# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/RalliV1_2.py from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame 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 import technical.indicators as ftt buy_params = { "base_nb_candles_buy": 14, "ewo_high": 2.327, "ewo_high_2": -2.327, "ewo_low": -20.988, "low_offset": 0.975, "low_offset_2": 0.955, "rsi_buy": 60, "rsi_buy_2": 45 } sell_params = { "base_nb_candles_sell": 24, "high_offset": 0.991, "high_offset_2": 0.997 } def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif class Github_remiotore_freqtrade__RalliV1_2__20260111_210550(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.04, "40": 0.032, "87": 0.018, "201": 0 } stoploss = -0.3 base_nb_candles_buy = IntParameter( 5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter( 5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter( 0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) low_offset_2 = DecimalParameter( 0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=True) high_offset = DecimalParameter( 0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter( 0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True) 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( 2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True) ewo_high_2 = DecimalParameter( -6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) rsi_buy_2 = IntParameter(30, 70, default=buy_params['rsi_buy_2'], space='buy', optimize=True) trailing_stop = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.01 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 = 200 plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } protections = [ { "method": "CooldownPeriod", "stop_duration_candles": 2 } ] def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] if ((rate > last_candle['close'])) : return False return True 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) buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag else: trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) buy_signal = dataframe.loc[dataframe['date'] < trade_open_date] if not buy_signal.empty: buy_signal_candle = buy_signal.iloc[-1] buy_tag = buy_signal_candle['buy_tag'] if buy_signal_candle['buy_tag'] != '' else 'empty' buy_tags = buy_tag.split() current_profit = trade.calc_profit_ratio(current_rate) if (current_profit <= -0.3): return 'stoploss ( ' + buy_tag + ')' trade_time_40 = trade.open_date_utc + timedelta(minutes=40) trade_time_87 = trade.open_date_utc + timedelta(minutes=87) trade_time_201 = trade.open_date_utc + timedelta(minutes=201) if (current_time < trade_time_40): if(current_profit >= 0.04): return 'roi 0 ( ' + buy_tag + ')' elif (current_time >= trade_time_40) and (current_time < trade_time_87): if(current_profit >= 0.032): return 'roi 40 ( ' + buy_tag + ')' elif (current_time >= trade_time_87) and (current_time < trade_time_201): if(current_profit >= 0.018): return 'roi 87 ( ' + buy_tag + ')' elif (current_time >= trade_time_201): if(current_profit >= 0): return 'roi 201 ( ' + buy_tag + ')' last_candle = dataframe.iloc[-1] sell_1 = ( (last_candle['hma_50'] > last_candle['ema_100'])& (last_candle['close'] > last_candle['sma_9'])& (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (last_candle['rsi_fast'] > last_candle['rsi_slow']) ) sell_2 = ( (last_candle['close'] < last_candle['ema_100'])& (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (last_candle['rsi_fast'] > last_candle['rsi_slow']) ) sell_3 = (last_candle['rsi'] > 45 ) sell_4 = (last_candle['hma_50'] < last_candle['ema_100']) if (sell_1 and sell_3): return 'sell 1-3 ( ' + buy_tag + ')' elif (sell_1 and sell_4): return 'sell 1-4 ( ' + buy_tag + ')' elif (sell_2 and sell_3): return 'sell 2-3 ( ' + buy_tag + ')' elif (sell_2 and sell_4): return 'sell 2-4 ( ' + buy_tag + ')' return None use_custom_stoploss = True def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) candle = df.iloc[-1].squeeze() if current_profit < 0.001 and current_time - timedelta(minutes=140) > trade.open_date_utc: return -0.005 return 1 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: 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['hma_9'] = qtpylib.hull_moving_average(dataframe['close'], window=9) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) dataframe['ema_9'] = ta.EMA(dataframe, timeperiod=9) 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) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' buy_1 = ( (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] < dataframe['ema_100'])& (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'])& (dataframe['rsi_fast'] <35)& (dataframe['rsi_fast'] >4)& (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_2.value) & (dataframe['volume'] > 0)& (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) dataframe.loc[buy_1, 'buy_tag'] += '1 ' conditions.append(buy_1) buy_2 = ( (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] < dataframe['ema_100'])& (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'])& (dataframe['rsi_fast'] <35)& (dataframe['rsi_fast'] >4)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy_2.value) & (dataframe['volume'] > 0)& (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))& (dataframe['rsi']<25) ) dataframe.loc[buy_2, 'buy_tag'] += '2 ' conditions.append(buy_2) buy_3 = ( (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] < dataframe['ema_100'])& (dataframe['sma_9'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'])& (dataframe['rsi_fast'] < 35)& (dataframe['rsi_fast'] >4)& (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['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) dataframe.loc[buy_3, 'buy_tag'] += '3 ' conditions.append(buy_3) buy_4 = ( (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] > dataframe['ema_100'])& (dataframe['rsi_fast'] <35)& (dataframe['rsi_fast'] >4)& (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['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) dataframe.loc[buy_4, 'buy_tag'] += '4 ' conditions.append(buy_4) buy_5 = ( (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] > dataframe['ema_100'])& (dataframe['rsi_fast'] <35)& (dataframe['rsi_fast'] >4)& (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0)& (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))& (dataframe['rsi']<25) ) dataframe.loc[buy_5, 'buy_tag'] += '5 ' conditions.append(buy_5) buy_6 = ( (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] > dataframe['ema_100'])& (dataframe['rsi_fast'] < 35)& (dataframe['rsi_fast'] >4)& (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['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) ) dataframe.loc[buy_6, 'buy_tag'] += '6 ' conditions.append(buy_6) if conditions: dataframe.loc[:, 'buy'] = reduce(lambda x, y: x | y, conditions) return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'sell'] = 0 return dataframe