# source: https://raw.githubusercontent.com/stash86/MultiMA_TSL/a14d2844b9fe8cd260770ffde4e5a71d27338194/user_data/strategies/Cenderawasih_kucoin/Cenderawasih_2_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 from technical.indicators import zema import talib.abstract as ta import math import pandas_ta as pta import logging from logging import FATAL import time logger = logging.getLogger(__name__) ########################################################################################################### ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ## ## ########################################################################################################### ## 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 ## ########################################################################################################### 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) -> 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['close'], math.floor(length / 2)) - tv_wma(dataframe['close'], length) tv_hma = tv_wma(h, math.floor(math.sqrt(length))) # dataframe.drop("h", inplace=True, axis=1) return tv_hma def rvol(dataframe, window=24): av = ta.SMA(dataframe['volume'], timeperiod=int(window)) rvol = dataframe['volume'] / av return rvol # patreon class github_stash86_MultiMA_TSL__Cenderawasih_2_kucoin__20230807_103659 (IStrategy): def version(self) -> str: return "v2_kucoin" INTERFACE_VERSION = 3 # ROI table: minimal_roi = { "0": 100.0 } # Buy hyperspace params: buy_params = { "base_nb_candles_buy_vwma": 44, "low_offset_vwma": 0.931, } # Sell hyperspace params: sell_params = { "base_nb_candles_sell_ema": 58, "high_offset_ema": 0.951, "base_nb_candles_sell_ema2": 5, "high_offset_ema2": 0.908, "base_nb_candles_sell_ema3": 49, "high_offset_ema3": 0.914, } # 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 dummy = IntParameter(20, 70, default=61, space='buy', optimize=False) # rsi_buy_ema = IntParameter(20, 70, default=61, space='buy', optimize=False) # buy_rsi_1 = IntParameter(0, 70, default=50, optimize=False) # buy_rsi_fast_1 = IntParameter(0, 70, default=50, optimize=False) # optimize_buy_hma = False # base_nb_candles_buy_hma = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_hma) # low_offset_hma = DecimalParameter(0.9, 0.99, default=0.95, space='buy', optimize=optimize_buy_hma) # optimize_buy_hma2 = False # base_nb_candles_buy_hma2 = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_hma2) # low_offset_hma2 = DecimalParameter(0.9, 0.99, default=0.95, space='buy', optimize=optimize_buy_hma2) # 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, 1.1, default=1, space='buy', optimize=optimize_buy_ema) # optimize_buy_ema2 = False # base_nb_candles_buy_ema2 = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_ema2) # low_offset_ema2 = DecimalParameter(0.9, 1.1, default=1, space='buy', optimize=optimize_buy_ema2) 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_vwma_2 = False # base_nb_candles_buy_vwma_2 = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_vwma_2) # low_offset_vwma_2 = DecimalParameter(0.9, 0.99, default=0.9, space='buy', optimize=optimize_buy_vwma_2) # optimize_buy_vwma_3 = False # base_nb_candles_buy_vwma_3 = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_vwma_3) # low_offset_vwma_3 = DecimalParameter(0.9, 0.99, default=0.9, space='buy', optimize=optimize_buy_vwma_3) # optimize_buy_vwma_4 = False # base_nb_candles_buy_vwma_4 = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_vwma_4) # low_offset_vwma_4 = DecimalParameter(0.9, 0.99, default=0.9, space='buy', optimize=optimize_buy_vwma_4) # optimize_buy_vwma2 = False # base_nb_candles_buy_vwma2 = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_vwma2) # low_offset_vwma2 = DecimalParameter(0.9, 0.99, default=0.9, space='buy', optimize=optimize_buy_vwma2) # 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=optimize_buy_volatility) # buy_max_volatility = DecimalParameter(0.5, 2, default=1, decimals = 2, space='buy', optimize=optimize_buy_volatility) # optimize_buy_volatility_2 = True # buy_length_volatility_2 = IntParameter(10, 200, default=72, space='buy', optimize=optimize_buy_volatility_2) # buy_min_volatility_2 = DecimalParameter(0, 0.2, default=0, decimals = 2, space='buy', optimize=optimize_buy_volatility_2) # buy_max_volatility_2 = DecimalParameter(0.5, 2, default=1, decimals = 1, space='buy', optimize=optimize_buy_volatility_2) # optimize_buy_volatility_hma = False # buy_length_volatility_hma = IntParameter(10, 200, default=72, space='buy', optimize=optimize_buy_volatility_hma) # buy_min_volatility_hma = DecimalParameter(0, 0.5, default=0, decimals = 2, space='buy', optimize=optimize_buy_volatility_hma) # buy_max_volatility_hma = DecimalParameter(0.5, 2, default=1, decimals = 2, space='buy', optimize=optimize_buy_volatility_hma) # optimize_buy_volatility2 = False # buy_length_volatility2 = IntParameter(10, 200, default=72, space='buy', optimize=optimize_buy_volatility2) # buy_min_volatility2 = DecimalParameter(0, 0.5, default=0, decimals = 2, space='buy', optimize=False) # buy_max_volatility2 = DecimalParameter(0.5, 2, default=1, decimals = 2, space='buy', optimize=optimize_buy_volatility2) # optimize_buy_volume = False # buy_length_volume = IntParameter(5, 100, default=6, optimize=optimize_buy_volume) # buy_volume_volatility = DecimalParameter(0.5, 3, default=1, decimals=2, optimize=optimize_buy_volume) # buy_rsi_vwma = IntParameter(10, 70, default=50, optimize=False) # buy_rsi4_vwma = IntParameter(10, 70, default=50, optimize=False) # buy_rsx_vwma = IntParameter(20, 70, default=61, optimize=False) # buy_rsx4_vwma = IntParameter(20, 70, default=61, optimize=False) # optimize_rsi_rsx_vwma_2 = False # buy_rsi_vwma_2 = IntParameter(10, 70, default=50, optimize=optimize_rsi_rsx_vwma_2) # buy_rsi4_vwma_2 = IntParameter(10, 70, default=50, optimize=optimize_rsi_rsx_vwma_2) # buy_rsx_vwma_2 = IntParameter(20, 70, default=61, optimize=optimize_rsi_rsx_vwma_2) # buy_rsx4_vwma_2 = IntParameter(20, 70, default=61, optimize=optimize_rsi_rsx_vwma_2) # optimize_rsi_rsx_hma = False # buy_rsi_hma = IntParameter(10, 70, default=50, optimize=optimize_rsi_rsx_hma) # buy_rsi4_hma = IntParameter(10, 70, default=50, optimize=optimize_rsi_rsx_hma) # buy_rsx_hma = IntParameter(20, 70, default=61, optimize=optimize_rsi_rsx_hma) # buy_rsx4_hma = IntParameter(20, 70, default=61, optimize=optimize_rsi_rsx_hma) # optimize_2_stars = False # buy_rsx_2_stars = IntParameter(10, 70, default=61, optimize=optimize_2_stars) # optimize_3_stars = False # buy_rsx_3_stars = IntParameter(10, 70, default=61, optimize=optimize_3_stars) # optimize_4_stars = False # buy_rsx_4_stars = IntParameter(10, 70, default=61, optimize=optimize_4_stars) # optimize_5_stars = False # buy_rsx_5_stars = IntParameter(10, 70, default=61, optimize=optimize_5_stars) # buy_rsx_hma = IntParameter(20, 70, default=61, optimize=False) # buy_rsx4_hma = IntParameter(20, 70, default=61, optimize=False) # Sell # optimize_sell_hma = False # base_nb_candles_sell_hma = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_hma) # high_offset_hma = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_hma) 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) # 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 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) 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_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 return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] 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[:, 'buy'] = 0 add_check = ( dataframe['live_data_ok'] & dataframe['age_filter_ok_1d'] ) buy_offset_vwma = ( ((dataframe['close'] < dataframe['vwma_offset_buy'])) ) 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, 'buy', ]= 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # if self.optimize_sell_hma: # dataframe['hma_offset_sell'] = tv_hma(dataframe, int(self.base_nb_candles_sell_hma.value)) *self.high_offset_hma.value 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 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 ' add_check = ( (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions) & add_check, 'sell' ] = 1 return dataframe