dataframe['close'] + (dataframe['close'] - dataframe['close'] .shift(lag)) return ta.EMA(ema_data, timeperiod = timeperiod) def VWAPB(dataframe, window_size=20, num_of_std=1): df = dataframe.copy() df['vwap'] = qtpylib.rolling_vwap(df,window=window_size) rolling_std = df['vwap'].rolling(window=window_size).std() df['vwap_low'] = df['vwap'] - (rolling_std * num_of_std) df['vwap_high'] = df['vwap'] + (rolling_std * num_of_std) return df['vwap_low'], df['vwap'], df['vwap_high'] def 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'] def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe['volume'] cmf = (mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum()) if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf') class Github_remiotore_freqtrade__price_down_latest_v25__20260111_210550(IStrategy): INTERFACE_VERSION = 2 """ minimal_roi = { "0": 0.08, "20": 0.04, "40": 0.032, "87": 0.016, "201": 0, "202": -1 } """ @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 5 }, { "method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 20, "stop_duration_candles": 4, "max_allowed_drawdown": 0.2 }, { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "only_per_pair": False }, { "method": "LowProfitPairs", "lookback_period_candles": 6, "trade_limit": 2, "stop_duration_candles": 60, "required_profit": 0.02 }, { "method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "required_profit": 0.01 } ] minimal_roi = { "0": 0.50, } def informative_pairs(self): pairs = self.dp.current_whitelist informative_pairs.extend = [(pair, '1h') for pair in pairs] informative_pairs.extend = [(pair, '1d') for pair in pairs] informative_pairs = [(pair, '1d') for pair in pairs] return informative_pairs def is_support(self, row_data) -> bool: conditions = [] for row in range(len(row_data)-1): if row < len(row_data)/2: conditions.append(row_data[row] > row_data[row+1]) else: conditions.append(row_data[row] < row_data[row+1]) return reduce(lambda x, y: x & y, conditions) stoploss = -0.99 buy_31_ma_offset = 0.962 buy_31_ewo = -10.4 buy_31_wr = -90.0 buy_31_cti = -0.89 buy_34_ma_offset = 0.93 buy_34_dip = 0.005 buy_34_ewo = -6.0 buy_34_cti = -0.88 buy_34_volume = 2.0 buy_44_ma_offset = 0.982 buy_44_ewo = -18.143 buy_44_cti = -0.8 buy_44_r_1h = -75.0 is_optimize_clucha = False buy_clucha_bbdelta_close = DecimalParameter(0.01,0.05, default=0.02206, optimize=is_optimize_clucha) buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=1.02515, optimize=is_optimize_clucha) buy_clucha_close_bblower = DecimalParameter(0.001, 0.05, default=0.03669, optimize=is_optimize_clucha) buy_clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=0.04401, optimize=is_optimize_clucha) buy_clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=0.47782, optimize=is_optimize_clucha) is_optimize_local_uptrend = False buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize = is_optimize_local_uptrend) buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize = False) buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize = is_optimize_local_uptrend) sell_rsi_bb_2 = DecimalParameter(55.0, 75.0, default=70, space='sell', decimals=1, optimize=False, load=True) sell_rsi_main_3 = DecimalParameter(60.0, 90.0, default=82, space='sell', decimals=1, optimize=False, load=True) sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='sell', decimals=1, optimize=False, load=True) sell_custom_profit_11 = DecimalParameter(0.16, 0.45, default=0.20, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_11 = DecimalParameter(28.0, 40.0, default=34.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=34.0, space='sell', decimals=3, optimize=False, load=True) sell_custom_profit_1 = DecimalParameter(0.01, 0.1, default=0.02, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=35.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_2 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_2 = DecimalParameter(30.0, 50.0, default=37.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_3 = DecimalParameter(0.01, 0.1, default=0.04, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_3 = DecimalParameter(30.0, 50.0, default=42.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_4 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_4 = DecimalParameter(35.0, 50.0, default=43.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_5 = DecimalParameter(0.01, 0.1, default=0.06, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_5 = DecimalParameter(35.0, 50.0, default=45.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_6 = DecimalParameter(0.01, 0.1, default=0.07, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_6 = DecimalParameter(38.0, 55.0, default=48.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_7 = DecimalParameter(0.01, 0.1, default=0.08, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_7 = DecimalParameter(40.0, 58.0, default=54.