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Jérôme Delacotte
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Zeus_8_3_3_3.py
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756
Zeus_8_3_3_3.py
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# Zeus Strategy: First Generation of GodStra Strategy with maximum
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# AVG/MID profit in USDT
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
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# --- Do not remove these libs ---
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from datetime import timedelta, datetime
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from typing import Optional
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from freqtrade import data
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from freqtrade.persistence import Trade
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from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter, IntParameter, BooleanParameter
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from numpy.lib import math
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from freqtrade.strategy.interface import IStrategy
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import pandas
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from pandas import DataFrame
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import time
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import logging
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import calendar
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from freqtrade.loggers import setup_logging
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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# --------------------------------
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# Add your lib to import here
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import ta
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from functools import reduce
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import numpy as np
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import talib.abstract as talib
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from random import shuffle
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logger = logging.getLogger(__name__)
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class Zeus_8_3_3_3(IStrategy):
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# ROI table:
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minimal_roi = {
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"0": 0.564,
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"567": 0.273,
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"2814": 0.12,
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"7675": 0
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}
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# Stoploss:
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stoploss = -1 #0.256
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# Buy hypers
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timeframe = '5m'
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market_overview = {'up': 0, 'down': 0}
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market_overview_pct5 = 0
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market_overview_pct1 = 0
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max_open_trades = 5
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max_amount = 40
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stop_buy_for_all = False
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stop_buying = {}
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# DCA config
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position_adjustment_enable = True
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#max_dca_orders = 2 # n - 1
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max_dca_multiplier = 7 # (2^n - 1)
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dca_trigger = 0
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plot_config = {
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"main_plot": {
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"min200": {
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"color": "#86c932"
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},
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"min50": {
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"color": "white"
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},
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# "max200": {
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# "color": "yellow"
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# },
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"sma3_1d": {
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"color": "pink"
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},
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"sma5_1d": {
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"color": "blue"
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},
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"sma10_1d": {
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"color": "orange"
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},
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"close_1d": {
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"color": "#73e233",
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},
