# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BinClucMadDevelop.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter from functools import reduce import logging logger = logging.getLogger(__name__) 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__BinClucMadDevelop__20260111_210550(IStrategy): INTERFACE_VERSION = 2 minimal_roi = {"0": 0.20, "38": 0.074, "78": 0.025, "194": 0} stoploss = -0.99 timeframe = "5m" informative_timeframe = "1h" use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 ignore_roi_if_buy_signal = True trailing_stop = True trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.05 use_custom_stoploss = False process_only_new_candles = True startup_candle_count: int = 200 buy_params = { "buy_minimum_conditions": 1, "v6_buy_condition_0_enable": False, # avg 0.47 dd 27% "v6_buy_condition_1_enable": True, # no trade "v6_buy_condition_2_enable": True, # avg 2.32 "v6_buy_condition_3_enable": True, # avg 1.12 dd 6% "v8_buy_condition_0_enable": True, # avg 0.74 "v8_buy_condition_1_enable": False, # avg 0.41 dd 37% "v8_buy_condition_2_enable": True, # avg 1.37 "v8_buy_condition_3_enable": False, # avg 0.41 "v8_buy_condition_4_enable": True, # avg 1.29 "v9_buy_condition_0_enable": False, "v9_buy_condition_1_enable": True, "v9_buy_condition_2_enable": True, "v9_buy_condition_3_enable": True, "v9_buy_condition_4_enable": False, "v9_buy_condition_5_enable": True, "v9_buy_condition_6_enable": True, "v9_buy_condition_7_enable": True, "v9_buy_condition_8_enable": False, "v9_buy_condition_9_enable": False, "v9_buy_condition_10_enable": False, } sell_params = { "v9_sell_condition_0_enable": False, "v8_sell_condition_0_enable": True, "v8_sell_condition_1_enable": True, } cust_log_verbose = False v6_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_sell_condition_0_enable = CategoricalParameter([True, False], default=True, space="sell", optimize=False, load=True) v8_sell_condition_1_enable = CategoricalParameter([True, False], default=True, space="sell", optimize=False, load=True) v8_sell_rsi_main = DecimalParameter(72.0, 90.0, default=80, space="sell", decimals=2, optimize=False, load=True) buy_dip_threshold_0 = DecimalParameter(0.001, 0.1, default=0.015, space="buy", decimals=3, optimize=False, load=True) buy_dip_threshold_1 = DecimalParameter(0.08, 0.2, default=0.12, space="buy", decimals=2, optimize=False, load=True) buy_dip_threshold_2 = DecimalParameter(0.02, 0.4, default=0.28, space="buy", decimals=2, optimize=False, load=True) buy_dip_threshold_3 = DecimalParameter(0.25, 0.44, default=0.36, space="buy", decimals=2, optimize=False, load=True) buy_bb40_bbdelta_close = DecimalParameter(0.005, 0.04, default=0.031, space="buy", optimize=False, load=True) buy_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.021, space="buy", optimize=False, load=True) buy_bb40_tail_bbdelta = DecimalParameter(0.2, 0.4, default=0.264, space="buy", optimize=False, load=True) buy_bb20_close_bblowerband = DecimalParameter(0.8, 1.1, default=0.992, space="buy", optimize=False, load=True) buy_bb20_volume = IntParameter(18, 36, default=29, space="buy", optimize=False, load=True) buy_rsi_diff = DecimalParameter(34.0, 60.0, default=50.48, space="buy", decimals=2, optimize=False, load=True) buy_min_inc = DecimalParameter(0.005, 0.05, default=0.01, space="buy", decimals=2, optimize=False, load=True) buy_rsi_1h = DecimalParameter(40.0, 70.0, default=67.0, space="buy", decimals=2, optimize=False, load=True) buy_rsi = DecimalParameter(30.0, 40.0, default=38.5, space="buy", decimals=2, optimize=False, load=True) buy_mfi = DecimalParameter(36.0, 65.0, default=36.0, space="buy", decimals=2, optimize=False, load=True) buy_volume_1 = DecimalParameter(1.0, 10.0, default=2.0, space="buy", decimals=2, optimize=False, load=True) buy_ema_open_mult_1 = DecimalParameter(0.01, 0.05, default=0.02, space="buy", decimals=3, optimize=False, load=True) sell_custom_roi_profit_1 = DecimalParameter( 0.01, 0.03, default=0.02, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_rsi_1 = DecimalParameter(40.0, 56.0, default=50, space="sell", decimals=2, optimize=True, load=True) sell_custom_roi_profit_2 = DecimalParameter( 0.01, 0.20, default=0.04, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_rsi_2 = DecimalParameter(42.0, 56.0, default=50, space="sell", decimals=2, optimize=True, load=True) sell_custom_roi_profit_3 = DecimalParameter( 0.15, 0.30, default=0.08, space="sell", decimals=2, optimize=True, 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=True, 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=True, load=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space="sell", decimals=3, optimize=True, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space="sell", decimals=2, optimize=True, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space="sell", decimals=3, optimize=True, load=True) sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.02, space="sell", decimals=3, optimize=True, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space="sell", decimals=2, optimize=True, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space="sell", decimals=3, optimize=True, load=True) sell_custom_stoploss_1 = DecimalParameter( -0.15, -0.03, default=-0.05, space="sell", decimals=2, optimize=True, load=True ) v9_buy_condition_0_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_buy_condition_1_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_buy_condition_2_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_buy_condition_3_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_buy_condition_4_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_buy_condition_5_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_buy_condition_6_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_buy_condition_7_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_buy_condition_8_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_buy_condition_9_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_buy_condition_10_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True) v9_sell_condition_0_enable = CategoricalParameter([True, False], default=False, space="sell", optimize=False, load=True) buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.99, space="buy", optimize=False, load=True) buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space="buy", optimize=False, load=True) buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space="buy", decimals=1, optimize=False, load=True) buy_volume_drop_1 = DecimalParameter(1, 10, default=4, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space="buy", decimals=1, optimize=False, load=True) buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space="buy", decimals=2, optimize=False, load=True) buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space="buy", decimals=2, optimize=False, load=True) buy_minimum_conditions = IntParameter(1, 2, default=1, space='buy', optimize=False, load=True) sell_roi_profit_1 = DecimalParameter(0.08, 0.16, default=0.1, space='sell', decimals=2, optimize=False, load=True) sell_roi_rsi_1 = DecimalParameter(30.0, 38.0, default=34, space='sell', decimals=2, optimize=False, load=True) sell_roi_profit_2 = DecimalParameter(0.02, 0.05, default=0.05, space='sell', decimals=2, optimize=False, load=True) sell_roi_rsi_2 = DecimalParameter(34.0, 44.0, default=38, space='sell', decimals=2, optimize=False, load=True) sell_roi_profit_3 = DecimalParameter(0.0, 0.0, default=0.03, space='sell', decimals=2, optimize=False, load=True) sell_roi_rsi_3 = DecimalParameter(48.0, 56.0, default=50, space='sell', decimals=2, optimize=False, load=True) def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: return True dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if (last_candle is not None): current_profit = trade.calc_profit_ratio(rate) if (sell_reason == 'roi'): if (current_profit > self.sell_roi_profit_1.value): if (last_candle['rsi'] > self.sell_roi_rsi_1.value): return False elif (current_profit > self.sell_roi_profit_2.value): if (last_candle['rsi'] > self.sell_roi_rsi_2.value): return False elif (current_profit > self.sell_roi_profit_3.value): if (last_candle['rsi'] > self.sell_roi_rsi_3.value): return False return True def custom_stoploss( self, pair: str, trade: "Trade", current_time: datetime, current_rate: float, current_profit: float, **kwargs ) -> float: if current_profit > 0: return 0.99 else: trade_time_50 = trade.open_date_utc + timedelta(minutes=240) if current_time > trade_time_50: try: number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) candle = dataframe.iloc[-number_of_candle_shift].squeeze() if (candle["sma_200_dec"]) & (candle["sma_200_dec_1h"]): return 0.01 if candle["rsi_1h"] < 30: return 0.99 if candle["close"] > candle["ema_200"]: if current_rate * 1.025 < candle["open"]: return 0.01 if current_rate * 1.015 < candle["open"]: return 0.01 except IndexError as error: return 0.1 return 0.99 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() if last_candle is not None: if (current_profit > self.sell_custom_roi_profit_4.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_4.value ): return "roi_target_4" elif (current_profit > self.sell_custom_roi_profit_3.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_3.value ): return "roi_target_3" elif (current_profit > self.sell_custom_roi_profit_2.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_2.value ): return "roi_target_2" elif (current_profit > self.sell_custom_roi_profit_1.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_1.value ): return "roi_target_1" elif ( (current_profit > 0) & (current_profit < self.sell_custom_roi_profit_5.value) & (last_candle["sma_200_dec"]) ): return "roi_target_5" elif ( (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" return None def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_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=self.informative_timeframe) 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["sma_200_dec"] = informative_1h["sma_200"] < informative_1h["sma_200"].shift(20) informative_1h["rsi"] = ta.RSI(informative_1h, timeperiod=14) ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) informative_1h["ssl_down"] = ssl_down_1h informative_1h["ssl_up"] = ssl_up_1h informative_1h["ssl-dir"] = np.where(ssl_up_1h > ssl_down_1h, "up", "down") return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bb_40 = qtpylib.bollinger_bands(dataframe["close"], window=40, stds=2) dataframe["lower"] = bb_40["lower"] dataframe["mid"] = bb_40["mid"] dataframe["bbdelta"] = (bb_40["mid"] - dataframe["lower"]).abs() dataframe["closedelta"] = (dataframe["close"] - dataframe["close"].shift()).abs() dataframe["tail"] = (dataframe["close"] - dataframe["low"]).abs() 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["volume_mean_slow"] = dataframe["volume"].rolling(window=30).mean() dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12) 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_5"] = ta.EMA(dataframe, timeperiod=5) dataframe["sma_200"] = ta.SMA(dataframe, timeperiod=200) dataframe["sma_200_dec"] = dataframe["sma_200"] < dataframe["sma_200"].shift(20) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["mfi"] = ta.MFI(dataframe, timeperiod=14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, "v9_buy_condition_1_enable"] = False dataframe.loc[:, "v9_buy_condition_2_enable"] = False dataframe.loc[:, "v9_buy_condition_3_enable"] = False dataframe.loc[:, "v9_buy_condition_4_enable"] = False dataframe.loc[:, "v9_buy_condition_5_enable"] = False dataframe.loc[:, "v9_buy_condition_6_enable"] = False dataframe.loc[:, "v9_buy_condition_7_enable"] = False dataframe.loc[:, "v9_buy_condition_8_enable"] = False dataframe.loc[:, "v9_buy_condition_9_enable"] = False dataframe.loc[:, "v9_buy_condition_10_enable"] = False dataframe.loc[:, "v6_buy_condition_0_enable"] = False dataframe.loc[:, "v6_buy_condition_1_enable"] = False dataframe.loc[:, "v6_buy_condition_2_enable"] = False dataframe.loc[:, "v6_buy_condition_3_enable"] = False dataframe.loc[:, "v8_buy_condition_0_enable"] = False dataframe.loc[:, "v8_buy_condition_1_enable"] = False dataframe.loc[:, "v8_buy_condition_2_enable"] = False dataframe.loc[:, "v8_buy_condition_3_enable"] = False dataframe.loc[:, "v8_buy_condition_4_enable"] = False dataframe.loc[:, "conditions_count"] = 0 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["lower"].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["lower"].shift()) & dataframe["close"].le(dataframe["close"].shift()) & (self.v8_buy_condition_0_enable.value == True) ), "v8_buy_condition_0_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) & (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["close"] < dataframe["ema_50"]) & (dataframe["close"] < self.buy_bb20_close_bblowerband.value * dataframe["bb_lowerband"]) & (dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * self.buy_bb20_volume.value)) & (self.v8_buy_condition_1_enable.value == True) ), "v8_buy_condition_1_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] < dataframe["sma_5"]) & (dataframe["ssl_up_1h"] > dataframe["ssl_down_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["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_3.value ) & (dataframe["rsi"] < dataframe["rsi_1h"] - self.buy_rsi_diff.value) & (self.v8_buy_condition_2_enable.value == True) ), "v8_buy_condition_2_enable", ] = 1 dataframe.loc[ ( (dataframe["sma_200"] > dataframe["sma_200"].shift(20)) & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(16)) & ( ((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["open"].rolling(24).min() - dataframe["close"]) / dataframe["close"]) > self.buy_min_inc.value ) & (dataframe["rsi_1h"] > self.buy_rsi_1h.value) & (dataframe["rsi"] < self.buy_rsi.value) & (dataframe["mfi"] < self.buy_mfi.value) & (self.v8_buy_condition_3_enable.value == True) ), "v8_buy_condition_3_enable", ] = 1 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"])) & (self.v8_buy_condition_4_enable.value == True) ), "v8_buy_condition_4_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb20_close_bblowerband_safe_1.value) & (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value) & (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)) & ( dataframe["open"] - dataframe["close"] < dataframe["bb_upperband"].shift(2) - dataframe["bb_lowerband"].shift(2) ) & (self.v9_buy_condition_1_enable.value == True) ), "v9_buy_condition_1_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb20_close_bblowerband_safe_2.value) & (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value) & (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)) & ( dataframe["open"] - dataframe["close"] < dataframe["bb_upperband"].shift(2) - dataframe["bb_lowerband"].shift(2) ) & (self.v9_buy_condition_2_enable.value == True) ), "v9_buy_condition_2_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["close"] < dataframe["bb_lowerband"]) & (dataframe["rsi"] < self.buy_rsi_3.value) & (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)) & (self.v9_buy_condition_3_enable.value == True) ), "v9_buy_condition_3_enable", ] = 1 dataframe.loc[ ( (dataframe["rsi_1h"] < self.buy_rsi_1h_1.value) & (dataframe["close"] < dataframe["bb_lowerband"]) & (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)) & (self.v9_buy_condition_4_enable.value == True) ), "v9_buy_condition_4_enable", ] = 1 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"] * self.buy_macd_1.value)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & (dataframe["close"] < (dataframe["bb_lowerband"])) & (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)) & (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value) & (self.v9_buy_condition_5_enable.value == True) ), "v9_buy_condition_5_enable", ] = 1 dataframe.loc[ ( (dataframe["ema_26"] > dataframe["ema_12"]) & ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_macd_2.value)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & (dataframe["close"] < (dataframe["bb_lowerband"])) & (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)) & (self.v9_buy_condition_6_enable.value == True) ), "v9_buy_condition_6_enable", ] = 1 dataframe.loc[ ( (dataframe["rsi_1h"] < self.buy_rsi_1h_2.value) & (dataframe["ema_26"] > dataframe["ema_12"]) & ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_macd_1.value)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)) & (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value) & (self.v9_buy_condition_7_enable.value == True) ), "v9_buy_condition_7_enable", ] = 1 dataframe.loc[ ( (dataframe["rsi_1h"] < self.buy_rsi_1h_3.value) & (dataframe["rsi"] < self.buy_rsi_1.value) & (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)) & (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value) & (self.v9_buy_condition_8_enable.value == True) ), "v9_buy_condition_8_enable", ] = 1 dataframe.loc[ ( (dataframe["rsi_1h"] < self.buy_rsi_1h_4.value) & (dataframe["rsi"] < self.buy_rsi_2.value) & (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value)) & (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value) & (self.v9_buy_condition_9_enable.value == True) ), "v9_buy_condition_9_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] < dataframe["sma_5"]) & (dataframe["ssl_up_1h"] > dataframe["ssl_down_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["rsi"] < dataframe["rsi_1h"] - 43.276) & (self.v9_buy_condition_10_enable.value == True) ), "v9_buy_condition_10_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["close"] < dataframe["ema_50"]) & (dataframe["close"] < 0.99 * dataframe["bb_lowerband"]) & ( (dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * 21)) | (dataframe["volume_mean_slow"] > (dataframe["volume_mean_slow"].shift(30) * 0.4)) ) & (self.v6_buy_condition_0_enable.value == True) ), "v6_buy_condition_0_enable", ] = 1 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)) & (self.v6_buy_condition_1_enable.value == True) ), "v6_buy_condition_1_enable", ] = 1 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"])) & (self.v6_buy_condition_2_enable.value == True) ), "v6_buy_condition_2_enable", ] = 1 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"])) & (self.v6_buy_condition_3_enable.value == True) ), "v6_buy_condition_3_enable", ] = 1 dataframe.loc[:, "conditions_count"] = ( dataframe["v9_buy_condition_1_enable"].astype(int) + dataframe["v9_buy_condition_2_enable"].astype(int) + dataframe["v9_buy_condition_3_enable"].astype(int) + dataframe["v9_buy_condition_4_enable"].astype(int) + dataframe["v9_buy_condition_5_enable"].astype(int) + dataframe["v9_buy_condition_6_enable"].astype(int) + dataframe["v9_buy_condition_7_enable"].astype(int) + dataframe["v9_buy_condition_8_enable"].astype(int) + dataframe["v9_buy_condition_9_enable"].astype(int) + dataframe["v9_buy_condition_10_enable"].astype(int) + dataframe["v6_buy_condition_0_enable"].astype(int) + dataframe["v6_buy_condition_1_enable"].astype(int) + dataframe["v6_buy_condition_2_enable"].astype(int) + dataframe["v6_buy_condition_3_enable"].astype(int) + dataframe["v8_buy_condition_0_enable"].astype(int) + dataframe["v8_buy_condition_1_enable"].astype(int) + dataframe["v8_buy_condition_2_enable"].astype(int) + dataframe["v8_buy_condition_3_enable"].astype(int) + dataframe["v8_buy_condition_4_enable"].astype(int) ) conditions.append(dataframe["conditions_count"] >= self.buy_minimum_conditions.value) conditions.append(dataframe["volume"].gt(0)) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), "buy"] = 1 if self.cust_log_verbose == True: for index, row in dataframe.iterrows(): if row["buy"] == 1: buy_cond_details = f"count={int(row['conditions_count'])}/v9_1={int(row['v9_buy_condition_1_enable'])}/v9_2={int(row['v9_buy_condition_2_enable'])}/v9_3={int(row['v9_buy_condition_3_enable'])}/v9_4={int(row['v9_buy_condition_4_enable'])}/v9_5={int(row['v9_buy_condition_5_enable'])}/v9_6={int(row['v9_buy_condition_6_enable'])}/v9_7={int(row['v9_buy_condition_7_enable'])}/v9_8={int(row['v9_buy_condition_8_enable'])}/v9_9={int(row['v9_buy_condition_9_enable'])}/v9_10={int(row['v9_buy_condition_10_enable'])}/v6_0={int(row['v6_buy_condition_0_enable'])}/v6_1={int(row['v6_buy_condition_1_enable'])}/v6_2={int(row['v6_buy_condition_2_enable'])}/v6_3={int(row['v6_buy_condition_3_enable'])}/v8_0={int(row['v8_buy_condition_0_enable'])}/v8_1={int(row['v8_buy_condition_1_enable'])}/v8_2={int(row['v8_buy_condition_2_enable'])}/v8_3={int(row['v8_buy_condition_3_enable'])}/v8_4={int(row['v8_buy_condition_4_enable'])}" logger.info(f"{metadata['pair']} - candle: {row['date']} - buy condition - details: {buy_cond_details}") return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.v9_sell_condition_0_enable.value: conditions.append( ( (dataframe["close"] > dataframe["bb_middleband"] * 1.01) & (dataframe["volume"] > 0) # Don't be gready, sell fast # Make sure Volume is not 0 ) ) if self.v8_sell_condition_0_enable.value: conditions.append( ( (dataframe["close"] > dataframe["bb_upperband"]) & (dataframe["close"].shift(1) > dataframe["bb_upperband"].shift(1)) & (dataframe["close"].shift(2) > dataframe["bb_upperband"].shift(2)) & (dataframe["close"].shift(2) > dataframe["bb_upperband"].shift(2)) & (dataframe["volume"] > 0) ) ) if self.v8_sell_condition_1_enable.value: conditions.append(((dataframe["rsi"] > self.v8_sell_rsi_main.value) & (dataframe["volume"] > 0))) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), "sell"] = 1 return dataframe def SSLChannels_ATR(dataframe, length=7): """ SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/ Credit to @JimmyNixx for python """ 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"]