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_8 = DecimalParameter(0.06, 0.1, default=0.09, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_8 = DecimalParameter(40.0, 50.0, default=55.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_9 = DecimalParameter(0.05, 0.14, default=0.1, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_9 = DecimalParameter(40.0, 60.0, default=54.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_10 = DecimalParameter(0.1, 0.14, default=0.12, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_10 = DecimalParameter(38.0, 50.0, default=42.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_profit_11 = DecimalParameter(0.16, 0.45, default=0.20, space='sell', decimals=3, optimize=False, load=True) sell_custom_rsi_11 = DecimalParameter(28.0, 40.0, default=34.0, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_profit_1 = DecimalParameter(0.01, 0.03, default=0.01, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_rsi_1 = DecimalParameter(40.0, 56.0, default=50, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_profit_2 = DecimalParameter(0.01, 0.20, default=0.04, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_rsi_2 = DecimalParameter(42.0, 56.0, default=50, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_profit_3 = DecimalParameter(0.15, 0.30, default=0.08, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_rsi_3 = DecimalParameter(44.0, 58.0, default=56, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_profit_4 = DecimalParameter(0.3, 0.7, default=0.14, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_rsi_4 = DecimalParameter(44.0, 60.0, default=58, space='sell', decimals=2, optimize=False, load=True) sell_custom_roi_profit_5 = DecimalParameter(0.01, 0.1, default=0.04, space='sell', decimals=2, optimize=False, load=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_2 = DecimalParameter(0.04, 0.1, default=0.04, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.11, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='sell', decimals=3, optimize=False, load=True) sell_custom_stoploss_1 = DecimalParameter(-0.15, -0.03, default=-0.05, space='sell', decimals=2, optimize=False, load=True) sell_rsi_main = DecimalParameter(72.0, 90.0, default=80, space='sell', decimals=2, optimize=True, load=True) sell_custom_under_profit_0 = DecimalParameter(0.01, 0.4, default=0.01, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_0 = DecimalParameter(28.0, 40.0, default=35.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=56.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.03, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=57.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_3 = DecimalParameter(0.01, 0.10, default=0.04, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=58.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_4 = DecimalParameter(0.02, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_4 = DecimalParameter(50.0, 68.0, default=59.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_5 = DecimalParameter(0.02, 0.1, default=0.06, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_5 = DecimalParameter(46.0, 62.0, default=60.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_6 = DecimalParameter(0.03, 0.1, default=0.07, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_6 = DecimalParameter(44.0, 60.0, default=56.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_7 = DecimalParameter(0.04, 0.1, default=0.08, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_7 = DecimalParameter(46.0, 60.0, default=54.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_8 = DecimalParameter(0.06, 0.12, default=0.09, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_8 = DecimalParameter(40.0, 58.0, default=55.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_9 = DecimalParameter(0.08, 0.14, default=0.1, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_9 = DecimalParameter(40.0, 60.0, default=54.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_10 = DecimalParameter(0.1, 0.16, default=0.12, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_10 = DecimalParameter(30.0, 50.0, default=42.0, space='sell', decimals=1, optimize=False, load=True) sell_custom_under_profit_11 = DecimalParameter(0.16, 0.3, default=0.2, space='sell', decimals=3, optimize=False, load=True) sell_custom_under_rsi_11 = DecimalParameter(24.0, 40.0, default=34.0, space='sell', decimals=1, optimize=False, load=True) base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True) lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True) lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True) lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True) is_optimize_cofi = False buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97 , optimize = is_optimize_cofi) buy_fastk = IntParameter(20, 30, default=20, optimize = is_optimize_cofi) buy_fastd = IntParameter(20, 30, default=20, optimize = is_optimize_cofi) buy_adx = IntParameter(20, 30, default=30, optimize = is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi) fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0,default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter(3.0, 3.4, default=buy_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=False) 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) trailing_stop = False trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True use_sell_signal = True sell_profit_only = True sell_profit_offset = 0.025 ignore_roi_if_buy_signal = False order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } timeframe = '5m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 400 plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() previous_candle_1 = dataframe.iloc[-1].squeeze() previous_candle_2 = dataframe.iloc[-2].squeeze() previous_candle_3 = dataframe.iloc[-3].squeeze() max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate) if (last_candle is not None): if (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_1.value)): return 'trail_target_1' elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_2.value)): return 'trail_target_2' elif (current_profit > 3) & (last_candle['rsi'] > 85): return 'RSI-85 target' if (current_profit > 0) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (last_candle['rsi']>50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']): return 'sell signal1' if (current_profit > 0) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']): return 'sell signal2' return 'sell stoploss1' if ( (current_profit < self.sell_deadfish_profit.value) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) ): return f"sell_stoploss_deadfish" if (-0.12 <= current_profit < -0.08): if (last_candle['close'] < last_candle['atr_high_thresh_1']) and (last_candle['cmf'] < -0.0): return 'sell_stoploss_atr_1' if (-0.16 <= current_profit < -0.12): if (last_candle['close'] < last_candle['atr_high_thresh_2']) and (last_candle['cmf'] < -0.0): return 'sell_stoploss_atr_2' if (-0.2 <= current_profit < -0.16): if (last_candle['close'] < last_candle['atr_high_thresh_3']) and (last_candle['cmf'] < -0.0): return 'sell_stoploss_atr_3' if (-0.3 <= current_profit < -0.2): if (last_candle['close'] < last_candle['atr_high_thresh_4']) and (last_candle['cmf'] < -0.0): return 'sell_stoploss_atr_4' if (current_profit < -0.3): if (last_candle['close'] < last_candle['atr_high_thresh_5']) and (last_candle['cmf'] < -0.0): return 'sell_stoploss_atr_5' def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1d') for pair in pairs] if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" informative_pairs.append((btc_info_pair, self.timeframe)) informative_pairs.append((btc_info_pair, self.inf_1h)) return informative_pairs @informative('1h') def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) sup_series = dataframe['low'].rolling(window = 5, center=True).apply(lambda row: self.is_support(row), raw=True).shift(2) dataframe['sup_level'] = Series(np.where(sup_series, np.where(dataframe['close'] < dataframe['open'], dataframe['close'], dataframe['open']), float('NaN'))).ffill() dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['cti'] = pta.cti(dataframe["close"], length=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['r_480'] = williams_r(dataframe, period=480) inf_heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_close'] = inf_heikinashi['close'] dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=168) dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) return dataframe def pump_dump_protection(self, dataframe: DataFrame, metadata: dict) -> DataFrame: df36h = dataframe.copy().shift( 432 ) # TODO FIXME: This assumes 5m timeframe df24h = dataframe.copy().shift( 288 ) # TODO FIXME: This assumes 5m timeframe dataframe['volume_mean_short'] = dataframe['volume'].rolling(4).mean() dataframe['volume_mean_long'] = df24h['volume'].rolling(48).mean() dataframe['volume_mean_base'] = df36h['volume'].rolling(288).mean() dataframe['volume_change_percentage'] = (dataframe['volume_mean_long'] / dataframe['volume_mean_base']) dataframe['rsi_mean'] = dataframe['rsi'].rolling(48).mean() dataframe['pnd_volume_warn'] = np.where((dataframe['volume_mean_short'] / dataframe['volume_mean_long'] > 5.0), -1, 0) return dataframe def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['price_trend_long'] = (dataframe['close'].rolling(8).mean() / dataframe['close'].shift(8).rolling(144).mean()) ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) return