# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/sniper_s.py import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from functools import reduce import technical.indicators as technicali import technical.pivots_points as technicalp from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_remiotore_ccxt_freqtrade__sniper_s__20260111_210550(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.166, "109": 0.115, "180": 0.054, "538": 0 } stoploss = -0.252 buy_params = { "buy_aroondown": 11, "buy_aroonup": 69, "buy_arrondown_cat": False, "buy_arronup_cat": True, "buy_emas_cat": False, "buy_td": 5, "buy_td_cat": True, "buy_tke_cat": True, "buy_tke_val": 39, "buy_vfi_cat": True, "buy_vfi_cat2": False, "buy_vfi_cat3": True, "buy_vwmacd_cat": False, "buyema": 112, } sell_params = { "sell_aroondown": 96, "sell_aroonup": 16, "sell_arrondown_cat": False, "sell_arronup_cat": True, "sell_td_cat": False, "sell_tkcros_cat": True, "sell_tke_cat": True, "sell_tke_val": 64, } trailing_stop = True trailing_stop_positive = 0.164 trailing_stop_positive_offset = 0.187 trailing_only_offset_is_reached = True use_custom_stoploss = False timeframe = '15m' process_only_new_candles = True use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True startup_candle_count: int = 30 order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } plot_config = { 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, } } } buy_aroonup = IntParameter(40, 100, default = 77, space = 'buy') buy_aroondown = IntParameter(0, 40, default = 24, space = 'buy' ) buyema = IntParameter(45,120, default= 70, space ='buy') buy_tke_val = IntParameter(1, 40, default = 37, space = 'buy') buy_td = IntParameter(3, 9, default = 9, space = 'buy') sell_aroonup = IntParameter(0, 75, default = 24, space = 'sell' ) sell_aroondown = IntParameter(40, 100, default = 68, space = 'sell') sell_tke_val = IntParameter(60, 90, default = 78, space = 'sell') buy_arrondown_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_arronup_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_emas_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_tke_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_td_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_vwmacd_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_vfi_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_vfi_cat2 = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_vfi_cat3 = CategoricalParameter([True, False], default = True, space = 'buy' ) sell_arronup_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_arrondown_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_tkcros_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_tke_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_td_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: vfi = technicali.vfi(dataframe) aroon = ta.AROON(dataframe) tke = technicali.TKE(dataframe) td = technicali.td_sequential(dataframe) vwmacd = technicali.vwmacd(dataframe) dataframe['aroonup'] = aroon['aroonup'] dataframe['aroondown'] = aroon['aroondown'] dataframe['td'] = td['TD_count'] dataframe['ema200'] = ta.EMA(dataframe, timeperiod = self.buyema.value) dataframe['vfi'] = vfi[0] dataframe['vfima'] = vfi[1] dataframe['vwmacd'] = vwmacd['vwmacd'] dataframe['vwmacds'] = vwmacd['signal'] dataframe['TKE'] = tke[0] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.buy_vfi_cat == True: conditions.append(qtpylib.crossed_above(dataframe['vfi'], dataframe['vfima'])) if self.buy_vfi_cat2 == True: conditions.append(qtpylib.crossed_above(dataframe['vfi'], 0)) if self.buy_vfi_cat3 == True: conditions.append(qtpylib.crossed_above(dataframe['vfima'], 0)) if self.buy_vwmacd_cat.value == True: conditions.append(qtpylib.crossed_above(dataframe['vwmacd'], dataframe['vwmacds'])) if self.buy_tke_cat.value == True: conditions.append(qtpylib.crossed_above(dataframe['TKE'], self.buy_tke_val.value)) if self.buy_arronup_cat.value == True: conditions.append(qtpylib.crossed_above(dataframe['aroonup'], self.buy_aroonup.value)) if self.buy_arrondown_cat.value == True: conditions.append(qtpylib.crossed_above(dataframe['aroondown'], self.buy_aroondown.value)) if self.buy_emas_cat.value == True: conditions.append(qtpylib.crossed_above(dataframe["close"], dataframe['ema200'])) conditions.append(dataframe['volume'] > 0) if self.buy_td_cat == True: conditions.append(dataframe['td'] == self.buy_td.value) 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 = [] if self.sell_tke_cat.value == True: conditions.append(qtpylib.crossed_below(dataframe['TKE'], self.sell_tke_val.value)) if self.sell_arrondown_cat.value == True: conditions.append(qtpylib.crossed_above(dataframe['aroondown'], self.sell_aroondown.value)) if self.sell_arronup_cat.value == True: conditions.append(qtpylib.crossed_below(dataframe['aroonup'], self.sell_aroonup.value)) conditions.append(dataframe['volume'] > 0) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe