# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/abbasPublic.py from freqtrade.strategy import (IStrategy, informative) from typing import Dict, List from pandas import DataFrame, Series import pandas_ta as pta import numpy as np import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter, BooleanParameter from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real from datetime import datetime from technical.indicators import RMI buy_params = { "buy_clucha_bbdelta_close": 0.017, "buy_clucha_bbdelta_tail": 0.5, "buy_clucha_closedelta_close": 0.009, "buy_clucha_rocr_1h": 0.82, "buy_vwap_closedelta": 14.8, "buy_vwap_cti": -0.01, "buy_vwap_width": 3.9, "ewo_candles_buy": 23, "ewo_candles_sell": 4, "ewo_high_offset": 1.18302, "ewo_low": -9.842, "ewo_low_offset": 1.10711, "ewo_low_rsi_4": 49, "fast_ewo": 6, "slow_ewo": 188, "buy_bb40_bbdelta_close": 0.043, "buy_bb40_closedelta_close": 0.016, "buy_bb40_tail_bbdelta": 0.302, "buy_c10_1": -107, "buy_c10_2": -0.8, "buy_c2_1": 0.02, "buy_c2_2": 0.984, "buy_c2_3": -0.5, "buy_c9_1": 42, "buy_c9_2": -74, "buy_c9_3": -69, "buy_c9_4": 38, "buy_c9_5": 20, "buy_c9_6": 87, "buy_c9_7": -103, "buy_cci": -110, "buy_cci_length": 33, "buy_con3_1": 0.011, "buy_con3_2": 0.99, "buy_con3_3": 0.971, "buy_con3_4": -0.88, "buy_dip_threshold_1": 0.32, "buy_dip_threshold_2": 0.3, "buy_dip_threshold_3": 0.32, "buy_dip_threshold_5": 0.064, "buy_dip_threshold_6": 0.083, "buy_dip_threshold_7": 0.063, "buy_dip_threshold_8": 0.25, "buy_ema_open_mult_1": 0.016, "buy_macd_41": 0.07, "buy_mfi_1": 33, "buy_min_inc_1": 0.049, "buy_pump_pull_threshold_1": 2.54, "buy_pump_threshold_1": 0.913, "buy_rmi_length": 15, "buy_rsi_1": 20, "buy_rsi_1h_42": 23, "buy_rsi_1h_max_1": 81, "buy_rsi_1h_min_1": 39, "buy_volume_1": 4, "buy_volume_drop_41": 2.0, "buy_volume_pump_41": 0.1, "bzv7_buy_macd_1": 0.07, "bzv7_buy_rsi_1h_1": 30, "bzv7_buy_rsi_1h_2": 29, "bzv7_buy_rsi_1h_4": 17, "bzv7_buy_rsi_2": 13, "bzv7_buy_volume_drop_1": 8.7, "bzv7_buy_volume_pump_1": 0.5 } def EWO(dataframe, ema_length=5, ema2_length=35): ema1 = ta.EMA(dataframe, timeperiod=ema_length) ema2 = ta.EMA(dataframe, timeperiod=ema2_length) return (ema1 - ema2) / dataframe["low"] * 100 def VWAPB(dataframe, window_size=20, num_of_std=1): df = dataframe.copy() df["vwap"] = qtpylib.rolling_vwap(df,window=window_size) rolling_std = df["vwap"].rolling(window=window_size).std() df["vwap_low"] = df["vwap"] - (rolling_std * num_of_std) df["vwap_high"] = df["vwap"] + (rolling_std * num_of_std) return df["vwap_low"], df["vwap"], df["vwap_high"] def ha_typical_price(bars): res = (bars["ha_high"] + bars["ha_low"] + bars["ha_close"]) / 3. return Series(index=bars.index, data=res) def williams_r(dataframe: DataFrame, period: int = 14) -> Series: highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return WR * -100 def SSLChannels(dataframe, length=7): df = dataframe.copy() df["ATR"] = ta.ATR(df, timeperiod=14) df["smaHigh"] = df["high"].rolling(length).mean() + df["ATR"] df["smaLow"] = df["low"].rolling(length).mean() - df["ATR"] df["hlv"] = np.where(df["close"] > df["smaHigh"], 1, np.where(df["close"] < df["smaLow"], -1, np.NAN)) df["hlv"] = df["hlv"].ffill() df["sslDown"] = np.where(df["hlv"] < 0, df["smaHigh"], df["smaLow"]) df["sslUp"] = np.where(df["hlv"] < 0, df["smaLow"], df["smaHigh"]) return df["sslDown"], df["sslUp"] class Github_remiotore_ccxt_freqtrade__abbasPublic__20260111_210550(IStrategy): def version(self) -> str: return "v9.13" INTERFACE_VERSION = 3 class HyperOpt: def stoploss_space(): return [SKDecimal(-0.12, -0.03, decimals=3, name="stoploss")] def trailing_space() -> List[Dimension]: return[ Categorical([True], name="trailing_stop"), SKDecimal(0.0003, 0.0020, decimals=4, name="trailing_stop_positive"), SKDecimal(0.010, 