# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/vinfast5.py from logging import FATAL 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": 8, "ewo_high": 2.675, "ewo_high_2": 4.516, "ewo_low": -9.263, "lookback_candles": 22, "low_offset": 0.988, "low_offset_2": 0.915, "profit_threshold": 1.0408, "rsi_buy": 57, "rsi_fast_buy": 49 } sell_params = { "base_nb_candles_sell": 24, "high_offset": 0.998, "high_offset_2": 1, "sell_rsi_main": 87.43 } def zlema2(dataframe, fast): df = dataframe.copy() zema1=ta.EMA(df['close'], fast) zema2=ta.EMA(zema1, fast) d1=zema1-zema2 df['zlema2']=zema1+d1 return df['zlema2'] 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_ccxt_freqtrade__vinfast5__20260111_210550(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.215, "40": 0.032, "64": 0.03, "87": 0.016, "201": 0 } stoploss = -0.15 base_nb_candles_buy = IntParameter( 2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter( 2, 25, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) low_offset = DecimalParameter( 0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=False) low_offset_2 = DecimalParameter( 0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=False) high_offset = DecimalParameter( 0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=False) high_offset_2 = DecimalParameter( 0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=False) sell_rsi_main = DecimalParameter(72.0, 90.0, default=sell_params['sell_rsi_main'], space='sell', decimals=2, optimize=False, load=True) fast_ewo = 50 slow_ewo = 200 lookback_candles = IntParameter( 1, 36, default=buy_params['lookback_candles'], space='buy', optimize=False) profit_threshold = DecimalParameter(0.99, 1.05, default=buy_params['profit_threshold'], space='buy', optimize=False) ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=False) ewo_high = DecimalParameter( 2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=False) ewo_high_2 = DecimalParameter( -6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=False) rsi_buy = IntParameter(10, 80, default=buy_params['rsi_buy'], space='buy', optimize=False) rsi_fast_buy = IntParameter( 10, 50, default=buy_params['rsi_fast_buy'], space='buy', optimize=False) trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.010 trailing_only_offset_is_reached = True use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 ignore_roi_if_buy_signal = False order_time_in_force = { 'buy': 'gtc', 'sell': 'ioc' } timeframe = '5m' inf_15m = '15m' inf_1h = '1h' process_only_new_candles = True startup_candle_count = 200 use_custom_stoploss = False plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, 'subplots': { 'rsi': { 'rsi': {'color': 'orange'}, 'rsi_fast': {'color': 'red'}, 'rsi_slow': {'color': 'green'}, }, 'ewo': { 'EWO': {'color': 'orange'} }, } } slippage_protection = { 'retries': 3, 'max_slippage': -0.02 } 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: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] current_profit = trade.calc_profit_ratio(rate) if 'bb_bull' in trade.buy_tag and current_profit > 0.01: return True if (last_candle is not None): if (sell_reason in ['sell_signal']): if (last_candle['hma_50'] > last_candle['ema_100']) and (last_candle['rsi'] < 45): #*1.2 return False if (last_candle is not None): if (sell_reason in ['sell_signal']): if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951): # *1.2 return False if (trade.buy_tag == 'bb_bull'): if (sell_reason in ['sell_signal'])or (sell_reason in ['roi']): return False 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 informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '15m') 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.inf_1h) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) return informative_1h 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=self.inf_15m) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) return informative_15m def normal_tf_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['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_10'] = zlema2(dataframe, 10) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) dataframe['sma_9'] = ta.SMA(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) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) 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['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() dataframe['vol_7_max'] = dataframe['volume'].rolling(window=20).max() dataframe['vol_14_max'] = dataframe['volume'].rolling(window=14).max() dataframe['vol_7_min'] = dataframe['volume'].rolling(window=20).min() dataframe['vol_14_min'] = dataframe['volume'].rolling(window=14).min() dataframe['roll_7'] = 100*((dataframe['volume']-dataframe['vol_7_max'])/(dataframe['vol_7_max']-dataframe['vol_7_min'])) dataframe['vol_base']=ta.SMA(dataframe['roll_7'], timeperiod=5) dataframe['vol_ma_26'] = ta.EMA(dataframe['volume'], timeperiod=26) dataframe['vol_ma_200'] = ta.EMA(dataframe['volume'], timeperiod=200) dataframe['vol_ma_26_front'] = ((ta.EMA(dataframe['volume'], timeperiod=26).max())-(ta.EMA(dataframe['volume'], timeperiod=26).min()))/2 return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_15m = self.informative_15m_indicators(dataframe, metadata) dataframe = merge_informative_pair( dataframe, informative_15m, self.timeframe, self.inf_15m, ffill=True) dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dont_buy_conditions = [] dont_buy_conditions.append( ( (dataframe['close_15m'].rolling(self.lookback_candles.value).max() < (dataframe['close'] * self.profit_threshold.value)) ) ) dataframe.loc[ ( (dataframe['vol_base']<-80) & (dataframe['ema_10'].rolling(10).mean() > dataframe['ema_100'].rolling(10).mean()) & (dataframe['lower'].shift().gt(0)) & (dataframe['bbdelta'].gt(dataframe['close'] * 0.031)) & (dataframe['closedelta'].gt(dataframe['close'] * 0.018)) & (dataframe['tail'].lt(dataframe['bbdelta'] * 0.233)) & (dataframe['close'].lt(dataframe['lower'].shift())) & (dataframe['close'].le(dataframe['close'].shift())) & (dataframe['volume'] > 0) ) | ( (dataframe['vol_base']<-80) & (dataframe['ema_10'].rolling(10).mean() > dataframe['ema_100'].rolling(10).mean()) & (dataframe['close'] > dataframe['ema_100']) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.993 * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 21)) & (dataframe['volume'] > 0) ), ['buy', 'buy_tag']] = (1, 'bb_bull') if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'buy'] = 0 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ((dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) ) | ( (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ]=1 return dataframe