dataframe def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi_8'] = ta.RSI(dataframe, timeperiod=8) ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.config['stake_currency'] in ['USDT','BUSD']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" 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 = 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'] bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb20_2_low'] = bb_20_std2['lower'] dataframe['bb20_2_mid'] = bb_20_std2['mid'] dataframe['bb20_2_upp'] = bb_20_std2['upper'] 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'] bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() 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) dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['atr_high_thresh_1'] = (dataframe['high'] - (dataframe['atr'] * 7.0)) dataframe['atr_high_thresh_2'] = (dataframe['high'] - (dataframe['atr'] * 5.6)) dataframe['atr_high_thresh_3'] = (dataframe['high'] - (dataframe['atr'] * 5.0)) dataframe['atr_high_thresh_4'] = (dataframe['high'] - (dataframe['atr'] * 3.8)) dataframe['atr_high_thresh_5'] = (dataframe['high'] - (dataframe['atr'] * 2.0)) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['r_480'] = williams_r(dataframe, period=480) dataframe['tpct_change__2'] = self.top_percent_change_dca(dataframe,2) dataframe['tpct_change__12'] = self.top_percent_change_dca(dataframe,12) dataframe['tpct_change__144'] = self.top_percent_change_dca(dataframe,144) vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1) dataframe['vwap_low'] = vwap_low dataframe['tcp_percent_4'] = top_percent_change(dataframe , 4) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['moderi_32'] = moderi(dataframe, 32) dataframe['moderi_64'] = moderi(dataframe, 64) dataframe['moderi_96'] = moderi(dataframe, 96) dataframe['zlema_68'] = zlema(dataframe, 68) inf_heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_close'] = inf_heikinashi['close'] dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=168) btc_info_tf = self.dp.get_pair_dataframe(btc_info_pair, self.inf_1h) btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.inf_1h, ffill=True) drop_columns = [f"{s}_{self.inf_1h}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) btc_base_tf = self.dp.get_pair_dataframe(btc_info_pair, self.timeframe) btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True) drop_columns = [f"{s}_{self.timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) inf_tf = '1d' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) informative['rsi'] = ta.RSI(informative, timeperiod=7) for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) 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_28'] = ta.SMA(dataframe, timeperiod=28) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['cci_25'] = ta.CCI(dataframe, source='hlc3', timeperiod=25) # doplnene na testovanie z NFIX dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) # doplnene na testovanie z NFIX 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['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['is_pump'] = (dataframe['high'] / dataframe['low'].shift(36) > 1.2).astype('int') dataframe['pump_warning'] = (dataframe['is_pump'].rolling(192).max() > 0).astype('int') dataframe['downtrend1']=(dataframe['high'] / dataframe['low'].shift(36)) dataframe['downtrend2']=(dataframe['high'] / dataframe['low'].shift(200)) dataframe['downtrend_final']=dataframe['downtrend1'] - dataframe['downtrend2'] dataframe['long_term_price_drop_4_20'] = np.where((dataframe['close'].rolling(4).max() * 0.96 > dataframe['close'].rolling(6).mean()), 1, 0) dataframe['long_term_price_drop_4_25'] = np.where((dataframe['close'].rolling(5).max() * 0.96 > dataframe['close'].rolling(6).mean()), 1, 0) dataframe['long_term_price_drop_5_30'] = np.where((dataframe['close'].rolling(6).max() * 0.95 > dataframe['close'].rolling(6).mean()), 1, 0) dataframe['long_term_price_drop_5_40'] = np.where((dataframe['close'].rolling(8).max() * 0.95 > dataframe['close'].rolling(6).mean()), 1, 0) dataframe['long_term_price_drop_6_40'] = np.where((dataframe['close'].rolling(8).max() * 0.94 > dataframe['close'].rolling(6).mean()), 1, 0) dataframe['long_term_price_drop_8_60'] = np.where((dataframe['close'].rolling(12).max() * 0.94 > dataframe['close'].rolling(6).mean()), 1, 0) dataframe['long_term_price_drop_10_60'] = np.where((dataframe['close'].rolling(12).max() * 0.90 > dataframe['close'].rolling(6).mean()), 1, 0) dataframe['long_term_price_drop_15_8h'] = np.where((dataframe['close'].rolling(96).max() * 0.85 > dataframe['close'].rolling(6).mean()), 1, 0) dataframe['long_term_price_drop_20_12h'] = np.where((dataframe['close'].rolling(144).max() * 0.8 > dataframe['close'].rolling(8).mean()), 1, 0) dataframe['long_term_price_drop_25_24h'] = np.where((dataframe['close'].rolling(288).max() * 0.75 > dataframe['close'].rolling(12).mean()), 1, 0) dataframe['long_term_price_drop_30_36h'] = np.where((dataframe['close'].rolling(432).max() * 0.7 > dataframe['close'].rolling(24).mean()), 1, 0) dataframe['long_term_price_drop'] = np.where( ( ( dataframe['close'].rolling(4).max() * 0.96 > dataframe['close'].rolling(6).mean() #0 buys 20220101 ) | ( dataframe['close'].rolling(6).max() * 0.96 > dataframe['close'].rolling(6).mean() #0 buys 20220101 ) | ( dataframe['close'].rolling(6).max() * 0.95 > dataframe['close'].rolling(6).mean() #0 buys 20220101 ) | ( dataframe['close'].rolling(8).max() * 0.95 > dataframe['close'].rolling(6).mean() #0 buys 20220101 ) | ( dataframe['close'].rolling(8).max() * 0.94 > dataframe['close'].rolling(6).mean() ) | ( dataframe['close'].rolling(12).max() * 0.92 > dataframe['close'].rolling(6).mean() ) | ( dataframe['close'].rolling(24).max() * 0.90 > dataframe['close'].rolling(6).mean() ) | ( dataframe['close'].rolling(96).max() * 0.85 > dataframe['close'].rolling(6).mean() ) | ( dataframe['close'].rolling(144).max() * 0.8 > dataframe['close'].rolling(8).mean() ) | ( dataframe['close'].rolling(288).max() * 0.75 > dataframe['close'].rolling(12).mean() ) | ( dataframe['close'].rolling(432).max() * 0.7 > dataframe['close'].rolling(24).mean() ) ), 1, 0) dataframe = self.pump_dump_protection(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' lambo2_drop_4_20 = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_4_20'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_4_20 , 'buy_tag'] += 'L_d_4_20 ' conditions.append(lambo2_drop_4_20 ) lambo2_drop_4_25 = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_4_25'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_4_25 , 'buy_tag'] += 'L_d_4_25 ' conditions.append(lambo2_drop_4_25 ) lambo2_drop_5_30 = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_5_30'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_5_30 , 'buy_tag'] += 'L_d_5_30 ' conditions.append(lambo2_drop_5_30 ) lambo2_drop_5_40 = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_5_40'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_5_40 , 'buy_tag'] += 'L_d_5_40 ' conditions.append(lambo2_drop_5_40 ) lambo2_drop_6_40 = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_6_40'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_6_40 , 'buy_tag'] += 'L_d_6_40 ' conditions.append(lambo2_drop_6_40 ) lambo2_drop_8_60 = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_8_60'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_8_60 , 'buy_tag'] += 'L_d_8_60 ' conditions.append(lambo2_drop_8_60 ) lambo2_drop_10_60 = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_10_60'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_10_60 , 'buy_tag'] += 'L_d_10_60 ' conditions.append(lambo2_drop_10_60 ) lambo2_drop_15_8h = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_15_8h'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_15_8h , 'buy_tag'] += 'L_d_15_8h ' conditions.append(lambo2_drop_15_8h ) lambo2_drop_20_12h = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_20_12h'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_20_12h , 'buy_tag'] += 'L_d_20_12h ' conditions.append(lambo2_drop_20_12h ) lambo2_drop_25_24h = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_25_24h'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_25_24h , 'buy_tag'] += 'L_d_25_24h ' conditions.append(lambo2_drop_25_24h ) lambo2_drop_30_36h = ( (dataframe['pump_warning'] == 0) & (dataframe['long_term_price_drop_30_36h'] == 1) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < dataframe['sma_15'] * 0.936) & # doplnene na testovanie z NFIX (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2_drop_30_36h , 'buy_tag'] += 'L_d_30_36h ' conditions.append(lambo2_drop_30_36h ) buy1ewo = ( (dataframe['pump_warning'] == 0) & (dataframe['rsi_fast'] <35)& (dataframe['long_term_price_drop'] == 1) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0)& (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) dataframe.loc[buy1ewo, 'buy_tag'] += 'buy1eworsi_' conditions.append(buy1ewo) NFIX29 = ( (dataframe['close'] > (dataframe['sup_level_1h'] * 0.72)) & (dataframe['close'] < (dataframe['ema_16'] * 0.982)) & (dataframe['EWO'] < -10.0) & (dataframe['cti'] < -0.9) ) dataframe.loc[NFIX29, 'buy_tag'] += 'NFIX29_' conditions.append(NFIX29) NFIX9= ( (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['close'] > (dataframe['sup_level_1h'] * 0.88)) & (dataframe['close'] < dataframe['sma_30'] * 0.99) & (dataframe['cti'] < -0.92) & (dataframe['EWO'] < -5.9) & (dataframe['cti_1h'] < -0.75) & (dataframe['crsi_1h'] > 8.0) & (dataframe['volume_mean_12'] > (dataframe['volume_mean_24'] * 1.05)) ) dataframe.loc[NFIX9, 'buy_tag'] += 'NFIX9_' conditions.append(NFIX9) NFINext31= ( (dataframe['moderi_64'] == False) & (dataframe['close'] < dataframe['zlema_68'] * self.buy_31_ma_offset ) & (dataframe['EWO'] < self.buy_31_ewo) & (dataframe['r_480'] < self.buy_31_wr) & (dataframe['cti'] < self.buy_31_cti) ) dataframe.loc[NFINext31, 'buy_tag'] += 'NFINext31_' conditions.append(NFINext31) NFINext34= ( (dataframe['cti'] < self.buy_34_cti) & ((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.buy_34_dip) & (dataframe['close'] < dataframe['ema_13'] * self.buy_34_ma_offset) & (dataframe['EWO'] < self.buy_34_ewo) & (dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_34_volume)) ) dataframe.loc[NFINext34, 'buy_tag'] += 'NFINext34_' conditions.append(NFINext34) NFINext44= ( (dataframe['close'] < (dataframe['ema_16'] * self.buy_44_ma_offset)) & (dataframe['EWO'] < self.buy_44_ewo) & (dataframe['cti'] < self.buy_44_cti) & (dataframe['r_480_1h'] < self.buy_44_r_1h) ) dataframe.loc[NFINext44, 'buy_tag'] += 'NFINext44_' conditions.append(NFINext44) 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['close'] > (dataframe['sup_level_1h'] * 0.88)) & (dataframe['ha_close'] < dataframe['ha_close'].shift()) ) dataframe.loc[clucHA, 'buy_tag'] += 'clucHA_' conditions.append(clucHA) clucHA2 = ( (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h.value ) & (dataframe['ha_close'] < dataframe['ema_slow']) & (dataframe['ha_close'] < self.buy_clucha_close_bblower.value * dataframe['bb_lowerband2']) ) dataframe.loc[clucHA2, 'buy_tag'] += 'clucHA2_' conditions.append(clucHA2) local_uptrend = ( (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 ) ) dataframe.loc[local_uptrend, 'buy_tag'] += 'local_uptrend_' conditions.append(local_uptrend) buy2ewo = ( (dataframe['pump_warning'] == 0) & (dataframe['rsi_fast'] < 35)& (dataframe['long_term_price_drop'] == 1) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0)& (dataframe['rsi_1d'] > 20) & (dataframe['cci_25'] < -120.0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) dataframe.loc[buy2ewo, 'buy_tag'] += 'buy2ewo_' conditions.append(buy2ewo) vwap = ( (dataframe['pump_warning'] == 0) & (dataframe['rsi_1d'] > 20) & (dataframe['close'] < dataframe['vwap_low']) & (dataframe['tcp_percent_4'] > 0.05) & (dataframe['cti'] < -0.8) & (dataframe['rsi'] < 35) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['volume'] > 0) ) dataframe.loc[vwap, 'buy_tag'] += 'vwap_' conditions.append(vwap) 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 = [] conditions.append( ( (dataframe['close']>dataframe['hma_50'])& (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['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' ]=0 return dataframe def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, current_time: datetime, **kwargs) -> bool: trade.sell_reason = sell_reason + "_" + trade.buy_tag return True def pct_change(a, b): return (b - a) / a class Price_down_dca_latest_v25(Github_remiotore_freqtrade__price_down_latest_v25__20260111_210550): initial_safety_order_trigger = -0.018 max_safety_orders = 8 safety_order_step_scale = 1.1 safety_order_volume_scale = 1.4 def top_percent_change_dca(self, dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_indicators(dataframe, metadata) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['tpct_change_0'] = self.top_percent_change_dca(dataframe,0) dataframe['tpct_change_2'] = self.top_percent_change_dca(dataframe,2) dataframe['tpct_change_12'] = self.top_percent_change_dca(dataframe,12) dataframe['tpct_change_144'] = self.top_percent_change_dca(dataframe,144) return dataframe def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): if current_profit > self.initial_safety_order_trigger: return None dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() previous_candle = dataframe.iloc[-2].squeeze() previous2_candle = dataframe.iloc[-3].squeeze() if (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']): return None count_of_buys = 0 for order in trade.orders: if order.ft_is_open or order.ft_order_side != 'buy': continue if order.status == "closed": count_of_buys += 1 if 1 <= count_of_buys <= self.max_safety_orders: safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1)) if current_profit <= (-1 * abs(safety_order_trigger)): try: stake_amount = self.wallets.get_trade_stake_amount(trade.pair, None) stake_amount = stake_amount * math.pow(self.safety_order_volume_scale,(count_of_buys - 1)) amount = stake_amount / current_rate logger.info(f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}") return stake_amount except Exception as exception: logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}') return None return None def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name="{0} Williams %R".format(period), ) return WR * -100