0.030, decimals=3, name="trailing_stop_positive_offset_p1"), Categorical([True], name="trailing_only_offset_is_reached"), ] def roi_space() -> List[Dimension]: return [ Integer( 6, 34, name="roi_t1"), Integer( 20, 64, name="roi_t2"), Integer( 50, 104, name="roi_t3"), Integer( 80, 194, name="roi_t4"), Integer(180, 264, name="roi_t5"), Integer(250, 400, name="roi_t6"), ] def generate_roi_table(params: Dict) -> Dict[int, float]: roi_table = {} roi_table[params["roi_t1"]] = 0.015 roi_table[params["roi_t2"]] = 0.010 roi_table[params["roi_t3"]] = 0.005 roi_table[params["roi_t4"]] = 0 roi_table[params["roi_t5"]] = -0.02 roi_table[params["roi_t6"]] = -0.04 return roi_table timeframe = "5m" info_timeframes = ["15m", "30m", "1h"] minimal_roi = { "23": 0.015, "63": 0.01, "96": 0.005, "176": 0, "254": -0.02, "387": -0.04 } order_time_in_force = { "entry": "gtc", "exit": "ioc" } slippage_protection = { "retries": 3, "max_slippage": -0.002 } stoploss = -0.067 trailing_stop = True trailing_stop_positive = 0.0002 trailing_stop_positive_offset = 0.026 trailing_only_offset_is_reached = True use_exit_signal = False ignore_roi_if_entry_signal = False process_only_new_candles = True startup_candle_count = 449 ewo1_low_optimize = False ewo_candles_buy = IntParameter(2, 30, default=buy_params['ewo_candles_buy'], space='buy', optimize=ewo1_low_optimize) ewo_candles_sell = IntParameter(2, 35, default=buy_params['ewo_candles_sell'], space='buy', optimize=ewo1_low_optimize) ewo_low_offset = DecimalParameter(0.7, 1.2, default=buy_params['ewo_low_offset'], decimals=5, space='buy', optimize=ewo1_low_optimize) ewo_high_offset = DecimalParameter(0.75, 1.5, default=buy_params['ewo_high_offset'], decimals=5, space='buy', optimize=ewo1_low_optimize) ewo_low_rsi_4 = IntParameter(1, 50, default=buy_params['ewo_low_rsi_4'], space='buy', optimize=ewo1_low_optimize) ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=ewo1_low_optimize) fast_ewo = IntParameter(5,30, default=buy_params["fast_ewo"], space="buy", optimize=ewo1_low_optimize) slow_ewo = IntParameter(120,250, default=buy_params["slow_ewo"], space="buy", optimize=ewo1_low_optimize) is_optimize_clucha = False buy_clucha_bbdelta_close = DecimalParameter(0.010, 0.060, default=buy_params["buy_clucha_bbdelta_close"], space="buy", decimals=3, optimize = is_optimize_clucha) # 0.060-0.010 = 0.050 | 50 buy_clucha_bbdelta_tail = DecimalParameter(0.40, 1.00, default=buy_params["buy_clucha_bbdelta_tail"], space="buy", decimals=2, optimize = is_optimize_clucha) # 1.00-0.40 = 0.60 | 60 buy_clucha_closedelta_close = DecimalParameter(0.001, 0.030, default=buy_params["buy_clucha_closedelta_close"], space="buy", decimals=3, optimize = is_optimize_clucha) # 0.030-0.001 = 0.029 | 29 buy_clucha_rocr_1h = DecimalParameter(0.050, 1.00, default=buy_params["buy_clucha_rocr_1h"], space="buy", decimals=2, optimize = is_optimize_clucha) # 1.00-0.050 = 0.950 | 95 is_optimize_vwap = False buy_vwap_width = DecimalParameter(0.5, 10.0, default=buy_params["buy_vwap_width"], space="buy", decimals=1, optimize = is_optimize_vwap) # 10.0-0.5 = 9.5 | 95 buy_vwap_closedelta = DecimalParameter(10.0, 30.0, default=buy_params["buy_vwap_closedelta"], space="buy", decimals=1, optimize = is_optimize_vwap) # 30.0-10.0 = 20.0 | 200 buy_vwap_cti = DecimalParameter(-0.90, -0.00, default=buy_params["buy_vwap_cti"], space="buy", decimals=2, optimize = is_optimize_vwap) # -0.00-(-0.90) = 0.90 | 90 optc1 = True buy_rmi_length = IntParameter(8, 20, default=buy_params["buy_rmi_length"], space='buy', optimize= optc1) # 20-8 = 12 buy_cci_length = IntParameter(25, 45, default=buy_params["buy_cci_length"], space='buy', optimize= optc1) # 45-25 = 20 buy_cci = IntParameter(-135, -90, default=buy_params["buy_cci"], space='buy', optimize= optc1) # -90-(-135) = 45 optc2 = True buy_c2_1 = DecimalParameter(0.010, 