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"low": {
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"color": "cyan",
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},
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"bb_lowerband": {
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"color": "#da59a6"},
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"bb_upperband": {
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"color": "#da59a6",
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}
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},
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"subplots": {
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# "Ind": {
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# "trend_ichimoku_base": {
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# "color": "#dd1384"
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# },
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# "trend_kst_diff": {
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# "color": "#850678"
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# }
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# },
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# "BB": {
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# "bb_width": {
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# "color": "white"
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# },
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# "bb_lower_5": {
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# "color": "yellow"
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# }
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# },
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"Rsi": {
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"rsi_1d": {
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"color": "pink"
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},
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# "rsi_1h": {
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# "color": "green"
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# },
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"rsi5": {
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"color": "yellow"
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},
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"rsi3_1d": {
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"color": "red"
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}
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},
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# "Percent": {
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# "pct_change_1_1d": {
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# "color": "green"
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# },
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# "pct_change_3_1d": {
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# "color": "orange"
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# },
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# "pct_change_5_1d": {
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# "color": "red"
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# }
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# }
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}
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}
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trades = list()
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buy_min_horizon = IntParameter(50, 200, default=72, space='buy')
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buy_rsi = IntParameter(20, 90, default=72, space='buy')
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buy_bb_width_n = DecimalParameter(1, 10, decimals=1, default=5, space='buy')
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buy_min_max_nh = IntParameter(1, 48, default=24, space='buy')
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buy_min_max_n = DecimalParameter(0, 0.2, decimals=2, default=0.05, space='buy')
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buy_min_max_rsi = IntParameter(50, 90, default=72, space='buy')
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buy_min_max_coef = DecimalParameter(1, 1.01, decimals=3, default=1.002, space='buy')
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buy_min_max_decalage = IntParameter(2, 10, default=2, space='buy')
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profit_b_no_change = BooleanParameter(default=True, space="sell")
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profit_b_quick_lost = BooleanParameter(default=True, space="sell")
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profit_b_sma5 = BooleanParameter(default=True, space="sell")
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profit_b_sma10 = BooleanParameter(default=True, space="sell")
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profit_b_sma20 = BooleanParameter(default=True, space="sell")
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profit_b_quick_gain = BooleanParameter(default=True, space="sell")
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profit_b_quick_gain_3 = BooleanParameter(default=True, space="sell")
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profit_b_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_b_very_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_b_over_rsi = BooleanParameter(default=True, space="sell")
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profit_b_short_loss = BooleanParameter(default=True, space="sell")
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sell_b_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_b_percent3 = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_b_candels = IntParameter(0, 48, default=12, space='sell')
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sell_b_too_old_day = IntParameter(0, 10, default=5, space='sell')
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sell_b_too_old_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_b_profit_no_change = DecimalParameter(0, 0.02, decimals=3, default=0.005, space='sell')
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sell_b_profit_percent10 = DecimalParameter(0, 0.002, decimals=4, default=0.001, space='sell')
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sell_b_RSI = IntParameter(70, 98, default=88, space='sell')
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sell_b_RSI2 = IntParameter(70, 98, default=88, space='sell')
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sell_b_RSI3 = IntParameter(70, 98, default=80, space='sell')
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sell_b_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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# sell_b_expected_profit = DecimalParameter(0, 0.01, decimals=3, default=0.01, space='sell')
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profit_h_no_change = BooleanParameter(default=True, space="sell")
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profit_h_quick_lost = BooleanParameter(default=True, space="sell")
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profit_h_sma5 = BooleanParameter(default=True, space="sell")
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profit_h_sma10 = BooleanParameter(default=True, space="sell")
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profit_h_sma20 = BooleanParameter(default=True, space="sell")
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profit_h_quick_gain = BooleanParameter(default=True, space="sell")
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profit_h_quick_gain_3 = BooleanParameter(default=True, space="sell")
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profit_h_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_h_very_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_h_over_rsi = BooleanParameter(default=True, space="sell")
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profit_h_short_loss = BooleanParameter(default=True, space="sell")
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sell_h_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_h_percent3 = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_h_candels = IntParameter(0, 48, default=12, space='sell')
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sell_h_too_old_day = IntParameter(0, 10, default=5, space='sell')
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sell_h_too_old_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_h_profit_no_change = DecimalParameter(0, 0.02, decimals=3, default=0.005, space='sell')
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sell_h_profit_percent10 = DecimalParameter(0, 0.002, decimals=4, default=0.001, space='sell')
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sell_h_RSI = IntParameter(70, 98, default=88, space='sell')
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sell_h_RSI2 = IntParameter(70, 98, default=88, space='sell')
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sell_h_RSI3 = IntParameter(70, 98, default=80, space='sell')
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sell_h_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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protection_percent_buy_lost = IntParameter(1, 30, default=3, space='protection')
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protection_nb_buy_lost = IntParameter(1, 3, default=3, space='protection')
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def calc_profit(self, price: float, current: float) -> float:
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fee = 1.0007
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profit = ((current*fee) -
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(price*fee))
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return float(f"{profit:.8f}")
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
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current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
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allow_to_buy = True
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# info_previous_last_candle = informative.iloc[-2].squeeze()
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if self.stop_buying.get(pair, None) is None:
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print("enable buying tag", pair)
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self.stop_buying[pair] = False
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informative, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
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info_last_candle = informative.iloc[-1].squeeze()
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if ((info_last_candle['rsi_1h'] >= 76) & (info_last_candle['close_1h'] >= info_last_candle['bb_upperband_1h'])) \
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& (self.stop_buying[pair] is False):
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logger.info("1 - Disable buying %s date %s", pair, info_last_candle['date'])
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self.stop_buying[pair] = True
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if self.stop_buying[pair] is True:
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if ((info_last_candle['rsi_1h'] <= 35) | (info_last_candle['close_1h'] < info_last_candle['bb_lowerband_1h'])):
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logger.info("2 - Enable buying %s date %s", pair, info_last_candle['date'])
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self.stop_buying[pair] = False
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if self.stop_buying[pair]:
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allow_to_buy = False
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logger.info("3 - cancel buying %s date %s", pair, info_last_candle['date'])
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else:
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logger.info("3 - accept buying %s date %s", pair, info_last_candle['date'])
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return allow_to_buy
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def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
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current_profit: float, **kwargs):
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = dataframe.iloc[-1].squeeze()
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previous_last_candle = dataframe.iloc[-2].squeeze()
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previous_previous_last_candle = dataframe.iloc[-2].squeeze()
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previous_5_candle = dataframe.iloc[-5].squeeze()
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return (previous_last_candle['rsi5'] > 60) \
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& (previous_last_candle['rsi5'] >= last_candle['rsi5']) \
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& (previous_last_candle['rsi5'] >= previous_previous_last_candle['rsi5']) \
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& (last_candle['close'] > last_candle['close_1d']) & (last_candle['percent'] < 0)
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# expected_profit = 0.01
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# #print(last_candle['buy_tag'])
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#
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# days = (current_time - trade.open_date_utc).days
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# ######
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#
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# if (last_candle['mrsi3_1h'] < previous_last_candle['mrsi3_1h']): #(self.market_overview_pct5 < 0) | (last_candle['pct_change_1_4h'] < 0):
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# max_percent = 0.004 # last_candle['bb_width'] / 3.5 # 0.005
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# max_profit = 0.004 # last_candle['bb_width'] * 3 / 4 # 0.015
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#
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# if (current_profit > 0.01) & \
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# (last_candle['percent10'] < -0.005) & ((current_time - trade.open_date_utc).seconds >= 3600):
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# return 'b_percent10'
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# if (current_profit > max_profit) & \
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# ((last_candle['percent'] < - max_percent) | (last_candle['percent3'] < -max_percent) | (
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# last_candle['percent5'] < -max_percent)):
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# return 'b_percent_quick'
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#
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# if (current_profit >= - self.sell_b_too_old_percent.value) & (days >= self.sell_b_too_old_day.value)\
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# & (days < self.sell_b_too_old_day.value * 2)\
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# & (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
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# return "b_too_old_0.01"
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# if (current_profit >= - self.sell_b_too_old_percent.value * 2) & (days >= self.sell_b_too_old_day.value * 2)\
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# & (days < self.sell_b_too_old_day.value * 3) \
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# & (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
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# return "b_too_old_0.02"
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# if (current_profit >= - self.sell_b_too_old_percent.value * 3) & (days >= self.sell_b_too_old_day.value * 3) \
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# & (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
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# return "b_too_old_0.03"
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#
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# if self.profit_b_quick_lost.value and (current_profit >= max_profit) & (
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# last_candle['percent3'] < -max_percent):
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# return "b_quick_lost"
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#
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# if self.profit_b_no_change.value and (current_profit > self.sell_b_profit_no_change.value) \
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# & (last_candle['percent10'] < self.sell_b_profit_percent10.value) & (last_candle['percent5'] < 0) \
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# & ((current_time - trade.open_date_utc).seconds >= 3600):
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# return "b_no_change"
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#
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# if (current_profit > self.sell_b_percent.value) & (last_candle['percent3'] < - self.sell_b_percent3.value) \
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# & ((current_time - trade.open_date_utc).seconds <= 300 * self.sell_b_candels.value):
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# return "b_quick_gain_param"
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#
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# if self.profit_b_sma5.value:
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# if (current_profit > expected_profit) \
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# & ((previous_5_candle['sma5'] > last_candle['sma5']) \
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# | (last_candle['percent3'] < -expected_profit) | (
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# last_candle['percent5'] < -expected_profit)) \
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# & ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
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# # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'b_sma5'
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#
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# if self.profit_b_sma10.value:
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# if (current_profit > expected_profit) \
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# & ((previous_5_candle['sma10'] > last_candle['sma10']) \
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# | (last_candle['percent3'] < -expected_profit) | (
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# last_candle['percent5'] < -expected_profit)) \
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# & ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
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# # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'b_sma10'
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#
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# if self.profit_b_sma20.value:
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# if (current_profit > max_percent) \
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# & (previous_last_candle['sma10'] > last_candle['sma10']) \
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# & ((current_time - trade.open_date_utc).seconds >= 3600) \
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# & ((previous_last_candle['sma20'] > last_candle['sma20']) &
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# ((last_candle['percent5'] < 0) | (last_candle['percent10'] < 0) | (
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# last_candle['percent20'] < 0))):
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# # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'b_sma20'
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#
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# if self.profit_b_over_rsi.value:
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# if (current_profit > 0) & (previous_last_candle[
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# 'rsi'] > self.sell_b_RSI.value): # & (last_candle['percent'] < 0): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
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# # print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'b_over_rsi'
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#
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# if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_b_RSI2.value) & \
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# (last_candle[
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# 'percent'] < - self.sell_b_RSI2_percent.value): # | (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
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# # print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'b_over_rsi_2'
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#
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# if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_b_RSI3.value) & \
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# (last_candle['close'] >= last_candle['max200']) & (last_candle[
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# 'percent'] < - self.sell_b_RSI2_percent.value): # | (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
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# # print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'b_over_rsi_max'
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||||
#
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||||
# if self.profit_b_short_loss.value:
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||||
# if (current_profit > -expected_profit) & (previous_last_candle['percent10'] > 0.04) & (
|
||||
# last_candle['percent'] < 0) \
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||||
# & (days >= 1): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
||||
# # print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
# return 'b_short_lost'
|
||||
# else:
|
||||
# max_percent = 0.005 # last_candle['bb_width'] / 3.5 # 0.005
|
||||
# max_profit = 0.01 # last_candle['bb_width'] * 3 / 4 # 0.015
|
||||
#
|
||||
# if (current_profit > max_profit) & (
|
||||
# (last_candle['percent'] < -max_percent) | (last_candle['percent3'] < -max_percent) | (
|
||||
# last_candle['percent5'] < -max_percent)):
|
||||
# return 'h_percent_quick'
|
||||
#
|
||||
# # if (last_candle['bb_width'] < 0.02) & (current_profit > 0) & (last_candle['close'] > bb_width_up) & \
|
||||
# # ((last_candle['percent'] < - bb_width_lim) | (last_candle['percent3'] < - bb_width_lim) | (last_candle['percent5'] < - bb_width_lim)):
|
||||
# # return 'h_bb_width_max'
|
||||
#
|
||||
# if (current_profit >= - self.sell_h_too_old_percent.value) & (days >= self.sell_h_too_old_day.value)\
|
||||
# & (days < self.sell_h_too_old_day.value * 2)\
|
||||
# & (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
|
||||
# return "h_too_old_0.01"
|
||||
# if (current_profit >= - self.sell_h_too_old_percent.value * 2) & (days >= self.sell_h_too_old_day.value * 2)\
|
||||
# & (days < self.sell_h_too_old_day.value * 3) \
|
||||
# & (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
|
||||
# return "h_too_old_0.02"
|
||||
# if (current_profit >= - self.sell_h_too_old_percent.value * 3) & (days >= self.sell_h_too_old_day.value * 3) \
|
||||
# & (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
|
||||
# return "h_too_old_0.03"
|
||||
#
|
||||
# if self.profit_h_quick_lost.value and (current_profit >= max_profit) & (
|
||||
# last_candle['percent3'] < -max_percent):
|
||||
# return "h_quick_lost"
|
||||
#
|
||||
# if self.profit_h_no_change.value and (current_profit > self.sell_h_profit_no_change.value) \
|
||||
# & (last_candle['percent10'] < self.sell_h_profit_percent10.value) & (last_candle['percent5'] < 0) \
|
||||
# & ((current_time - trade.open_date_utc).seconds >= 3600):
|
||||
# return "h_no_change"
|
||||
#
|
||||
# if (current_profit > self.sell_h_percent.value) & (last_candle['percent3'] < - self.sell_h_percent3.value) \
|
||||
# & ((current_time - trade.open_date_utc).seconds <= 300 * self.sell_h_candels.value):
|
||||
# return "h_quick_gain_param"
|
||||
#
|
||||
# if self.profit_h_sma5.value:
|
||||
# if (current_profit > expected_profit) \
|
||||
# & ((previous_5_candle['sma5'] > last_candle['sma5']) \
|
||||
# | (last_candle['percent3'] < -expected_profit) | (
|
||||
# last_candle['percent5'] < -expected_profit)) \
|
||||
# & ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
|
||||
# # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
# return 'h_sma5'
|
||||
#
|
||||
# if self.profit_h_sma10.value:
|
||||
# if (current_profit > expected_profit) \
|
||||
# & ((previous_5_candle['sma10'] > last_candle['sma10']) \
|
||||
# | (last_candle['percent3'] < -expected_profit) | (
|
||||
# last_candle['percent5'] < -expected_profit)) \
|
||||
# & ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
|
||||
# # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
# return 'h_sma10'
|
||||
#
|
||||
# if self.profit_h_sma20.value:
|
||||
# if (current_profit > max_percent) \
|
||||
# & (previous_last_candle['sma10'] > last_candle['sma10']) \
|
||||
# & ((current_time - trade.open_date_utc).seconds >= 3600) \
|
||||
# & ((previous_last_candle['sma20'] > last_candle['sma20']) &
|
||||
# ((last_candle['percent5'] < 0) | (last_candle['percent10'] < 0) | (
|
||||
# last_candle['percent20'] < 0))):
|
||||
# # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
# return 'h_sma20'
|
||||
#
|
||||
# if self.profit_h_over_rsi.value:
|
||||
# if (current_profit > 0) & (previous_last_candle[
|
||||
# 'rsi'] > self.sell_h_RSI.value): # & (last_candle['percent'] < 0): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
||||
# # print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
# return 'h_over_rsi'
|
||||
#
|
||||
# if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_h_RSI2.value) & \
|
||||
# (last_candle[
|
||||
# 'percent'] < - self.sell_h_RSI2_percent.value): # | (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
||||
# # print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
# return 'h_over_rsi_2'
|
||||
#
|
||||
# if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_h_RSI3.value) & \
|
||||
# (last_candle['close'] >= last_candle[
|
||||
# 'max200']): # | (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
||||
# # print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
# return 'h_over_rsi_max'
|
||||
#
|
||||
# if self.profit_h_short_loss.value:
|
||||
# if (current_profit > -expected_profit) & (previous_last_candle['percent10'] > 0.04) & (
|
||||
# last_candle['percent'] < 0) \
|
||||
# & (days >= 1): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
||||
# # print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
||||
# return 'h_short_lost'
|
||||
|
||||
def informative_pairs(self):
|
||||
# get access to all pairs available in whitelist.
|
||||
pairs = self.dp.current_whitelist()
|
||||
informative_pairs = [(pair, '1d') for pair in pairs]
|
||||
# informative_pairs += [(pair, '4h') for pair in pairs]
|
||||
informative_pairs += [(pair, '1h') for pair in pairs]
|
||||
|
||||
return informative_pairs
|
||||
|
||||
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=12 * self.buy_min_max_nh.value)
|
||||
dataframe['max_n'] = talib.MAX(dataframe['close'], timeperiod=12 * self.buy_min_max_nh.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['bb_lower_var_5'] = (dataframe['bb_lowerband'] - dataframe['min50']).rolling(5).var()
