# source: https://raw.githubusercontent.com/stash86/MultiMA_TSL/a14d2844b9fe8cd260770ffde4e5a71d27338194/user_data/strategies/Cenderawasih_kucoin/Cenderawasih_3_kucoin.py 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 talib.abstract as ta import math import pandas_ta as pta import logging from logging import FATAL import time import requests import threading logger = logging.getLogger(__name__) ########################################################################################################### ## DONATIONS for stash86 ## ## ## ## Real-life money : https://patreon.com/stash86 ## ## BTC: 1FghqtgGLpD9F21BNDMje4iyj4cSzVPZPb ## ## ETH (ERC20): 0x689c16451889824d3d3a79ad6fc867909dc8874d ## ## BEP20/BSC (USDT): 0x689c16451889824d3d3a79ad6fc867909dc8874d ## ## TRC20/TRON (USDT): TKMuRHJppPok3ik2siZp2SYRdBdfdSWxrt ## ## ## ## REFERRAL LINKS ## ## ## ## Binance: https://accounts.binance.com/en/register?ref=143744527 ## ## Kucoin: https://www.kucoin.com/ucenter/signup?rcode=r3BWY2T ## ## Vultr (you get $100 credit that expires in 14 days) : https://www.vultr.com/?ref=8944192-8H ## ########################################################################################################### class github_stash86_MultiMA_TSL__Cenderawasih_3_kucoin__20230807_103659 (IStrategy): def version(self) -> str: return "cend_3_kucoin" INTERFACE_VERSION = 3 # ROI table: minimal_roi = { "0": 100.0 } # Buy hyperspace params: buy_params = { "base_nb_candles_buy_vwma": 31, "low_offset_vwma": 0.989, "buy_rsi_vwma": 52, "base_nb_candles_buy_ema": 19, "low_offset_ema": 0.912, "buy_ema_length_15m": 30, } # Sell hyperspace params: sell_params = { "base_nb_candles_sell_ema": 61, "high_offset_ema": 0.942, "base_nb_candles_sell_ema2": 5, "high_offset_ema2": 0.908, "base_nb_candles_sell_ema3": 7, "high_offset_ema3": 0.947, "base_nb_candles_sell_ema4": 14, "high_offset_ema4": 1.088, } # Protection hyperspace params: protection_params = { "cooldown_lookback": 2, # value loaded from strategy } cooldown_lookback = IntParameter(2, 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 optimize_buy_ema = False base_nb_candles_buy_ema = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_ema) low_offset_ema = DecimalParameter(0.9, 0.99, default=0.9, space='buy', optimize=optimize_buy_ema) optimize_buy_vwma = False base_nb_candles_buy_vwma = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_vwma) low_offset_vwma = DecimalParameter(0.9, 0.99, default=0.9, space='buy', optimize=optimize_buy_vwma) optimize_buy_ema_length_15m = False buy_ema_length_15m = CategoricalParameter([5, 10, 15, 20, 25, 30, 35, 40], default=10, optimize=optimize_buy_ema_length_15m) buy_rsi_vwma = IntParameter(10, 70, default=50, optimize=False) optimize_sell_ema = False base_nb_candles_sell_ema = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema) high_offset_ema = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema) optimize_sell_ema2 = False base_nb_candles_sell_ema2 = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema2) high_offset_ema2 = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema2) optimize_sell_ema3 = False base_nb_candles_sell_ema3 = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema3) high_offset_ema3 = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema3) optimize_sell_ema4 = False base_nb_candles_sell_ema4 = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema4) high_offset_ema4 = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema4) # Stoploss: stoploss = -0.99 # Trailing stop: trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.01 trailing_only_offset_is_reached = True # Sell signal 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 = 999 use_custom_stoploss = True def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: sl_new = 1 if (current_profit > 0.2): sl_new = 0.05 elif (current_profit > 0.1): sl_new = 0.03 elif (current_profit > 0.06): sl_new = 0.02 elif (current_profit > 0.03): sl_new = 0.01 elif (current_profit > 0.015): sl_new = 0.005 return sl_new 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 @informative('15m') def populate_indicators_15m(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if (self.config['runmode'].value in ('hyperopt')) and self.optimize_buy_ema_length_15m: for val in self.buy_ema_length_15m.range: dataframe[f'ema_{val}'] = ta.EMA(dataframe, timeperiod=int(val)) else: dataframe[f'ema_{self.buy_ema_length_15m.value}'] = ta.EMA(dataframe, timeperiod=int(self.buy_ema_length_15m.value)) 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: # # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) if not self.optimize_buy_ema: dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value if not self.optimize_buy_vwma: dataframe['vwma_offset_buy'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma.value)) *self.low_offset_vwma.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_ema: dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value if self.optimize_buy_vwma: dataframe['vwma_offset_buy'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma.value)) *self.low_offset_vwma.value dataframe.loc[:, 'enter_tag'] = '' dataframe.loc[:, 'enter_long'] = 0 add_check = ( dataframe['live_data_ok'] & dataframe['age_filter_ok_1d'] ) buy_offset_vwma = ( ((dataframe['close'] < dataframe['vwma_offset_buy'])) & (dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['rsi'] < self.buy_rsi_vwma.value) & (dataframe['close'] < dataframe[f'ema_{self.buy_ema_length_15m.value}_15m']) ) dataframe.loc[buy_offset_vwma, 'enter_tag'] += 'vwma ' conditions.append(buy_offset_vwma) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions) & add_check, 'enter_long', ]= 1 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_cond_2 = ( (dataframe['close'] > dataframe['ema_offset_sell']) ) conditions.append(sell_cond_2) dataframe.loc[sell_cond_2, 'exit_tag'] += 'EMA_1 ' sell_cond_4 = ( (dataframe['close'] < dataframe['ema_offset_sell2']) ) conditions.append(sell_cond_4) dataframe.loc[sell_cond_4, 'exit_tag'] += 'EMA_2 ' sell_cond_3 = ( ((dataframe['close'] < dataframe['ema_offset_sell3']).rolling(2).min() > 0) ) conditions.append(sell_cond_3) dataframe.loc[sell_cond_3, 'exit_tag'] += 'EMA_3 ' sell_cond_1 = ( (dataframe['close'] > dataframe['ema_offset_sell4']).rolling(2).min() > 0 ) conditions.append(sell_cond_1) dataframe.loc[sell_cond_1, 'exit_tag'] += 'EMA_4 ' add_check = ( (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions) & add_check, 'exit_long' ] = 1 return dataframe