0.025, default=buy_params["buy_c2_1"], space='buy', decimals=3, optimize=optc2) # 0.025-0.010 = 0.015 | 15 buy_c2_2 = DecimalParameter(0.980, 0.995, default=buy_params["buy_c2_2"], space='buy', decimals=3, optimize=optc2) # 0.995-0.980 = 0.015 | 15 buy_c2_3 = DecimalParameter(-0.8, -0.3, default=buy_params["buy_c2_3"], space='buy', decimals=1, optimize=optc2) # -0.3-(-0.8) = 0.5 | 5 optc3 = True buy_con3_1 = DecimalParameter(0.010, 0.025, default=buy_params["buy_con3_1"], space='buy', decimals=3, optimize=optc3) # 0.025-0.010 = 0.015 | 15 buy_con3_2 = DecimalParameter(0.980, 0.995, default=buy_params["buy_con3_2"], space='buy', decimals=3, optimize=optc3) # 0.995-0.980 = 0.015 | 15 buy_con3_3 = DecimalParameter(0.955, 0.975, default=buy_params["buy_con3_3"], space='buy', decimals=3, optimize=optc3) # 0.975-0.955 = 0.020 | 20 buy_con3_4 = DecimalParameter(-0.95, -0.70, default=buy_params["buy_con3_4"], space='buy', decimals=2, optimize=optc3) # -0.70-(-0.95) = 0.25 | 25 optc4 = True buy_rsi_1h_42 = IntParameter(10, 50, default=buy_params["buy_rsi_1h_42"], space='buy', optimize=optc4) # 50-10 = 40 buy_macd_41 = DecimalParameter(0.01, 0.09, default=buy_params["buy_macd_41"], space='buy', decimals=2, optimize=optc4) # 0.09-0.01 = 0.08 | 8 buy_volume_pump_41 = DecimalParameter(0.1, 0.9, default=buy_params["buy_volume_pump_41"], space='buy', decimals=1, optimize=optc4) # 0.9-0.1 = 0.8 | 8 buy_volume_drop_41 = DecimalParameter(1, 10, default=buy_params["buy_volume_drop_41"], space='buy', decimals=1, optimize=optc4) # 10-1 = 9 | 90 optc8 = True buy_min_inc_1 = DecimalParameter(0.010, 0.050, default=buy_params["buy_min_inc_1"], space='buy', decimals=3, optimize=optc8) # 0.050-0.010 = 0.040 | 40 buy_rsi_1h_min_1 = IntParameter(25, 40, default=buy_params["buy_rsi_1h_min_1"], space='buy', optimize=optc8) # 40-25 = 15 buy_rsi_1h_max_1 = IntParameter(70, 90, default=buy_params["buy_rsi_1h_max_1"], space='buy', optimize=optc8) # 90-70 = 20 buy_rsi_1 = IntParameter(20, 40, default=buy_params["buy_rsi_1"], space='buy', optimize=optc8) # 40-20 = 20 buy_mfi_1 = IntParameter(20, 40, default=buy_params["buy_mfi_1"], space='buy', optimize=optc8) # 40-20 = 20 optc9 = True buy_c9_1 = IntParameter(25, 44, default=buy_params["buy_c9_1"], space='buy', optimize=optc9) # 44-25 = 19 buy_c9_2 = IntParameter(-80, -67, default=buy_params["buy_c9_2"], space='buy', optimize=optc9) # -67-(-80) = 13 buy_c9_3 = IntParameter(-80, -67, default=buy_params["buy_c9_3"], space='buy', optimize=optc9) # -67-(-80) = 13 buy_c9_4 = IntParameter(35, 54, default=buy_params["buy_c9_4"], space='buy', optimize=optc9) # 54-35 = 19 buy_c9_5 = IntParameter(20, 44, default=buy_params["buy_c9_5"], space='buy', optimize=optc9) # 44-20 = 24 buy_c9_6 = IntParameter(65, 94, default=buy_params["buy_c9_6"], space='buy', optimize=optc9) # 94-65 = 29 buy_c9_7 = IntParameter(-110, -80, default=buy_params["buy_c9_7"], space='buy', optimize=optc9) # -80-(-110) = 30 optc10 = True buy_c10_1 = IntParameter(-110, -80, default=buy_params["buy_c10_1"], space='buy', optimize=optc10) # -80-(-110) = 30 buy_c10_2 = DecimalParameter(-1.00, -0.50, default=buy_params["buy_c10_2"], space='buy', decimals=2, optimize=optc10) # -0.50-(-1.00) = 0.50 | 50 dip_optimize = True buy_dip_threshold_5 = DecimalParameter(0.020, 0.070, default=buy_params["buy_dip_threshold_5"], space='buy', decimals=3, optimize=dip_optimize) ## 0.070-0.020 = 0.050 | 50 buy_dip_threshold_6 = DecimalParameter(0.050, 0.100, default=buy_params["buy_dip_threshold_6"], space='buy', decimals=3, optimize=dip_optimize) # 0.100-0.050 = 0.050 | 50 buy_dip_threshold_7 = DecimalParameter(0.050, 0.100, default=buy_params["buy_dip_threshold_7"], space='buy', decimals=3, optimize=dip_optimize) # 0.100-0.050 = 0.050 | 50 buy_dip_threshold_8 = DecimalParameter(0.150, 0.250, default=buy_params["buy_dip_threshold_8"], space='buy', decimals=3, optimize=dip_optimize) # 0.250-0.150 = 0.100 | 100 buy_pump_optimize = True buy_pump_pull_threshold_1 = DecimalParameter(1.50, 3.00, default=buy_params["buy_pump_pull_threshold_1"], space='buy', decimals=2, optimize=buy_pump_optimize) # 3.00-1.50 = 1.50 | 150 buy_pump_threshold_1 = DecimalParameter(0.600, 1.000, default=buy_params["buy_pump_threshold_1"], space='buy', decimals=3, optimize=buy_pump_optimize) # 1.000-0.600 = 0.400 | 400 pump_dump_optimize = True bzv7_buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=buy_params["bzv7_buy_volume_pump_1"], space="buy", decimals=1, optimize=pump_dump_optimize) bzv7_buy_volume_drop_1 = DecimalParameter(1, 10, default=buy_params["bzv7_buy_volume_drop_1"], space="buy", decimals=1, optimize=pump_dump_optimize) bzv7_rsi_optimize = True bzv7_buy_rsi_1h_1 = IntParameter(8, 30, default=buy_params["bzv7_buy_rsi_1h_1"], space="buy", optimize=bzv7_rsi_optimize) bzv7_buy_rsi_1h_2 = IntParameter(20, 45, default=buy_params["bzv7_buy_rsi_1h_2"], space="buy", optimize=bzv7_rsi_optimize) bzv7_buy_rsi_1h_4 = IntParameter(10, 30, default=buy_params["bzv7_buy_rsi_1h_4"], space="buy", optimize=bzv7_rsi_optimize) bzv7_buy_rsi_2 = IntParameter(4, 20, default=buy_params["bzv7_buy_rsi_2"], space="buy", optimize=bzv7_rsi_optimize) # 40-7 = 33 bzv7_macd_optimize = True bzv7_buy_macd_1 = DecimalParameter(0.01, 0.09, default=buy_params["bzv7_buy_macd_1"], space="buy", decimals=2, optimize=bzv7_macd_optimize) # 0.09-0.01 = 0.08 | 8 buy_dip_threshold_optimize = True buy_dip_threshold_1 = DecimalParameter(0.20, 0.40, default=buy_params["buy_dip_threshold_1"], space="buy", decimals=2, optimize=buy_dip_threshold_optimize) buy_dip_threshold_2 = DecimalParameter(0.20, 0.50, default=buy_params["buy_dip_threshold_2"], space="buy", decimals=2, optimize=buy_dip_threshold_optimize) buy_dip_threshold_3 = DecimalParameter(0.30, 0.60, default=buy_params["buy_dip_threshold_3"], space="buy", decimals=2, optimize=buy_dip_threshold_optimize) bcmbigz0 = True buy_bb40_bbdelta_close = DecimalParameter(0.025, 0.045, default=buy_params["buy_bb40_bbdelta_close"], space="buy", decimals=3, optimize=bcmbigz0) buy_bb40_closedelta_close = DecimalParameter(0.010, 0.030, default=buy_params["buy_bb40_closedelta_close"], space="buy", decimals=3, optimize=bcmbigz0) buy_bb40_tail_bbdelta = DecimalParameter(0.250, 0.350, default=buy_params["buy_bb40_tail_bbdelta"], space="buy", decimals=3, optimize=bcmbigz0) bcmbigz4 = True buy_volume_1 = IntParameter(1, 10, default=buy_params["buy_volume_1"], space="buy", optimize=bcmbigz4) buy_ema_open_mult_1 = DecimalParameter(0.010, 0.050, default=buy_params["buy_ema_open_mult_1"], space="buy", decimals=3, optimize=bcmbigz4) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [] for info_timeframe in self.info_timeframes: informative_pairs.extend([(pair, info_timeframe) for pair in pairs]) return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." informative_1h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="1h") informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12) informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) informative_1h['r_480'] = williams_r(dataframe, period=480) informative_1h['safe_pump_24'] = ((((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / informative_1h['close'].rolling(24).min()) < self.buy_pump_threshold_1.value) | (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / self.buy_pump_pull_threshold_1.value) > (informative_1h['close'] - informative_1h['close'].rolling(24).min()))) informative_1h['cti'] = pta.cti(informative_1h["close"], length=20) ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) informative_1h["ssl-dir"] = np.where(ssl_up_1h > ssl_down_1h, "up", "down") inf_heikinashi = qtpylib.heikinashi(informative_1h) informative_1h["ha_close"] = inf_heikinashi["close"] informative_1h["rocr"] = ta.ROCR(informative_1h["ha_close"], timeperiod=168) return informative_1h def informative_30m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." informative_30m = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="30m") return informative_30m def informative_15m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." informative_15m = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="15m") return informative_15m def base_tf_5m_indicators(self, metadata: dict, dataframe: DataFrame) -> DataFrame: dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_buy.value)) dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_sell.value)) dataframe["ewo"] = EWO(dataframe, int(self.fast_ewo.value), int(self.slow_ewo.value)) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe["volume_mean_slow_30"] = dataframe["volume"].rolling(window=30).mean() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() bb_21268 = qtpylib.bollinger_bands(dataframe["close"], window=21, stds=2.68) dataframe["bb_lowerband"] = bb_21268["lower"] dataframe["bb_upperband"] = bb_21268["upper"] dataframe["bbdelta"] = (bb_21268["mid"] - bb_21268["lower"]).abs() dataframe["macd"], dataframe["signal"], dataframe["hist"] = ta.MACD(dataframe["close"], fastperiod=12, slowperiod=26, signalperiod=9) for val in self.buy_cci_length.range: dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val) dataframe['cci'] = ta.CCI(dataframe, 26) for val in self.buy_rmi_length.range: dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean() dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_32'] = williams_r(dataframe, period=32) dataframe['r_64'] = williams_r(dataframe, period=64) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['mfi'] = ta.MFI(dataframe) dataframe['safe_dips_strict'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_5.value) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_6.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_7.value) & (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_8.value)) heikinashi = qtpylib.heikinashi(dataframe) dataframe["ha_close"] = heikinashi["close"] dataframe["ha_high"] = heikinashi["high"] dataframe["ha_low"] = heikinashi["low"] dataframe["rsi_84"] = ta.RSI(dataframe, timeperiod=84) dataframe["rsi_112"] = ta.RSI(dataframe, timeperiod=112) vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1) dataframe["vwap_upperband"] = vwap_high dataframe["vwap_middleband"] = vwap dataframe["vwap_lowerband"] = vwap_low dataframe["vwap_width"] = ( (dataframe["vwap_upperband"] - dataframe["vwap_lowerband"]) / dataframe["vwap_middleband"] ) * 100 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_delta_cluc"] = (dataframe["bb_middleband2_40"] - dataframe["bb_lowerband2_40"]).abs() dataframe["ha_closedelta"] = (dataframe["ha_close"] - dataframe["ha_close"].shift()).abs() dataframe["ha_tail"] = (dataframe["ha_close"] - dataframe["ha_low"]).abs() dataframe["cti"] = pta.cti(dataframe["close"], length=20) dataframe["closedelta"] = (dataframe["close"] - dataframe["close"].shift()).abs() 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: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) try: state = self.slippage_protection["__pair_retries"] except KeyError: state = self.slippage_protection["__pair_retries"] = {} candle = dataframe.iloc[-1].squeeze() slippage = (rate / candle["close"]) - 1 if slippage < self.slippage_protection["max_slippage"]: pair_retries = state.get(pair, 0) if pair_retries < self.slippage_protection["retries"]: state[pair] = pair_retries + 1 return False state[pair] = 0 return True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.base_tf_5m_indicators(metadata, dataframe) informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, "1h", ffill=True) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['rsi_4'] < self.ewo_low_rsi_4.value) & (dataframe['close'] < (dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] * self.ewo_low_offset.value)) & (dataframe['ewo'] < self.ewo_low.value) & (dataframe['close'] < (dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] * self.ewo_high_offset.value)) ), ["enter_long", "enter_tag"]] = (1, "ewolow__") dataframe.loc[ ( (dataframe["close"] < dataframe["vwap_lowerband"]) & (dataframe["vwap_width"] > self.buy_vwap_width.value) & (dataframe["closedelta"] > dataframe["close"] * self.buy_vwap_closedelta.value / 1000 ) & (dataframe["cti"] < self.buy_vwap_cti.value) & (dataframe["rsi_84"] < 60) & (dataframe["rsi_112"] < 60) ), ["enter_long", "enter_tag"]] = (1, "vwap") dataframe.loc[ ( (dataframe["rocr_1h"] > self.buy_clucha_rocr_1h.value ) & (dataframe["bb_lowerband2_40"].shift() > 0) & (dataframe["bb_delta_cluc"] > dataframe["ha_close"] * self.buy_clucha_bbdelta_close.value) & (dataframe["ha_closedelta"] > dataframe["ha_close"] * self.buy_clucha_closedelta_close.value) & (dataframe["ha_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()) & (dataframe["rsi_84"] < 60) & (dataframe["rsi_112"] < 60) ), ["enter_long", "enter_tag"]] = (1, "clucha") dataframe.loc[ ( (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_c2_1.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_c2_2.value)) & (dataframe['cti_1h'] > self.buy_c2_3.value) ), ["enter_long", "enter_tag"]] = (1, "con2") dataframe.loc[ ( (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_con3_1.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_con3_2.value)) & (dataframe['close'] < dataframe['ema_20'] * self.buy_con3_3.value) & (dataframe['cti'] < self.buy_con3_4.value) ), ["enter_long", "enter_tag"]] = (1, "con3") dataframe.loc[ ( (dataframe['rsi_1h'] < self.buy_rsi_1h_42.value) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_41.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_41.value)) & (dataframe['volume_mean_slow_30'] > dataframe['volume_mean_slow_30'].shift(48) * self.buy_volume_pump_41.value) & (dataframe['volume_mean_slow_30'] * self.buy_volume_pump_41.value < dataframe['volume_mean_slow_30'].shift(48)) ), ["enter_long", "enter_tag"]] = (1, "con4") dataframe.loc[ ( (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(50)) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_1h']) & (((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_1.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_1.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_1.value) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) ), ["enter_long", "enter_tag"]] = (1, "con8") dataframe.loc[ ( (((dataframe['close'] - dataframe['open'].rolling(12).min()) / dataframe['open'].rolling(12).min()) > 0.032) & (dataframe['rsi'] < self.buy_c9_1.value) & (dataframe['r_14'] < self.buy_c9_2.value) & (dataframe['r_32'] < self.buy_c9_3.value) & (dataframe['mfi'] < self.buy_c9_4.value) & (dataframe['rsi_1h'] > self.buy_c9_5.value) & (dataframe['rsi_1h'] < self.buy_c9_6.value) & (dataframe['r_480_1h'] > self.buy_c9_7.value) ), ["enter_long", "enter_tag"]] = (1, "con9") dataframe.loc[ ( (dataframe['close'].shift(4) < (dataframe['close'].shift(3))) & (dataframe['close'].shift(3) < (dataframe['close'].shift(2))) & (dataframe['close'].shift(2) < (dataframe['close'].shift())) & (dataframe['close'].shift(1) < (dataframe['close'])) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['close'] > (dataframe['open'])) & (dataframe['cci'].shift() < dataframe['cci']) & (dataframe['ssl-dir_1h'] == 'up') & (dataframe['cci'] < self.buy_c10_1.value) & (dataframe['cti'] < self.buy_c10_2.value) ), ["enter_long", "enter_tag"]] = (1, "con10") dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_1.value) & (((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"])< self.buy_dip_threshold_2.value) & dataframe["bb_lowerband"].shift().gt(0) & dataframe["bbdelta"].gt(dataframe["close"] * self.buy_bb40_bbdelta_close.value) & dataframe["closedelta"].gt(dataframe["close"] * self.buy_bb40_closedelta_close.value) & dataframe["tail"].lt(dataframe["bbdelta"] * self.buy_bb40_tail_bbdelta.value) & dataframe["close"].lt(dataframe["bb_lowerband"].shift()) & dataframe["close"].le(dataframe["close"].shift()) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 0") dataframe.loc[ ( (dataframe["close"] > dataframe["ema_100_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) & (((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_1.value) & (((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_2.value) & (((dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_3.value) & (dataframe["volume"].rolling(4).mean() * self.buy_volume_1.value > dataframe["volume"]) & (dataframe["ema_26"] > dataframe["ema_12"]) & ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_ema_open_mult_1.value)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & (dataframe["close"] < (dataframe["bb_lowerband"])) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 4") dataframe.loc[ ( (dataframe["close"] < dataframe["ema_50"]) & (dataframe["close"] < 0.975 * dataframe["bb_lowerband"]) & ((dataframe["volume"]< (dataframe["volume_mean_slow"].shift(1) * 20)) | (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * 0.4)) & (dataframe["rsi_1h"] < 15) # Don't buy if someone drop the market. & (dataframe["volume"] < (dataframe["volume"].shift() * 4)) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 7") dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_26"] > dataframe["ema_12"]) & ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.02)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & ((dataframe["volume"] < (dataframe["volume"].shift() * 4)) | (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * 0.4)) & (dataframe["close"] < (dataframe["bb_lowerband"])) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 8") dataframe.loc[ ( (dataframe["ema_26"] > dataframe["ema_12"]) & ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & (dataframe["volume"] < (dataframe["volume"].shift() * 4)) & (dataframe["close"] < (dataframe["bb_lowerband"])) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 9") dataframe.loc[ ( (dataframe['close'] > dataframe['ema_200']) & (dataframe['rsi'] < 30) & (dataframe['close'] * 1.024 < dataframe['open'].shift(3)) & (dataframe['rsi_1h'] < 71) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 10") dataframe.loc[ ( (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['bb_lowerband'] * 0.985) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 12") dataframe.loc[ ( (dataframe['rsi_1h'] < self.bzv7_buy_rsi_1h_1.value) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 14") dataframe.loc[ ( (dataframe['rsi_1h'] < self.bzv7_buy_rsi_1h_2.value) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.bzv7_buy_macd_1.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 17") dataframe.loc[ ( (dataframe['rsi_1h'] < self.bzv7_buy_rsi_1h_4.value) & (dataframe['rsi'] < self.bzv7_buy_rsi_2.value) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 19") dataframe.loc[ ( (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * 0.993) & (dataframe['low'] < dataframe['bb_lowerband'] * 0.985) & (dataframe['close'].shift() > dataframe['bb_lowerband']) & (dataframe['rsi_1h'] < 72.8) & (dataframe['open'] > dataframe['close']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & ((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) ), ["enter_long", "enter_tag"]] = (1, "BCMBİGZ 22") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe