# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ViTradeTuned.py import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from typing import Optional, Union from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) from freqtrade.strategy import merge_informative_pair import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from technical.consensus import Consensus PAIR_INF_TIMEFRAME = '1h' class Github_remiotore_freqtrade__ViTradeTuned__20260111_210550(IStrategy): INTERFACE_VERSION = 3 can_short: bool = False timeframe = '5m' process_only_new_candles = True trailing_stop = True trailing_stop_positive = 0.01032 trailing_stop_positive_offset = 0.03518 trailing_only_offset_is_reached = True minimal_roi = { "0": 0.109, "34": 0.056, "87": 0.032, "143": 0 } stoploss = -0.1 trailing_stop = True # value loaded from strategy trailing_stop_positive = 0.049 # value loaded from strategy trailing_stop_positive_offset = 0.13 # value loaded from strategy trailing_only_offset_is_reached = True # value loaded from strategy buy_signal_buy_score_consensus = IntParameter(low=1, high=100, default=21, space='buy', optimize=True, load=True) buy_signal_sell_score_consensus = IntParameter(low=1, high=100, default=41, space='buy', optimize=True, load=True) buy_signal_buy_score_delta_threshold = DecimalParameter(low=0.01, high=0.1, default=0.03, space='buy', optimize=True, load=True) buy_signal_bb_perc = DecimalParameter(low=0.1, high=1, default=0.83, space='buy', optimize=True, load=True) buy_signal_bb_std_dev = IntParameter(low=1, high=3, default=1, space='buy', optimize=True, load=True) buy_signal_bb_window = IntParameter(low=8, high=300, default=83, space='buy', optimize=True, load=True) buy_signal_rolling_window_higher_tf = IntParameter(low=1, high=50, default=21, space='buy', optimize=True, load=True) buy_signal_skip_spikes_higher_tf = DecimalParameter(low=0.01, high=0.25, default=0.2, space='buy', optimize=True, load=True) sell_signal_sell_score_consensus = IntParameter(low=1, high=100, default=18, space='sell', optimize=True, load=True) sell_signal_bb_perc = DecimalParameter(low=0.1, high=0.5, default=0.3, space='sell', optimize=True, load=True) use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = True startup_candle_count = 100 order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } plot_config = { 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, } } } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=PAIR_INF_TIMEFRAME) informative['rolling_max'] = informative['close'].rolling(window=21).max() informative['roc'] = informative['close'].pct_change() dataframe = merge_informative_pair(dataframe, informative, self.timeframe, PAIR_INF_TIMEFRAME, ffill=True) c = Consensus(dataframe) c.evaluate_hull() c.evaluate_adx() c.evaluate_macd() c.evaluate_cci() c.evaluate_osc() c.evaluate_vwma() score = c.score() dataframe['score_sell'] = score['sell'] dataframe['score_buy'] = score['buy'] dataframe['buy_agreement'] = score['buy_agreement'] dataframe['buy_disagreement'] = score['buy_disagreement'] dataframe['sell_agreement'] = score['sell_agreement'] dataframe['sell_disagreement'] = score['sell_disagreement'] mid, lower = bollinger_bands(dataframe['low'], window_size=self.buy_signal_bb_window.value, num_of_std=self.buy_signal_bb_std_dev.value) dataframe['mid'] = np.nan_to_num(mid) dataframe['lower'] = np.nan_to_num(lower) dataframe['bbdelta'] = (dataframe['mid'] - dataframe['lower']).abs() dataframe['pricedelta'] = (dataframe['open'] - dataframe['close']).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=self.buy_signal_bb_std_dev.value) dataframe['bb_low'] = bollinger['lower'] dataframe['bb_mid'] = bollinger['mid'] dataframe['bb_upper'] = bollinger['upper'] dataframe['bb_perc'] = (dataframe['close'] - dataframe['bb_low']) / (dataframe['bb_upper'] - dataframe['bb_low']) dataframe['roc'] = dataframe['close'].pct_change() dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ dataframe.loc[ ( (dataframe["close_"+PAIR_INF_TIMEFRAME].rolling(self.buy_signal_rolling_window_higher_tf.value).max().ge(dataframe['close'])) & (dataframe['roc_'+PAIR_INF_TIMEFRAME] < self.buy_signal_skip_spikes_higher_tf.value) & ( dataframe['score_sell'].ge(self.buy_signal_sell_score_consensus.value) | dataframe['score_buy'].ge(self.buy_signal_buy_score_consensus.value) ) & (dataframe['bb_perc'].le(self.buy_signal_bb_perc.value)) & (dataframe['closedelta'].gt(dataframe['close'] * self.buy_signal_buy_score_delta_threshold.value)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'enter_long'] = 1 dataframe.loc[ ( dataframe["close_" + PAIR_INF_TIMEFRAME].rolling( self.buy_signal_rolling_window_higher_tf.value).min().le(dataframe['close']) & (dataframe['roc_' + PAIR_INF_TIMEFRAME].gt(-self.buy_signal_skip_spikes_higher_tf.value)) & dataframe['score_buy'].le(self.buy_signal_sell_score_consensus.value) & dataframe['bb_perc'].ge(1 - self.buy_signal_bb_perc.value) & dataframe['closedelta'].lt(dataframe['close'] * self.buy_signal_buy_score_delta_threshold.value) & dataframe['volume'] > 0 ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['bb_perc'] > self.sell_signal_bb_perc.value) & (dataframe['score_sell'].ge(self.sell_signal_sell_score_consensus.value)) ), 'exit_long'] = 1 dataframe.loc[ ( dataframe['bb_perc'].lt(1 - self.sell_signal_bb_perc.value) & dataframe['score_buy'].ge(self.sell_signal_sell_score_consensus.value) ), 'exit_short'] = 1 return dataframe def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - (rolling_std * num_of_std) return rolling_mean, lower_band