# Zeus Strategy: First Generation of GodStra Strategy with maximum # AVG/MID profit in USDT # Author: @Mablue (Masoud Azizi) # github: https://github.com/mablue/ # IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta) # freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus # --- Do not remove these libs --- from datetime import timedelta, datetime from typing import Optional from freqtrade import data from freqtrade.persistence import Trade from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter, IntParameter, BooleanParameter from numpy.lib import math from freqtrade.strategy.interface import IStrategy import pandas from pandas import DataFrame import time import logging import calendar from freqtrade.loggers import setup_logging from freqtrade.strategy.strategy_helper import merge_informative_pair # -------------------------------- # Add your lib to import here import ta from functools import reduce import numpy as np import talib.abstract as talib from freqtrade.strategy.strategy_helper import merge_informative_pair import freqtrade.vendor.qtpylib.indicators as qtpylib from random import shuffle logger = logging.getLogger(__name__) class Zeus_8d_2(IStrategy): # Buy hyperspace params: buy_params = { "buy_min_horizon": 23, "buy_min_max_n": 0.16, "buy_rsi": 10, "decalage": 8, } # Sell hyperspace params: sell_params = { "sell_b_RSI": 75, "sell_percent": 0.1, "sell_profit_percent": 0.1, } # Protection hyperspace params: protection_params = { "protection_nb_buy_lost": 3, "protection_percent_buy_lost": 17, "protection_start_buying_rsi_1d": 17, "protection_stop_buying_rsi_1d": 51, } # ROI table: # value loaded from strategy minimal_roi = { "0": 10 } # Stoploss: stoploss = -1.0 # value loaded from strategy # Trailing stop: trailing_stop = False # value loaded from strategy trailing_stop_positive = None # value loaded from strategy trailing_stop_positive_offset = 0.0 # value loaded from strategy trailing_only_offset_is_reached = False # value loaded from strategy # Buy hypers timeframe = '4h' stop_buying = {} # DCA config # position_adjustment_enable = True plot_config = { "main_plot": { "min200": { "color": "#86c932" }, "min50": { "color": "white" }, # "max200": { # "color": "yellow" # }, "sma3_1d": { "color": "pink" }, "sma5_1d": { "color": "blue" }, "sma10_1d": { "color": "orange" }, "close_1d": { "color": "#73e233", }, "low": { "color": "cyan", }, "bb_lowerband": { "color": "#da59a6"}, "bb_upperband": { "color": "#da59a6", } }, "subplots": { # "Ind": { # "trend_ichimoku_base": { # "color": "#dd1384" # }, # "trend_kst_diff": { # "color": "#850678" # } # }, # "BB": { # "bb_width": { # "color": "white" # }, # "bb_lower_5": { # "color": "yellow" # } # }, "Rsi": { "rsi_1d": { "color": "pink" }, # "rsi_1h": { # "color": "green" # }, "rsi5": { "color": "yellow" }, "rsi3_1d": { "color": "red" } }, # "Percent": { # "pct_change_1_1d": { # "color": "green" # }, # "pct_change_3_1d": { # "color": "orange" # }, # "pct_change_5_1d": { # "color": "red" # } # } } } buy_min_horizon = IntParameter(1, 100, default=7, space='buy') buy_rsi = IntParameter(1, 30, default=12, space='buy') buy_min_max_n = DecimalParameter(0.01, 0.2, decimals=2, default=0.05, space='buy') buy_percent = DecimalParameter(1.005, 1.02, decimals=3, default=1.01, space='buy') decalage = IntParameter(1, 12, default=6, space='buy') sell_b_RSI = IntParameter(60, 98, default=60, space='sell') sell_profit_percent = DecimalParameter(0.1, 1.5, decimals=1, default=0.8, space='sell') sell_percent = DecimalParameter(0.01, 0.30, decimals=2, default=0.05, space='sell') # protection_percent_buy_lost = IntParameter(1, 30, default=3, space='protection') # protection_nb_buy_lost = IntParameter(1, 3, default=3, space='protection') protection_stop_buying_rsi_1d = IntParameter(50, 100, default=76, space='protection') protection_start_buying_rsi_1d = IntParameter(1, 50, default=30, space='protection') # def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, # current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool: # allow_to_buy = True # # info_previous_last_candle = informative.iloc[-2].squeeze() # # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # # last_candle = dataframe.iloc[-1].squeeze() # # return allow_to_buy # def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, # current_profit: float, **kwargs): # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # last_candle = dataframe.iloc[-1].squeeze() # previous_last_candle = dataframe.iloc[-2].squeeze() # previous_previous_last_candle = dataframe.iloc[-3].squeeze() # # if (current_profit > self.buy_min_max_n.value * self.sell_profit_percent.value): # # \ # # & (previous_last_candle['rsi5'] > self.sell_b_RSI.value) \ # # & (previous_last_candle['rsi5'] > last_candle['rsi5']) \ # # & (previous_last_candle['rsi5'] > previous_previous_last_candle['rsi5']): # logger.info("Sell ==> %s ", pair + " " + str(current_time) + " " + str(current_profit) # + " " + str(self.buy_min_max_n.value * self.sell_profit_percent.value) # + " " + str(previous_previous_last_candle['rsi5']) + " " + str( # previous_last_candle['rsi5']) + " " + str(last_candle['rsi5']) # ) # return 'profit_1' # + str(self.sell_percent.value) # # return None def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Add all ta features dataframe['pct_change'] = dataframe['close'].pct_change(5) dataframe['min'] = talib.MIN(dataframe['close'], timeperiod=self.buy_min_horizon.value) # dataframe['min10'] = talib.MIN(dataframe['close'], timeperiod=10) # dataframe['min20'] = talib.MIN(dataframe['close'], timeperiod=20) # dataframe['min50'] = talib.MIN(dataframe['close'], timeperiod=50) dataframe['min200'] = talib.MIN(dataframe['close'], timeperiod=200) # dataframe['min200_1'] = dataframe['min200'] * 1.005 # dataframe['moy200_12'] = dataframe['min200'].rolling(12).mean() dataframe['max50'] = talib.MAX(dataframe['close'], timeperiod=50) dataframe['max200'] = talib.MAX(dataframe['close'], timeperiod=200) dataframe['min_max200'] = (dataframe['max200'] - dataframe['min200']) / dataframe['min200'] dataframe['min_n'] = talib.MIN(dataframe['close'], timeperiod=self.buy_min_horizon.value) dataframe['max_n'] = talib.MAX(dataframe['close'], timeperiod=self.buy_min_horizon.value) dataframe['min_max_n'] = (dataframe['max_n'] - dataframe['min_n']) / dataframe['min_n'] dataframe['rsi'] = talib.RSI(dataframe) dataframe['rsi5'] = talib.RSI(dataframe, timeperiod=5) dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5) # dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10) # dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20) # dataframe['sma50'] = talib.SMA(dataframe, timeperiod=50) # dataframe['sma100'] = talib.SMA(dataframe, timeperiod=100) dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"] dataframe["percent5"] = dataframe["percent"].rolling(5).sum() # dataframe["percent3"] = dataframe["percent"].rolling(3).sum() dataframe["percent10"] = dataframe["percent"].rolling(10).sum() # dataframe["percent20"] = dataframe["percent"].rolling(20).sum() # dataframe["percent50"] = dataframe["percent"].rolling(50).sum() # dataframe['percent_lost_n'] = dataframe["percent"].rolling(self.protection_lost_candles.value).sum() # dataframe["volume10"] = dataframe["volume"].rolling(10).mean() # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] ) dataframe['distance_min'] = (dataframe['close'] - dataframe['min']) / dataframe['close'] dataframe['min1.1'] = 1.01 * dataframe['min'] dataframe['normal'] = 100 * (dataframe['close'] / dataframe['close'].rolling(200).mean()) dataframe['min_max_close'] = ( (dataframe['max200'] - dataframe['close']) / (dataframe['close'] - dataframe['min200'])) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['rsi5'] < self.protection_stop_buying_rsi_1d.value) & (dataframe['rsi5'] > self.protection_start_buying_rsi_1d.value) # & (dataframe['rsi5'].shift(1) < self.buy_rsi.value) # & (dataframe['rsi5'].shift(1) < dataframe['rsi5']) # & (dataframe['rsi5'].shift(1) < dataframe['rsi5'].shift(2)) & (dataframe['min_n'].shift(self.decalage.value) == dataframe['min_n']) & (dataframe['min_max_n'] >= self.buy_min_max_n.value) & (dataframe['close'] <= dataframe['min_n'] * self.buy_percent.value) ), ['buy', 'buy_tag']] = (1, 'buy_adx_inf') return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe