# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/sniper_v1.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_freqtrade__sniper_v1__20260111_210550(IStrategy): INTERFACE_VERSION = 2 buy_params = { "b_rsi_val": 16, "buy_emalongb": 178, "buy_emamediumb": 66, "buy_emas1_cat": False, "buy_emas2_cat": True, "buy_emashortb": 49, "buy_stoch": 50, "buy_stoch_cat": False, "buy_stochv_cat": True, "buy_tke_cat": False, "buy_tke_val": 29, "buy_vfi_cat": True, "buy_vfi_cat2": False, "buy_vfi_cat3": False, "buy_vwmacd_cat": False, } sell_params = { "s_rsi_val": 13, "sell_ema1_cat": True, "sell_ema2_cat": True, "sell_emalongs": 155, "sell_emamediums": 48, "sell_emashorts": 28, "sell_stoch": 68, "sell_stoch_cat": False, "sell_stochv_cat": True, "sell_tke_cat": True, "sell_tke_val": 69, "sell_vfi_cat": True, "sell_vfi_cat2": True, "sell_vfi_cat3": False, "sell_vwmacd_cat": True, } minimal_roi = { "0": 0.393, "95": 0.098, "214": 0.054, "395": 0 } stoploss = -0.288 trailing_stop = True trailing_stop_positive = 0.051 trailing_stop_positive_offset = 0.073 trailing_only_offset_is_reached = False use_custom_stoploss = False timeframe = '15m' informative_timeframe = '1h' 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'}, } } } ''''def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] if self.dp: for pair in pairs: informative_pairs += [(pair, "1d")] return informative_pairs ''''' buy_emalongb = IntParameter( 130, 200, default = 140, space = 'buy') buy_emamediumb = IntParameter(40, 100, default = 50, space = 'buy') buy_emashortb = IntParameter(15, 50, default = 20, space = 'buy') buy_stoch= IntParameter(5, 50, default=40, space = 'buy') buy_tke_val = IntParameter(1, 40, default = 37, space = 'buy') b_rsi_val = IntParameter(8, 25, default = 14, space = 'buy') sell_stoch= IntParameter(50, 100, default=80, space = 'sell') sell_tke_val = IntParameter(60, 90, default = 78, space = 'sell') s_rsi_val = IntParameter(8, 25, default = 14, space = 'sell') sell_emalongs = IntParameter( 130, 200, default = 140, space = 'sell') sell_emamediums = IntParameter(40, 100, default = 50, space = 'sell') sell_emashorts = IntParameter(15, 50, default = 20, space = 'sell') buy_emas1_cat = CategoricalParameter ( [True, False], default = True, space = 'buy') buy_emas2_cat = CategoricalParameter ( [True, False], default = True, space = 'buy') buy_stochv_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' ) buy_stoch_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_stochv_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) buy_tke_cat = CategoricalParameter([True, False], default = True, space = 'buy' ) sell_vfi_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_vfi_cat2 = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_vfi_cat3 = CategoricalParameter([True, False], default = True, space = 'sell' ) buy_stochv_cat = CategoricalParameter ( [True, False], default = True, space = 'buy') sell_ema1_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_ema2_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_vwmacd_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_stoch_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_stochv_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) sell_tke_cat = CategoricalParameter([True, False], default = True, space = 'sell' ) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: vwmacd = technicali.vwmacd(dataframe) fastkb, fastdb = ta.STOCHRSI(dataframe["close"], timeperiod=self.b_rsi_val.value, fastk_period=5, fastd_period=3, fastd_matype=0) fastks, fastds = ta.STOCHRSI(dataframe["close"], timeperiod=self.s_rsi_val.value, fastk_period=5, fastd_period=3, fastd_matype=0) vfi = technicali.vfi(dataframe) tke = technicali.TKE(dataframe) dataframe['emalongb'] = ta.EMA(dataframe, timeperiod = self.buy_emalongb.value) dataframe['emamedb'] = ta.EMA(dataframe, timeperiod = self.buy_emamediumb.value) dataframe['emashortb'] = ta.EMA(dataframe, timeperiod = self.buy_emashortb.value) dataframe['emalong'] = ta.EMA(dataframe, timeperiod = self.sell_emalongs.value) dataframe['emameds'] = ta.EMA(dataframe, timeperiod = self.sell_emamediums.value) dataframe['emashorts'] = ta.EMA(dataframe, timeperiod = self.sell_emashorts.value) dataframe['vwmacd'] = vwmacd['vwmacd'] dataframe['vwmacds'] = vwmacd['signal'] dataframe['stochfb'] = fastdb dataframe['stochkb'] = fastkb dataframe['stochfs'] = fastds dataframe['stochks'] = fastks dataframe['vfi'] = vfi[0] dataframe['vfima'] = vfi[1] dataframe['TKE'] = tke[0] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.buy_emas1_cat == True: conditions.append(qtpylib.crossed_above(dataframe['emamedb'], dataframe['emalongb'])) if self.buy_emas2_cat == True: conditions.append(qtpylib.crossed_above(dataframe['emashortb'], dataframe['emalongb'])) if self.buy_vwmacd_cat.value == True: conditions.append(qtpylib.crossed_above(dataframe['vwmacd'], dataframe['vwmacds'])) conditions.append(qtpylib.crossed_above(dataframe['stochfb'], dataframe['stochkb'])) if self.buy_stochv_cat.value == True: conditions.append(dataframe['stochkb'] > self.buy_stoch.value) if self.buy_vfi_cat == True: conditions.append(qtpylib.crossed_above(dataframe['vfi'], dataframe['vfima'])) 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_tke_cat.value == True: conditions.append(qtpylib.crossed_above(dataframe['TKE'], self.buy_tke_val.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_ema1_cat == True: conditions.append(qtpylib.crossed_below(dataframe['emameds'], dataframe['emalongs'])) if self.sell_ema2_cat == True: conditions.append(qtpylib.crossed_below(dataframe['emashorts'], dataframe['emalongs'])) if self.sell_vwmacd_cat.value == True: conditions.append(qtpylib.crossed_below(dataframe['vwmacd'], dataframe['vwmacds'])) if self.sell_stoch_cat.value == True: conditions.append(qtpylib.crossed_below(dataframe['stochfs'], dataframe['stochks'])) conditions.append(dataframe['stochks'] > self.sell_stoch.value) if self.sell_vfi_cat.value == True: conditions.append(qtpylib.crossed_below(dataframe['vfi'], dataframe['vfima'])) conditions.append(qtpylib.crossed_below(dataframe['vfi'], 0)) if self.sell_vfi_cat3 == True: conditions.append(qtpylib.crossed_below(dataframe['vfima'], 0)) if self.buy_tke_cat.value == True: conditions.append(qtpylib.crossed_above(dataframe['TKE'], self.sell_tke_val.value)) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe