import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy import IStrategy, informative from freqtrade.strategy import (merge_informative_pair, DecimalParameter, IntParameter, BooleanParameter, CategoricalParameter, stoploss_from_open) from pandas import DataFrame, Series from typing import Dict, List, Optional, Tuple from functools import reduce from freqtrade.persistence import Trade from datetime import datetime, timedelta, timezone from freqtrade.exchange import timeframe_to_prev_date import math import talib.abstract as ta import logging from logging import FATAL logger = logging.getLogger(__name__) class Matoa(IStrategy): def version(self) -> str: return "Matoa-v1" INTERFACE_VERSION = 3 minimal_roi = { "0": 100 } buy_params = { "base_nb_candles_buy_hma": 62, "low_offset_hma": 0.88, "base_nb_candles_buy_hma2": 118, "low_offset_hma2": 0.86, "base_nb_candles_buy_hma3": 46, "low_offset_hma3": 0.883, "buy_length_volatility": 95, "buy_max_volatility": 1.19, "buy_length_volatility2": 110, "buy_max_volatility2": 1.55, } sell_params = { "base_nb_candles_sell_ema": 49, "high_offset_ema": 1.07, "base_nb_candles_sell_ema2": 36, "high_offset_ema2": 0.95, "base_nb_candles_sell_ema3": 104, "high_offset_ema3": 0.94, "base_nb_candles_sell_ema4": 5, "high_offset_ema4": 1.01, } protection_params = { "cooldown_lookback": 1, # value loaded from strategy } cooldown_lookback = IntParameter(1, 48, default=2, space="protection", optimize=False) @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) return prot dummy = IntParameter(20, 70, default=61, space='buy', optimize=True) optimize_buy_hma = False base_nb_candles_buy_hma = IntParameter(5, 150, default=6, space='buy', optimize=optimize_buy_hma) low_offset_hma = DecimalParameter(0.7, 0.99, default=0.98, decimals=2, space='buy', optimize=optimize_buy_hma) optimize_buy_hma2 = False base_nb_candles_buy_hma2 = IntParameter(5, 150, default=6, space='buy', optimize=optimize_buy_hma2) low_offset_hma2 = DecimalParameter(0.7, 0.99, default=0.95, decimals=2, space='buy', optimize=optimize_buy_hma2) optimize_buy_hma3 = False base_nb_candles_buy_hma3 = IntParameter(5, 150, default=6, space='buy', optimize=optimize_buy_hma3) low_offset_hma3 = DecimalParameter(0.7, 0.99, default=0.95, space='buy', optimize=optimize_buy_hma3) optimize_buy_volatility = False buy_length_volatility = IntParameter(10, 200, default=72, space='buy', optimize=optimize_buy_volatility) buy_min_volatility = DecimalParameter(0, 0.5, default=0, decimals = 2, space='buy', optimize=False) buy_max_volatility = DecimalParameter(0.5, 2, default=1, decimals = 2, space='buy', optimize=optimize_buy_volatility) optimize_buy_volatility2 = False buy_length_volatility2 = IntParameter(10, 200, default=72, space='buy', optimize=optimize_buy_volatility2) buy_max_volatility2 = DecimalParameter(0.5, 2, default=1, decimals = 2, space='buy', optimize=optimize_buy_volatility2) optimize_sell_ema = False base_nb_candles_sell_ema = IntParameter(5, 150, default=6, space='sell', optimize=optimize_sell_ema) high_offset_ema = DecimalParameter(1, 1.2, default=1.02, decimals=2, space='sell', optimize=optimize_sell_ema) optimize_sell_ema2 = False base_nb_candles_sell_ema2 = IntParameter(5, 150, default=6, space='sell', optimize=optimize_sell_ema2) high_offset_ema2 = DecimalParameter(0.7, 0.99, default=0.98, decimals=2, space='sell', optimize=optimize_sell_ema2) optimize_sell_ema3 = False base_nb_candles_sell_ema3 = IntParameter(5, 150, default=6, space='sell', optimize=optimize_sell_ema3) high_offset_ema3 = DecimalParameter(0.7, 0.99, default=0.95, decimals=2, space='sell', optimize=optimize_sell_ema3) optimize_sell_ema4 = False base_nb_candles_sell_ema4 = IntParameter(5, 150, default=6, space='sell', optimize=optimize_sell_ema4) high_offset_ema4 = DecimalParameter(1, 1.2, default=1, decimals=2, space='sell', optimize=optimize_sell_ema4) stoploss = -0.08 trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.08 trailing_only_offset_is_reached = True use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False timeframe = '5m' process_only_new_candles = True startup_candle_count = 300 age_filter = 30 @informative('1d') def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['age_filter_ok'] = (dataframe['volume'].rolling(window=self.age_filter, min_periods=self.age_filter).min() > 0) if not self.config['runmode'].value in ('dry_run', 'live'): drop_columns = ['open', 'high', 'low', 'close', 'volume'] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) if not self.optimize_buy_hma: dataframe['hma_offset_buy'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value if not self.optimize_buy_hma2: dataframe['hma_offset_buy2'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma2.value)) *self.low_offset_hma2.value if not self.optimize_buy_hma3: dataframe['hma_offset_buy3'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma3.value)) *self.low_offset_hma3.value if not self.optimize_buy_volatility: df_std = dataframe['close'].rolling(int(self.buy_length_volatility.value)).std() dataframe["volatility"] = (df_std > self.buy_min_volatility.value) & (df_std < self.buy_max_volatility.value) if not self.optimize_buy_volatility2: df_std = dataframe['close'].rolling(int(self.buy_length_volatility2.value)).std() dataframe["volatility2"] = (df_std > self.buy_min_volatility.value) & (df_std < self.buy_max_volatility2.value) if not self.optimize_sell_ema: dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema.value)) *self.high_offset_ema.value if not self.optimize_sell_ema2: dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema2.value)) *self.high_offset_ema2.value if not self.optimize_sell_ema3: dataframe['ema_offset_sell3'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema3.value)) *self.high_offset_ema3.value if not self.optimize_sell_ema4: dataframe['ema_offset_sell4'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema4.value)) *self.high_offset_ema4.value return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.optimize_buy_hma: dataframe['hma_offset_buy'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value if self.optimize_buy_hma2: dataframe['hma_offset_buy2'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma2.value)) *self.low_offset_hma2.value if self.optimize_buy_hma3: dataframe['hma_offset_buy3'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma3.value)) *self.low_offset_hma3.value if self.optimize_buy_volatility: df_std = dataframe['close'].rolling(int(self.buy_length_volatility.value)).std() dataframe["volatility"] = (df_std > self.buy_min_volatility.value) & (df_std < self.buy_max_volatility.value) if self.optimize_buy_volatility2: df_std = dataframe['close'].rolling(int(self.buy_length_volatility2.value)).std() dataframe["volatility2"] = (df_std > self.buy_min_volatility.value) & (df_std < self.buy_max_volatility2.value) dataframe['enter_tag'] = '' add_check = ( dataframe['live_data_ok'] & dataframe['age_filter_ok_1d'] & (dataframe['close'] < dataframe['open']) ) buy_offset_hma = ( (dataframe['close'] < dataframe['hma_offset_buy']) & (dataframe['volatility'].shift() == True) ) dataframe.loc[buy_offset_hma, 'enter_tag'] += 'hma ' conditions.append(buy_offset_hma) buy_offset_hma2 = ( ((dataframe['close'] < dataframe['hma_offset_buy2']).rolling(2).min() > 0) & (dataframe['volatility2'].shift() == True) ) dataframe.loc[buy_offset_hma2, 'enter_tag'] += 'hma_2 ' conditions.append(buy_offset_hma2) buy_offset_hma3 = ( (dataframe['close'] < dataframe['hma_offset_buy3']).rolling(3).min() > 0 ) dataframe.loc[buy_offset_hma3, 'enter_tag'] += 'hma_3 ' conditions.append(buy_offset_hma3) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions) & add_check, 'enter_long', ]= 1 dataframe.loc[ buy_offset_hma & buy_offset_hma2 & np.invert(buy_offset_hma3), 'enter_long' ]= 0 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.optimize_sell_ema: dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema.value)) *self.high_offset_ema.value if self.optimize_sell_ema2: dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema2.value)) *self.high_offset_ema2.value if self.optimize_sell_ema3: dataframe['ema_offset_sell3'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema3.value)) *self.high_offset_ema3.value if self.optimize_sell_ema4: dataframe['ema_offset_sell4'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema4.value)) *self.high_offset_ema4.value dataframe.loc[:, 'exit_tag'] = '' conditions = [] sell_ema_1 = ( (dataframe['close'] > dataframe['ema_offset_sell']) ) conditions.append(sell_ema_1) dataframe.loc[sell_ema_1, 'exit_tag'] += 'EMA_up ' sell_ema_2 = ( (dataframe['close'] < dataframe['ema_offset_sell2']) ) conditions.append(sell_ema_2) dataframe.loc[sell_ema_2, 'exit_tag'] += 'EMA_down ' sell_ema_3 = ( (dataframe['close'] < dataframe['ema_offset_sell3']).rolling(2).min() > 0 ) conditions.append(sell_ema_3) dataframe.loc[sell_ema_3, 'exit_tag'] += 'EMA_down_2 ' sell_ema_4 = ( (dataframe['close'] > dataframe['ema_offset_sell4']).rolling(3).min() > 0 ) conditions.append(sell_ema_4) dataframe.loc[sell_ema_4, 'exit_tag'] += 'EMA_up_3 ' add_check = ( (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions) & add_check, 'exit_long' ] = 1 return dataframe def tv_wma(df, length = 9) -> DataFrame: """ Source: Tradingview "Moving Average Weighted" Pinescript Author: Unknown Args : dataframe : Pandas Dataframe length : WMA length field : Field to use for the calculation Returns : dataframe : Pandas DataFrame with new columns 'tv_wma' """ norm = 0 sum = 0 for i in range(1, length - 1): weight = (length - i) * length norm = norm + weight sum = sum + df.shift(i) * weight tv_wma = (sum / norm) if norm > 0 else 0 return tv_wma def tv_hma(dataframe, length = 9, field = 'close') -> DataFrame: """ Source: Tradingview "Hull Moving Average" Pinescript Author: Unknown Args : dataframe : Pandas Dataframe length : HMA length field : Field to use for the calculation Returns : dataframe : Pandas DataFrame with new columns 'tv_hma' """ h = 2 * tv_wma(dataframe[field], math.floor(length / 2)) - tv_wma(dataframe[field], length) tv_hma = tv_wma(h, math.floor(math.sqrt(length))) return tv_hma