# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/MarketChyperHyperStrategy.py import numpy as np # noqa import pandas as pd # noqa import talib.abstract as ta from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy.interface import IStrategy from freqtrade.strategy.hyper import (CategoricalParameter, DecimalParameter, IntParameter, RealParameter) class Github_remiotore_freqtrade__MarketChyperHyperStrategy__20260111_210550(IStrategy): debuggable_weighted_signal_dataframe = False buy_params = { 'buy_downtrend_oslevel': -37, 'buy_downtrend_rsi_div_value': 79, 'buy_downtrend_rsi_divergence_weight': 50, 'buy_downtrend_total_signal_needed': 70, 'buy_downtrend_wavetrend_weight': 96, 'buy_sideways_oslevel': -28, 'buy_sideways_rsi_div_value': 45, 'buy_sideways_rsi_divergence_weight': 35, 'buy_sideways_total_signal_needed': 80, 'buy_sideways_wavetrend_weight': 5, 'buy_uptrend_oslevel': -43, 'buy_uptrend_rsi_div_value': 93, 'buy_uptrend_rsi_divergence_weight': 84, 'buy_uptrend_total_signal_needed': 25, 'buy_uptrend_wavetrend_weight': 91 } sell_params = { 'sell_downtrend_oblevel': 75.67481, 'sell_downtrend_rsi_divergence_weight': 90, 'sell_downtrend_total_signal_needed': 51, 'sell_downtrend_wavetrend_weight': 30, 'sell_sideways_oblevel': 1.93351, 'sell_sideways_rsi_divergence_weight': 59, 'sell_sideways_total_signal_needed': 36, 'sell_sideways_wavetrend_weight': 83, 'sell_uptrend_oblevel': 37.98512, 'sell_uptrend_rsi_divergence_weight': 99, 'sell_uptrend_total_signal_needed': 93, 'sell_uptrend_wavetrend_weight': 43 } minimal_roi = { "0": 0.23733, "238": 0.20624, "846": 0.08939, "1834": 0 } stoploss = -0.12447 trailing_stop = True trailing_stop_positive = 0.01658 trailing_stop_positive_offset = 0.10224 trailing_only_offset_is_reached = True timeframe = '1h' process_only_new_candles = False use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False startup_candle_count: int = 400 order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } buy___trades_when_downwards = \ CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy___trades_when_sideways = \ CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy___trades_when_upwards = \ CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_uptrend_total_signal_needed = IntParameter(0, 100, default=65, space='buy', optimize=True, load=True) buy_uptrend_wavetrend_weight = \ IntParameter(0, 100, default=0, space='buy', optimize=True, load=True) buy_uptrend_rsi_divergence_weight = \ IntParameter(0, 100, default=0, space='buy', optimize=True, load=True) buy_uptrend_rsi_div_value = \ IntParameter(0, 100, default=0, space='buy', optimize=True, load=True) buy_uptrend_oslevel = \ IntParameter(-100, 0, default=0, space='buy', optimize=True, load=True) buy_sideways_total_signal_needed = IntParameter(0, 100, default=65, space='buy', optimize=True, load=True) buy_sideways_wavetrend_weight = \ IntParameter(0, 100, default=0, space='buy', optimize=True, load=True) buy_sideways_rsi_divergence_weight = \ IntParameter(0, 100, default=0, space='buy', optimize=True, load=True) buy_sideways_rsi_div_value = \ IntParameter(0, 100, default=0, space='buy', optimize=True, load=True) buy_sideways_oslevel = \ IntParameter(-100, 0, default=0, space='buy', optimize=True, load=True) buy_downtrend_total_signal_needed = IntParameter(0, 100, default=65, space='buy', optimize=True, load=True) buy_downtrend_wavetrend_weight = \ IntParameter(0, 100, default=0, space='buy', optimize=True, load=True) buy_downtrend_rsi_divergence_weight = \ IntParameter(0, 100, default=0, space='buy', optimize=True, load=True) buy_downtrend_rsi_div_value = \ IntParameter(0, 100, default=0, space='buy', optimize=True, load=True) buy_downtrend_oslevel = \ IntParameter(-100, 0, default=0, space='buy', optimize=True, load=True) sell___trades_when_downwards = \ CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell___trades_when_sideways = \ CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell___trades_when_upwards = \ CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell_uptrend_total_signal_needed = IntParameter(0, 100, default=65, space='sell', optimize=True, load=True) sell_uptrend_wavetrend_weight = \ IntParameter(0, 100, default=0, space='sell', optimize=True, load=True) sell_uptrend_rsi_divergence_weight = \ IntParameter(0, 100, default=0, space='sell', optimize=True, load=True) sell_uptrend_oblevel = \ RealParameter(0, 100, default=0, space='sell', optimize=True, load=True) sell_sideways_total_signal_needed = IntParameter(0, 100, default=65, space='sell', optimize=True, load=True) sell_sideways_wavetrend_weight = \ IntParameter(0, 100, default=0, space='sell', optimize=True, load=True) sell_sideways_rsi_divergence_weight = \ IntParameter(0, 100, default=0, space='sell', optimize=True, load=True) sell_sideways_oblevel = \ RealParameter(0, 100, default=0, space='sell', optimize=True, load=True) sell_downtrend_total_signal_needed = IntParameter(0, 100, default=65, space='sell', optimize=True, load=True) sell_downtrend_wavetrend_weight = \ IntParameter(0, 100, default=0, space='sell', optimize=True, load=True) sell_downtrend_rsi_divergence_weight = \ IntParameter(0, 100, default=0, space='sell', optimize=True, load=True) sell_downtrend_oblevel = \ RealParameter(0, 100, default=0, space='sell', optimize=True, load=True) 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: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) # 14 timeperiods is usually used for ADX dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=25) dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=25) dataframe['rsi'] = ta.RSI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] # MACD - Blue TradingView Line (Bullish if on top) dataframe['macdsignal'] = macd['macdsignal'] # Signal - Orange TradingView Line (Bearish if on top) dataframe['total_buy_signal_strength'] = dataframe['total_sell_signal_strength'] = 0 dataframe = self.divergences(dataframe) dataframe.loc[(dataframe['adx'] > 20) & (dataframe['plus_di'] < dataframe['minus_di']), 'trend'] = 'downwards' dataframe.loc[dataframe['adx'] < 20, 'trend'] = 'sideways' dataframe.loc[(dataframe['adx'] > 20) & (dataframe['plus_di'] > dataframe['minus_di']), 'trend'] = 'upwards' return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe = self.market_cipher(dataframe) dataframe.loc[(dataframe['trend'] == 'downwards'), 'wtOversold'] = (dataframe['wt2'] <= self.buy_downtrend_oslevel.value) dataframe.loc[(dataframe['trend'] == 'sideways'), 'wtOversold'] = (dataframe['wt2'] <= self.buy_sideways_oslevel.value) dataframe.loc[(dataframe['trend'] == 'upwards'), 'wtOversold'] = (dataframe['wt2'] <= self.buy_uptrend_oslevel.value) dataframe.loc[(dataframe['trend'] == 'downwards') & (dataframe['wtCrossUp'] & dataframe['wtOversold']), 'buy_wavetrend_weight'] = self.buy_downtrend_wavetrend_weight.value dataframe.loc[(dataframe['trend'] == 'sideways') & (dataframe['wtCrossUp'] & dataframe['wtOversold']), 'buy_wavetrend_weight'] = self.buy_sideways_wavetrend_weight.value dataframe.loc[(dataframe['trend'] == 'upwards') & (dataframe['wtCrossUp'] & dataframe['wtOversold']), 'buy_wavetrend_weight'] = self.buy_uptrend_wavetrend_weight.value dataframe['total_buy_signal_strength'] += dataframe['buy_wavetrend_weight'] dataframe.loc[(dataframe['trend'] == 'downwards') & (dataframe['bullish_div'] & (dataframe['rsi'] <= self.buy_downtrend_rsi_div_value.value)), 'buy_rsi_divergence_weight'] = self.buy_downtrend_rsi_divergence_weight.value dataframe.loc[(dataframe['trend'] == 'sideways') & (dataframe['bullish_div'] & (dataframe['rsi'] <= self.buy_sideways_rsi_div_value.value)), 'buy_rsi_divergence_weight'] = self.buy_sideways_rsi_divergence_weight.value dataframe.loc[(dataframe['trend'] == 'upwards') & (dataframe['bullish_div'] & (dataframe['rsi'] <= self.buy_uptrend_rsi_div_value.value)), 'buy_rsi_divergence_weight'] = self.buy_uptrend_rsi_divergence_weight.value dataframe['total_buy_signal_strength'] += dataframe['buy_rsi_divergence_weight'] dataframe.loc[ ( (dataframe['trend'] == 'downwards') & (dataframe['total_buy_signal_strength'] >= self.buy_downtrend_total_signal_needed.value) ) | ( (dataframe['trend'] == 'sideways') & (dataframe['total_buy_signal_strength'] >= self.buy_sideways_total_signal_needed.value) ) | ( (dataframe['trend'] == 'upwards') & (dataframe['total_buy_signal_strength'] >= self.buy_uptrend_total_signal_needed.value) ), 'buy'] = 1 if not self.buy___trades_when_downwards.value: dataframe.loc[dataframe['trend'] == 'downwards', 'buy'] = 0 if not self.buy___trades_when_sideways.value: dataframe.loc[dataframe['trend'] == 'sideways', 'buy'] = 0 if not self.buy___trades_when_upwards.value: dataframe.loc[dataframe['trend'] == 'upwards', 'buy'] = 0 return dataframe return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.market_cipher(dataframe) dataframe.loc[(dataframe['trend'] == 'downwards'), 'wtOverbought'] = (dataframe['wt2'] >= self.sell_downtrend_oblevel.value) dataframe.loc[(dataframe['trend'] == 'sideways'), 'wtOverbought'] = (dataframe['wt2'] >= self.sell_sideways_oblevel.value) dataframe.loc[(dataframe['trend'] == 'upwards'), 'wtOverbought'] = (dataframe['wt2'] >= self.sell_uptrend_oblevel.value) dataframe.loc[(dataframe['trend'] == 'downwards') & (dataframe['wtCrossDown'] & dataframe['wtOverbought']), 'sell_wavetrend_weight'] = self.sell_downtrend_wavetrend_weight.value dataframe.loc[(dataframe['trend'] == 'sideways') & (dataframe['wtCrossDown'] & dataframe['wtOverbought']), 'sell_wavetrend_weight'] = self.sell_sideways_wavetrend_weight.value dataframe.loc[(dataframe['trend'] == 'upwards') & (dataframe['wtCrossDown'] & dataframe['wtOverbought']), 'sell_wavetrend_weight'] = self.sell_uptrend_wavetrend_weight.value dataframe['total_sell_signal_strength'] += dataframe['sell_wavetrend_weight'] dataframe.loc[(dataframe['trend'] == 'downwards') & (dataframe['bearish_div']), 'sell_rsi_divergence_weight'] = self.sell_downtrend_rsi_divergence_weight.value dataframe.loc[(dataframe['trend'] == 'sideways') & (dataframe['bearish_div']), 'sell_rsi_divergence_weight'] = self.sell_sideways_rsi_divergence_weight.value dataframe.loc[(dataframe['trend'] == 'upwards') & (dataframe['bearish_div']), 'sell_rsi_divergence_weight'] = self.sell_uptrend_rsi_divergence_weight.value dataframe['total_sell_signal_strength'] += dataframe['sell_rsi_divergence_weight'] dataframe.loc[ ( (dataframe['trend'] == 'downwards') & (dataframe['total_sell_signal_strength'] >= self.sell_downtrend_total_signal_needed.value) ) | ( (dataframe['trend'] == 'sideways') & (dataframe['total_sell_signal_strength'] >= self.sell_sideways_total_signal_needed.value) ) | ( (dataframe['trend'] == 'upwards') & (dataframe['total_sell_signal_strength'] >= self.sell_uptrend_total_signal_needed.value) ), 'sell'] = 1 return dataframe def market_cipher(self, dataframe) -> DataFrame: self.n1 = 10 #WT Channel Length self.n2 = 21 #WT Average Length dataframe['ap'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 dataframe['esa'] = ta.EMA(dataframe['ap'], self.n1) dataframe['d'] = ta.EMA((dataframe['ap']-dataframe['esa']).abs(), self.n1) dataframe['ci'] = ( dataframe['ap']-dataframe['esa'] ) / (0.015 * dataframe['d']) dataframe['tci'] = ta.EMA(dataframe['ci'], self.n2) dataframe['wt1'] = dataframe['tci'] dataframe['wt2'] = ta.SMA(dataframe['wt1'],4) dataframe['wtVwap'] = dataframe['wt1'] - dataframe['wt2'] dataframe['wtCrossUp'] = dataframe['wt2'] - dataframe['wt1'] <= 0 dataframe['wtCrossDown'] = dataframe['wt2'] - dataframe['wt1'] >= 0 dataframe['crossed_above'] = qtpylib.crossed_above(dataframe['wt2'], dataframe['wt1']) dataframe['crossed_below'] = qtpylib.crossed_below(dataframe['wt2'], dataframe['wt1']) return dataframe def divergences(self,dataframe) -> DataFrame: dataframe['bullish_div'] = ( ( dataframe['close'].shift(4) > dataframe['close'].shift(2) ) & ( dataframe['close'].shift(3) > dataframe['close'].shift(2) ) & ( dataframe['close'].shift(2) < dataframe['close'].shift(1) ) & ( dataframe['close'].shift(2) < dataframe['close'] ) ) dataframe['bearish_div'] = ( ( dataframe['close'].shift(4) < dataframe['close'].shift(2) ) & ( dataframe['close'].shift(3) < dataframe['close'].shift(2) ) & ( dataframe['close'].shift(2) > dataframe['close'].shift(1) ) & ( dataframe['close'].shift(2) > dataframe['close'] ) ) return dataframe