|
||||
dataframe['bb_lower_5'] = 100 * ((dataframe['bb_lowerband'].rolling(5).mean() / dataframe['bb_lowerband']) - 1)
|
||||
dataframe['bb_lower_width_5'] = (dataframe['bb_lowerband'] * (1 + dataframe['bb_width'] / self.buy_bb_width_n.value))
|
||||
|
||||
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']))
|
||||
|
||||
# profit = False
|
||||
# profit_percent = False
|
||||
# percent_lower = False
|
||||
# current_price = dataframe['close'].iloc[-1]
|
||||
#
|
||||
# dataframe['should_sell'] = False
|
||||
# dataframe['should_buy'] = False
|
||||
#
|
||||
# # Get the previous trade
|
||||
# trade = Trade.get_trades_proxy(is_open=False, pair=metadata['pair'])
|
||||
# if trade:
|
||||
# trade = trade[-1]
|
||||
# lsp = trade.close_rate
|
||||
# if lsp:
|
||||
# percent_lower = self.calc_percentage_lower(price=lsp, current=current_price)
|
||||
# # Found a bug? When force selling it doesnt close it
|
||||
# else:
|
||||
# lsp = trade.open_rate
|
||||
# if lsp:
|
||||
# percent_lower = self.calc_percentage_lower(price=lsp, current=current_price)
|
||||
# else:
|
||||
# lsp = 0.00
|
||||
#
|
||||
# # Get the current Trade
|
||||
# trade = Trade.get_trades_proxy(is_open=True, pair=metadata['pair'])
|
||||
# if trade:
|
||||
# trade = trade[-1]
|
||||
# lbp = trade.open_rate
|
||||
# open_trade = True
|
||||
# profit = self.calc_profit(price=lbp, current=current_price)
|
||||
# profit_percent = (profit/lbp)*100
|
||||
# else:
|
||||
# lbp = 0.00
|
||||
# open_trade = False
|
||||
# profit = False
|
||||
# profit_percent = False
|
||||
#
|
||||
# print("------------")
|
||||
#
|
||||
# print("Last Sold For:", lsp)
|
||||
#
|
||||
# if open_trade:
|
||||
# print("Bought for: ", lbp)
|
||||
# print("Current Price: ", current_price)
|
||||
# if profit:
|
||||
# print("Current Profit: ", profit, " ", float(f"{profit_percent:.8f}"), "%")
|
||||
# if percent_lower and not open_trade:
|
||||
# print("Percent Lower: ", float(f"{percent_lower:.8f}"), "%")
|
||||
#
|
||||
# # Should we Sell?
|
||||
# if profit_percent:
|
||||
# if profit_percent > 1:
|
||||
# dataframe['should_sell'] = True
|
||||
#
|
||||
# # Should we buy?
|
||||
# if not open_trade:
|
||||
# if (lsp == 0.00 ) & (lbp == 0.00):
|
||||
# dataframe['should_buy'] = True
|
||||
# # Is the percentage of what we sold for and the current price 2% lower
|
||||
# if percent_lower > 2:
|
||||
# dataframe['should_buy'] = True
|
||||
#
|
||||
# dataframe['last_sell_price'] = lsp
|
||||
# dataframe['last_buy_price'] = lbp
|
||||
|
||||
################### INFORMATIVE 1D
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
|
||||
informative["rsi"] = talib.RSI(informative)
|
||||
informative["rsi3"] = talib.RSI(informative, 3)
|
||||
informative["mrsi3"] = informative["rsi"].rolling(3).mean()
|
||||
informative["max3"] = talib.MAX(informative['close'], timeperiod=3)
|
||||
informative["min3"] = talib.MIN(informative['close'], timeperiod=3)
|
||||
informative['pct_change_1'] = informative['close'].pct_change(1)
|
||||
informative['pct_change_3'] = informative['close'].pct_change(3)
|
||||
informative['pct_change_5'] = informative['close'].pct_change(5)
|
||||
informative['sma3'] = talib.SMA(informative, timeperiod=3)
|
||||
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
||||
informative['sma10'] = talib.SMA(informative, timeperiod=10)
|
||||
informative['sar'] = talib.SAR(informative)
|
||||
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
|
||||
informative['bb_lowerband'] = bollinger['lower']
|
||||
informative['bb_middleband'] = bollinger['mid']
|
||||
informative['bb_upperband'] = bollinger['upper']
|
||||
informative["bb_percent"] = (
|
||||
(informative["close"] - informative["bb_lowerband"]) /
|
||||
(informative["bb_upperband"] - informative["bb_lowerband"])
|
||||
)
|
||||
informative["bb_width"] = (
|
||||
(informative["bb_upperband"] - informative["bb_lowerband"]) / informative["bb_middleband"]
|
||||
)
|
||||
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
||||
|
||||
# ######################## INFORMATIVE 4h
|
||||
# informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="4h")
|
||||
# informative["rsi"] = talib.RSI(informative)
|
||||
# informative['pct_change_1'] = informative['close'].pct_change(1)
|
||||
# informative['pct_change_3'] = informative['close'].pct_change(3)
|
||||
# informative['pct_change_5'] = informative['close'].pct_change(5)
|
||||
# informative['sma3'] = talib.SMA(informative, timeperiod=3)
|
||||
# informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
||||
# informative['sma10'] = talib.SMA(informative, timeperiod=10)
|
||||
# dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "4h", ffill=True)
|
||||
|
||||
######################## INFORMATIVE 1h
|
||||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
|
||||
informative["rsi"] = talib.RSI(informative)
|
||||
informative["rsi3"] = talib.RSI(informative, 3)
|
||||
informative["mrsi3"] = informative["rsi"].rolling(3).mean()
|
||||
informative['pct_change_1'] = informative['close'].pct_change(1)
|
||||
informative['pct_change_3'] = informative['close'].pct_change(3)
|
||||
informative['pct_change_5'] = informative['close'].pct_change(5)
|
||||
informative['sma3'] = talib.SMA(informative, timeperiod=3)
|
||||
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
||||
informative['sma10'] = talib.SMA(informative, timeperiod=10)
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
|
||||
informative['bb_lowerband'] = bollinger['lower']
|
||||
informative['bb_middleband'] = bollinger['mid']
|
||||
informative['bb_upperband'] = bollinger['upper']
|
||||
|
||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
# (dataframe['min_max_n'] >= self.buy_min_max_n.value)
|
||||
(dataframe['rsi5'].shift(1) < 12)
|
||||
& (dataframe['rsi5'].shift(1) < dataframe['rsi5'])
|
||||
& (dataframe['rsi5'].shift(1) < dataframe['rsi5'].shift(2))
|
||||
# & (dataframe['close'] < dataframe['min_n'] * self.buy_min_max_coef.value)
|
||||
#& (dataframe['min_n'].shift(self.buy_min_max_decalage.value) == dataframe['min_n'])
|
||||
# & (dataframe['pct_change_1_1d'] > 0)
|
||||
& (dataframe['close'] <= dataframe['close_1d'])
|
||||
& (dataframe['close_1d'] <= dataframe['sma3_1d'])
|
||||
& (dataframe['close_1d'] <= dataframe['sma5_1d'])
|
||||
& (dataframe['close_1d'] <= dataframe['sma10_1d'])
|
||||
& (dataframe['low'] <= dataframe['min200'])
|
||||
& (dataframe['min'].shift(2) == dataframe['min50'])
|
||||
# & (dataframe['rsi3_1d'].shift(288) <= dataframe['rsi3_1d'])
|
||||
), ['buy', 'buy_tag']] = (1, 'buy_rsi5')
|
||||
|
||||
return dataframe
|
||||
|
||||
# def bot_loop_start(self, **kwargs) -> None:
|
||||
# pairs = self.dp.current_whitelist()
|
||||
# print("Calcul des pairs informatives")
|
||||
# for pairname in pairs:
|
||||
# self.stop_buying[pairname] = True
|
||||
# print("Fin Calcul des pairs informatives")
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# (dataframe['rsi5'].shift(1) > 60)
|
||||
# & (dataframe['rsi5'].shift(1) >= dataframe['rsi5'])
|
||||
# & (dataframe['rsi5'].shift(1) >= dataframe['rsi5'].shift(2))
|
||||
# & (dataframe['close'] > dataframe['close_1d'])
|
||||
# # & (dataframe['close_1d'] > dataframe['sma3_1d'])
|
||||
# # & (dataframe['close_1d'] > dataframe['sma5_1d'])
|
||||
# # & (dataframe['close_1d'] > dataframe['sma10_1d'])
|
||||
# # & (dataframe['close'] < dataframe['min_n'] * self.buy_min_max_coef.value)
|
||||
#
|
||||
# # & (dataframe['rsi3_1d'].shift(288) > dataframe['rsi3_1d'])
|
||||
# # (dataframe['close_1d'] > dataframe['sma3_1d'])
|
||||
# # & (dataframe['rsi3_1d'] > 72)
|
||||
# #& (dataframe['last_buy_price'] < (dataframe['close']))
|
||||
# #& (dataframe['should_sell'] == True)
|
||||
# ), ['sell', 'sell_tag']] = (1, 'sell_close_1d')
|
||||
# print("dans sell" + metadata['pair'])
|
||||
return dataframe
|
||||
|
||||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||||
current_rate: float, current_profit: float, min_stake: float,
|
||||
max_stake: float, **kwargs):
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
||||
if (len(dataframe) < 1):
|
||||
return None
|
||||
|
||||
# dataframe = self.populate_buy_trend(dataframe, {'pair': trade.pair})
|
||||
# if not (self.populate_buy_trend(dataframe, {'pair': trade.pair})):
|
||||
# return None
|
||||
if (self.stop_buying.get(trade.pair, None) == None):
|
||||
print("-----------" + trade.pair + " Init stop buying " + str(current_profit) + " " + str(current_time) + "---------------------")
|
||||
self.stop_buying[trade.pair] = True
|
||||
|
||||
if (self.stop_buying[trade.pair] == True):
|
||||
print("-----------" + trade.pair + " Canceled " + str(current_profit) + " " + str(current_time) + "---------------------")
|
||||
return None
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
previous_last_candle = dataframe.iloc[-2].squeeze()
|
||||
previous_previous_last_candle = dataframe.iloc[-3].squeeze()
|
||||
|
||||
# last_candle_5 = dataframe.iloc[-3].squeeze()
|
||||
# last_candle_decalage = dataframe.iloc[- self.buy_min_max_decalage.value].squeeze()
|
||||
# print(last_candle['buy'])
|
||||
|
||||
condition = (
|
||||
# (last_candle['min_max_n'] >= self.buy_min_max_n.value)
|
||||
(previous_last_candle['rsi5'] < 12)
|
||||
& (previous_last_candle['rsi5'] < last_candle['rsi5'])
|
||||
& (previous_last_candle['rsi5'] < previous_previous_last_candle['rsi5'])
|
||||
# & (last_candle['close'] < last_candle['min_n'] * self.buy_min_max_coef.value)
|
||||
& (last_candle['close'] <= last_candle['close_1d'])
|
||||
& (last_candle['close_1d'] <= last_candle['sma3_1d'])
|
||||
& (last_candle['close_1d'] <= last_candle['sma5_1d'])
|
||||
& (last_candle['close_1d'] <= last_candle['sma10_1d'])
|
||||
# & (dataframe['close'] < dataframe['min_n'] * self.buy_min_max_coef.value)
|
||||
)
|
||||
if not (condition):
|
||||
return None
|
||||
|
||||
# min_d = min(last_candle['sma3_4h'], last_candle['close_1d'])
|
||||
|
||||
filled_buys = trade.select_filled_orders('buy')
|
||||
count_of_buys = len(filled_buys)
|
||||
# days = (current_time - trade.open_date_utc).days
|
||||
# minutes = (current_time - trade.open_date_utc).seconds / 60
|
||||
# condition = condition & ((last_candle['min50'] == last_candle_5['min50']) & (last_candle['close'] <= last_candle['close_1h']))
|
||||
p = self.protection_percent_buy_lost.value
|
||||
percents = [p, p * 2, p * 3, p * 4, p * 5, p * 6, p * 7, p * 8, p * 9]
|
||||
|
||||
if (0 < count_of_buys <= self.protection_nb_buy_lost.value) \
|
||||
& (current_profit < - (percents[count_of_buys - 1] / 100)) & (condition):
|
||||
try:
|
||||
p = self.config['stake_amount']
|
||||
factors = [p, p, p, p, 2 * p, 4 * p, 5 * p, 6 * p]
|
||||
|
||||
stake_amount = factors[count_of_buys - 1]# filled_buys[0].cost
|
||||
# This then calculates current safety order size
|
||||
print("-----------" + trade.pair + " " + str(current_profit) + " " + str(count_of_buys) + " " + str(stake_amount) +
|
||||
" " + str(current_time) + "---------------------")
|
||||
return stake_amount
|
||||
except Exception as exception:
|
||||
print(exception)
|
||||
return None
|
||||
return None
|
||||
Reference in New Issue
Block a user